Klaus Solberg Söilen

The Researcher’s Journey

A Guide to Methodology and Academia in Social Sciences

Klaus Solberg Söilen

School of Business, Halmstad University, Halmstad, Sweden

ISBN 978-3-031-91564-2e-ISBN 978-3-031-91565-9

https://doi.org/10.1007/978-3-031-91565-9

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

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Dedicated to our daughter

Anastasia Mont Söilen

“With AI, the world you inherit now has two brains instead of one, so you can think faster and dream bigger.”

Preface

Why this Book?

The aim of this book is to guide any student aspiring to build a research career. Designed for early-career academic scholars, as well as PhD and master’s students with an interdisciplinary focus in the social sciences, this book provides practical insights and strategies to address the challenges of academic and scientific pursuits.

So, whether you have the guidance of a great supervisor, are navigating this journey on your own, or are simply curious about the world of research, I hope to be your companion as you explore and grow in this field.

The book’s content closely follows the structure of two courses I have taught for many years: Advanced Scientific Methods and Classics of Business Administration. Drawing on over 25 years of teaching experience, it inevitably carries a personal touch—an aspect I hope the reader will excuse. When I reference my own journey in lectures, it is not to center the discussion on myself but to make abstract concepts more relatable and engaging from the perspective I know best. Rest assured, my intent is not self-indulgence—I promise, I’m really not that interesting.

What, then, are the topics needed for a PhD program, and what does this book offer (strengths indicated by shades of green)? (Table 1)

Table 1

Courses or topics covered in a PhD program

As can be seen then, the book covers most of the essential topics for a PhD program, including professional development, academic career skills, grant writing, and proposal development. Although it does not help you through many statistical calculations, it guides you on when and why to use specific statistical methods. Additionally, Appendix A provides an overview of all the statistical concepts you may need to know, and more.

A suitable title for a course using this book could be “Foundations of Social Science Research: Methods, Process, and Practical Application.” The only additional courses or modules you might need beyond what is covered in this book would be one where you can practice statistical calculations, a course on academic teaching and mentorship, a module on academic writing, and perhaps a short session on the specific structure and format of the PhD dissertation—though it’s worth noting that dissertation requirements vary significantly by country.

This book, although comprehensive, does, of course, have limitations. For example, I assume you have decided to stay in academia, so there is no discussion on career transitions beyond academia—such as how PhD students can move into non-academic careers in industry, government, or non-profits. Additionally, the book does not address the mental health challenges often faced by PhD students or provide strategies for building resilience, handling setbacks, and seeking support, beyond acknowledging that the journey can be mentally taxing and outlining some of the reasons why. I felt these topics would be better covered by experts in psychiatry or career counselling, areas I know little about. This book also assumes that you have already been accepted into a PhD program and does not cover the admission process—though I can’t resist offering a few pieces of advice along the way. Generally, a strong academic record and applying to multiple programs improve your chances. Personal visits to institutions can also make a positive impression.

The book provides accessible guidance for early-career researchers, though it occasionally prioritizes simplicity over depth in methodological explanations, potentially limiting its appeal to advanced readers. Its focus on Scandinavian and European contexts, while reflecting the author's expertise and constraints of length—given the book already exceeds 450 pages—may reduce its global applicability, a trade-off the author hopes readers will understand and forgive.

This book was born out of frustration with existing PhD program materials that left crucial topics uncovered. Not only were essential topics missing, but there was also a lack of comprehensive guides on navigating the research journey—how to build a career and understand the academic workplace.

When I began my PhD in Germany, I was given one of the many standard textbooks widely used for this purpose in the country¹. These types of books, familiar to countless graduate and PhD students in Germany, have been published by different authors in numerous editions over the years and have become staples in academic preparation, but do not provide much advice beyond form and style.

For insights into research methodologies, I first turned to statistics books, but they focused narrowly on performing calculations, which I already understood, without offering a broader perspective. They rarely explained why certain methods would be chosen over others or reflected on different methodological choices based on the specific problem being addressed. It felt like, “this is just how we do things,” which might be effective for a PhD student but, in my view, not fulfilling.

Many of the Anglo-Saxon books on “thesis writing” I could find emphasized writing and authorial challenges, which was useful, though they sometimes felt more like they were made for courses in English literature. As a result, when I began teaching research master’s and PhD students, I started developing my own materials tailored to their needs. It’s the fruits of those notes that you are holding in your hands.

Book Structure

The first chapter introduces the nature of science, emphasizing the distinctions and connections between the natural and social sciences. Chapter two dives deeper into research methods, offering a guide to conducting rigorous and methodologically sound studies. The third chapter explores the "academic ecosystem," the professional environment where scientists work, covering essential topics such as academic structures, publishing practices, and networking within the field.

The fourth and final chapter offers a reflective journey on establishing your own place within the landscape of science, focusing on theoretical foundations, perspectives, and schools of thought. The goal is to guide you in selecting a philosophical grounding for your research and mapping knowledge frameworks across disciplines and foundational philosophies.

This textbook is designed with numerous tables to provide clear overviews and serve as guides for making important decisions as a researcher. It emphasizes processes, detailed step-by-step procedures, and practical lessons to help carry specific ideas forward. Keywords that require specific knowledge or definitions are marked in bold and included in the index for easy reference, while words that are emphasized but may not need definitions are marked in italics. Major authors marked in italics within the text can be found in the index at the back, with references to other places in the book where they are mentioned. If an author's name appears in the index but not in italics, it indicates that they are listed in the tables. To allow for more comprehensive text, the number of figures has been minimized, with tables highlighting concepts or ideas that benefit from an overview.

I have included references in the footnotes and full references and selected readings at the end of each chapter to guide readers toward foundational or supplementary texts that enhance their understanding of the topics discussed. I have cited most of these books for all four chapters because they offer comprehensive insights or frameworks relevant to all, such as overarching methodologies or philosophical foundations, but some are also cited twice. Although most of the content in this book is based on my own personal experience and thus lacks references, I am confident that I could find many sources that express similar views. The reason I chose not to add more references is to keep the book focused on personal experience and original insights. Besides, too many references would risk shifting the tone, making it feel overly instrumental and detracting from its practical and personal approach.

Given the limitations of length and the need to avoid delving into an anthropological dimension beyond the book's initial scope, it was impossible to include examples from countries worldwide. Instead, I have focused on what I know best: the Scandinavian countries, particularly Sweden, along with Denmark and Norway, where I occasionally lecture, as well as Germany, France, the USA, and England.

Personal stories, cases, notes, and exercises are included in separate boxes, providing additional pedagogical tools to enhance understanding and engagement, though they can be skipped if the reader prefers. The book purposefully cites a wide range of works with full references in the text, not only to emphasize the importance of these resources but also to encourage reflection. All referenced books are also listed in the reference section at the end of the main text, making it easy for readers to explore further.

Originally, I considered a chapter on future research trends, but predicting scientific developments is speculative and will quickly become outdated. I also contemplated a separate chapter on research ethics in the social sciences but felt that integrating key ethical considerations throughout the book would avoid a prescriptive tone while still addressing these important issues. There is no absolute "right" or "wrong" philosophical approach here either—only the need to build a scientifically sound and defensible foundation for one’s research.

Who Is This Book For?

This book is intended for students and early-career researchers, ideally as a course book for PhD programs or two-year research master’s programs. Most of the content is drawn from slides and teaching materials developed over more than a decade of teaching scientific methods in a research-focused master’s program at Halmstad University in Sweden. My approach included a foundational course on scientific methods in the first year, followed by a more advanced, comprehensive course in the second year, timed to prepare students for their master’s research. Additionally, I also taught a parallel course titled “Classics in Business Administration,” which focused on the history of economic thought and exploring Nobel prize winners in Economics and their contributions. Some of that has been added to chapter four. I have also had the opportunity to teach aspects of this material to a number of programs, for example, master’s students at the University of Agder in Norway and PhD students at the University of Riga in Latvia. I am grateful to these institutions for providing enriching teaching experiences.

Although I am an economist, this book is intended for all the social sciences, as no specific social science has its own exclusive scientific methods. All scientific methods are shared across the social sciences, even though each discipline tends to have its preferred approaches to conducting research. I aim to demonstrate, and hopefully convince you, that this tendency to rely on specific methods is more of a limitation than an advantage.

This book aims to bridge the gap in understanding the research process and academic landscape for today’s PhD students, who often have limited time to acclimate to the broader research profession. With some programs lasting as few as three years, students are frequently busy with their specific research topics without the opportunity to engage deeply with the wider scientific arena. This book seeks to fill that gap, making it suitable, I hope, for both PhD students and master’s students.

Primarily targeted at the social sciences, the book also offers valuable insights for those in the natural sciences. While examples mostly come from economics and business studies, reflecting areas of my expertise, the approach is interdisciplinary. Intellectually, I draw from a European continental tradition, which views economics within its social contexts—a perspective similar to the British Philosophy, Politics, and Economics (PPE) model, where disciplines are seen as interconnected fields.

The book’s structure supports a range of disciplines, including sociology, psychology, political science, economics, anthropology, and education. Unlike many modern books that emphasize statistical techniques, this book addresses the entire scientific journey, from entering academia to—ideally—attaining full professorship one day.

The project to write this book began ten years after I became a full professor in Business Administration, making it both a reflection of this journey and a celebration of the experience gained along the way.

AI plays a significant role in demonstrating how new technology can enhance research processes, from descriptive statistics and hypothesis testing to regression analysis directly from Excel files, potentially replacing traditional statistical software. The book also considers the broader implications of AI for the future of social sciences. While ChatGPT assisted with syntax and grammar, the wording remains the author’s own.

Most of the individuals I need to thank for this book are acknowledged within its pages. Many are authors I have never met, yet their work has been a profound source of inspiration and guidance—an approach that may seem unconventional for a scholarly work.

I am especially grateful to Oksana Mont, Lars Andersson, and Mats Alvesson for their valuable comments on different parts of the manuscript. Looking further back, I extend my sincere thanks to Per Brattrok, Johannes Lie, Oddmund Søilen, Carl Brønn, Klaus Lange, Silvia Föhr, Herbert Gooch, Jerry Slattum, Joseph Le Bihan, and Russell A. Berman for their inspiration as teachers, mentors, and supervisors. Their insights and encouragement, in various ways, contributed to the development of this book. I also deeply appreciate everyone I have had the opportunity to write and collaborate with on both small and large research projects along the way.

A special thank you goes to Vice-Chancellor Susanna Öhman for granting me dedicated time to focus on writing this book over the past year. I am equally grateful to my doctoral colleagues, who have allowed me to serve in various capacities—PhD Examiner, PhD Committee Board Member, PhD Supervisor, and PhD Co-Supervisor—across institutions such as Halmstad University and KTH in Sweden, L’Université Toulouse III—Paul Sabatier and Université Paris-Est in France, and the Institut National des Postes et Télécommunications in Rabat, Morocco. These experiences have been invaluable in shaping my academic perspective.

Finally, I extend my sincere gratitude to my editor at Springer, Felix Serrano. When I first approached him, my intention was to write a more statistics-centered book. However, he encouraged me to broaden its scope and leave the detailed calculations for other works. I took his advice—and this book is the result.

References

Gerhards, F. (1991). Seminar-, Diplom- und Doktorarbeit: Muster und Empfehlungen zur Gestaltung von rechts- und wirtschaftswissenschaftlichen Prüfungsarbeiten. Verlag Recht und Wirtschaft.

Footnotes

1

Gerhards, F. (1991).

List of Figures

Fig.​ 1.​1 Logical structure of this chapter2

Fig.​ 1.​2 Place de la Sorbonne54

Fig.​ 1.​3 The statue of John Stuart Mill located in Victoria Embankment Gardens in London58

Fig.​ 2.​1 Logical structure of Chap.​ 272

Fig.​ 2.​2 The process for finding a problem to solve82

Fig.​ 2.​3 Research proposal template93

Fig.​ 2.​4 Research proposal template, extended version95

Fig.​ 2.​5 Defining our area of study using the tree of porphyry96

Fig.​ 2.​6 Searching for the literature104

Fig.​ 2.​7 How to do the literature review118

Fig.​ 2.​8 Example of research model148

Fig.​ 2.​9 Deciding on goodness of measure157

Fig.​ 2.​10 Deciding what sampling techniques to use165

Fig.​ 2.​11 The process for finding causation206

Fig.​ 2.​12 How to submit an article on a journal’s webpage220

Fig.​ 2.​13 Login into the journal’s webpage220

Fig.​ 3.​1 Logical structure of this chapter238

Fig.​ 4.​1 The scholarly positioning framework356

Fig.​ 4.​2 The intellectual journey of a scholar358

Fig.​ 4.​3 Brahe, Kepler, … and the author as a young scholar371

Fig.​ 4.​4 Scholarly development cycles394

Fig.​ 4.​5 Schools of thought441

Fig.​ 4.​6 Schools of political economy442

Fig.​ 4.​7 Disciplines, schools of thought, scientific perspectives, and philosophical schools452

Fig. A.1 The process for distinct purposes of statistical techniques490

List of Tables

Table 1.​1 Criteria for something to be called a science9

Table 1.​2 Dimensions of complexity in the social science and how they can be mitigated11

Table 1.​3 Extent of biases in the social science vs.​ the natural science14

Table 1.​4 Certainty expressions in social science writing18

Table 1.​5 Well-established social facts23

Table 1.​6 Examples of social facts that have been abandoned25

Table 1.​7 Types of explicandum and their application33

Table 1.​8 Type of explicandum, origin, and scientific strength36

Table 1.​9 Best fit of types of explicandum for specific problems37

Table 1.​10 Primary forms of scientific reasoning39

Table 1.​11 Context in the social sciences and how to handle it41

Table 1.​12 The scientific rigor and applicability evaluation framework (SRAF)47

Table 1.​13 Possible outcomes of hypotheses50

Table 1.​14 Statistical techniques used for testing hypotheses52

Table 1.​15 The birth of the social sciences56

Table 1.​16 Dimensions of differences between the natural sciences and the social sciences65

Table 1.​17 Combinations of blending disciplines68

Table 2.​1 The research development process with our example78

Table 2.​2 Types of problems to be solved at the university81

Table 2.​3 Types of levels in the problem formulation taken from the project title97

Table 2.​4 Methodological differences between social sciences108

Table 2.​5 Preferred mathematical branches used in the study of economics110

Table 2.​6 Types of literature review113

Table 2.​7 Elements are to be included in the results, analysis, and discussion section121

Table 2.​8 Summary of elements to include in the conclusion123

Table 2.​9 Example of journal ranking, ABS list extraction125

Table 2.​10 Other rankings of business journals126

Table 2.​11 Choosing between quantitative or qualitative methods133

Table 2.​12 Types of research using research questions instead of hypotheses136

Table 2.​13 Examples of study purpose138

Table 2.​14 Examples of researcher interference139

Table 2.​15 Examples of study settings141

Table 2.​16 Exploring different data collection methods143

Table 2.​17 The rational for choosing one method over another144

Table 2.​18 Variables and how to measure146

Table 2.​19 Terms used to describe models that show variables and hypotheses in a research paper148

Table 2.​20 What kind of scales to use150

Table 2.​21 Likert scale of 5153

Table 2.​22 Likert scale of 7153

Table 2.​23 Assessing and ensuring the goodness of measure154

Table 2.​24 Choosing the unit of analysis158

Table 2.​25 How to use different time horizons161

Table 2.​26 Types of sampling design and when to use them163

Table 2.​27 How to set the confidence level, margin of error, and population proportion169

Table 2.​28 Salming for different population (P) sizes171

Table 2.​29 Criteria for distinguishing between basic and applied research in the social sciences177

Table 2.​30 List of statistical techniques frequently used179

Table 2.​31 Statistical techniques used in Oubrich et al.​ (2021)181

Table 2.​32 Additional statistical techniques that could improve the paper Oubrich et al.​ (2021)182

Table 2.​33 Ways of gathering data in social science and potential risks184

Table 2.​34 Types of controlled experiments for the social sciences191

Table 2.​35 Types of case studies195

Table 2.​36 Reason why controlled experiments are not done more often198

Table 2.​37 Experimental research methods200

Table 2.​38 How to try to prove causation205

Table 2.​39 Types of comparative analysis208

Table 2.​40 Other forms of analysis used209

Table 2.​41 A framework for interpreting results in the social sciences213

Table 2.​42 Types of outlets for research215

Table 2.​43 Topics covered by “for authors” on journal’s webpage219

Table 2.​44 Types of documents to be uploaded to the journal’s webpage222

Table 2.​45 Assessment of author contributions225

Table 2.​46 Authors’ answers to reviewers231

Table 3.​1 Components of the academic ecosystem237

Table 3.​2 Roles, functions, and decision-making at the university240

Table 3.​3 Degree of political control in governing boards (Support for these claims can be found in OECD (2019), Capano and Piattoni (2021), Klemperer (2021), Fielden (2010), and Lane and Johnstone (2013))244

Table 3.​4 Qualifications needed for key positions at the university246

Table 3.​5 Diplomas offered at the university, what they contain and mean249

Table 3.​6 Administrative personnel and their functions252

Table 3.​7 Comparing the use of conferences and colloquiums263

Table 3.​8 The increase in the number of social scientists and research papers written265

Table 3.​9 Collaborative research models270

Table 3.​10 Collaboration tools and platforms271

Table 3.​11 Example of a detailed list of roles in a research team275

Table 3.​12 Example of a practical list of roles in a research team278

Table 3.​13 Stages and positions in an academic career282

Table 3.​14 Estimated number of social science journals and their major owners (Based on data from the National Science Foundation (NSF))288

Table 3.​15 Example of websites that offer bibliometrics308

Table 3.​16 Services that assess journals and outputs310

Table 3.​17 Examples of research grants319

Table 3.​18 Grant writing techniques329

Table 3.​19 Examples of public policy area and types of decisions scientists help with344

Table 3.​20 Examples of how scientists shape cultural and intellectual development350

Table 4.​1 Classic Greek and Roman books related to science368

Table 4.​2 Major books in philosophy important for the development of science388

Table 4.​3 Examples of methods used in the social sciences397

Table 4.​4 Assumptions in the social sciences when we write “ceteris parabus”402

Table 4.​5 Alternatives for applying a scientific perspective in your research407

Table 4.​6 Scientific perspectives414

Table 4.​7 Which disciplines tend to use which scientific perspective415

Table 4.​8 Different forms of epistemologies used in the social sciences418

Table 4.​9 The deductive-nomological (D-N) model420

Table 4.​10 Difference between Hempel’s and Carnap’s and on logical positivism421

Table 4.​11 Schools of empiricism423

Table 4.​12 Schools of philosophy427

Table 4.​13 Suggested reading for self-studies in the philosophy of science430

Table 4.​14 Examples of compilations on the philosophy of science431

Table 4.​15 Examples of professions working intensively with information433

Table 4.​16 Schools of thought in the example of economics439

Table 4.​17 A selection of schools of thinking in the social sciences444

Table 4.​18 Main disciplines that all study economic factors455

Table 4.​19 Other disciplines that all study economic factors456

Table 4.​20 More disciplines and their subsequent sub-disciplines459

Table 4.​21 A division of the social sciences into problems to be solved466

Table 4.​22 AI functions supporting the scientific process471

Table 4.​23 Problems with interdisciplinar​y research473

Table 4.​24 Locating your academic tribe477

Table A.1 Deciding on what statistics to do based on what available data485

Table A.2 Statistical techniques used in the book487

Table A.3 More common statistical techniques used in social science research504

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

K. Solberg SöilenThe Researcher's Journeyhttps://doi.org/10.1007/978-3-031-91565-9_1

1. Introduction to Science

Klaus Solberg Söilen1  

(1)

School of Business, Halmstad University, Halmstad, Sweden

Klaus Solberg Söilen

Email: klaus.solberg_soilen@hh.se

This chapter sets the stage by exploring the essential nature of science, establishing a foundation for understanding the social sciences within a larger scientific context.

We begin by examining science in general and then highlight the unique aspects that differentiate the social sciences from the natural sciences. This includes questions such as what is science and what qualifies as good science? We look at the question of the complexity in the social sciences, the meaning of theory, and why, unlike in the natural sciences, social sciences lack universal “laws” and employ few theorems.

We also examine the concepts of fact and “truth” in research and how scientific inquiry differs from common sense. Additionally, we address assumptions frequently made in the social sciences—such as rationality and free will—and consider whether they are realistic.

In the following chapter, we’ll cover the practical aspects of conducting research. This chapter, however, focuses on the theoretical and philosophical foundations essential for understanding what it means to carry out high-quality scientific work.

The logical structure of the chapter is as follows (Fig. 1.1):

Fig. 1.1

Logical structure of this chapter

The logical structure of this chapter begins with an overarching introduction to science, setting the stage for the topics that follow. It proceeds to outline the scientific method, emphasizing its foundational role in guiding inquiry and research. A detailed exploration of science is then presented in eleven lessons, covering key aspects such as the complexity of social sciences, the definition of laws and facts, and the interplay between political influence and academic independence. The discussion delves into philosophical concepts like the notion of truth, contextualization of facts, and the progression from correlation to causation, highlighting their relevance to scientific reasoning. The chapter concludes by connecting these lessons, offering a cohesive framework that synthesizes the foundational principles of science with practical and intellectual applications.

1.1 The Scientific Method

Why do we do science? This should be our first question. Even primitive men discovered that:

first, knowledge gives controlling power over the environment and consequently makes life easier for him who possesses it; and second, that learning what others already know is far more economical than acquiring this knowledge by one’s own experience. (Georgescu-Roegen 1966, p. 3)¹

From this, we can say that science is stored communal knowledge.

The problem of storing and preserving knowledge soon led to the profession of scholars and to the institutions of teaching. (p. 5)

I wish I could say that science is universally applied in the same way by all nations once new knowledge is brought forward and universities are built regardless of where, but this is not always the case. Instead, those nations and cultures that excel in science tend to prosper. We saw this in Mesopotamia and Egypt, which were cradles of early scientific and mathematical thought; in classical Greece, where philosophy and science flourished; and in the Arab world during the medieval period, which preserved and expanded upon ancient knowledge while much of Europe experienced intellectual stagnation. Today, a similar pattern is emerging as several Asian nations, such as China, Japan, and South Korea, take the lead in advancing new discoveries and technologies. At any given time in history, only a handful of nations have managed to use science to their competitive advantage—mostly because it is difficult, and we tend to forget or grow complacent. In fact, all great civilizations are defined by their success in doing so.

At the heart of science lies its method. The scientific method is a systematic way of investigating phenomena, acquiring knowledge, and refining what we already know.

This method generally includes forming hypotheses, conducting experiments or observations, analyzing data, and drawing conclusions based on empirical evidence, always allowing for revisions as new tests or information become available. Although this approach is standard in the natural sciences, it is also employed in the social sciences—albeit with significant differences, as we shall explore.

In social sciences, research is often more complex due to the unpredictable nature of human behavior, social structures, and cultural factors, which aren’t as easily isolated or controlled as physical variables. Unlike the often-controlled lab conditions in natural sciences, social scientists frequently rely on observational studies, surveys, and case studies. Though experiments can and should be done in social sciences, when possible, they’re also more demanding because of these complexities and take more time and resources to do, a topic we will return to.

Interpretation also plays a crucial role in social science, as findings usually need to be considered within specific cultural or historical contexts. This makes broad generalizations trickier, and ethical concerns add another layer, as researchers must prioritize participant consent, confidentiality, and well-being, which limits, for example, certain experimental methods.

Replication, a cornerstone of scientific rigor, is more complicated in social sciences as well, as studies are highly influenced by unique social and cultural settings that are hard to recreate. This doesn’t mean social science studies shouldn’t be replicated—they should. However, practical challenges mean they often aren’t, and methodology sections in research papers are frequently too brief or vague to allow others to replicate the study exactly.

Addressing this issue would require encouraging and funding more replication studies, establishing dedicated publication spaces for such research, and fostering a culture that values replication as a key part of scientific rigor. This would help social sciences align more closely with the rigorous testing traditions of natural sciences, strengthening the validity of findings and advancing the domain.

This gives us the first exercise to do:

Exercise: Replicate a Study

Find a social science article with a detailed methodology section—one that specifies each step clearly enough to allow for an exact replication of its empirical component. Start by carefully reviewing the article’s methodology to ensure that the steps for data collection and analysis are explicitly outlined. Once you have selected an article, conduct the empirical part yourself, gathering data according to the methods specified. Be meticulous in following the described procedures to maintain consistency with the original study. This will take you a few days.

After gathering the data, perform the statistical analysis as outlined in the original article, using the same or equivalent statistical techniques to assess the results. Next, compare your findings with those of the original study. Are they similar? If your results differ from those reported in the article, what could be the reasons?

These discrepancies might arise from variations in sample characteristics, potential ambiguities in the methodology that weren’t fully addressed, or even differences stemming from the influence of time and context on human behavior.

“Context” here refers to the specific conditions and circumstances in which a study is conducted, which can influence the outcomes in ways that may not be replicable elsewhere. It encompasses factors such as cultural, social, economic, and temporal conditions that shape human behavior and responses. Finally, consider how methodological clarity and completeness contribute to the replicability of social science research, shedding light on the importance of transparency in research practices.

If we carefully followed the methods and performed the same statistical analysis as the original study yet obtained very different results, it suggests that the findings may be highly context-dependent. This variability highlights the complexity of studying human behavior, as social phenomena can be influenced by unseen or subtle factors unique to each specific time, place, or population. Such outcomes can lead us to question the generalizability of findings within the social sciences, as different contexts might yield different behaviors or responses, even under similar conditions.

On the other hand, if replication yields quite similar results, it’s a positive sign for the robustness and reliability of the paper. Likely, some studies will show results that align closely across different contexts, while others may diverge significantly. Does this mean that studies with divergent results represent “bad” science? In a sense, it can—if differing results stem from poorly defined methods or lack of clarity in procedures, it can indicate flaws in the study’s design. However, some will argue that the variability in outcomes may also reflect the social sciences’ strength, as they capture the dynamic nature of human societies and how context affects findings. It’s a complicated line of thinking, but it comes down to the fact that variability in results helps researchers identify the boundaries within which certain theories hold true. Of course, if you only conduct a single study, you’re unlikely to fully understand these boundaries, as you’re likely to assume that your chosen method is sound (otherwise, you wouldn’t have selected it). While replication that yields similar results enhances the robustness and reliability of findings, it is equally important to encourage diverse approaches and interpretations to avoid the pitfalls of conformity and foster scientific innovation.

It’s the follow-up study, particularly one with differing results, that begins to reveal and define these limits. Unfortunately, in the social sciences, relatively few follow-up studies are conducted, meaning this critical boundary exploration often remains unaddressed. This gap makes it harder to assess the full applicability and robustness of social science theories across diverse contexts.

At the end of the exercise, write down your observations about the differences you found in the two studies and what might have caused them. More importantly, reflect on what this exercise reveals about the value of the social sciences.

Consider whether the variability highlights the field’s adaptability in capturing complex, context-dependent human behavior or whether it suggests challenges in replicating findings.

You may also find that significant differences in results can arise simply due to the passage of time, even within the scope of a localized study.

While replication that yields similar results enhances the robustness and reliability of findings, it is equally important to encourage diverse approaches and interpretations to avoid the pitfalls of conformity and foster scientific innovation.

This exercise may take a week to do, but it is well worth it.

Having presented now an idea about the scientific method, how are we do understand and do science?

1.2 Science in Eleven Lessons

This modular approach to social science epistemology organizes the teaching of science into eleven focused lessons, offering a systematic progression from foundational concepts to practical applications in social research. This framework emphasizes the interplay of theory and practice, addressing key themes such as complexity, objectivity, contextualization of facts, and hypothesis testing. It aligns with Kuhn’s idea of paradigm shifts by exploring how political and academic ideals influence research frameworks² and with Popper’s emphasis on falsifiability through its focus on hypotheses and testing.³ By addressing both epistemological underpinnings and methodological applications, it mirrors Kuhn’s view of scientific revolutions as well as Popper’s logic of scientific discovery.

The lessons include nuanced discussions on the role of common sense, the transition from correlation to causation, and the contextualization of facts, emphasizing adaptability and critical thinking in the face of changing paradigms. This structured approach ensures that learners understand not only the philosophical foundations of science but also their practical implications for social research. By bridging abstract theory with empirical inquiry, the modular approach fosters a comprehensive, balanced understanding of social science epistemology. It serves as a guide for navigating the complexity of research while remaining grounded in methodological rigor and philosophical insight.

When we have a collection of facts organized in a catalog—say, details about bird species, economic trends, or insights into human cognition—does that alone make it a science?

Let’s say we label these collections as biology, economics, psychology, or neurology. Does the organization of information itself mean we’ve created a scientific field? No, not necessarily. A collection of data or facts alone, no matter how thorough, is as much a science as a drawer of screws is an engineering project or a stamp collection is a study of communication. Science requires something more: principles, methodologies, and structured inquiry that explain how these facts interconnect, revealing insights about underlying processes and causal relationships:

Science is about understanding, not just compiling.

Science relies on the development of theories. Theories go beyond simply describing what happens; they offer explanations for why things happen, providing frameworks that help us understand patterns and predict outcomes. For instance, a theory might explain why some people lack adequate clothing, why individuals engage in theft, or what drives them to give gifts. In social sciences, theories like Maslow’s Hierarchy of Needs help explain human motivation, suggesting that unmet basic needs lead to specific behaviors. Similarly, theories like social exchange theory explore why people give gifts, proposing that giving often involves an expectation of reciprocal benefits. For each field, theories serve as essential building blocks that move us from isolated facts to meaningful, interpretable patterns.

Theories in the social sciences operate differently from those in the natural sciences, where a specific cause (A) reliably leads to a specific effect (B). This consistency allows natural sciences to define theories with strong predictive power. In contrast, social science theories often work with probabilities rather than certainties.

For example, if A leads to B only 25% of the time, can we consider it a theory? In many cases, yes, as the social sciences grapple with so many complex variables that influence human behavior. A 25% occurrence rate might represent a significant advance by helping to eliminate competing explanations if there are hundreds of them. After all, social sciences never achieve 100% predictability for anything, so far. In this context, a lower predictability threshold can still offer valuable insights and support a theoretical framework, even if it lacks the same level of certainty we find in natural sciences.

Exercise: The “Money Acceptance Theory”⁴

Everyone wants money, right? Wrong. A certain percentage of people will refuse money, even when it’s offered for free. As we’ll see in this exercise, the decision to accept or decline depends on a variety of factors.

To illustrate how cause A does not always lead to effect B, let’s dive deeper into my “Money Acceptance Theory,” an exercise I regularly use in my scientific method classes. This experiment aims to challenge the assumption that human behavior is always predictable, much like how we often assume that everyone would accept money when offered. The Money Acceptance Theory proposes that people, being universally motivated by material gain, will always accept money if it’s freely given. To demonstrate this theory, I walk down the line of students, offering a bill from my wallet to each one, assuming that everyone will take the money.

However, the results often contradict expectations. Many students decline, some because they suspect a hidden agenda or feel uneasy about accepting something they haven’t earned. This deviation from the predicted outcome demonstrates that cause A (offering money) does not always lead to effect B (acceptance of money).

The reasons for this are grounded in various psychological and sociological theories, including social contract theory (Hobbes 1651) and behavioral economics (Kahneman 2011). These theories argue that individuals don’t always act out of self-interest and that moral considerations, fairness, and societal norms play critical roles in decision-making.

Why are we doing this?

The aim of this exercise is not to prove that money isn’t universally appealing—clearly, it is. Rather, it highlights the complexities of human behavior and decision-making. We are working through the concept that human actions often involve factors beyond pure self-interest. This point is especially valuable in discussions of rational choice theory (Becker 1976), where assumptions about rationality are sometimes challenged by real-world behavior.

This simple experiment also touches on the cognitive biases that affect our judgments and decisions, such as loss aversion and anchoring bias (Tversky and Kahneman 1974). People may perceive accepting the money as potentially leading to an obligation or as a social faux pas, which interferes with the predicted rational choice of taking the free money.

Finally, theories of social norms (Cialdini 2003) and normative influence explain why people may reject free offers if they feel it violates accepted social behavior. In this case, the rejection of free money becomes a means of maintaining personal integrity or adherence to a socially acceptable standard.

This extended exercise provides an understanding of why cause and effect may not always align in social sciences, especially when studying human behavior. What results did you get, how many % took the money?

Unlike in the natural sciences, where water predictably boils at exactly 100 °C under standard conditions, 100% of the time, human behavior has exceptions, even for examples we assume won’t have any.

Although social science theories may not reach the certainty found in natural sciences, they provide valuable insights into the probabilities and tendencies within complex social behaviors, and that is an important point because it shows the value of the social sciences. The point is we don’t need to be 100% certain to make contributions of value that can benefit society. It’s often enough that we make small advancements.

So, how do we determine if we are conducting good science? In the table below, several criteria serve as hallmarks for quality science, regardless of whether it’s in the natural or social sciences. We explore how each field applies these criteria in practice, showcasing the distinctive ways that each branch meets, or sometimes adapts, these standards (Table 1.1).

Table 1.1

Criteria for something to be called a science

Criteria

Description

Natural sciences

Social sciences

Theories

Develops frameworks to explain why phenomena occur, often with predictive power

Essential, highly emphasized

Essential, with probabilistic emphasis

Empirical evidence

Based on observable and measurable evidence, often through experiments and data collection

Required, fundamental

Required, though less experimental

Causation

Identifying cause-and-effect relationships, enabling predictive conclusions

Expected, generally strong

Expected, often probabilistic or correlational

Reproducibility

Findings should be repeatable under similar conditions by independent researchers

Critical requirement

Important but variable in outcomes

Testable hypotheses

Theories must produce hypotheses that can be empirically tested and potentially falsified

Essential and required

Essential but more challenging in practice

Laws or generalized principles

Develops universal laws (e.g., gravity) or general principles; while common in natural sciences, it’s rare in social sciences

Common and required

Not expected; general principles are developed

Peer review and scientific methodology

Utilizes peer review and follows a systematic method, including hypothesis formation, experimentation, and conclusion

Standard requirement

Standard requirement

Objectivity and neutrality

Commitment to unbiased data interpretation and analysis

Highly prioritized

Expected, though subjectivity may vary

We see here that the natural sciences generally demand strict adherence to these criteria, with a heavy focus on causation, reproducibility, and forming universal laws or theorems where possible. Theories are often highly predictive and rely on rigorous empirical testing.

The social sciences require most of these criteria but interpret them differently due to the complexity of human behavior. While theories and hypotheses are central, social sciences often accept probabilistic causation and do not expect universal laws, as human behavior is influenced by so many variables. Let’s have a look at some of these.

1.2.1 Complexity in Social Sciences

Social science complexity is multilayered, arising from the extensive range of variables in any sequence of events involving multiple people. Each scenario is unique, making it nearly impossible to comprehensively map every factor. Moreover, every measurement captures a singular moment in time that cannot be replicated, as societies continuously evolve. This means that a phenomenon observed at time T − 1 cannot be directly applied to a future time T + 1 without accounting for changes that have occurred in the intervening period.

Additionally, each observation is influenced by the specific culture in which it was conducted, with results closely tied to cultural context. This limits the transferability of conclusions across different cultures or even across different periods within the same culture, as social dynamics and cultural frameworks are in constant flux.

Then comes the complexity in data capture, with numerous identified and unidentified biases affecting the accuracy of observations at any given time. Among the many identifiable biases, we can make some adjustments to mitigate their impact, though complete elimination is rare, to say impossible. However, every study also carries the risk of hidden biases that can compromise data quality. These are errors that often go unnoticed, typically because we haven’t examined our own project with sufficient scrutiny before starting.

For instance, if a participant realizes they are being observed, they might modify their behavior to make a better impression. The researcher might remain unaware that the participant knows they’re being studied, which introduces a significant bias that distorts the data. This phenomenon, known as the Hawthorne effect, is just one example of how unforeseen biases can render data unreliable, underscoring the intricate challenges of obtaining truly objective social science data (Table 1.2).

Table 1.2

Dimensions of complexity in the social science and how they can be mitigated

Dimension of complexity

Description

Example of distortion

Mitigation strategy

Interdependence of social factors

Social factors are often interlinked, meaning a change in one may influence others.

Studying education’s effect on income can be confounded by economic status, as wealthier students may have better access to education.

Use statistical controls to isolate factors or use structural equation modeling to account for relationships among variables.

Dynamic social contexts

Social contexts change over time due to political, economic, or cultural shifts.

Findings from a study on public trust in government pre-crisis may not apply during a major political event like an election or scandal.

Conduct longitudinal studies to observe changes over time or contextualize results within specific time frames.

Individual vs. collective behavior

Contrasts exist between personal actions and group dynamics, complicating findings on collective trends.

An individual may act sustainably alone but conform to unsustainable group norms in social settings.

Use mixed methods to capture both individual and group-level insights and distinguish findings by scale (individual vs. group) in analysis.

Complexity of subjectivity

Personal experiences and perceptions are subjective, introducing biases.

When surveyed, participants’ self-reported happiness levels can be highly subjective, influenced by mood or context.

Use standardized measures and triangulate with observational or objective data to reduce subjective bias.

Ethical and access constraints

Privacy, consent, and ethics may limit data collection, especially in sensitive populations.

Studying substance abuse among teens may miss those most affected, as they avoid participation due to privacy concerns.

Utilize anonymized surveys, gain ethical approval, and establish trust to encourage broader participation.

Nonlinearity of social systems

Small changes may lead to disproportionately large or unpredictable effects.

A small policy change intended to improve education access could inadvertently worsen inequalities by benefitting only specific groups.

Model potential unintended consequences, such as through systems dynamics modeling, before implementing and assessing the policy’s impact on diverse groups.

Influence of language and interpretation

Language nuances, cultural idioms, and regional dialects influence how questions are understood and answered.

In multilingual studies, participants might interpret translated questions differently, leading to inconsistent responses across languages.

Conduct pilot tests, employ back-translation, and involve local experts to ensure the accuracy and consistency of survey translations.

Multitude of variables

Many variables can influence outcomes, especially in complex social phenomena.

Analyzing the causes of educational attainment may miss hidden variables like parental encouragement or neighborhood safety.

Use comprehensive data collection techniques and employ factor analysis to reduce variables to the most impactful ones.

Historical and cultural context dependence

Social phenomena are often context-specific, varying across cultures and time periods.

A theory on individualism developed in Western contexts might not apply to collectivist societies.

Test theories across different cultural contexts to determine generalizability or adapt them contextually while noting limitations.

Awareness of observation (Hawthorne effect)

Individuals alter their behavior when they know they are being studied, which skews results.

Participants in a productivity study may work harder knowing their performance is being observed, leading to inflated results.

Unobtrusive observation methods, such as indirect measures or delayed observation periods, can be used to reduce participant awareness and thus mitigate this effect.

Unidentified biases

Hidden biases, which researchers may not be aware of, can distort findings.

An unconscious cultural bias may influence how researchers interpret data on immigration policies, affecting results.

Engage diverse research teams and regularly review methodologies to identify and address biases; conduct peer reviews and seek feedback from external experts.

In contrast to the social sciences, natural sciences face fewer complexities because their subjects—like physical objects, chemical reactions, or biological processes—are typically less influenced by context, subjective perceptions, or cultural variations.

For example, an atom of hydrogen behaves consistently in any location, unaffected by cultural or historical context, which simplifies experimentation and replication. Natural sciences can also avoid the impact of the “Hawthorne effect” since particles and chemicals do not alter behavior when observed, unlike human subjects who might adjust actions when they know they are being studied, consciously or unconsciously. Additionally, the multitude of variables in social sciences, where countless factors such as economic status, educational background, and societal norms can affect outcomes, is far less of a concern in natural sciences, where experiments can often be controlled more tightly to isolate specific variables. From this perspective, the natural sciences have it much easier and are much easier to carry out (Table 1.3).

Table 1.3

Extent of biases in the social science vs. the natural science

Bias

Description

Extent of problem in natural sciences

Confirmation bias

The tendency to search for, interpret, or prioritize information in a way that confirms one’s preconceptions.

Medium: Less pervasive due to emphasis on empirical evidence, but still a concern, particularly in hypothesis-driven research or during data interpretation.

Selection bias

Arises when the sample studied is not representative of the population, often due to nonrandom sampling or specific inclusion criteria.

Medium: Common in natural sciences, especially in studies with practical constraints (e.g., clinical trials, field research) but generally mitigated through statistical adjustments.

Publication bias

A tendency for journals to publish studies with positive results more frequently than those with null or negative findings.

High: Present in both fields, with substantial impact on meta-analyses and research validity; affects reproducibility in natural sciences by prioritizing novel findings over null results.

Hindsight bias

The inclination to see events as having been predictable after they have occurred.

Low: More frequent in social sciences, where outcomes are often interpreted through theoretical lenses; less impactful in natural sciences, though can influence historical interpretations of discoveries.

Anchoring bias

Relying too heavily on the first piece of information encountered (the “anchor”) when making decisions.

Medium: This can influence initial hypothesis formation and experimental design; however, peer review and replication reduce its impact.

Observer bias

When researchers’ expectations or subjective interpretations influence the study’s outcomes.

Low: Important in social sciences, especially in observational studies; generally, less of an issue in natural sciences where measurements are more objective, though still possible in observational fields.

Self-serving bias

The tendency to attribute positive outcomes to one’s abilities and negative outcomes to external factors.

Low: Predominantly a social science issue; in natural sciences, it may subtly affect interpretations of research results, especially in individual-driven research or theoretical debates.

Social desirability bias

The tendency of respondents to answer questions in a way that will be viewed favorably by others.

Low: Primarily affects social sciences due to reliance on self-reported data; limited in natural sciences, except in research involving human subjects, such as clinical studies.

Cognitive dissonance bias

The discomfort of holding conflicting information leads researchers to alter beliefs or interpretations to maintain consistency.

Low: More relevant in social sciences; may affect hypothesis adherence despite contradictory evidence in natural sciences but is relatively rare.

Availability heuristic

A cognitive shortcut where people rely on immediate examples that come to mind, often leading to overestimating the likelihood of similar instances occurring.

Low: Common in social sciences; may affect initial hypothesis formation in natural sciences, though rigorous data collection typically mitigates it.

Fundamental attribution error

The tendency to overemphasize personality traits and underestimate situational factors when explaining others’ behavior.

Low: Primarily affects social sciences; in natural sciences, limited to fields involving human behavior or animal studies where assumptions about causality may be biased.

Survivorship bias

Concentrating on people or things that “survived” some process while overlooking those that did not, potentially skewing results or conclusions.

Medium: Seen in both fields, though more pronounced in historical, social science studies, in natural sciences it may influence evolutionary biology or experimental outcomes.

Groupthink

When the desire for harmony or conformity in a group leads to irrational or dysfunctional decision-making.

Low: Predominantly an issue in collaborative social science research and policymaking; less common in natural sciences, though group consensus can occasionally influence scientific agendas.

Endowment effect

The tendency to overvalue something simply because one owns it or is personally invested in it.

Low: Notable in social sciences, particularly in economics; in natural sciences, personal attachment to theories may be a factor, but peer review provides checks.

Recency bias

Giving undue weight to recent information or events compared to older information.

Low: Common in social sciences, where new theories may overshadow established ones; in natural sciences, this bias is mitigated by the cumulative nature of evidence and established paradigms.

Illusory correlation

The perception of a relationship between two variables when no such relationship exists.

Low: Affects both fields but is more common in social sciences; controlled experiments in natural sciences often clarify correlations.

Halo effect

Allowing positive qualities in one area to influence judgments in other areas.

Low: Primarily a social science issue, especially in psychology; less common in natural sciences but may subtly affect the perception of researchers’ findings.

False consensus effect

The tendency to overestimate how much others share one’s beliefs, values, or opinions.

Low: Primarily, social sciences, in natural sciences, may affect assumptions in fields that rely on human perception, such as cognitive science or social psychology.

Experimenter effect

When researchers unintentionally influence participants through cues or behaviors, potentially skew results.

Medium: Important in social sciences; in natural sciences, controlled conditions generally minimize this effect but can still be relevant in behavioral and clinical studies.

Overconfidence bias

The tendency to overestimate one’s own knowledge or abilities.

Medium: Seen across both fields; in natural sciences, more rigor in validation and replication often mitigates its effects.

Framing effect

The way information is presented (framed) affects decisions and judgments.

Low: Common in social sciences, especially in surveys and qualitative research; less prevalent in natural sciences where data presentation is standardized.

We understand that this makes the natural sciences more suited to producing universal laws and generalizable results, as phenomena like gravity or chemical reactions can be studied in highly controlled environments, minimizing the impact of unknown variables.

Also, natural sciences often have clear cause-and-effect relationships that are testable under repeatable conditions, unlike social systems where causality is more complex and multilayered. Thus, the natural sciences tend to produce more deterministic results, while the social sciences must navigate a multitude of confounding factors, resulting in findings that are often probabilistic and, for lack of a better term, more “tentative” rather than definitive (Table 1.4).

Table 1.4

Certainty expressions in social science writing

Expression

Meaning

Example

It is well established that…

The finding is strongly supported by multiple studies and widely accepted in the field.

It is well established that socioeconomic status influences access to higher education.

Research consistently shows that…

Multiple studies have found similar results, suggesting strong evidence.

Research consistently shows that early childhood education improves long-term academic outcomes.

The evidence strongly suggests that…

The findings indicate a high probability of truth, though not absolute certainty.

The evidence strongly suggests that social networks influence career opportunities.

Studies indicate that…

Findings from research point toward a certain trend or pattern but do not claim absolute certainty.

Studies indicate that leadership styles affect employee motivation.

There is substantial evidence to suggest that…

There is significant support for the claim, but findings are not entirely conclusive.

There is substantial evidence to suggest that urbanization affects voting behavior.

Findings suggest that…

The results imply a connection, but more research is needed for stronger confirmation.

Findings suggest that remote work increases productivity for some employees.

In general,…

The statement applies broadly, but exceptions exist.

In general, economic recessions lead to increased unemployment rates.

Often,…

The phenomenon occurs frequently, but not always.

Often, political instability leads to economic downturns.

In some cases,…

The finding applies in specific instances but not universally.

In some cases, social media use is linked to increased political engagement.

There appears to be a correlation between…

A relationship is observed, but causation is not established.

There appears to be a correlation between income level and life expectancy.

Some research suggests that…

A few studies have found this result, but evidence is limited or inconclusive.

Some research suggests that bilingualism enhances cognitive flexibility.

It is possible that…

The claim is hypothetical and lacks strong empirical backing.

It is possible that artificial intelligence will reshape labor markets in unforeseen ways.

It is speculated that…

The statement is a hypothesis or informed guess rather than an established fact.

It is speculated that cultural values influence economic growth in developing country N.

It has been proposed that…

Someone has suggested this theory, but it has not been widely tested or accepted.

It has been proposed that universal basic income could reduce poverty rates.

It remains unclear whether…

There is no definitive answer yet due to conflicting or insufficient research.

It remains unclear whether social media increases or decreases political polarization.

This table ranks expressions commonly used in social science research based on their degree of certainty, from the most conclusive to the most tentative. The ranking reflects the strength of empirical support, with phrases like “It is well established that…” at the top, indicating strong consensus and repeated validation across studies. As certainty decreases, expressions such as “Findings suggest that…” and “In some cases,…” acknowledge variability and limitations in generalizability. The lowest-ranked expressions, like “It remains unclear whether…” and “It is speculated that…”, indicate uncertainty, lack of strong empirical evidence, or ongoing debate in the field.

Of course, this is deeply frustrating for a natural scientist—especially the fact that there is no universal agreement on when to use these terms—but this is as far as we have come to this point.

1.2.2 Laws and Theorems

Based on the above, could we conclude that the natural sciences are inherently “more scientific” than the social sciences? In some ways, yes: natural sciences often operate under stricter criteria, especially regarding reproducibility and universal laws, but because they can. However, the foundational method of inquiry is the same in both fields.

Social sciences face unique challenges because human behavior is inherently complex and context-dependent, which makes it difficult to apply the same rigorous standards as in the natural sciences. This doesn’t imply that the social sciences are “flawed” science—rather, it highlights that they tackle broader, more intricate issues. The social sciences, still relatively young, face significant challenges in their development, particularly during periods of heightened social and economic difficulties. It is in these moments that they are truly tested, as expectations on their contributions increase. We are now entering such a phase, especially in the Western parts of the world—the first since the major expansion of the social sciences following the Second World War. In this new era, there is likely to be greater scrutiny from voters and society at large regarding the relevance and impact of our work.⁵

In essence, the social sciences require methodologies that can account for variability and nuances, as they seek to uncover patterns within dynamic and culturally diverse human behavior. Acknowledging these challenges reflects a realistic understanding of the field’s complexity and ongoing growth. The complexity of social sciences comes from the vast number of variables influencing human behavior and the difficulty of reducing or eliminating biases. It’s within these limits and constraints that we must make progress, or the project will falter.

With the advancement of AI, however, we now have tools to tackle some of this complexity. AI can process millions of articles and texts, identify patterns, assess methodologies, highlight biases, and even track support for theories across studies. If anything, AI might pave the way for a new era in social science by allowing us to systematically sort through much of this complexity. In this sense, social sciences could be on the brink of a significant breakthrough, suggesting that genuine progress may be just ahead, in this very century. More about this at the end of Chap. 4.

Does this mean we can also hope to establish laws for the social sciences? I think there are reasons to be carefully optimistic.

In science, laws are universally accepted principles describing consistent and repeatable phenomena in the natural world. Examples include Newton’s law of universal gravitation (Newton 1687), which quantifies gravitational attraction between masses, and Ohm’s law (Ohm 1827), explaining the relationship between voltage, current, and resistance in electrical circuits. Laws are generally deterministic, meaning that they describe effects that consistently follow certain causes without exception (e.g., the laws of thermodynamics, which define energy conservation and entropy). In chemistry, Boyle’s law describes the relationship between the pressure and volume of a gas at a constant temperature. These laws are foundational and universally applicable because they predictably describe interactions in the natural world, allowing for precision and replicability in science.

A law in the social sciences would probably look quite different from those in the natural sciences. Rather than having deterministic laws like those in physics, a social science law would likely take the form of a probabilistic statement. For example, it might be said that “under certain conditions, individuals tend to conform to these group norms,” but would allow for variation due to factors like cultural differences, personality, and situational influences. So, if we add another culture, C, to the equation, we will automatically get another outcome.

We could envision these social science laws as part of a comprehensive software model, where variables are selected from dropdown menus based on the conditions and factors relevant to a particular situation. Behind this setup would lie an enormous database built from the vast array of social datasets gathered over time. Such a system, in the form of an AI agent, could also guide future research, as the software would indicate what additional data or variables are needed to refine the model’s accuracy. This kind of approach is no longer an unrealistic scenario for the development of the social sciences. Instead of being rigid, these laws would resemble adaptable statistical trends rather than absolute rules, requiring regular testing and adjustments across different societies to ensure their applicability. This system is all within our reach now.

So, according to this, laws in the social sciences, if discovered, would potentially be possible. How about theorems, can and should we implement them more often?

Theorems are logically proven statements based on established principles, most common in mathematics and fields grounded in formal logic. Pythagoras’ theorem in geometry, which states that:

Pythagoras’ theorem

a2+b2=c2

(1.1)

In statistics, Bayes’ theorem provides a framework for updating the probability of a hypothesis based on new evidence. Social sciences often borrow theorems, especially in economics, for example, with arrow’s impossibility theorem.

Arrow’s impossibility theorem, developed by economist Kenneth Arrow in 1951, reveals the fundamental limitations of creating a fair voting system. It demonstrates that no voting system can translate individual preferences into a collective decision while satisfying all key fairness criteria when there are three or more options. Specifically, arrow identified four criteria that seem necessary for a fair voting process:

1.

Unrestricted domain, meaning the system should accommodate any combination of individual preferences.

2.

Non-dictatorship, ensuring that no single voter’s preference should dictate the outcome for the entire group.

3.

Pareto efficiency, which requires that if everyone prefers one option over another, the group’s decision should reflect that preference.

4.

Independence of irrelevant alternatives, where the group’s ranking between two options should only depend on preferences between those two options and not be influenced by a third, unrelated option.

His impossibility theorem shows that no voting system can perfectly convert individual preferences into a fair group decision if there are three or more choices. Kenneth Arrow proved that, under reasonable criteria (such as non-dictatorship and Pareto efficiency), it is impossible to satisfy all conditions of fairness in voting without conflicts. This theorem highlights a flaw in democratic systems, as every voting method will inevitably fail to meet at least one fairness standard, revealing the limitations of collective decision-making.

Unlike laws, theorems in the social sciences tend to be less rigid and more adaptable to varying social contexts. This flexibility allows for broader application and makes theorems more accessible in disciplines with complex and variable phenomena. Developing theorems remains a noble goal for any social science, as they provide structured insights without demanding the universality of natural science laws.

However, disciplines outside of economics, such as sociology and psychology, infrequently rely on formal theorems. Instead, approaches like game theory and decision theory—which apply structured reasoning to model social and psychological dynamics—are sometimes used to understand social interactions, particularly within social psychology. While formal theorems may be rare, these frameworks serve to bridge structured analysis with the nuanced, contextual nature of human behavior. As such, we should continue to strive for them in our research.

To reach that goal, however, we first need to step back and establish some foundational concepts in the social sciences—specifically, the notion of a fact, what we mean by “truths,” and how to distinguish between subjective and objective perspectives.

In this book, I aim to demonstrate that theory and theorems are possible when certain rules are followed, which we define here as lessons.

1.2.3 Defining Facts in Social Sciences

In the social sciences, a fact is something we can assert with confidence and substantiate with evidence. Proof implies that the fact has a basis in the physical world, which is crucial because many concepts exist solely in our minds, from what we have heard.

Strict proofs are tricky for the social sciences. We often rely on facts that emerge from observation and evidence rather than formal proofs. For example, it is a widely accepted fact that economic inequality can lead to social unrest. Numerous historical and contemporary studies support this observation by showing a correlation between high inequality and increased protests or social tensions, yet there is no formal “proof” in the mathematical sense. This fact is accepted because it consistently appears in diverse cases and contexts, supported by empirical data. In social sciences, many facts, such as this, are grounded in patterns and correlations rather than deductive proofs, which are more characteristic of mathematics and formal logic.

Some scholars argue that facts are not universal but instead shaped by paradigms and the theoretical frameworks through which they are interpreted. However, even these critics must rely on evidence and observations to support their claims, effectively gathering “facts” to argue against the universality of facts. This highlights a paradox: while questioning the objectivity of facts, the process of substantiating claims through evidence remains essential to scholarly inquiry. Ultimately, this debate underscores the complexity and contextual nature of truth in the social sciences.

To show another layer of complexity, I like to remind my students that the neurosciences show that we, as humans, are not made for the truth. Our minds are, first, adept at constructing ideas that align with our interests, making us more like lawyers than scientists. Therefore, we must approach what others say with caution—not because they intend to deceive, but because we naturally present ourselves and the information we share in a way that serves our purposes, including how we describe reality. In other words, from an evolutionary perspective, humans are not wired to tell the truth. So, we need to make an extra effort to discover it.

So, what is a truth in the social sciences? Here are five examples of well-established social facts that social scientists have consistently found to be true (Table 1.5):

Table 1.5

Well-established social facts

Social fact

Description

Example/explanation

Attributed to

References

Social stratification

Societies are structured in hierarchical layers based on factors like wealth, income, education, and occupation.

People from higher socioeconomic backgrounds tend to have better educational and health outcomes compared to those from lower socioeconomic backgrounds.

Max Weber,

Karl Marx

Weber, M. (1946). From Max Weber: Essays in Sociology.

Marx, K. (1848). The Communist Manifesto.

Cultural universals

Certain cultural practices and norms are found across all human societies.

Practices such as marriage, family units, religious rituals, and moral codes are present in all societies, although their specific forms and meanings may vary.

George Murdock

Murdock, G. P. (1945). “The Common Denominator of cultures.” In Linton, R. (Ed.), The Science of Man in the World Crisis.

The influence of socialization

Socialization is the process through which individuals learn and internalize the values, norms, and behaviors of their society.

Family, schools, peer groups, and media play critical roles in socializing individuals from a young age, shaping their identities, beliefs, and behaviors throughout their lives.

Émile Durkheim,

George Herbert Mead

Durkheim, E. (1956). Education and Sociology.

Mead, G. H. (1934). Mind, Self, and Society.

Group dynamics and conformity

Individuals often conform to group norms and behaviors.

Classic experiments, such as those by Solomon Asch, demonstrate that people tend to conform to group pressure, even when it contradicts their own perceptions or beliefs.

Solomon Asch

Asch, S. E. (1956). “Studies of Independence and Conformity: I. A Minority of One Against a Unanimous Majority.” Psychological Monographs: General and Applied, 70(9), 1–70.

The impact of socioeconomic status on health

There is a strong correlation between socioeconomic status (SES) and health outcomes.

Individuals with higher SES typically experience better health and longer life expectancy compared to those with lower SES, due to factors like access to healthcare and nutritious food.

Michael Marmot,

Richard G. Wilkinson

Marmot, M. (2005). The Status Syndrome: How Social Standing Affects Our Health and Longevity.

Wilkinson, R. G. (1996). Unhealthy Societies: The Afflictions of Inequality.

Others may again object to some of these so-called facts. For example, social structures like stratification may not be as clearly defined or universally applicable as claimed, given that the boundaries between socioeconomic groups are often fluid and context-specific. Additionally, the concept of family, highlighted as a central agent of socialization, differs greatly across cultures and historical periods and is particularly diverse in today’s era of nontraditional family structures. While Durkheim’s ideas on socialization remain foundational, they were formulated in a more rigidly organized society and may not fully address the complexities of contemporary, dynamic social environments. Furthermore, while the correlation between socioeconomic status and health is well-documented, other factors, such as cognitive abilities or access to quality education, might also significantly influence SES and health outcomes, calling for a broader perspective on these relationships.

Social facts are records of what was observed and understood at a specific moment in time. As human beings evolve, so do our tastes, preferences, values, and desires. This means that social facts may be subject to change as we gain new insights. In other words, social facts represent our best understanding now (when the research was conducted), but they do not exclude the possibility that future research might offer different interpretations or conclusions about the same phenomena. In fact, that often happens. In this respect, the social sciences differ significantly from the natural sciences.

For example, the table below lists social facts that were once accepted but later abandoned as new evidence emerged (Table 1.6).

Table 1.6

Examples of social facts that have been abandoned

Abandoned social fact

Description

New knowledge

Description of new knowledge

Phrenology

The belief that the shape and size of the skull determine personality traits and mental abilities

Modern neuroscience

Understanding that personality and mental abilities are determined by brain structure and function, not skull shape

Social Darwinism

The application of Darwin’s theory of natural selection to justify social inequality and imperialism

Modern social theories

Recognition that social inequality is influenced by a complex interplay of biological, historical, economic, and social factors

Lombroso’s Theory of Atavism

The belief that criminals are born with certain physical characteristics that make them prone to crime

Modern criminology

Understanding that criminal behavior is influenced by a range of social, economic, psychological, and biological factors.

The Blank Slate Theory

The belief that individuals are born without any built-in mental content and all knowledge comes from experience

Evolutionary psychology

Recognition that humans are born with certain cognitive predispositions shaped by evolution

The Homogeneous Family Model

The belief that the nuclear family (a mother, father, and children) is the universal and only functional family structure

Diverse family structures

Understanding that functional family structures can vary widely and include single-parent families, extended families, and same-sex parent families

Again, even these truths may alter as new evidence is found. We can observe certain patterns in how these changes in theories occur. New technologies and scientific advancements enable us to observe and understand phenomena with greater accuracy.

For example, neuroscience now complements and expands traditional psychology, offering deeper insights into brain function and behavior.⁶ Similarly, breakthroughs in biology and DNA research provide more precise information about human origins, development, and the inheritance of traits across generations.⁷ While discoveries can emerge from within the same discipline, they are often more likely to come from other fields. This is because established bodies of knowledge tend to be conservative, protective of their frameworks, and resistant to change. In contrast, researchers from other disciplines, without such vested interests, are often more open and encouraged to critically examine the same or similar phenomena but from another perspective.

1.2.4 Balancing Facts and Freedom: Political Influence vs. Academic Independence

Changes in science may arise not only from internal developments but also from external pressures, such as political influence on research directions and outcomes through funding allocations and policy directives.⁸ Such interventions can skew research priorities, pushing scientists to focus on topics that align with political agendas rather than areas of genuine scientific need. This often leads to a narrowing of inquiry, limiting innovation and progress in areas deemed less important by policymakers.

The counterargument is that political influence on research funding and policy can also ensure that scientific efforts address pressing societal issues, such as public health, climate change, or economic inequality.⁹ By prioritizing these areas, policymakers help channel resources toward challenges with immediate relevance and impact, potentially accelerating advancements in fields that directly benefit society. What’s more, democratic oversight of research funding allows elected representatives to reflect public values and needs, aligning scientific inquiry with broader social goals.

The problem is that different politicians have very different, and sometimes diametrically opposed, convictions. As a result, when one political party rises to power, it may completely reverse the policies of the previous government. This can cause significant damage to research efforts, as reversing course may no longer be feasible due to the lost momentum. It often takes years, if not a decade or more, to catch up in areas where a country was once leading.

It can be argued that political interference in scientific research represents a subjective fact because the impact of such influence is often interpreted differently depending on one’s perspective on governance, public needs, and the role of science.

It could also be argued that scientists, due to their training in objectivity and commitment to advancing knowledge for the betterment of humanity, may be better positioned to make impartial judgments about research priorities. Scientists are generally trained to evaluate evidence critically and design studies that minimize bias, aiming for discoveries that benefit society at large rather than specific interest groups. Their expertise and focus on empirical rigor may help them prioritize issues based on scientific merit and potential impact rather than political agendas. Thus, some might contend that leaving more of these decisions to scientists could foster a research landscape that emphasizes genuine scientific needs and innovative exploration over short-term political goals. However, opinions differ on this topic.

It’s a complex issue, as there’s a risk of going too far in one direction where politicians dictate what scientists should prioritize. This is why academic freedom is essential for science to thrive; it allows researchers to pursue knowledge independently, free from external pressures that might compromise the integrity and objectivity of their work. In the long run, a climate of academic freedom fosters diverse, independent exploration—key to advancing science and delivering lasting benefits to society.

This brings us to the next lesson, being able to differentiate between objective and subjective facts.

1.2.5 Objective vs. Subjective Understanding of Facts

In the social sciences, we frequently encounter both objective and subjective facts and understanding the distinction between the two is crucial.

Objective facts are those that can be universally verified—facts that remain consistent regardless of who is observing or when. For example, a country’s population size at a given time is an objective fact, as it is measurable and not influenced by personal opinions.

On the other hand, subjective facts arise from personal experiences, cultural contexts, and individual interpretations. These might include opinions on social norms, attitudes toward certain behaviors, or the emotional impact of societal changes. They are “facts,” but only for that observer.

In contrast to the natural sciences, where objective facts dominate, the social sciences are most often left to carefully navigate between both realms. This mix adds additional layers of complexity to research, as subjective experiences—while essential for understanding human behavior—can be difficult to quantify or validate across different contexts. As researchers, we must, therefore, develop methodologies that can separate, or at least clarify, these different types of truths.

At the same time, subjective facts play an important role in shaping societies and are often central to policy debates. For example, a population’s perception of fairness or justice, though subjective, can influence notable political or economic outcomes. As such, it is important that someone—preferably a researcher—tries to find these truths and report on them as they are valuable for our understanding of society.

This interplay between objective and subjective facts makes the social sciences both challenging and frustrating. Subjective facts can change rapidly. People often change their minds about preferences without fully understanding why or for seemingly arbitrary reasons. What is important here is that researchers never confuse subjective perspectives with universal truths while still recognizing the value of these perspectives in understanding human behavior. The challenge lies in developing robust methods to analyze and interpret subjective facts without losing sight of the objectivity needed for scientific rigor.

1.2.6 The Notion of Truth

A fact is an observable or verifiable piece of information that may be subjective or objective. For example, personal preferences or perceptions can create subjective facts (like a taste preference), while objective facts are universally observable, such as the freezing point of water at sea level. Truth, on the other hand, is a concept aligned with reality and accuracy and has only one standard—it must conform to what is real or factual. If something is not true, it is considered false or even a lie and lies can significantly distort society’s understanding and decisions. Lies disrupt trust and clarity, making truth an essential component of social stability and collective knowledge.

When all else fails, and our universities remain untainted, our collective knowledge stands as truth’s final bastion—the ultimate foundation for renewing a fractured society. You might also envision it as akin to an encyclopedia: even if every university were to be lost, much of its knowledge could be reconstructed from a single, well-preserved copy of a comprehensive encyclopedia.

All great Western civilizations have their encyclopedias, often regarded as the backbone of their cultural heritage. The French have Encyclopædia Universalis, the British have Britannica—originally a Scottish endeavor but since the 1960s managed by Americans and closely associated with the University of Chicago—and the Germans had Meyer’s Konversations-Lexikon, later replaced by Brockhaus. These encyclopedias serve as monumental repositories of knowledge, comparable to modern cathedrals of learning. They stand as guardians against falsehood, offering a kind of guarantee or insurance for knowledge—especially crucial in today’s world. While Wikipedia at the current is some 160 times larger than Britannica, it is also vulnerable; it could be altered, taken offline, or even lost entirely if the internet were to collapse. In such a scenario, where do we turn when we suspect something is untrue?

To illustrate the full horror of the problem, lies are also sometimes knowingly spread by individuals aware that their statements are not factual. In these cases, they act against the foundational aim of science—to advance society by building a reliable base of knowledge. This intentional spread of misinformation not only misguides public understanding but also undermines trust in scientific and academic integrity, potentially stalling progress. When such deliberate deception enters public discourse, it creates obstacles to forming well-informed policies and decisions. Thus, intentional falsehoods in science are especially harmful to the mission of promoting societal development through truth.

The internet is both a blessing and a challenge when it comes to the pursuit of truth. It provides access to more accurate and reliable information than ever before in human history, but it also makes it easier to become trapped in echo chambers where falsehoods are reinforced simply because you are surrounded by like-minded individuals. This duality makes the internet a powerful yet dangerous tool, susceptible to manipulation. On one hand, it offers access to podcasts and vlogs featuring some of the brightest scientific minds on the planet. On the other hand, it fosters the risk of isolating ourselves within forums and communities that perpetuate misinformation, making it increasingly difficult to remain critical and objective. This phenomenon is relatively new, and society has yet to develop effective ways to address it. Paradoxically, the role of universities and the scientific method in safeguarding truth and promoting critical thinking has become more crucial in the age of the internet, not less.

Science is a systematic method for discovering and verifying truths—statements that accurately describe reality. In mathematics, this concept is represented by the symbol “=”, meaning “equals,” which signifies equivalence or factual correctness. The value of these truths lies in their ability to help us, as human beings, navigate and manage our environment, ultimately aiding our survival and prosperity, both as individuals and as a species. This fundamental purpose underpins the entire endeavor of scientific inquiry.

This is important because it allows us to evaluate and rank the significance of individual contributions to science. The first criterion for scientific research is ensuring that the findings are true, but the second is ensuring that these truths hold relevance for society. We must ask, “Is this important?” If not, it might be better to start a different research project. In terms of the first criterion, we want results that are not only true but widely applicable, rather than findings that hold value for only a narrow audience. For instance, knowing that a specific apple-cinnamon shampoo is popular among teenagers in Alaska might not make for groundbreaking science, but it could be highly relevant to a shampoo company targeting that market—and might even spark some curiosity among other persons living near the polar circle.

In a longer answer, science is not just about discovering truths but also about testing ideas in ways that allow them to be proven wrong, a concept known as falsifiability. It relies on empirical evidence gathered through observation and experimentation to support or challenge hypotheses. Scientific inquiry is driven by building and refining theories that explain patterns and predict future outcomes. Importantly, science is an iterative process, constantly evolving as new data and perspectives emerge. Ultimately, its strength lies in not just what it discovers, but in its openness to revision and self-correction. As such, we may say it’s a piece of machinery for combatting falsehood whose job will never be completed.

We publish these truths in scientific papers, which are published in scientific journals and are subject to critical comments and scrutiny—this is known as the double-blind or peer review process, which all scientific journals must show they follow. If what is submitted is deemed to be good science according to the rules of science, it is published; if not, it is rejected, or the authors receive comments to improve the paper. Not all journals are equally rigorous and do not maintain the same high standards, but this is something we will return to later in the book.

Now, let’s look at some examples of how we have discovered truths and what this has meant.

Examples: Scurvy and Car Accidents

Scurvy

In the eighteenth century, scurvy was a common and deadly disease among sailors who spent long periods at sea without access to fresh fruits and vegetables. The prevailing false belief was that scurvy was caused by factors like bad air or poor hygiene. However, James Lind, a Scottish naval surgeon, conducted one of the first clinical trials in 1747 and discovered that scurvy could be prevented and treated by consuming citrus fruits like lemons and oranges. The truth—that scurvy was caused by a deficiency of vitamin C—saved countless lives by leading to the adoption of citrus fruits as a staple in sailors’ diets, effectively eradicating the disease among seafaring populations.

Car accidents

An example closer to our own time: for many years, some people believed that seat belts were unnecessary and even dangerous, thinking they could trap you in a car during an accident. However, extensive research and crash tests have proven the truth: seat belts are one of the most effective ways to protect passengers in a car accident. They significantly reduce the risk of death and serious injury by preventing occupants from being thrown from the vehicle. This truth is now widely accepted, and wearing seat belts has not only become a standard safety practice but is even law in most countries. As a result, countless lives are saved every year because people wear seat belts, reducing fatalities and injuries in car accidents.

We saw that, in the eighteenth century, James Lind’s experiments disproved prevailing myths about scurvy, demonstrating that it was caused by a lack of vitamin C and could be treated with citrus fruits. Similarly, modern research on seat belts proved their lifesaving potential, overturning the mistaken belief that they posed more harm than benefit.

Both examples illustrate how science not only corrects errors but also saves lives by leading to better practices and solutions.

So, is everyone satisfied, with all problems solved now that we have vitamin C and seat belts? No. In science, it’s not enough just to resolve issues—we also seek to understand what is happening, the why. Why was it, for instance, that people who ate oranges didn’t become ill? Understanding the “why” provides us with a deeper insight into underlying factors, which can help us avoid similar issues in the future. This is where the concept of the explicandum comes in.

1.2.7 The Explicandum

Once a fact is identified, the next step is to explain it. For instance, why is the sum of consecutive odd integers starting from 1 always a perfect square? Take the sequence 1 + 3 + 5 + 7 = 161 + 3 + 5 + 7 = 161 + 3 + 5 + 7 = 16, which equals 4². Each sequence of consecutive odd numbers forms a pattern that can be shown to sum to a perfect square, providing a clear example of the kind of relationships we aim to explain. Here, identifying the rule isn’t enough; understanding the underlying reason—why this sum behaves in such a way—requires exploration and explanation.

The reason the sum of consecutive odd numbers starting from 1 always results in a perfect square comes from a mathematical pattern related to how numbers are arranged in a sequence. Specifically, if you add the first n odd numbers (starting with 1), the sum will equal n². This pattern occurs because each sequence of odd numbers forms a structure that can visually or algebraically be represented as a square.

The explicandum of scurvy prevention includes understanding why sailors on long voyages developed scurvy and why vitamin C, specifically, prevents it. While James Lind identified that citrus fruits could treat and prevent scurvy in 1747 through his clinical trials, he didn’t uncover the underlying biochemical reason. The exact role of vitamin C as essential for collagen synthesis, however, was discovered much later in the 1930s by the Hungarian physiologist Albert Szent-Györgyi and the American biochemist Charles Glen King.

Szent-Györgyi first isolated vitamin C in 1928 while studying compounds in paprika and later proved its efficacy in scurvy prevention. King’s work, in parallel, identified vitamin C’s structure and its function in preventing scurvy through experiments on guinea pigs, who, like humans, cannot synthesize vitamin C. (This happens frequently in science, by the way—that great minds work on similar problems in parallel, sometimes without even knowing about it. The classic example is Leibniz and Newton, both developing calculus). Their combined work explained the biochemical reason for scurvy: vitamin C is necessary for collagen synthesis. Without it, the body cannot maintain skin, blood vessels, or connective tissues, leading to scurvy’s characteristic symptoms.

This full explicandum—why sailors contracted scurvy and why vitamin C is essential for health—bridged the gap between Lind’s empirical discovery and a scientific understanding of vitamin C’s biochemical role.

The explicandum of seat belt efficacy encompasses understanding both why seat belts are effective in reducing injury and death in car accidents and why this became widely accepted despite initial resistance. Initially, some believed that seat belts could be dangerous, fearing they might trap individuals in vehicles during accidents. However, extensive research, crash tests, and real-world data demonstrated that seat belts did indeed significantly lower the risk of fatality and severe injury by restraining passengers and preventing ejection from the vehicle.

The underlying reason for this efficacy, explained by physics and biomechanics, is that seat belts help distribute the force of a collision over more durable parts of the body and prevent sudden, fatal impact against the car’s interior or ejection. This scientific evidence led to policy changes and public acceptance, with seat belt laws now standard in most countries. The explicandum, therefore, addresses both the physical mechanism of seat belt safety and the process through which scientific evidence overcame public skepticism, leading to one of the most effective interventions in vehicle safety today.

We can attribute much of the early science and development of modern seat belts to Volvo Cars, a pioneer in automotive safety. In 1959, Volvo engineer Nils Bohlin invented the three-point seat belt, a groundbreaking design that combines a lap belt with a diagonal shoulder strap to distribute collision forces across the chest and pelvis, reducing the risk of injury. This innovation was based on extensive biomechanical studies and crash tests, demonstrating the importance of a restraint system that could secure passengers more effectively.

Volvo then took a remarkable step in public safety by making the three-point seat belt design freely available to other car manufacturers, prioritizing public health over competitive advantage. The effectiveness of seat belts has since been supported by research from institutions worldwide, with crash test data consistently showing the reduction of fatalities and serious injuries by as much as 50% for passengers who wear seat belts. The introduction of crash test dummies in the 1970s allowed further refinement, enabling researchers to measure the impact of collisions on the human body, ultimately helping establish seat belts as a standard safety feature. The story is valuable also because it shows that it’s not only researchers who do science, who engage in advanced experiments; employees in industries do as well.

The next step is to identify what kind of explicandum we are dealing with for a given problem (Table 1.7).

Table 1.7

Types of explicandum and their application

Type of explanation

Definition

Usage in social sciences

Deductive explanation

Uses general principles or theories to explain specific instances (if A, then B).

Common in theory testing, where a hypothesis derived from a general theory is tested empirically. Example: Using social contract theory to explain patterns of compliance within different societies.

Probabilistic explanation

Describes the likelihood or probability of an outcome occurring rather than certainty.

Often used in sociology and psychology, where behaviors or events are likely but not guaranteed (e.g., “those with higher education levels are more likely to vote”).

Functional or teleological

Explains phenomena by their function or purpose within a system.

Common in anthropology and sociology to explain practices or institutions by the purpose they serve within a society. For instance, religion may be explained by its function in social cohesion.

Genetic explanation

Explains current phenomena by tracing their origins or historical development.

Used to understand how present-day social systems or norms emerged over time, such as tracing the origins of capitalism or democracy.

Causal explanation

Identifies specific cause-and-effect relationships between variables.

Often found in experimental research or case studies, where an independent variable is shown to directly affect a dependent variable (e.g., examining how economic inequality impacts crime rates).

Interpretative explanation

Focuses on the meanings and subjective experiences of individuals to explain social phenomena.

Common in qualitative research, where researchers seek to understand individuals’ motivations and perceptions (e.g., explaining protest participation by studying participants’ personal grievances and values).

Narrative explanation

Uses storytelling or sequences of events to explain social processes, often including context.

Applied in historical and sociological research, particularly in case studies or ethnographies, to describe how events unfold over time (e.g., explaining social movements by tracing key events and leaders’ roles).

Normative explanation

Explains phenomena by referencing norms, values, or ideals.

Often used in political science or ethics, where social behaviors are understood in relation to normative standards (e.g., explaining voting behavior through civic duty or societal expectations).

Comparative explanation

Uses comparisons across different groups or contexts to identify patterns and causes.

Utilized in cross-cultural studies or cross-national research to highlight differences and similarities that may explain behaviors or institutions (e.g., comparing welfare systems across countries to understand variations in social support policies).

Systemic explanation

Looks at how parts of a system interact to produce an outcome, often within a larger structure.

Employed in political science, economics, and sociology to examine how different institutions or components interact within society to create stability or change (e.g., explaining economic growth through interactions between government policy and market forces).

Choosing the right type of explanation, or explicandum, in social sciences is challenging because each type offers different strengths and limitations in terms of scientific rigor. Deductive and causal explanations, for example, are often seen as more “scientific” because they rely on clear cause-and-effect relationships and generalizable theories, mirroring approaches in natural sciences. Probabilistic explanations, while less definitive, are also widely used and accepted in social sciences, as they allow researchers to capture trends without assuming certainty.

Functional explanations, which emphasize the purpose of behaviors within a social system, and interpretative explanations, focusing on subjective meaning, are often considered less “scientific” because they rely more on context and individual perspectives, making them harder to test or generalize. Historical or genetic explanations add depth by tracing the origins of phenomena, yet they can lack experimental rigor and often deal with unique, non-replicable events. Finally, normative and narrative explanations help understand phenomena within moral or cultural contexts, but these explanations tend to be descriptive rather than predictive, leading to debates about their scientific validity.

The choice of explicandum thus depends on the research goal: to produce empirically testable results, a deductive or causal explanation might be preferred, while for understanding subjective experiences, interpretative approaches are more appropriate. Each type adds value in specific contexts, but matching the explanation method to the research question to balance scientific rigor with meaningful insights is essential.

These various forms of understanding the reasons behind events didn’t develop overnight. Many foundational ideas emerged either during what we call the Classical Period in ancient Greece or within the broader ancient world. Others evolved later, particularly during the Enlightenment, mostly across central Europe and the British Isles.

The Classical Period laid the groundwork in philosophy and logical inquiry, establishing concepts like deductive reasoning, while the Enlightenment accelerated advancements in scientific methodology and empirical research. Through this historical progression, we inherited multiple frameworks for examining causality, probability, and explanation, each building on the contributions of earlier thinkers to deepen our understanding of scientific inquiry. It is this tradition that many Asian countries are following today, often making greater advancements than we are and, in a sense, taking over this (allowing the sentimentality for a second) beautiful intellectual tradition. That is also ok, as science is a gift to all of humanity (Table 1.8).

Table 1.8

Type of explicandum, origin, and scientific strength

Type of explanation

Scientific rigor

Age

Author(s)

References

Deductive explanation

High, due to clear, testable predictions.

Ancient (fourth century BCE)

Aristotle, Isaac Newton

Posterior Analytics; Principia Mathematica (1687)

Causal explanation

High, particularly in experimental research.

Enlightenment (eighteenth century)

David Hume, J.S. Mill

An Enquiry Concerning Human Understanding (1748); A System of Logic (1843)

Probabilistic explanation

Moderate to high, empirical application.

Early modern (eighteenth century)

David Hume, Pierre-Simon Laplace

A Treatise of Human Nature (1739); Théorie Analytique des Probabilités (1814)

Comparative explanation

Moderate, context-dependent but useful.

Early modern (nineteenth century)

Alexis de Tocqueville, Arend Lijphart

Democracy in America (1835); Comparative Politics (1971)

Functional/teleological

Moderate, context-dependent and hard to generalize.

Ancient (fourth century BCE)

Aristotle, Émile Durkheim

Physics; The Division of Labor in Society (1893)

Genetic explanation

Moderate, explores unique phenomena.

Modern (nineteenth century)

Charles Darwin, Max Weber

On the Origin of Species (1859); The Protestant Ethic and the Spirit of Capitalism (1905)

Systemic explanation

Moderate, useful for complex systems.

Modern (twentieth century)

Ludwig von Bertalanffy, Talcott Parsons

General System Theory (1968); The Social System (1951)

Interpretative explanation

Moderate to low, descriptive.

Modern (nineteenth century)

Wilhelm Dilthey, Max Weber

Introduction to the Human Sciences (circa 1883); Economy and Society (1922)

Normative explanation

Moderate to low, primarily descriptive.

Ancient (fourth century BCE)

Aristotle, John Stuart Mill

Nicomachean Ethics; On Liberty (1859)

Narrative explanation

Moderate, descriptive with low predictive power.

Ancient (fifth century BCE)

Herodotus, Clifford Geertz

Histories; The Interpretation of Cultures (1973)

Looking at this long list, normative interpretations are not inherently weak science; in fact, they are crucial when studying phenomena closely tied to cultural values. For example, when analyzing the competitive advantages of specific cultures, considering normative elements is essential to avoid oversimplifying and missing key nuances. Ignoring these aspects can lead to an incomplete picture, sacrificing accuracy for simplicity. By incorporating normative perspectives, researchers can better capture the complex interplay between cultural values and economic or social outcomes, providing a more comprehensive and realistic analysis.

This also means that there is usually, or often, a best-fit type of explicandum for a given problem (Table 1.9).

Table 1.9

Best fit of types of explicandum for specific problems

Type of problem

Best-suited explicandum(s)

Explanation

Predicting natural phenomena

Deductive

Deductive explanations work well for predictable, cause-and-effect relationships found in natural sciences, such as in physics or chemistry, where A consistently leads to B, as in Newton’s Laws of motion.

Understanding social trends

Probabilistic

Probabilistic explanations are useful for examining social trends, where outcomes cannot be predicted with certainty but can be associated with likelihoods, such as forecasting voting behaviors or economic shifts.

Explaining biological evolution

Genetic

Genetic explanations are well-suited to problems of evolutionary biology, where tracing the historical development and adaptation of species provides insight into current forms and functions, such as in Darwinian evolution.

Evaluating policy impacts

Comparative, causal

Comparative explanations allow for policy impact evaluations by comparing outcomes across regions or demographics, while causal explanations help determine if a policy directly influenced certain results, like comparing healthcare systems.

Explaining cultural behaviors

Functional, interpretative

Functional and interpretative explanations are valuable for understanding cultural behaviors, focusing on the purposes these behaviors serve within a society and the subjective meanings held by individuals, as in studies of rituals or traditions.

Assessing moral frameworks

Normative

Normative explanations are ideal for assessing ethical or moral issues, such as the fairness of justice systems, as they examine values and societal standards, providing context for discussions on “what should be.”

Understanding systemic inequalities

Systemic, probabilistic

Systemic explanations address how structural factors contribute to inequalities, while probabilistic explanations identify trends or likelihoods in affected populations, useful for understanding income disparities or educational access.

Analyzing historical events

Genetic, narrative

Genetic explanations analyze the origins and development of historical events, while narrative explanations provide a chronological account that contextualizes individual and group actions, as in analyzing the causes of a war or a social movement.

Assessing public health interventions

Causal, probabilistic

Causal explanations are used to determine the effectiveness of public health interventions, such as vaccination programs, while probabilistic explanations are used to predict health trends, such as disease spread patterns.

Investigating ecological systems

Systemic, functional

Systemic explanations capture the interdependence within ecosystems, while functional explanations address the roles that species or processes play, useful for problems in conservation biology and sustainability studies.

For a student or researcher, this means we should not only articulate the reasons for selecting a particular method but also explain why alternative methods were not chosen. This approach demonstrates a thorough understanding of the research landscape and shows the researcher has critically assessed multiple methodologies, ultimately selecting the best fit for the specific problem. Being able to discuss both the chosen and dismissed methods strengthens the validity of the research and highlights the alignment of the method with the research objectives. This critical evaluation process is essential for ensuring that the method is not only effective but also contextually appropriate for addressing the research question.

Validity refers to the extent to which a concept, conclusion, or measurement is well-founded and accurately represents what it is intended to measure or describe. In other words, it ensures that you achieve what you set out to do. Reliability refers to the consistency, dependability, or repeatability of a measurement, process, or result. This means that if you repeated the same test under the same conditions, using the same method, you would obtain the same results.

There are two primary forms of scientific reasoning: the deductive method, introduced by classical thinkers such as Aristotle, and the inductive method, which was more formally developed much later by Francis Bacon. You either start with observations and attempt to build a theory, or you begin with a theoretical idea and then gather evidence to support it. What becomes clear here, when you think about it for a second, is that the deductive method, in a way, relies on the inductive process, as theories don’t simply emerge out of thin air (Table 1.10).

Table 1.10

Primary forms of scientific reasoning

Method

Definition

Example

First described by

Book

Inductive

Starts with specific observations, which are used to form broader generalizations and theories. It is often called “bottom-up” reasoning.

Observing that apples fall from trees leads to the theory of gravity.

Francis Bacon

Novum Organum (1620)

Deductive

Begins with a general theory or hypothesis and tests this with specific cases or observations. Often referred to as “top-down” reasoning.

Starting with the hypothesis that “all humans are mortal,” then deducing that “Socrates is human, therefore Socrates is mortal.”

Aristotle

Prior analytics (fourth century BCE)

In the social sciences, a third general form of reasoning, called abductive reasoning, is also commonly used. Introduced by the American philosopher Charles Sanders Peirce in various papers in the early 1900s, abductive reasoning involves forming a likely hypothesis or explanation based on incomplete evidence, often relying on educated guessing.¹⁰ For example, noticing that the grass is wet and inferring that it probably rained uses abduction; rain is the simplest explanation for wet grass. We could describe abductive reasoning as a kind of “premature inductive method,” since it involves making a provisional inference based on incomplete information. Unlike inductive reasoning, which builds a general theory from repeated observations, abduction leaps to the most plausible explanation based on limited evidence, often without full confirmation. It’s also a practical method and likely aligns more closely with how we make judgments in daily life, as we rarely have complete information. However, many would argue that this method is less scientific.

An often-overlooked dimension in methodologies is not only explaining which methods were chosen for a specific study but also providing a full description of the context in which the study was conducted.

1.2.8 Contextualizing Facts in Social Science

The role of context in social science facts is fundamental to how we understand and interpret human behavior and societal structures. Unlike in natural sciences, where facts are often seen as universally applicable, social facts are deeply intertwined with the specific conditions of time, place, and culture.

A practice that may seem logical and acceptable in one society could be entirely alien or even unacceptable in another. For example, economic systems, family structures, and social norms all vary greatly depending on historical and cultural contexts. Researchers need to recognize that the conclusions drawn from a study in one context may not be generalizable to other contexts without major adjustments.

This fluid nature of social facts also means that they can evolve over time as societal values and norms change. As a result, social scientists must be cautious when interpreting data, ensuring they understand the full spectrum of influences that shape behavior within specific environments. Ignoring context can lead to flawed analyses and, ultimately, inaccurate conclusions. In fact, some of the most valuable insights in social science come from examining how different contexts shape similar behaviors or outcomes.

In anthropology, accounting for context is crucial for an accurate understanding of a culture. For instance, the practice of gift-giving has different meanings across societies. In some Indigenous cultures, gift-giving is not merely a gesture of kindness but part of a system of social obligation and reciprocity. If an anthropologist were to study this practice without understanding its cultural context, they might mistakenly interpret it as simply an exchange of goods, missing its deeper societal role.

To properly account for context, anthropologists immerse themselves in the social and cultural norms of the community they are studying, often through long-term fieldwork and participant observation. By doing so, they can start to grasp how such practices are embedded in larger social systems, ensuring a more accurate and respectful interpretation of the facts they uncover (Table 1.11).

Table 1.11

Context in the social sciences and how to handle it

Dimension of context

How to account for it

What can go wrong (false interpretations)

Cultural context

Conduct fieldwork and understand local customs, language, and values. Use cultural insiders to interpret behaviors.

Misinterpretation of cultural practices, assuming behaviors hold the same meaning across cultures.

Historical context

Study the historical background, policies, and significant events influencing the society.

Overlooking historical influences can lead to oversimplified analyses or ignoring long-term social changes.

Economic context

Include economic factors like income distribution, job opportunities, and market dynamics. Use economic data alongside social data.

Failing to account for economic conditions can distort social behavior, making it appear as a personal preference rather than an economic necessity.

Political context

Examine government policies, laws, power dynamics, and political ideologies shaping the environment.

Ignoring political systems can lead to misunderstanding the motivations behind actions, missing how power or regulation constrains behavior.

Geographic context

Consider the geographic location, urban vs rural settings, and environmental factors.

Misapplying urban data to rural settings (or vice versa) can distort findings, as behaviors are often tied to geographic realities.

Social norms and values

Observe social expectations, taboos, and the moral frameworks in place. Use qualitative methods (e.g., interviews) to gather insights.

Assuming uniformity across societies can cause misunderstandings, such as interpreting practices as immoral or irrational in a different cultural lens.

Technological context

Consider the level of technological development and access to digital resources. Study how technology shapes social interaction.

Ignoring technological disparities can obscure access to information, digital literacy, and their impact on social behavior.

Institutional context

Investigate the role of institutions (schools, churches, legal systems) in structuring behavior.

Overlooking institutional influence can result in attributing behaviors to individual agency rather than systemic factors.

Demographic context

Account for age, gender, race, ethnicity, and social class in shaping behavior. Use diverse samples for broader generalization.

Ignoring demographic diversity can lead to overgeneralization or reinforce stereotypes about certain groups.

Temporal context

Acknowledge the time period and whether social norms and conditions have evolved. Compare data over time.

Treating past data as applicable to the present without accounting for temporal changes can result in outdated conclusions.

Psychological context

Consider individual cognitive biases, motivations, and personal experiences. Use psychological theories to interpret behavior.

Without accounting for psychological factors, interpretations might ignore the role of individual perception and cognition in shaping behavior.

Media/communication context

Evaluate how media consumption and communication methods shape perceptions and opinions.

Overlooking media influence can cause misinterpretation of public opinions or trends driven by media narratives.

So, when a reviewer or a research colleague asks about the context of our study, it’s not just an idle question meant to poke at us but a genuine concern that we may not have accounted for all the factors influencing our findings. Without considering these dimensions, our results might only apply to a specific group of people and lack generalizability.

At the university, our goal is to present findings that are as universal as possible—that’s why it’s called a “university,” where we study universal truths. If our research can be applied more broadly, more people will benefit. However, in most cases, this isn’t entirely realistic for the social sciences. This isn’t a failure of the discipline but a reflection of its inherent complexity. We learn to be grateful even for small advances, even if they only apply to certain groups at specific times and places. What’s crucial is that we are sure of our findings and how we present them: we must clearly explain the context and avoid overclaiming the scope or significance of our research.

Besides that, our findings should be interesting.

1.2.9 Common Sense in Social Science

What types of problems should science tackle? Are we to solve straightforward issues like “how to cross the street?” Is that important? For example, when crossing, we look left, then right, then left again before stepping off the curb. Why, do we do that? The final glance to the left—the side closest as we step forward—confirms that no car is approaching unexpectedly. The left-right-left method we teach children is very important but is often considered common sense, meaning it has become universally known and, in a way, already “solved.” (That it is not always practiced is another problem, and that could still be of interest to some social sciences, like the study of psychology.)

However, this method was not always common practice. It took time to develop, and, tragically, many children lost their lives before such precautions were widely adopted. Today, we are more conscious of enforcing these safety protocols, along with other traffic safety measures, to protect lives, not to “figure them out.”

Science might still be interested in specific aspects of this behavior, such as how crossing patterns vary with age or how quickly one should cross.

Similarly, while people have known how to make a fire for millennia, scientific methods can refine this knowledge. For example, research suggests lighting a fire from the top of the wood stack rather than the bottom allows it to burn more efficiently with better oxygen flow and less smoke. So even old “truths” can sometimes be revisited.

However, science’s primary purpose is to solve complex, unsolved problems that go beyond common sense—challenges like building reusable rockets, creating self-driving cars, or designing a financial system that avoids severe economic cycles. For instance, Elon Musk’s SpaceX succeeded in developing a reusable rocket, while tech companies are on the verge of mastering self-driving cars, or so they say—being a bit overoptimistic on the timing. However, economists still debate how to stabilize financial systems, and we seem still to be far away from a solution. Unlike crossing the street, these tasks are at the forefront of scientific inquiry precisely because they require our immediate attention. These are the types of problems we, as researchers, should tackle, delving deeply enough to uncover and understand their root causes.

1.2.10 From Correlation to Causation

Unfortunately, most social science studies halt at correlation analysis, identifying that two factors move together in a particular way. These studies might state there is a correlation, implying a potential causal relationship—but without proving causation, we are left with uncertainty. Often, authors subtly suggest causation by framing the correlation as significant, even though genuine causation has not been shown. This limitation highlights a common shortfall in social science research: studies frequently stop too soon. One way forward could be for journals to encourage or even require a deeper exploration of causation to foster a more rigorous standard of evidence.

But how do we move from correlation to causation? One approach is to consider multiple potential causes (C1, C2, … Cn) that contribute to an effect (E1). These causes may also lead to other effects (E2, … En), but studying or isolating all these variables is often not feasible. Researchers should isolate each variable without overlooking critical influences to establish causation, which might require a broader study design. Additionally, causation may involve intermediary steps—factors that aren’t immediately visible but significantly impact the ultimate outcome. Recognizing these intermediary effects can be essential to capturing the full causal pathway and moving closer to scientifically meaningful conclusions.

Exercise: How to Conduct a Real-World Causal Investigation

Let’s walk through how causation is determined in practice. Imagine the case of a sunken ship, a severe incident where many lives were lost. An investigation is launched with extensive resources, aiming to uncover the reasons for the sinking. The investigative committee’s objective is clear: to identify the cause—or series of causes—that led to the disaster, examining both direct and intermediate factors. They know the final effect, “the ship sank,” but need to work backwards to understand how and why.

The investigation begins with an inspection of physical evidence—specifically, the shipwreck on the seafloor. Experts carefully examine the structure and look for clues. For instance, they might notice that the bow door, where cars typically enter, is lying detached on the ocean floor. This prompts the question, why did the door fall off? An initial hypothesis might suggest a mechanical failure, such as rusted bolts. Divers are sent to examine the bolts more closely and confirm that they seem intact, so rust is ruled out. They then discover bulges around the door’s attachment points, suggesting the door was forced open by a significant external impact. Experts consider possible sources of such an impact—waves, currents, or even explosives. After further analysis, they concluded that the bulges indicate force from an explosive device. At this point, the investigators may pause their inquiry, not because they have uncovered the ultimate cause, but because they have reached the limits of what their expertise can determine. If explosives are indeed identified as the cause, new questions arise, such as who was responsible and why it was done. However, these questions are likely to be pursued by a different investigative team with the necessary expertise in these areas.

This process illustrates the technique of listing possible causes and systematically ruling out those that don’t match the evidence. The goal is to narrow down options to one or a few causes with strong supporting evidence. When only one cause remains, backed by substantial evidence, the team can confidently establish a cause-effect relationship, leading to a well-supported conclusion.

The question for students is: Is the investigation method above sufficient to deduce a clear cause-and-effect relationship? If not, why?

Students should consider whether all possible factors were thoroughly examined and ruled out if there was enough evidence to establish a direct link between the explosion and the sinking and if the timeline of events aligns with this explanation. They should also question whether alternative explanations might account for the damage observed and if the investigative tools used were precise and reliable enough to confirm the explosive force as the definitive cause.

After the discussion, consider the following: To critically assess whether the explosion that damaged the bolts and caused the door to fall off truly led to the sinking of the ship, students should consider several key questions. First, did the ship have other structural vulnerabilities that could have also contributed to its sinking, independent of the door failure? Second, was the door’s detachment due to the explosion sufficient to allow water to flood the ship, or might there have been other points of entry? Third, what evidence exists to show that the timeline of events—from the door’s detachment to the ship sinking—aligns with the sinking being a direct result of this breach? Fourth, is there a possibility that the explosion impacted other critical areas of the ship, such as the hull or engine compartments, which might also explain the rapid sinking? Fifth, could other environmental factors, like severe weather or strong currents, have exacerbated the breach once the door detached, accelerating the sinking? Sixth, is there documented evidence or eyewitness testimony that confirms the door was indeed the initial point of failure rather than another weak point in the ship’s structure? Finally, how reliable are the investigative methods and tools used to detect signs of an explosive force, and could any alternative explanations account for similar damage? These questions help ensure a thorough examination, moving closer to a well-supported causal conclusion.

Now, how many of these considerations did the students raise during the discussions for this exercise? Did they examine alternative explanations, question the reliability of the investigative tools, and assess whether the evidence fully supports a direct link between the explosion and the sinking? Additionally, did they explore the sufficiency of the timeline and consider other contributing factors? Tracking these points will reveal the depth of critical thinking applied in analyzing cause and effect in this scenario.

This exercise is valuable because it illustrates the progression of a logical line of thinking—questions and answers—as well as hypotheses and guesses, demonstrating what a scientific inquiry might look like in practice.

In our inquiry, we use different scientific methods to systematically gather information and test hypotheses, ensuring that our findings are reliable and valid. Common methods include deductive reasoning, which moves from general principles to specific conclusions, and inductive reasoning, which involves drawing general conclusions from specific observations. We also employ experimental methods, where variables are manipulated to observe causation, and observational methods, where data is gathered without interference to understand natural conditions. Finally, comparative methods are helpful in social sciences for drawing insights from differences and similarities between societies or groups, helping us interpret causation across varying contexts. Each of these methods contributes uniquely to our ability to rigorously analyze, explain, and predict outcomes.

We see in the exercise above that investigating causation can be a long and painstaking process. There are statistical techniques available that give us an approximate sense of causation, which are increasingly popular, likely because they require less time. Additionally, the format of scientific articles, which often follows the concise style of the natural sciences, doesn’t always allow room for in-depth, extended explanations of causation. This culture of brevity within academic publishing limits the exploration of longer, detailed causal analyses, which is unfortunate. More comprehensive explanations could provide a deeper understanding of complex relationships and mechanisms. Perhaps we should consider shifting to longer, in-depth inquiries instead of the 20-page quick fixes we often label as scientific articles. What are your thoughts?

The statistical techniques for identifying causation aim to go beyond simple correlation by uncovering directional relationships between variables. Regression analysis is commonly used, where multiple independent variables are tested to see if they predict an outcome variable, helping to control for other potential influences. Path analysis and structural equation modeling (SEM) extend regression by allowing researchers to examine more complex models, including mediating and moderating effects, to understand indirect causation pathways. Instrumental variable (IV) analysis helps address causation by using variables that are related to the independent variable but not directly to the dependent variable, which can control for unobserved confounders. Difference-in-differences (DiD) is another technique used mainly in policy analysis, where researchers compare outcomes over time in a treatment group and a control group to determine if a specific intervention caused a change. Finally, propensity score matching (PSM) is useful for causal inference by creating comparable groups in observational studies, mimicking the effects of random assignment and helping to reduce bias. Each of these techniques is designed to strengthen causal claims but always requires careful interpretation, as statistical evidence of causation does not imply absolute certainty.

If you take the example of the shipwreck investigation method and combine it with the suggested statistical techniques, the limitations of relying solely on statistics become obvious. Statistical methods like regression or structural equation modeling can indeed reveal relationships between variables, but they do not provide direct, tangible insights. Unlike a physical inspection of a shipwreck, which can reveal unexpected physical causes—such as damage from an external force—statistical models work with predefined variables and will miss all unknown or unmeasured factors. Without the ability to observe the physical evidence firsthand, researchers using only statistical techniques but only looking at a few variables easily at hand will likely overlook important causal relationships entirely. In cases where subtle or unknown variables play a role, these techniques alone will fall short, as they assume that all relevant variables are included in the model. Therefore, while useful for testing hypothesized relationships, statistical methods cannot replace detailed empirical investigation and will need to be complemented with physical observations or experimental controls for a comprehensive understanding of causation.

In the social sciences, where no universal laws exist, causation is normally as good as it gets.

Using two variables—causal strength and cross-cultural applicability—we can evaluate the degree to which a social science project aligns with scientific principles. This approach allows us to assess how well a study achieves rigor and generalizability, addressing whether it meets certain scientific standards effectively. By analyzing these aspects, we can better understand a project’s robustness and relevance across contexts (Table 1.12).

Table 1.12

The scientific rigor and applicability evaluation framework (SRAF)

Degree to which A leads to B

Low cross-cultural applicability (applicable to few cultures)

Medium cross-cultural applicability (applicable to some cultures)

High cross-cultural applicability (applicable to many cultures)

Low causal strength

Example: Cultural interpretations of beauty standards—Studies showing that beauty ideals vary widely across cultures, with little consistency between A (exposure to specific media) and B (personal beauty ideals).

Example: Workplace dynamics influenced by hierarchy—Studies where respect for hierarchy varies by culture, such as between the United States and Japan, but do not yield consistent patterns globally.

Example: Gender and job satisfaction—Research on the general tendency for job satisfaction to differ by gender, often found in some but not all cultural contexts.

Medium causal strength

Example: Attitudes toward individualism vs. collectivism—Studies showing that A (exposure to collectivist media) has a moderate influence on B (preference for collectivist values), mainly within Asian countries but not universally.

Example: Family dynamics and child behavior—Studies in Western vs. eastern cultures that show moderate correlations between parental involvement and child outcomes, often influenced by cultural norms.

Example: Advertising and consumer behavior—Studies indicating a medium level of causation where A (exposure to advertising) influences B (consumer purchase intent), generalizable across many cultures with variations.

High causal strength

Example: Language acquisition in isolated communities—Strong findings in isolated cultures showing A (exposure to specific language structures) directly leads to B (specific cognitive patterns), but limited outside that context.

Example: Educational techniques and academic performance—Studies with robust causation in countries with similar educational systems but less in others; shows moderate generalizability globally.

Example: Economic incentives and work productivity—High causal relationship between A (incentives) and B (productivity), consistently found across many cultures, though with some contextual differences.

Low cross-cultural applicability—meaning results are relevant only to a few cultures—does not imply that such studies lack value. In fact, these localized issues are often precisely the kind of problems that need targeted investigation. The same perspective applies to the degree to which A leads to B; many studies begin by identifying correlations and stop there. Yet, if we aim to advance rigorous science, we should strive to go beyond correlation and assess the extent of causation. By doing so, we contribute not only to understanding the variables but also to refining the scientific quality and potential generalizability of our findings.

Another crucial step toward achieving greater scientific rigor is understanding when and how to use hypotheses effectively. Throughout this book, no special preference is given to quantitative methods over qualitative ones. What truly matters is that, as a researcher, you capture the truth, clearly presenting the facts and providing explanations. The approach you use—whether qualitative or quantitative—is ultimately irrelevant if you follow a rigorous scientific method.

1.2.11 Hypotheses Testing

Several critical factors must be considered when formulating and working with hypotheses in research. A hypothesis is essentially a specific, testable prediction about the relationship between variables, so it must be structured clearly and align with your research question. We note that:

1.

Clarity and specificity: Hypotheses must be clear and specific like research questions. For instance, if you are studying the impact of social media usage on academic performance, a vague hypothesis like “social media affects grades” would not be sufficient. A clearer version could be “Increased social media usage leads to lower academic performance among university students.” This gives a direct expectation that can be tested.

2.

Testability: A hypothesis must be testable through empirical research methods. It should suggest relationships between measurable variables. For example, a hypothesis like “students who use social media for more than 2 hours a day will have a GPA 10% lower than students who use it less” can be tested using surveys and GPA data.

3.

Null and alternative Hypotheses: A strong hypothesis usually comes with a null hypothesis (H0), which assumes that there is no relationship between the variables, and an alternative hypothesis (H1), which states that there is a relationship. For instance:

H0: Social media usage has no effect on academic performance.

H1: Increased social media usage decreases academic performance.

The null hypothesis allows for a rigorous test because we are essentially testing the probability that the null hypothesis can be rejected based on the evidence.

4.

Directionality: Some hypotheses are directional, suggesting the specific direction of the relationship, such as “increased social media usage leads to decreased academic performance.” Others are non-directional and simply suggest that there is a relationship but without specifying the direction, such as “social media usage affects academic performance.”

5.

Consider the variables: Hypotheses should be constructed with careful consideration of both the independent variable (the variable you manipulate or observe changes in, such as the amount of social media usage) and the dependent variable (the outcome, such as academic performance). If these variables are not operationalized clearly, it becomes difficult to test the hypothesis meaningfully.

6.

Causality vs. correlation: Ensure that your hypothesis does not imply causality unless you are using a method that can test it, like a randomized controlled trial (RCT). For example, saying “social media causes lower academic performance” suggests a causal link, which is difficult to prove without controlled experimentation. In most observational studies, it is safer to suggest correlation rather than causation.

In summary, constructing a hypothesis involves ensuring specificity, testability, and a clear relationship between the independent and dependent variables. We must also account for the formulation of the null and alternative hypotheses, understand whether the hypothesis is directional or non-directional, and avoid implying causality unless it can be rigorously tested.

As we saw, hypothesis testing typically involves two main types of hypotheses: the alternative hypothesis (H1) and the null hypothesis (H0): H1 is a statement predicting that there is an effect or difference (something significant has happened), while H0 is a statement suggesting that there is no effect or difference (nothing significant has occurred). The outcome of hypothesis testing can lead to one of two decisions: Accept (A) or Reject (R), which we denote accordingly.

If the evidence supports H1, we reject H0, indicating that the hypothesis under test appears valid. In the same way, if the data aligns with H0, we fail to reject H0, suggesting that any observed effect is likely due to chance rather than a real phenomenon. Therefore, each hypothesis results in a decision matrix that helps clarify whether observed data supports a genuine effect or merely a random occurrence. This process of systematically testing hypotheses is central to both establishing new theories and verifying existing ones.

Here are example hypotheses about a drug’s effectiveness, covering both the null (H0) and alternative (H1) hypotheses which we shall use in this section, simply because it is easy to relate to:

1.

Null hypothesis (H0): “The drug has no statistically significant effect on reducing symptoms in patients compared to a placebo.”

2.

Alternative hypothesis (H1): “The drug significantly reduces symptoms in patients compared to a placebo.”

These hypotheses are used to test whether the drug provides any measurable improvement. The null hypothesis (H0) assumes no effect, meaning any observed changes are due to chance, while the alternative hypothesis (H1) assumes that the drug genuinely reduces symptoms. The hypothesis test would aim to either reject H0 or fail to reject it based on the data, supporting or questioning the drug’s effectiveness (Table 1.13).

Table 1.13

Possible outcomes of hypotheses

Hypothesis

Decision

Outcome

Interpretation

Example scenario

H0 (null)

Accept H0

Correct decision (true negative)

No effect is detected, and this is accurate.

A drug is tested, and it truly has no effect.

H0 (null)

Reject H0

Type I error (false positive)

Incorrectly claims there is an effect.

A new product seems effective but is not.

H1 (alternative)

Accept H1

Correct decision (true positive)

An effect is detected, which accurately exists.

The experiment shows a new teaching method works.

H1 (alternative)

Reject H1

Type II error (false negative)

Fails to detect an effect when there is one.

A safety measure is effective, but testing misses it.

We are going into details about the philosophy of science later in Chap. 4, but we have to mention two contributions related to how we are to understand hypotheses now, that all researchers should be aware of:

As my uncle once explained to me in his dusted library when I was still a child, his face lighting up with the enthusiasm that academics often exhibit when they’ve grasped a good idea, the Austrian–British philosopher Karl Popper argued that scientific theories can never be conclusively proven as true. Instead, he proposed that science advances through falsifiability—the idea that we can only truly prove what is not true by demonstrating when a hypothesis does not hold up under testing. This concept is known as the principle of falsification, which posits that a theory is scientific only if it can be tested in such a way that it can, in principle, be proven false.¹¹

For example, if a scientist claims, “all swans are white,” Popper would argue that this statement can never be definitively proven because it would require observing every swan in existence. Yet, finding a single black swan would falsify the hypothesis, proving that “all swans are white” is not universally true. This approach contrasts with traditional methods that seek confirmation, emphasizing that empirical science is marked not by verification but by rigorous attempts to disprove hypotheses.

Popper’s work significantly shaped the philosophy of science by setting falsifiability as a criterion for demarcating scientific statements from non-scientific ones.

However, it was the British philosopher Bertrand Russell who, for the most part, would come to shape how we use hypotheses today in the social sciences:

Bertrand Russell had reservations about Karl Popper’s principle of falsification. While both were committed to scientific rigor and empiricism, Russell didn’t fully accept Popper’s claim that falsification was the only legitimate foundation for scientific progress. Russell believed in the importance of both falsification and verification, and he argued that science, to be useful, must rely on a mix of both confirmatory evidence and attempts at refutation. And that is pretty much where we still are today for the social sciences.

Russell’s view of science was more inclined toward a probabilistic approach, where hypotheses gain or lose credibility based on accumulating evidence rather than absolute falsification alone. This perspective allows for theories to be considered “probably true” even if they can’t be definitively proven. Russell also appreciated the value of inductive reasoning—generalizing findings from specific observations—which Popper was critical of.

In summary, Russell shared Popper’s dedication to a skeptical, evidence-based approach to science, but he didn’t consider falsification alone adequate for scientific progress. One might argue that if we strictly followed Popper’s criteria, findings in the social sciences could potentially be less engaging, as the standards might be overly restrictive for exploring complex human phenomena.

An overview in greater detail of the statistical techniques we use to either accept or reject a hypothesis is listed below, followed by a description of who contributed to the mathematics (Table 1.14).

Table 1.14

Statistical techniques used for testing hypotheses

Statistical method

Description

Application to drug example

T-test

Compares the means of two groups to assess if they differ significantly. Often used in experiments with small sample sizes.

In testing the drug, a t-test can compare the mean health outcomes of the treatment group (receiving the drug) and the control group (not receiving the drug) to see if the difference in outcomes is statistically significant.

Z-test

Used to test if the sample mean differs significantly from the population mean, typically for larger samples.

Here, the Z-test could determine if the average effect of the drug in the sample deviates significantly from the known standard or population data, assuming a sufficiently large sample size.

Chi-square test

Assesses the association between categorical variables and is suitable for frequency data.

In the drug example, the chi-square test can evaluate if there’s an association between treatment (drug vs. placebo) and patient improvement (yes vs. no), determining if improvement rates differ by group.

ANOVA (analysis of variance)

Compares means across three or more groups to identify significant differences. Often used when multiple conditions are present.

If different dosages of the drug are tested, ANOVA can determine if mean outcomes differ significantly across dosage groups, helping to identify the most effective dosage level.

Regression analysis

Evaluates the relationship between dependent and independent variables, often for prediction.

Regression could model the relationship between dosage and improvement level, showing if dosage significantly predicts outcomes, while accounting for other factors like age or prior health status.

P-value and confidence intervals

The p-value indicates the probability of observing the data if the null hypothesis is true. Confidence intervals give a range where the true effect likely falls.

A low p-value (e.g., below 0.05) would suggest rejecting the null hypothesis (no effect of the drug). Confidence intervals around the mean improvement level would provide a range for the drug’s true effect, adding interpretative strength.

The table outlines statistical tools relevant to testing H₀ ​ (null hypothesis) and H₁ (alternative hypothesis) for drug efficacy, each offering insights into whether observed differences or relationships are meaningful.

Most of these contributions are a team effort done over less than half a century. The British statistician William Sealy Gosset (1876–1937) developed the t-test in 1908 while working at Guinness Brewery to address the challenge of small sample sizes in quality control. Karl Pearson (1857–1936), also a British mathematician, introduced the z-test and chi-square test, with the latter designed to analyze categorical data and associations, marking significant advances in statistical theory. Another British statistician, Sir Ronald Fisher (1890–1962), created ANOVA (analysis of variance) in the 1920s to examine mean differences across multiple groups, greatly benefiting experimental research, especially in agriculture. Francis Galton (1822–1911), another influential British scientist, laid the groundwork for regression analysis to investigate heredity, which Pearson and Fisher later expanded on his ideas. Fisher popularized the concept of the p-value and it remains essential in hypothesis testing. The confidence interval, essential for estimating precision, was formalized by the Polish statistician Jerzy Neyman (1894–1981), bringing rigor to inferential statistics. These foundational methods collectively underpin modern statistical analysis across scientific fields.

We have already introduced the names of some key scientists, so now let’s take a closer look at the history of the social sciences—not as historians, but from the perspective of what is essential and beneficial for a future researcher to understand.

1.3 Foundations of Social Sciences: Philosophy of Inquiry

Overture

If you visit the renowned Sorbonne University in Paris, head to the small square in front of the church, adjacent to Avenue Saint-Michel. Facing the church, look to the trees on your left, and you’ll find a statue—almost hidden, especially in the thick foliage of summer (Fig. 1.2). Unless you walk closer, you will not even see the head of the man which the statue was made for. This statue, somewhat discreet now, once stood prominently in the center of the square. Its relocation tells a story of how the perception of ideas change. Here is the story:

Fig. 1.2

Place de la Sorbonne. Source: https://​fr.​wikipedia.​org/​wiki/​Place_​de_​la_​Sorbonne#/​media/​Fichier:​Place_​de_​la_​Sorbonne,_​Paris_​5e.​jpg

The term social sciences began to be used in the nineteenth century, with thinkers like the French philosopher Auguste Comte, who formulated the doctrine of positivism, and the British philosopher John Stuart Mill, who refined the methodology of scientific inquiry in social and moral contexts.

While the study of human society dates to ancient times, it was not until the 1800s that these disciplines were systematized under the collective name we recognize today as the “social sciences.” Comte’s concept of positivism introduced a philosophical approach that emphasized knowledge based on observable, measurable facts rather than speculative reasoning. This shift mirrored the development in historical studies from Herodotus’s narrative-driven, anecdotal accounts to Thucydides’ more systematic, evidence-based approach, which sought to objectively explain events rather than simply recount them as stories.

Comte advanced this tradition by establishing a systematic, cross-disciplinary method for analyzing social phenomena, contending that society and human behavior could be examined with the same rigor as natural phenomena. His Discours sur l’ensemble du positivism, published in 1848, stands as a bold manifesto—a comprehensive project aiming to consolidate human knowledge about emotions or sentiments and channel them toward the progress of civilization. In many ways, Comte’s vision parallels Balzac’s ambition for his series Comédie Humaine, where Balzac sought to capture the essence of society through fiction. Comte, however, approached this goal from a scientific and technical perspective rather than an artistic one, envisioning a unified science of humanity.

I suppose you could argue, by the same token, that the great Russian novelists—including my favorite, Marcel Proust, with his series À la recherche du temps perdu—anticipated the field of psychology. Through their nuanced exploration of human motivations, inner conflicts, and complex relationships, these writers delved into the depths of the human psyche long before psychology became a formal discipline. Their works offer detailed insights into emotional experiences, memory, and the effects of society on individuals, themes that modern psychology also seeks to understand. In this way, literature not only preceded psychology but provided a foundation upon which much psychological inquiry builds.

While these literary works offer profound psychological insights, this book will not include lists or reviews of novels or other works of literature. Such an undertaking would be better suited to a separate project focused specifically on the intersection of literature and psychology. Similarly, we only engage with history insofar as it relates to the history of thought, rather than providing a broad historical account—though I cannot help but acknowledge some of my favorites, such as Gibbon, Mommsen, and Ranke from the past, and Niall Ferguson among the more compelling contemporary historians. We note that Ranke, in particular, was more committed to factual accuracy compared to Gibbon and Mommsen. We see this development in all the sciences.

The descriptions in social sciences also became more technical, contrasting with earlier narrative styles. While this shift fostered accuracy, some argue that human understanding and recall are often better supported by storytelling, which is why Herodotus remains popular for his engaging style, even if Thucydides is valued for his precision. Yet, this evolution in method answered a growing demand for factual accounts over purely inspirational stories, as leaders and scholars increasingly saw the advantages of truth over amusement.

Comte argued that knowledge should be based on empirical evidence and systematic observation, rejecting metaphysical or supernatural explanations. He introduced the law of three stages, which suggested that societies evolve through three intellectual stages: the theological, the metaphysical, and finally the scientific, or “positive,” stage. In the scientific stage, Comte believed that society could be understood and improved through the rigorous application of the scientific method to social phenomena. In this sense, we are all positivists as social scientists. His ideas laid the foundation for sociology as a discipline, aiming to study society with the same objectivity as natural sciences. Psychology was not the first social science, though; that would probably go to political science (Table 1.15).

Table 1.15

The birth of the social sciences

Social science

Founder(s)

Initial books

Approximate start date

Economics

Physiocrats (e.g., François Quesnay), Adam Smith

Quesnay’s Tableau Économique (1758), Smith’s The Wealth of Nations (1776)

1758

Sociology

Auguste Comte

Comte’s Course of Positive Philosophy (1830–1842)

1830

Political science

Aristotle (early ideas), later Niccolò Machiavelli and Thomas Hobbes

Machiavelli’s The Prince (1532), Hobbes’ Leviathan (1651)

Sixteenth century (modernized in the nineteenth century)

Psychology

Wilhelm Wundt

Wundt’s Principles of Physiological Psychology (1874)

1874

Anthropology

Edward Burnett Tylor, Franz Boas

Tylor’s Primitive Culture (1871), Boas’ The Mind of Primitive Man (1911)

Late nineteenth century

Criminology

Cesare Beccaria

Beccaria’s On Crimes and Punishments (1764)

1764

Public administration

Woodrow Wilson, Max Weber

Wilson’s The Study of Administration (1887), Weber’s Economy and Society (1922)

1887

Gender studies

Simone de Beauvoir, Betty Friedan

de Beauvoir’s The Second Sex (1949), Friedan’s The Feminine Mystique (1963)

Mid-twentieth century

The social sciences initially emerged as descriptive disciplines rooted in philosophical and speculative traditions, rather than adhering to the systematic methodologies of the natural sciences. For instance, early sociological works, such as Émile Durkheim’s “The Division of Labor in Society” (1893), were heavily influenced by philosophical thought and aimed to understand societal structures more intuitively than through empirical analysis. Similarly, in the early twentieth century, Max Weber’s “The Protestant Ethic and the Spirit of Capitalism” (1905) employed a blend of historical and interpretive analysis, a hallmark of speculative rather than empirical methodologies. Even in modern times, there are debates about the degree to which fields like anthropology or economics continue to incorporate philosophical perspectives alongside empirical ones, as seen in the works of figures like Michel Foucault and John Maynard Keynes, who blend theory with practice in complex ways (“Discipline and Punish,” 1975; The General Theory of Employment, Interest and Money, 1936).

Early contributions, such as Aristotle’s work in political thought and the Physiocrats’ economic theories, focused on normative and observational approaches. These intellectual efforts were more reflective of medieval and early modern scholarship, prioritizing description and interpretation over empirical rigor.

However, the nineteenth century marked a pivotal shift as social sciences began to define themselves more clearly as scientific disciplines. Auguste Comte’s sociology aimed to model the study of society on the methods of the natural sciences, emphasizing positivism and empirical observation. Similarly, Wilhelm Wundt established psychology as an experimental science, relying on structured methodologies akin to those used in biology and chemistry.

However, and here we come back to the main story, Comte’s vision of sociology as a kind of secular religion, known as the “Religion of Humanity,” also led to a decline in his influence over time. In his “third stage,” Comte suggested a positivist society with sociologists as “priests” of moral and social order, almost like a clergy. This concept backfired in terms of credibility because it clashed with the objective, empirical goals he initially set for his project. Rather than liberating science from metaphysical or religious confines, Comte ironically ended up creating a system that closely mirrored a quasi-religious institution.

This approach partly explains why his statue was relocated to a less prominent position on the Place de la Sorbonne, where his head is scarcely visible among the trees, especially in summer. How fitting, in a sense, that he who aimed to ground human understanding in empirical science would be symbolically overshadowed as if veiled by the very ideals he sought to advance. At the same time, I can’t help feeling sorry for him.

Intermezzo

History shows us that the fate of groundbreaking thinkers has not always been a kind one. Mendel and Semmelweis, for instance, were only acknowledged posthumously for their discoveries in genetics and antiseptics, respectively. Others suffered even more extreme consequences for challenging the status quo: Giordano Bruno was burned at the stake for his heretical views on an infinite universe, while Galileo faced threats of torture and was forced to recant his support for heliocentrism. Darwin famously delayed publishing his theory of evolution for years, fearing backlash from both the scientific and religious communities. Such cases reveal how new ideas can be met with skepticism, resistance, or outright persecution, even when the ideas prove transformative in hindsight.

John Stuart Mill, in contrast to Comte, is remembered today because his contributions were both flexible and broadly applicable, making them adaptable to a variety of intellectual frameworks. His inductive reasoning methods, especially the Methods of Agreement and Difference, provided systematic approaches to identifying causal relationships by comparing cases where a phenomenon occurs with cases where it does not. Known collectively as Mill’s methods, these tools offered a practical, accessible framework for exploring causal links in social phenomena and laid a methodological foundation for social sciences. This approach established a way to handle both quantitative and qualitative elements, acknowledging the complexity and variability inherent in human behavior.

Unlike Comte’s rigid positivism, Mill’s contributions allowed for the incorporation of individual agency and qualitative dimensions within social inquiry, bridging quantitative and qualitative methods. His works, particularly A System of Logic (1843), put forth principles of inductive reasoning that became instrumental across disciplines. Mill argued that while social phenomena could be studied scientifically, they required methods flexible enough to account for human complexity. Combining ethical insights, scientific rigor, and intellectual openness, this balanced approach made Mill’s ideas enduringly relevant and widely influential (Fig. 1.3).

Fig. 1.3

The statue of John Stuart Mill located in Victoria Embankment Gardens in London. Sources: https://​commons.​wikimedia.​org/​wiki/​File:​The_​John_​Stuart_​Mill_​Statue,_​Victoria_​Embankment_​-_​London.​jpg

The term sciences sociales (social sciences) likely came into wider use later and was indeed influenced by English usage. While sciences humaines focused broadly on all human-related studies, “sciences sociales” grew to specifically denote studies centered on societal structures and social relationships.

Initially, Comte and others used physique sociale (social physics) to describe the emerging study of society. This term was inspired by natural sciences and emphasized a systematic approach to understanding social behavior, drawing from the methods of physics and patterns of mechanics.¹² Yet, the phrase sciences humaines (human sciences) became prominent in France, especially as scholars expanded beyond sociology to include fields such as psychology, anthropology, and history. “Sciences humaines” emphasized the broader study of human experiences and behavior beyond just society’s structural aspects.

Grand Finale

Throughout the nineteenth and twentieth centuries, a range of thinkers shaped the foundations of what was to become the social sciences. The German polyglot Karl Marx (1818–1883) introduced the concept of class struggle and economic determinism in understanding societal development, positioning economics as a force in social change. Émile Durkheim (1858–1917) formalized sociology through concepts like “social facts,” which he believed exerted control over individuals, shaping behaviors and norms. Durkheim’s contemporary, Max Weber (1864–1920), focused on rationalization, authority, and bureaucracy, examining the ways in which culture and religion influence capitalism, most famously in The Protestant Ethic and the Spirit of Capitalism.

Georg Simmel (1858–1918) contributed nuanced ideas about social interactions, urban life, and the influence of money, which he explored in The Philosophy of Money. Around the same time, Sigmund Freud (1856–1939) introduced psychoanalytic theory, offering insight into the unconscious mind’s role in behavior, thus merging psychology with sociology and cultural studies. Italian sociologist Vilfredo Pareto (1848–1923) furthered economic sociology, particularly with his Pareto Principle, which has applications across social sciences in understanding inequality and resource distribution.

In the mid-twentieth century, Talcott Parsons (1902–1979) shaped functionalist theory, proposing his AGIL framework as a way of understanding how societies maintain stability. Herbert Marcuse (1898–1979), a key figure of the Frankfurt School, critiqued consumer culture and modern capitalism, introducing elements of Marxist and psychoanalytic thought to explore the impact of industrialized society on individuality. Each of these thinkers contributed critical methods and theories that allowed the social sciences to develop into systematic, interdisciplinary fields, offering tools to understand complex social phenomena within varied contexts.

Postwar Era

After World War II, the social sciences experienced significant transformations, both in focus and form. In the early postwar years, scholars were preoccupied with understanding social order, political stability, and economic growth, influenced by the war’s effects and the Cold War’s geopolitical tensions. This was the era when the United States began dominating research funding and academic thought, leading to the institutionalization of disciplines such as sociology, psychology, and political science within American universities. The rise of federal funding for research, especially in the United States, also encouraged a shift from book-length studies to shorter, peer-reviewed articles that were easier to produce quickly and disseminate widely.

During the 1960s and 1970s, the focus in the social sciences expanded to include topics like civil rights, gender equality, and social justice, fueled by the political and social movements of the time. This period also marked a shift toward quantitative research, further emphasizing data-driven analysis over purely theoretical frameworks. Journals became the primary venue for publishing research, and articles became more standardized, promoting methodological rigor over the more narrative, interpretive styles typical of earlier monographs. As a result, the preference for article publication solidified, with journals acting as a gatekeeper for what constituted legitimate research.

Anglo-Saxon Dominance

By the 1980s, English had overtaken French and German as the primary language of the social sciences, primarily due to the dominance of American academic institutions and journals. The global influence of American research universities and the spread of English-language journals like American Journal of Sociology and American Political Science Review reinforced this shift, while European researchers increasingly adopted English to reach a broader, international audience. This movement effectively marginalized German and French, which had been dominant in the social sciences during the nineteenth and early twentieth centuries.

Today, the preference for English and the emphasis on journal articles have shaped a global academic landscape. The standardization has allowed for greater international collaboration, though it has also raised concerns about the exclusion of diverse perspectives and the privileging of certain methodologies and topics aligned with “American priorities.” Despite these changes, the social sciences continue to evolve, balancing quantitative rigor with renewed interest in qualitative approaches and interdisciplinary work to address increasingly complex global challenges.

Asia Ascendency

It will be interesting to see how China and much of Asia will shape the future of the social sciences. When I first began visiting Chinese universities about 15 years ago, many still featured Confucian institutes at their core. I remember walking around Chinese campuses in different parts of the country and suddenly coming across a special building or department dedicated to the study of Confucius, amid the economics or political science department. I must admit, I initially found it a bit exotic, but over time, I have often wondered whether this might be an alternative—or at least a valuable complementary—path forward. It gave the impression of a cautious approach, as though social sciences were positioned alongside, or even as an extension of, moral philosophy, blending societal inquiry with traditional moral frameworks.

Path Forward: Rethinking the Future of Social Science

Evolution and popularity, though, are not the same as relevance and value, and as we move into the twenty-first century, critical perspectives on the social sciences have increased. Many argue that the social sciences often repurpose common sense, offering little genuinely new insight. Instead, the argument is that we tend to repackage familiar concepts under new terminology, relying on rebranded theories and buzzwords. This critique suggests that rather than advancing novel ideas, the field sometimes focuses more on linguistic innovation than substantive breakthroughs. The result can, at times, feel like a cycle of science-by-slogan, where established ideas are repeatedly reframed instead of rigorously tested or used for practical purposes.

There is also the view that, for some, the real benefit of studying these fields lies less in the content of the education and more in the access to school networks and social capital that a diploma provides. Critics suggest that such degrees can serve as credentials rather than indicators of actual expertise, raising questions about the fundamental value of social science education and its role in professional life. These critiques point to a broader issue: the extent to which social sciences can create tangible value beyond the ivory towers. Remember, we are not the humanities; we cannot as easily justify our work as a pursuit of building better humans (German: Bildung, in the sense “personal and intellectual cultivation,” “holistic education,” or “character formation”). We are set to solve problems.

Natural sciences face their share of critique, particularly around areas perceived to have stalled in innovation, like certain branches of physics. Many had expected breakthroughs in understanding dark matter or quantum mechanics by now, yet progress has been slow and sometimes elusive. The awarding of the recent Nobel Prize in Physics to two “engineers” for their advancements in AI has further sparked debate. Some argue that it highlights a shift in scientific priorities toward applied research and technological innovation over foundational theoretical work. Critics suggest that this trend, while valuable, may overshadow the need to explore fundamental questions in physics. At the same time, this development has drawn attention to the evolving relationship between natural and applied sciences, as AI’s impact is seen as transformative across disciplines, including physics itself.

Perhaps society has grown accustomed to constant, often rapid, progress—especially given the past 150 years of transformative technological, medical, and social change developments. This expectation may make periods of slower advancement, particularly in fundamental sciences, feel like stagnation or failure, even if they’re part of natural cycles in scientific exploration. For example, after major breakthroughs such as the development of electricity, antibiotics, or computing, the process of consolidating and building upon these discoveries often requires decades of incremental progress.

Then, almost unexpectedly, a new wave of innovation emerges, transforming our understanding and capabilities, as we’ve recently seen with the GLP-1 receptor breakthrough in medicine. Other examples include the impact of CRISPR in genetic engineering, the transformative potential of artificial intelligence after years of groundwork, and the possible advances in quantum computing. These sudden leaps forward are often the result of years, even decades, of foundational work that quietly sets the stage for revolutionary change.

Moreover, many technological advances over the past century have been so visible—think of aeroplanes, the internet, or smartphones—that they set a standard for the immediate impact that theoretical sciences, like physics, rarely match. When profound discoveries do emerge, such as in particle physics or cosmology, they often appear abstract and disconnected from everyday life. Yet, these fields often drive essential foundational knowledge, even if the societal impact isn’t immediately visible. The expectation for relentless progress may obscure the value of incremental advances and the natural ebb and flow in scientific discovery. As such, science is not a profession for the impatient.

1.3.1 Standards of Scientific Rigor

The natural sciences have long served as a gold standard for scientific rigor and methodological precision, setting benchmarks that many social sciences aspire to emulate.¹³ This influence is rooted in the reproducibility, predictability, and objectivity that natural sciences like physics, chemistry, and biology strive to achieve.

In social sciences, achieving similar rigor is challenging because human behavior and social dynamics are inherently more variable and context dependent. Yet, comparing methodologies between the two helps highlight where social sciences can evolve to incorporate more empirical rigor while acknowledging that exact replication of natural science standards may not always be feasible.

For instance, social scientists can refine statistical methodologies, adopt more systematic experimental designs, and increase data transparency to improve replicability and generalizability. Yet, social sciences may also need their own benchmarks to reflect the complexity of human society accurately.

As we move forward, it’s essential for the social sciences to draw inspiration from the natural sciences while also considering unique standards that account for cultural, historical, and situational variability. This approach keeps the focus on developing robust, credible research that balances scientific rigor with the distinctive nuances of studying human behavior.

While the natural sciences often set the standard for scientific rigor, the social sciences don’t necessarily need to mirror their methodologies or rely heavily on, say, complex mathematics to achieve their objectives.

Typically, our mathematics is straightforward and focuses more on clear reasoning than advanced computation. The priority is to ensure that conclusions are solidly based on observable facts. Beyond that, only basic arithmetic and descriptive statistics may be required. Even more essential, though, is a clear logical structure (syllogism)—displaying premises and conclusions in a way that is easy to follow within the text, making reasoning transparent and accessible to the reader.

The unique focus of social sciences—on human behavior, social structures, and cultural nuances—often requires a different set of approaches. Qualitative methods, interpretative analysis, and a sensitivity to context are often invaluable, offering insights that traditional natural science methods cannot easily capture. Mathematical modeling, statistics and empirical methods certainly have their place and can strengthen social science research, but the diversity of human experience and complexity of social systems often demand flexible approaches even here. By balancing empirical rigor with methodologies suited to human dynamics, the social sciences can hopefully continue to benefit from natural science standards without being limited to them.

Natural sciences are also, in various ways, drawn from social science insights, especially in fields that address human behavior and societal impacts. For instance, environmental science increasingly incorporates social science research to understand how human activities influence ecosystems and how policy can mitigate these impacts. Psychology has informed neuroscience by offering models of cognition and behavior, leading neuroscientists to investigate how brain structures support mental processes described by psychologists. In public health, social science research on socioeconomic factors has shaped epidemiological models, showing how factors like income and education levels impact health outcomes. Even in physics, science communication strategies borrow from social science to make complex theories accessible to the public, enhancing education and outreach. Overall, social science has broadened the scope of natural science research, highlighting the interconnectedness of human and natural systems. In other words, it’s a two-way system of collaboration and inspiration for the benefit of both.

What we should keep in mind are both the differences and similarities between these domains (Table 1.16).

Table 1.16

Dimensions of differences between the natural sciences and the social sciences

Dimension

Natural sciences

Social sciences

Example

Objective

Discover and explain natural laws governing the physical world.

Understand and explain human behavior, societies, and cultural systems.

Natural science aims to explain gravity, while social science examines the effects of globalization on societies.

Methods of investigation

Primarily quantitative methods, such as experiments and measurements, controlled variables.

A combination of qualitative and quantitative methods, such as surveys, interviews, case studies, and statistical analysis.

Physics uses controlled lab experiments, while sociology may use interviews to study societal trends.

Predictability

Often, highly predictable outcomes are based on laws of nature (e.g., gravity, thermodynamics).

Less predictable due to the complexity of human behavior and social interactions.

In chemistry, chemical reactions are highly predictable, but predicting human behavior in economics is more uncertain.

Variables

Often simpler with fewer variables that can be isolated and controlled.

Complex with many interacting and hard-to-isolate variables.

A physics experiment isolates temperature changes, whereas a social science study on education might deal with socioeconomic, psychological, and cultural factors.

Reproducibility

High reproducibility in controlled lab settings.

Lower reproducibility due to the variability of human and social contexts.

The same experiment can be replicated in biology with identical results, but replicating a social science study on voting behavior may produce different outcomes.

Ethics and impact

Ethical considerations usually involve the treatment of living organisms and environmental concerns.

Ethical concerns are often more complex, involving human rights, privacy, and consent.

A natural science experiment might ensure no harm to animals, while social sciences need to ensure informed consent and respect privacy in psychological studies.

Scope of generalization

Results are often generalized to all similar physical systems.

Results are often context-dependent, with more caution applied to generalization across different societies or groups.

Newton’s laws apply universally to objects, whereas a sociological finding on family dynamics in one culture may not apply in another.

Examples of studies

Study of chemical reactions, planetary motion, and genetic sequencing.

Study of education systems, human behavior, economics, politics, and culture.

A physicist studies the laws of motion, while a sociologist studies the impact of income inequality on crime rates.

It’s not that the social sciences should simply copy the natural sciences or that they are failing in comparison, but rather that, from time to time, they need a reminder of the rigor and standards required to conduct science. Even though the natural sciences themselves can go for long periods without significant progress, as we’ve seen with physics in recent decades, and also make mistakes, they maintain a commitment to scientific rigor. The social sciences, however, sometimes seem to forget—whether willingly or not—and drift away from these essential principles. In this respect, the natural sciences act as a wake-up call, urging them to pull themselves together and refocus.

Concluding Remarks

We will recall that the natural sciences have existed much longer, having developed significantly earlier than the social sciences. Their origins trace back to ancient civilizations, although the modern scientific method we use today truly took shape during the Scientific Revolution of the sixteenth and seventeenth centuries. This transformative period saw pioneering figures such as Galileo, Newton, Leibniz, and Bacon, who laid foundational principles in experimentation, observation, and reasoning. On the other hand, the social sciences have a history of less than two centuries to look back on, about half the age, you could say.

Then there is the much older cousin, the humanities, which encompass disciplines like history, philosophy, and literature. The humanities have deep roots in ancient traditions, focusing on understanding the human condition through critical analysis, interpretation, and reflection. Unlike the social sciences, which often employ empirical methods, the humanities emphasize qualitative approaches and the exploration of meaning.

The combination of social sciences, natural sciences, and humanities makes the study of science at the university an ideal place to understand science holistically. Each field contributes uniquely to our understanding, with natural sciences offering methods for testing physical phenomena, social sciences providing insights into human behavior, and the humanities cultivating a deeper exploration of human culture and thought. Using the scientific method in fields like history and linguistics, the humanities add essential perspectives on human experience and cultural evolution. Philosophy, meanwhile, extends these disciplines by questioning foundational assumptions, pushing us to contemplate the nature of knowledge, ethics, and the limits of human understanding.

I also want to emphasize the value of fiction in the social sciences; to me, fiction reveals the many ways we can explore and depict human existence, and it does so in language that is accessible and pleasant to explore. While social science often relies on structured frameworks and empirical data, fiction allows us to see the nuances and subtleties of human experience from a unique, narrative perspective. Through stories, novels, and essays, we gain insights into people’s motivations, cultural contexts, and emotional landscapes in ways that data alone may not capture. Or, as one of my mentors, Russell A. Berman, who was for many years the editor of Telos, says in his book title, “Fiction Sets You Free.”¹⁴ Literature does not only show you the limits of what social situations are possible but has the power to illustrate complex ideas through relatable scenarios, enabling readers to grasp social concepts on a more intuitive level. In this way, it complements the social sciences by deepening our understanding of human nature and society.

This blend of disciplines highlights how interconnected human inquiry is: sciences provide empirical rigor, social sciences address variability and context, and humanities examine meaning and values. Together, they empower students with a nuanced framework to explore challenges, ask questions, foster curiosity, and confidently engage with the world. For this reason, I find it impossible to study the social sciences without simultaneously engaging with the humanities, and I don’t think we should separate the two as researchers.

Embracing a blend of disciplines opens diverse pathways for integrating different fields in research. This integration is not simply a merging of ideas but can take various forms, each with unique benefits and approaches. For instance, we can approach problems from a multidisciplinary angle, where researchers from distinct fields work on a shared issue, but each maintains their methodologies and perspectives. Alternatively, an interdisciplinary approach allows for deeper integration, where researchers combine their expertise to create new frameworks and methods that bridge their respective fields.

Another research approach is cross-disciplinary research, which examines one discipline through the concepts or methodologies of another, providing fresh insights without necessarily merging them fully. Transdisciplinary research, though, takes integration further, bringing together academic and non-academic knowledge by involving stakeholders outside of academia—such as policymakers or community groups—to address complex societal challenges.

Each of these approaches enriches the research process and helps adapt methodologies to the problem at hand, whether it’s an academic query or a real-world issue needing a practical solution. By leveraging these varied methods, researchers can better address the multifaceted nature of contemporary challenges, making their work both novel and applicable across different domains (Table 1.17).

Table 1.17

Combinations of blending disciplines

Approach

Description

Difference

Multidisciplinary

Experts from different fields collaborate on a shared problem, each using their own disciplinary lens.

Combines distinct perspectives without blending methods or frameworks. Each discipline maintains autonomy.

Interdisciplinary

Integrates methods, theories, and frameworks from multiple disciplines to create a unified approach.

Blends disciplines to form a cohesive approach, aiming for synergy between fields.

Cross-disciplinary

Examines one discipline through the concepts or methods of another, often to gain new insights.

Uses another field’s viewpoint without full integration or collaboration across disciplines.

Transdisciplinary

Goes beyond traditional disciplines to address complex problems, often involving stakeholders outside academia (e.g., communities, policymakers).

Focuses on solving real-world problems by transcending disciplinary boundaries, merging knowledge beyond academia.

The ultimate benchmark for the social sciences should not be limited to how well they align with the natural sciences or the depth of specialization within specific disciplines, but rather to their capacity to improve human well-being by fostering stability, peace, and prosperity in society. This includes promoting critical thinking, encouraging reflection on ideas and institutions, and challenging assumptions that shape our understanding of social dynamics and their impact on humanity. Let this then be our project.

References

American Psychological Association (2022) Publication manual of the American Psychological Association (7th ed.). American Psychological Association.

Becker GS (1976) The economic approach to human behavior. University of Chicago PressCrossref

Berger A (2023) Society, politics, and economics in shaping scientific endeavors. FullSci. https://​fullsci.​net/​society-politics-and-economics/​

Berman RA (2007) Fiction sets you free: literature, liberty, and Western culture. University of Iowa PressCrossref

Cialdini RB (2003) Influence: the psychology of persuasion. Harper Business

Dauben JW, Richmond GJ, Schmidt JA (2022) Peirce on abduction and diagrams in mathematical reasoning. In: Danesi M (ed) Handbook of cognitive mathematics. Springer, Cham, pp 1–25. https://​doi.​org/​10.​1007/​978-3-031-03945-4_​25Crossref

Durkheim É (1893) The division of labor in society. Alcan, Paris

Foster J (2023, November 5) Natural sciences vs social sciences: a comparative guide. Jamie Foster Science. https://​www.​jamiefosterscien​ce.​com/​natural-science-vs-social-science/​

Foucault M (1975) Discipline and punish: the birth of the prison. Pantheon Books

Georgescu-Roegen N (1966) Analytical economics: issues and problems. Harvard University Press, Cambridge, MACrossref

Georgescu-Roegen N (1971) The entropy law and the economic process. Harvard University PressCrossref

Hobbes T (1651) Leviathan. Andrew Crooke

Kahneman D (2011) Thinking, fast and slow. Farrar, Straus and Giroux

Keynes JM (1936) The general theory of employment, interest and money. Harcourt, Brace & World

Kuhn TS (1962b) The structure of scientific revolutions (50th anniversary ed.). University of Chicago Press

Mill JS (1874) A system of logic, ratiocinative and inductive: being a connected view of the principles of evidence and the methods of scientific investigation, 8th edn. Harper & Brothers, New York

Miller CA, Neff MW (2022) The politicization of science: how political pressures influence research agendas and integrity. Sci Technol Hum Values 47(4):567–589. https://​doi.​org/​10.​1007/​s11024-022-09459-8Crossref

Newton I (1687) Philosophiæ naturalis principia mathematica [Mathematical principles of natural philosophy]. Royal Society.

National Human Genome Research Institute (2021) Human origins and ancestry. https://​www.​genome.​gov/​dna-day/​15-ways/​human-origins-ancestry

Ohm GS (1827) Die galvanische Kette, mathematisch bearbeitet [The galvanic circuit investigated mathematically]. T. H. Riemann.

Organisation for Economic Co-operation and Development (OECD) (2020) Addressing societal challenges using transdisciplinary research. https://​www.​oecd.​org/​en/​publications/​addressing-societal-challenges-using-transdisciplinar​y-research_​0ca0ca45-en.​html

Popper KR (1959) The logic of scientific discovery. Hutchinson

Popper KR (1963) Conjectures and refutations: the growth of scientific knowledge. Routledge & Kegan Paul

Popper KR (1983) Realism and the aim of science. Hutchinson

Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131Crossref

UNESCO (2016) World social science report 2016: challenging inequalities: pathways to a just world. https://​unesdoc.​unesco.​org/​ark:​/​48223/​pf0000245904

Weber M (1905) The Protestant ethic and the spirit of capitalism. Charles Scribner’s Sons

Selected Readings

Albert H (1991) Traktat über kritische Vernunft. Mohr Siebeck

Bachelard G (2002) La formation de l’esprit scientifique: Contribution à une psychanalyse de la connaissance objective. Presses Universitaires de FranceCrossref

Berthelot J-M (1996) Épistémologie des sciences sociales. Presses Universitaires de France

Bortz J, Döring N (2016) Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler, 5. Auflage. Springer

Bryman A (2016) Social research methods, 5th edn. Oxford University Press

Creswell JW, Creswell JD (2018) Research design: qualitative, quantitative, and mixed methods approaches, 5th edn. Sage

Desrosières A (2008) La politique des grands nombres: Histoire de la raison statistique. La Découverte

Esser H (1999) Soziologie: Allgemeine Grundlagen. Campus Verlag

Kuhn TS (1962a) The structure of scientific revolutions. University of Chicago Press

Maxwell JA (2013) Qualitative research design: an interactive approach, 3rd edn. Sage

Opp K-D (2020) Methoden der empirischen Sozialforschung, 3. Auflage. Springer VS

Passeron J-C (2006) Le raisonnement sociologique: Un espace non poppérien de l’argumentation. Albin Michel

Popper KR (2005) The logic of scientific discovery. RoutledgeCrossref

Quivy R, Van Campenhoudt L (2017) Manuel de recherche en sciences sociales, 5e édition. Dunod

Saldaña J (2021) The coding manual for qualitative researchers, 4th edn. Sag

Schäfer B, Kuhl R (2019) Einführung in die empirische Sozialforschung, 9. Auflage. Beltz Verlag

Footnotes

1

Georgescu-Roegen (1966).

2

Kuhn (1962b)

3

Popper (1959)

4

Basically, a version of Mill’s “desire for wealth theory,” the notion that everyone wants wealth argues that any sane individual is governed by two laws: that “a greater gain is preferred to a smaller” and that “the greatest quantity of wealth with the least labor and self-denial” (Mill, A System of Logic, 1874, New York, pp. 623 ff.).

5

See Organisation for Economic Co-operation and Development (OECD) (2020) and UNESCO (2016).

6

American Psychological Association (2022).

7

National Human Genome Research Institute (2021).

8

For example, Miller and Neff (2022).

9

For example, Berger (2023).

10

Dauben et al. (2022).

11

We find these arguments developed in Popper (1959, 1963, 1983).

12

See Georgescu-Roegen (1971).

13

See also Foster (2023, November 5).

14

Berman (2007).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

K. Solberg SöilenThe Researcher's Journeyhttps://doi.org/10.1007/978-3-031-91565-9_2

2. How to Conduct Scientific Research

Klaus Solberg Söilen1  

(1)

School of Business, Halmstad University, Halmstad, Sweden

Klaus Solberg Söilen

Email: klaus.solberg_soilen@hh.se

This chapter explores the process of conducting scientific research, emphasizing the journey from asking the right questions to presenting well-analyzed findings. It provides a structured approach to developing research skills, offering practical guidance for each step of the scientific inquiry process (Fig. 2.1).

Fig. 2.1

Logical structure of Chap. 2

This chapter begins with the concept of asking meaningful questions and understanding the role of observation in science, setting the stage for hypothesis formation and testing. These principles lead to the research development process, which includes identifying research problems, conducting literature reviews, and crafting methodologies.

Then, a set of practical steps, such as operationalizing variables, collecting data, and interpreting results, form the backbone of this process, ensuring scientific rigor. Alongside these technical aspects, the chapter highlights the importance of mentorship, proposal crafting, and structuring knowledge into fields and subdisciplines, which are essential for our academic navigation.

Special attention is given to literature search strategies and the frameworks needed to synthesize existing knowledge effectively. The chapter concludes with a detailed breakdown of research articles, guiding readers through the construction of essential sections like methodology, results, and discussions. The aim is to present a comprehensive guide for mastering the art and science of research in a logical and methodical way.

A prelude.

Part 1: Asking Questions

While out for a walk with my dog during lunchtime, I noticed that many of the cars parked on the street next to our house were white. It’s just a single observation, but still, in my head, a question is formulated: “Do consumers today prefer white cars?” Or how do I find out if this is true, to what extent it is true, and what implications does it have? (As the reader will discover, this moment can be considered a “Madeleine cake” moment for this book.)

From one perspective, this is just the way our brains work when we are out walking, I guess. We see things and ask ourselves questions the whole time. Much of this is not even conscious. At the same time, in this sequence of thoughts lies the opportunity for discovering truths but also the risks of fallacies, biases, and simply flat-out being wrong.

The hypothesis of determining what color cars customers prefer can be studied from various angles across different social sciences. In sociology, researchers could explore how societal norms or group behaviors influence the popularity of specific car colors. Psychology might examine the individual cognitive or emotional factors that lead people to choose one color over another. Economists would look at market trends, supply, demand, and pricing to understand how these variables impact customer choices. In political science, the focus could be on how regulations, environmental policies, or government incentives affect preferences, such as promoting eco-friendly car colors. Anthropologists could investigate cultural influences, exploring how traditions or regional customs shape car color preferences.

Seeing that several cars are white are points of data. A piece of data, such as “a white car,” represents a single data point. We are going to need to gather many of these points as part of what we call a sample if this is going to work, and what we aim to generalize about will be called a Population, with a capital P. Data of similar types are collected into variables; for example, “white cars” would be the dependent variable. If the prevalence of these cars is due to a trend, that trend is then defined as an independent variable. We are going to see if there is a correlation between them for a start—not necessarily meaning that one leads to the other as in causation, but that the data move in the same direction in a cartesian system. Lots of things correlate without being causation. For example, ice cream sales often increase at the same time as drowning incidents rise. Both phenomena can be observed during the summer months due to warmer weather, but eating ice cream doesn’t cause drowning, at least not unless you eat a lot of it and swim in deep water. They just happen at the same time.

As we saw in the first chapter, causation can be suggested through statistical techniques, but these techniques alone cannot definitively prove causality. Causation is a much more complex concept that requires both statistical evidence and logical reasoning. So, while we can suggest statistical techniques that indicate causality—such as correlation, regression analysis (which shows the strength and direction of relationships), and some lesser-known methods like Granger causality, structural equation modeling (SEM), and randomized controlled trials (RCTs)—these techniques can only suggest or strengthen the case for causality, not prove it. Instead, causality requires logical deduction: to make sense of the relationship between variables and rule out confounding factors. It also requires a theoretical framework: to understand why and how one variable might cause another. In some cases, controlled experiments are necessary, where causality can be more strongly inferred by manipulating one variable and observing the effect on another.

Thus, to truly understand causation, we need to use logical reasoning and grasp all the mechanisms at play, and even then, it is difficult to know for sure in most cases. It requires not only knowledge of statistics and logic but also expert knowledge in the field we are studying, which is why it’s dangerous to believe that the scientific method alone will always provide the answer. We need to draw on our experience in the relevant field, just as a Safety Investigation Authority is tasked with examining the cause of a ferry sinking in a previous exercise we described.

Part 2: The Role of Observation in Science

Science is dedicated to continuously testing, refining, and validating truths to ensure societies benefit from evidence-based knowledge. Achieving this requires research that gathers empirical evidence to either support or challenge hypotheses. Through careful observation and controlled experiments, researchers test ideas, uncover patterns, and establish cause-and-effect relationships, which lead to more accurate conclusions about the natural or social world.

To determine if something is true, we need evidence. James Lind, as we recall, observed the symptoms of scurvy among sailors and noticed a possible link between diet and health. Scurvy, which caused weakness, anemia, gum disease, and eventually death, seemed less prevalent among those who consumed fruits or vegetables. His initial observation was simply a hypothesis:

“To me, it seems like this could be related to diet, as those who eat fruits or vegetables seem to suffer less.”

This approach is similar to what is known as abductive reasoning—often described as an “inference to the best explanation” or an educated guess. Unlike deduction, which confirms outcomes from general principles, and induction, which generalizes from specific instances, abduction doesn’t assert that a conclusion is necessarily correct. Instead, it proposes a likely explanation that can be explored further. Abductive reasoning is particularly valuable in fields where hypotheses must be generated from incomplete or surprising data, such as diagnostics, criminal investigations, and early-stage scientific theory development, providing a starting point for deeper inquiry and testing.

At the time, there were various theories about the cause of scurvy, but none of these were scientifically tested. This situation somewhat resembles what we face with Alzheimer’s disease today; we understand the physiological manifestations but not the underlying cause. Yet, if we remain curious and continue to harness the power of observation, we may eventually uncover the answer—and, hopefully, sooner rather than later.

Part 2.1: Hypothesis Formation and Testing

Simply observing with our eyes isn’t enough to uncover solutions; we also need ideas about what those solutions might look like. These educated guesses, or predictions, let’s say, are what we call hypotheses.

Hypothesis formation and testing are developing a proposed explanation for an observed phenomenon and then conducting experiments or analysis to determine whether empirical evidence supports or refutes the hypothesis.

This was the scientific method in five steps pursued by Lind:

Step 1: Hypothesis Formation.

Lind observed the symptoms of scurvy among sailors, and he noted the potential link that existed between diet and health. He hypothesized that something in the sailors’ diet was responsible for preventing or curing scurvy, and he speculated that acidic substances might be effective.

Step 2: The Controlled Experiment.

In 1747, Lind conducted what is considered one of the first controlled clinical trials in medical history, where he selected 12 sailors suffering from scurvy and divided them into six pairs. Each pair received a different treatment while maintaining the same basic diet. The treatments included:

1.

Cider (a pint daily).

2.

Elixir of vitriol (sulfuric acid).

3.

Vinegar (six spoonfuls daily).

4.

Seawater (half a pint daily).

5.

Oranges and lemons (two oranges and one lemon daily).

6.

A spicy paste and a drink made from barley water.

Step 3: Findings.

He observed the effects of these treatments over a period of 2 weeks. He found that the sailors who received oranges and lemons recovered remarkably quickly, while those on the other treatments showed no significant improvement. The group that consumed citrus fruits experienced a dramatic reversal of symptoms, which provided strong evidence that these fruits were effective in preventing and curing scurvy.

Step 4: Conclusion and Implication.

Lind then concluded that citrus fruits contained something essential (later identified as vitamin C) that could prevent scurvy. His findings were eventually published in his 1753 book, A Treatise of the Scurvy, where he recommended citrus fruits as a preventive measure for sailors.

Step 5: Impact.

Though it took quite some time for Lind’s findings to be widely accepted, the British Navy eventually adopted his recommendations. By the late eighteenth century, the practice of providing sailors with lemon or lime juice became standard, which drastically reduced the incidence of scurvy and saved countless lives.

Lind’s work exemplifies the scientific method: starting with observations, forming a hypothesis, testing it through controlled experiments, and arriving at a conclusion based on empirical evidence.

But it’s more than that: it’s about being curious, always asking questions about the things you see around you. This approach ultimately led to a critical discovery that had a profound impact on public health.

“Now, wait a minute,” an observant and critical reader might ask, “is that example from the natural sciences really relevant to the social sciences?” In principle, yes, it is. Just as in the natural sciences, social scientists can form hypotheses based on observations of human behavior and test them through controlled studies or empirical analysis to determine causality.

For instance, researchers might examine whether specific interventions reduce social issues in fields like psychology or sociology, using structured experiments to identify effective strategies. The process of forming a hypothesis, testing it with evidence, and drawing conclusions is universal across these disciplines and plays a critical role in validating theories in the social sciences as well. If we do not formulate hypotheses, we use research questions (RQs), which serve a similar purpose, though we then use qualitative methods to find the answers.

Using research questions (RQs) rather than hypotheses is not inherently less scientific; though, RQs often rely on qualitative methods, which can be more open to interpretation and subjectivity. This approach may introduce a higher risk of bias, but careful methodology and rigorous analysis can yield reliable and meaningful insights.

Part 3: The Research Development Process

Now, let’s consider the observations of the cars parked on the street outside of our house. As I look at them, I start to wonder: are white cars more popular these days? Are they trendier now?

How might a research process unfold to explore this question? Fast forward, and the project might look something like this (Table 2.1):

Table 2.1

The research development process with our example

Step

Description

Example: ‘White cars are preferred by consumers today’

1. Area of study

Identifying the research problem

Determine whether white is the most preferred car colour among consumers

2. Search in literature for research gaps

Identifying gaps in existing research

Review existing studies on car colour preferences and identify if recent trends in car colour preferences have been sufficiently explored

3. Work out the methodology

Research approach: Decide on whether the study will be qualitative or quantitative

Research approach: Use a quantitative survey to gather data on car colour preferences

 – Qualitative: Choose appropriate techniques (e.g., interviews, case studies)

 – Quantitative: Choose appropriate techniques (e.g., surveys, experiments)

The purpose of the study: Conduct a descriptive study to determine current car colour preferences among consumers

The purpose of the study: Determine if the study is exploratory, explanatory, descriptive, or causal

Researcher interference: Minimize interference by using anonymous surveys

Researcher interference: Decide on the extent of researcher interference: Minimal, moderate, or excessive.

Study setting: Conduct the survey in a non-contrived setting, such as online or in car dealerships.

Study setting: Choose between a contrived (artificial) or non-contrived (natural) setting

Research strategies: Use surveys to collect data on car colour preferences from a large sample of consumers

Research strategies: Select a research strategy: Experiments, surveys, observations, or case studies

Population to be studied/unit of analysis: Survey individual car buyers across different regions

Population to be studied/unit of analysis: Identify the unit of analysis: Individuals, groups, organizations, or cultures

Time horizons: Perform a cross-sectional study to capture current preferences

Time horizons: Decide between cross-sectional, multiple cross-sectional, or longitudinal studies

Variables: Dependent variable: Car colour preference; independent variables: Age, gender, region

The variables: Identify the dependent, independent, moderating, and/or mediating variables

Sampling design: Use probability sampling to ensure a representative sample of the population

Sampling design: Determine the sampling design: Probability/non-probability sampling, and decide on the sample size

4. Full literature review

Conducting a comprehensive literature review to understand the context and background of the problem

Summarize findings from previous studies on car color preferences, focusing on geographic, demographic, and temporal trends, and identify any inconsistencies or gaps

5. Operationalization

Define how to measure and study abstract concepts in reality

Develop a survey that includes questions about car color preferences, demographic information, and reasons for color choices. Use online survey tools to distribute the questionnaire and collect data

Specify the tools, instruments, and procedures to be used in the study

Ensure that each aspect of the methodology is practically applicable in the research setting

6. Data collection

Execute the chosen methodology and collect data according to the operationalization plan

Distribute the survey to a sample of car buyers and collect responses on their preferred car colors

7. Data analysis

Analyze the collected data using appropriate statistical or qualitative methods

Use statistical analysis to determine the most preferred car color and identify any patterns based on demographic factors

8. Interpretation of results

Interpret the data to draw conclusions and answer the research questions

Conclude whether white is the most preferred car color and discuss any factors that influence this preference. Explain also what all of this means for all actors in the market/society and what we understand under implications

9. Reporting and documentation

Write up the research findings, including a discussion of the implications and limitations

Document the findings in a research paper, including the methodology, analysis, and conclusions about car color preferences

10. Find an outlet for the paper

Identify and select appropriate journals or conferences to publish the research

Submit the paper to a journal focused on consumer behavior or automotive industry trends

If all of this feels a bit overwhelming and you’re unsure about the terms and operations, that’s precisely the purpose of this prelude—to throw you right into the cold water, to push you out of the nest, so to speak. Don’t worry; I’ll guide you through each step in the following sections.

The next two sections outline different approaches to understanding the research process:

Section 2.1 provides a quick overview, summarizing the main stages of research, such as identifying gaps, drafting a proposal, conducting a literature review, structuring an article, and selecting a journal. This overview is intended as an accessible roadmap, ideal for those looking to grasp the basic flow of research without diving too deep into each part.

By contrast, Sect. 2.2 provides a different perspective on the research project, moving away from the chronological stages of a research article. Instead, it begins with what is often the most natural starting point—operationalization—and progresses step by step to address the major methodological questions and research details necessary for a thorough investigation. It covers foundational aspects like choosing the right research approach, clarifying objectives, handling sampling, and data analysis, along with guidance on specific tools, such as scales and statistical techniques. This section goes further, including practical elements like balancing qualitative and quantitative methods, selecting data collection strategies, and understanding publication requirements. Detailed instructions on post-submission processes and ethical considerations make this section particularly useful for advanced researchers. In essence, Sect. 2.1 gives a concise, high-level summary, while Sect. 2.2 serves as a comprehensive guide.

Curtain.

2.1 The Research Process in Seven Key Lessons

Disclaimer

Some may wonder why there are headings like “Science in Eleven Lessons,” “The Research Process in Seven Key Lessons,” and “Research Process in Detail.” Is there a significance to the numbers 7 and 11, and why isn’t there a specific count of steps in the last one?

The truth is, I didn’t plan the number of steps in any of these processes. However, I do believe in having and using structured processes when teaching a topic, and it just so happened that these processes ended up with 11 and 7 steps. In the final section, where I delve into more detail, there are a larger number of sub-steps, and numbering them didn’t feel meaningful, at least not to me. Nonetheless, I still find the numbers useful because they help with memory and organization.

It all starts with an interest in a field of study, which eventually leads to a focus on a topic and a problem worth solving. This interest might come from your personal curiosity or something you’ve encountered that sparked a desire to dig deeper. The specific source doesn’t matter as much as your enthusiasm and the relevance of the problem—it should be something genuinely worth exploring, not a trivial matter or something that could be considered common sense. After all, we’re not at university to solve commonsense issues.

Once we’ve identified a meaningful problem, we need to study it closely and develop some expertise before tackling it. With this foundation, we can begin thinking about how to solve the problem and make a scientific contribution.

We will note that most problem-solving happens outside of research, in everyday situations—like figuring out how to remove a door in a kitchen cabinet (a reoccurring problem in our house, it seems). Every day, people face these practical issues: How do I start? Where’s the screw? While these aren’t scientific problems, they’re important in their own way. And they need to be solved.

Yet, university research tackles more challenging issues, often because they’re complex or because there’s no one readily available to answer them. At the university, we also address problems theoretically, which is a key distinction. The reason we do this is that if the theory is good, it can help us solve similar problems.

Solving an issue in a kitchen doesn’t require sitting down with a pen and paper because it’s assumed to be simple enough for the average adult person to handle. However, at the university, we aim to solve the kinds of problems that demand deeper inquiry and specialized methods (Table 2.2).

Table 2.2

Types of problems to be solved at the university

Problem

Kitchen cupboard door

Popularity of white cars

Description

Removing a door from a kitchen cupboard

Investigating whether white cars are currently more popular and if there’s a trend behind this preference

Nature of problem

Practical, straightforward, and commonly encountered in daily life

Complex, involves understanding societal trends, preferences, and potential market implications

Required knowledge/skills

Basic handyman skills, common sense; no specialized training required

Knowledge of statistics, sociology, and trend analysis; requires systematic data collection and interpretation

Level of inquiry

Low; limited to immediate action with no broader implications

High; potentially reveals insights into consumer behavior, design trends, or cultural shifts

Potential for contribution to knowledge

Minimal; no significant contribution to a field or discipline

High; findings could contribute to fields like sociology, marketing, or economics

Suitability for university research

Unsuitable; simple solution readily available to most individuals

Suitable; the question is complex and requires research methods like surveys or data analysis to draw conclusions

We could address the second problem while working in a car company or dealership, much like we do in market research, so there’s nothing preventing scientific work from happening outside university walls. However, for the question to be research-worthy rather than just a consulting task, it should offer broader insights, potentially significant for society. Ideally, such research could identify general trends that reveal patterns in consumer preferences. For instance, if white is trending not only for cars but also for kitchen appliances or living room furniture, we might explore why this preference exists and how long it may continue. Insights like these could be valuable for various industries and even influence the broader economy.

Social science students must be careful not to confuse consultancy work with academic research. While consultancy can often be highly useful—sometimes even more so than research, depending on the context—research has a different set of priorities and standards. One key distinction is that research seeks to identify universally applicable problems and solutions, making generalization across different contexts a core objective. This means that the conclusions drawn must hold true beyond the specific case or project at hand.

Additionally, in academic research, we hold ourselves to much higher standards when it comes to how data is collected. The method must not only be scientifically sound but also transparent and replicable by others, ensuring the validity of the findings. Researchers must also be meticulous in showing the origins of all claims made, whether they are derived from existing literature, datasets, or empirical evidence. This process ensures that our conclusions are built upon a solid foundation, maintaining scientific integrity and contributing meaningfully to the body of knowledge in the field. Unlike consultancy, which may focus more on tailored, immediate solutions for specific clients, research provides the broader, more methodologically rigorous insights that advance understanding and can be applied across various contexts. That being said, consultancy reports can often be far more engaging and, in many cases, even more valuable than typical social science articles (Fig. 2.2).

Fig. 2.2

The process for finding a problem to solve

Identifying an area of interest and immersing ourselves in it until we become experts can take considerable time, requiring patience and a good portion of what the Germans call Sitzenfleisch. It’s essential to have a genuine interest in your chosen topic, as a lack of it often leads to boredom and disengagement—especially during the long, desert-like journey that is a PhD (imagine yourself here, sitting on a camel). It’s a desert journey that goes on and on and doesn’t seem to end, not because it is boring.

It’s often difficult to pinpoint exactly why we become interested in a particular topic. Sometimes, we start with a specific problem that intrigues us (problem-driven approach), only to discover that it’s best approached through a certain discipline. Other times, we choose a discipline first (discipline-driven approach) and then find problems within that field that we want to solve. In other cases, our focus may be on what we want to become or achieve (identity-driven approach), which then shapes both the discipline we choose and the problems we address. For many, including myself, it’s been a combination of these approaches.

While your discipline may influence the kinds of problems you’re drawn to, one of the core arguments of this book in favor of interdisciplinary studies is that you shouldn’t limit yourself to examining an issue solely from the perspective of a single discipline. Instead, focus on the problem itself—immerse yourself in it with genuine passion—without being overly concerned about adhering strictly to the boundaries of your field. Staying confined will only limit you and result in solutions that are fragmented, forced, and desperately tied to preselected theories. Instead, approach the social sciences as if they were a gigantic smorgasbord, offering a variety of tools and perspectives to explore and address the issue effectively.

Nearly all social sciences have defined themselves too narrowly—not only in the perspectives they choose to approach problems but also in the methods they’re accustomed to applying. There’s no need to be restricted in this way. In this book, I aim to show you how to select the most suitable perspectives and methods, regardless of where they’re traditionally used or currently popular. So, focus on an interesting problem to solve first.

Often, and especially in hindsight, it may feel as if the research problem chooses you, not the other way around, shaping your intellectual journey, sometimes without you even realizing it or remembering having made a conscious choice.

To give an example, here is a brief account of my own journey in choosing disciplines, areas of study, and topics. I don’t claim to be special in any way, as you will see.

Example: Guided by Chance: Reflections on a Researcher’s Journey

For me, the decision to study economics and business wasn’t a conscious, deliberate choice but rather shaped by broader societal influences—not my parents, but by the media and conversations that surrounded me growing up. I imagine it’s similar to how my father’s generation gravitated toward engineering because that was what society needed. Studying in the United States, though, was a deliberate choice, driven by a broader dream of experiencing California—not solely for academic reasons. Initially, I intended to pursue a BA in Business Administration, but after encountering inspiring political science professors at a liberal arts university, I ended up with a double BA. This shift, in hindsight, was mainly by chance.

My university choice was also somewhat coincidental; I was recruited at my high school in Lillehammer, received a scholarship, and could start in the second year, thanks to academic credits from my military service. None of the business courses particularly stood out, but a computer science course on Management Information Systems (MIS) sparked my interest in Business Intelligence. Reflecting on it, I’d say my upbringing—where discussions of strategy and world events were frequent—also influenced this direction.

During my master’s at HEC in the early 90s, finance was at its peak in popularity, which led me to write a thesis on option pricing models at the Borsa di Milano during an exchange at SDA Bocconi. Yet, as I delved deeper, I became critical of modern finance theory, finding it overly pretentious and often borrowing concepts from physics without adequate explanation. This realization shifted my focus toward accounting and auditing, areas where I worked for several years, including as an auditor at KPMG. However, this work soon felt intellectually unfulfilling, prompting me to move toward academia.

KPMG supported my request for a part-time PhD in Germany, and by then, I had already developed a multidisciplinary research proposal on what I would later define as Intelligence Studies, with support from an American professor in Oslo who generously shared his time (and will always be remembered for this). Finding the right PhD program was challenging, but I eventually landed in Leipzig at the Wirtschaftswissenschaftliche Fakultät (WWF). There, I was put in the interdisciplinary Zentrum für Höhere Studien (ZHS), which was connected to the Geistes- und Sozialwissenschaftliches Zentrum (GSZ) within the PhD group, “Ambivalenzen der Okzidentalisierung” (Ambivalences of Westernization). This interdisciplinary setup, which no doubt included a critique of the market economy, proved fitting for the specific issue I wanted to explore: industrial espionage. So, I ended up writing the first doctoral dissertation on industrial espionage.

When I later transitioned to teaching at CBS in Copenhagen, I was set to teach courses on international business, but my research interests were more centered on business intelligence and digital marketing, two fields that have somewhat converged with digitalization’s rise. I’ve aimed to identify and address research gaps within these areas ever since, consciously avoiding the influence of university strategies or financiers’ priorities—a decision that has made my path more difficult.

Ultimately, selecting research problems is far from straightforward. External funding, though often essential, tends to impose constraints on research agendas, as funders’ priorities may not align with a researcher’s original vision, leading to shifts from the intended focus.

So, for me, choosing research topics has been a complex interplay of personal interests, societal needs, external pressures, and pure chance—taking me across much of Western Europe, but also, occasionally, to Africa, South America and Asia.

Did I plan all of this from the beginning? No, it unfolded more like a long chain of events—some influenced by social pressures, some by my own decisions, and others by pure chance.

Our own decisions—and indeed the decisions and behaviors we study in the social sciences—are rarely as clear-cut, rational, or well-planned as they are often portrayed. Human actions are shaped by a mix of personal intent, societal influences, external pressures, and chance, making it difficult to fit them neatly into models of rational decision-making.

As social scientists, our task is not to oversimplify these processes but rather to analyze them critically—recognizing their complexity while still striving to bring clarity and understanding to an otherwise unpredictable world.

When you arrive at what you want to do for your PhD, it’s important to ask yourself if you’re certain, especially when considering a 3–6-year PhD program. This commitment also involves forfeiting potential income while remaining a student for longer than you might have originally anticipated.

Choosing the right area of study is equally important, as otherwise, you risk becoming bored or disillusioned along the way—perhaps even realizing that the problem you have chosen is trivial over time. Once you’ve identified your area of study, it’s time to go out and hunt for real problems to solve. If you’re writing a monograph, one set of problems may be sufficient, but most PhD students today write a compilation thesis, which typically consists of 4–6 research articles, each needing its own set of problems to tackle.

Once you’ve chosen a topic, you’re committed to it, so ensuring it will keep your interest throughout your PhD journey is essential. While there’s always a chance that someone else might solve the problem before you, in my experience, this is rarely an issue. This is partly because students tend to stop actively comparing their work to others after completing their literature review, and even if it does happen, PhD committee members are likely to overlook it.

Start by reading scientific articles you think look interesting. Toward the end of these, you’ll often find a section titled “future studies” or something similar, where authors suggest potential areas for further research. These are hints at unsolved problems, at least according to the authors—just make sure to check the date, as these issues may have been resolved since publication.

Finding problems on your own is more challenging, as it requires reading more broadly afterward. When you rely on others’ recommendations, you’re benefiting from their extensive expertise and reading, so for beginners, it can be wise to follow journal recommendations, especially if you’re working on a compilation thesis.

2.1.1 Identifying Research Gaps and Opportunities

A research gap is the specific question or questions we aim to address through our study, which, upon thorough investigation, reveal an area of knowledge that has not yet been adequately explored or answered.

It is essential that we can clearly demonstrate the need for addressing this gap, both to ourselves and to the broader academic community, establishing that the inquiry is relevant and will contribute to advancing understanding in the field. Convincing others of the importance of the gap often involves showing that existing research has either overlooked the issue or that previous answers are incomplete or outdated. It is not simply enough here to say we found it in the back of an article.

The idea of the research gap is simple: each researcher aims to add something new to their field, and the best way to do this is by reviewing existing research to see what has already been covered and then spotting specific areas that remain unaddressed. Yet, it’s crucial that these gaps represent issues that genuinely need attention, either by fulfilling a societal need or being recognized as important by peers.

In the social sciences, many research gaps exist, but some questions also remain unaddressed for a good reason because they are particularly difficult or even impossible to answer definitively. For example, trying to determine the exact influence of cultural heritage on individual decision-making is complicated due to the interplay of countless variables, including personal experiences and broader societal factors. Similarly, certain questions regarding human behavior, like predicting long-term societal changes based on short-term events, may be too complex or influenced by unpredictable factors to generate conclusive results. Additionally, research might be avoided if the necessary data is inaccessible, either because it’s impossible to gather or unethical to pursue, such as in sensitive populations or conflict zones. These gaps, though technically unaddressed, may not make for practical research questions.

In our case, a valid research gap could, just to take an example, involve examining whether white cars are more popular in regions with certain temperatures (high, low, medium). This research gap could be valid because it explores a potential relationship between environmental factors, such as temperature, and consumer preferences, which can provide insights into regional behavior and market demand. Understanding this correlation could help car manufacturers and marketers optimize their strategies for different climates and regions, making it a relevant and practical research question. On the other hand, investigating whether the number of clouds on the day of purchase correlates with buying a white car or whether owners of white cars prefer vanilla ice cream would be less meaningful. Such questions, though unexplored, lack relevance and fail to contribute significantly to the field. Likewise, asking whether car dealerships with white walls sell more white cars may sound intriguing and even a bit humorous but offers little real insight into consumer behavior. That does not mean the managers of some dealerships won’t face this question; they will be compelled to make a decision based on some form of reasoning.

In contrast, studying whether consumers prefer white cars, while seemingly trivial, has significant societal implications. Understanding preferences in car colors can influence marketing strategies and product offerings while reflecting broader cultural trends. Such insights can help align production with demand, reduce waste, and impact economic planning, including resale values and insurance premiums. Analyzing these preferences can also shed light on regional differences, which are valuable for both global businesses and local economies. This is also the reason why companies spend gazillions of dollars each year on market research.

The preferences for car colors typically change over intervals of several years, often aligned with broader trends in design, fashion, and cultural shifts. These intervals might range from 5 to 10 years, if not sooner, as new automotive models are released, and manufacturers update their designs and color palettes. Consumer preferences can also be influenced by external factors such as economic conditions, where, for instance, more conservative colors like white, black, or silver become popular during times of economic uncertainty. Additionally, advancements in paint technology and environmental considerations, such as the introduction of eco-friendly paints or fluorescent colors like for the first Teslas, can also drive changes in color preferences. Therefore, these intervals reflect a combination of market dynamics, cultural trends, and technological innovations.

So, to conclude, we should first emphasize the importance of situating the research gap within the broader academic conversation. This process involves not only understanding what has been thoroughly studied but also recognizing areas that are gaining attention in recent literature. Identifying a gap means showing that there is something theoretical or practical that remains unresolved or unexplored and making a case for why it is essential to address it now. This is especially important for establishing the relevance of our research both in advancing knowledge and in solving real-world problems.

When searching for gaps, it’s critical to review various types of literature, including seminal works, recent studies, and interdisciplinary sources. This ensures that the researcher has a full picture of the academic landscape. Research gaps can also emerge from contradictions in the existing literature. For instance, conflicting results between studies may suggest that further investigation is required to clarify the issue.

Additionally, some research gaps are time-sensitive, especially in fields that involve rapidly evolving technologies or emerging societal trends. What is a significant gap today may not be as relevant in the near future, which makes timely investigation crucial. It’s equally important to strike a balance between addressing gaps that are too narrow—yielding limited findings—and gaps that are too broad, making the study unmanageable.

Lastly, gaps can sometimes reflect localized or updated needs, where existing research may be outdated or not applicable to specific populations or contexts. Addressing these types of gaps allows you to provide more accurate, contextually relevant, and up-to-date findings. By clearly defining and justifying the gap, you contribute meaningfully to both theory and practice in their field.

Now, all of this is not something PhD students are expected to know, which is why you will need a PhD supervisor.

This book also assumes that you have already been accepted into a PhD program and does not provide guidance on how to gain admission. The reason for this omission is that the process is relatively straightforward: to get into a strong master’s or PhD program, you generally need a solid academic record from your previous studies and should apply to multiple programs to improve your chances. Additionally, making personal visits to prospective institutions, if possible, can significantly enhance your application by demonstrating genuine interest and commitment. Keep an eye on PhD positions posted on university websites, as they are typically listed alongside other job opportunities at the institution. Set up an alert on the website to receive notifications about new announcements, and you’ll be all set.

2.1.2 Dialogues with Mentors: Conversations in Research

Much of what we’ve discussed so far may already be on the student’s mind, but having a conversation with an experienced researcher as early as possible is invaluable—even before they formally agree to be your supervisor, or regardless of whether they will be one, or if you’ve been accepted into a PhD program. Researchers are often generous with their time if you reach out, even if you aren’t in their class. There are some grumpy exceptions, but they should probably not have become teachers in the first place. Early conversations with knowledgeable people can, therefore, provide crucial insights and save you time.

The usual path to finding a supervisor varies depending on the institution. Some universities assign supervisors upon admission to the PhD program, based on your proposed topic and available faculty. Others require you to secure a supervisor independently before applying. In this case, it’s beneficial to identify potential supervisors through your literature review or from professional networks and reach out with a clear, concise summary of your research interest. Building these connections early on can help set a strong foundation for your research journey.

Unfortunately, it’s not as simple as finding a brilliant professor and asking them to supervise your PhD, especially if you don’t have your own funding. I receive such requests nearly every week, and I often have to give the same realistic answer. A professor may be interested in supervising, but they may have no time allocated, already have enough PhD students, or simply prefer not to take on more. Some professors, especially those nearing retirement, may also wish to focus on other projects during their final years. It all differs.

Example: Building a Supervisor-Student Relationship Based on Mutual Respect Leads to Shared Success

Like many, I was assigned a PhD supervisor only after I was admitted to a program or after a sort of interview. My professor was a mathematician by training and a distinguished senior member of the faculty. We hit it off immediately. He was strict, of course (we were in Germany), but also jolly and positive, with a strong Leipzig or Saxony dialect. It was love at first sight for me, and I regarded him right then and there as my Doktorvater, so more than just a supervisor. I also had a co-supervisor, but we had less contact, especially as she was working abroad for a period.

I would meet with my supervisor a few times each year for a week and then drive back to Oslo again in my old VW. I would present any progress, and he would give me more tasks, and that was it. I never dared to ask when I would finish or if he would even allow me to defend my thesis. All I did was focus on completing each assignment and making small progress.

Today, PhD students “have rights,” make demands, and, if they don’t like their supervisor, may request a change. For me, that was unheard of. I’m not saying the system was better back then, but it was characterized by a greater sense of respect, which I believe benefited all parties. This respect seems partly lost today, but I hope it may return in time.

The first thing I noticed when I walked into his spacious office on the first floor of the old Handelshochschule Leipzig building on Jahnallee was the enormous portrait of a man hanging over the table where we were sitting. “Oh, that,” said my supervisor, “that is the old Felix” (“Ach, das, das ist der alte Felix”). I soon learned that this had been his supervisor, and since then, I sometimes thought about the relationship they must have had and how it may have been different from ours. Maybe he, too, felt that there had been more respect in the past and that something was lost. Difficult to know.

Professor Felix Burkhardt was a notable German statistician and mathematician, especially active during the mid-twentieth century. He made significant contributions to statistics and insurance mathematics in a time of rapid development in the field. Born in 1888 in Herwigsdorf, a village in Prussia, he studied natural sciences, mathematics, and economics at the universities of Jena and Leipzig.

At that moment, I decided that if I ever made it through this program, I would have a portrait of Prof. Klaus Lange on my desk where I work. And I have, ever since I graduated, a photo taken at the dinner table at the Auerbach Keller on the evening of the graduation.

I’ve often considered a PhD supervisor to represent a particular way of seeing the world, embodying a specific theoretical perspective and belonging to a distinct school of thought, which they pass on to their students. It’s a romantic idea, I admit, but I don’t think it is wrong. While the student may ultimately choose a different path, they are inevitably shaped by the supervisor’s intellectual approach and foundational beliefs or the lack of such. We will delve more into this in Chap. 4.

For me, in practice, this meant not only diving into some of the classic works in economics before even being directed toward the research articles but studying figures like Claude Shannon and Robert Axelrod, as well as Robert Rosen’s Anticipatory Systems, which eventually led me to social systems thinkers like Kenneth Boulding and, most significantly, Niklas Luhmann. From there, I stumbled upon a range of brilliant minds, though the most significant were probably Oskar Morgenstern, John von Neumann, and an absolute favorite, Nicholas Georgescu-Roegen. Long before that, I read everything I could find by Peter Drucker, whom I was also fortunate enough to meet as an undergraduate student in California, as well as works by Henri Fayol and Michael Porter who were considered to be classics.

My Doktorvater and his Doktorvater before him were straightforward, no-nonsense mathematicians with a deep understanding of statistics. I, however, saw myself as a social scientist and an economist with what often seemed to me an almost too wide interest and approach to economic thinking. Was I ever going to be able to specialize?

The PhD thesis combined systems theory, game theory, and strategic management, integrating insights that transcended traditional disciplinary boundaries. This perspective naturally aligned me with what some colleagues call the heterodox school—though I find that term contentious and prefer the label “evolutionary school,” a branch of evolutionary economics. More accurately, it reflects an evolutionary approach to economics, which remains a diverse field with various schools applying evolutionary theory to understand economic processes as dynamic, adaptive systems. This approach values both theoretical depth and practical applicability, promoting a more holistic view of how economies evolve and interact within broader systems. We will even discuss this at length in chapter four.

So, to conclude, my path to this perspective was very much shaped by the guidance of my supervisor and the distinctive environment at the institute where I completed my PhD. Sometimes, I like to think that all of these ideas were ingrained in the very walls, as they say. Hopefully, you, too, will find one of these old, overgrown places filled with the awkward breed of minds who are called researchers—or, in the past, scholars—and learn from their ways.

My advice to you as a student is to recognize the unique nature of the relationship between a supervisor and a student. At its best, this relationship can resemble the bond between a parent and a child, though without the complexities, but with the same foundation of mutual respect. Of course, this respect must be reciprocal, as it is the key to fostering a productive and supportive working dynamic.

I mention this story because, like me, you’ll likely be influenced by your PhD advisor’s perspective, whether consciously or not. You’ll find yourself positioned on the broader map of ideas, shaped by those who came before.

Understanding and reflecting on these influences is essential, as they subtly guide your thinking and will be evident to anyone who reads your work closely. Recognizing these influences helps you gain clarity about the perspective from which you write and the intellectual paths that shape your journey.

Your first meetings

Let’s assume you now have a supervisor and you’ve scheduled your first in-person meeting. The conversation will typically start there if the student doesn’t have a specific area of interest. Occasionally, the student may have identified some research gaps, though this is less common, so it’s often a productive place to begin. From the very start, we need structure, clarity of thought, and a logical presentation of ideas. To support this, I expect the student to start developing a thesis proposal by our second meeting.

To start, we use a simple template covering the essential elements: topics, areas of study, key problems, and methodology. We always begin with a template because one common challenge in thesis writing is losing focus—having a basic structure helps keep things on track. The template acts as a guide to ensure logical consistency and serves as a sort of contract between the supervisor and student. Once it’s in place, any changes to the research direction require updating the template to maintain alignment.

You might wonder why this structured approach is necessary, given that these are adult students. The truth is that even experienced researchers will lose their way, especially as the number of pages grows. As you dive deeper into your topic, new ideas will take you down unexpected paths, so you need to find your way back and know what is where in your document (Fig. 2.3).

Fig. 2.3

Research proposal template

2.1.3 Crafting a Research Proposal

In my view, the essentials of a research proposal template are straightforward. First, there’s a working title—just a starting point, which can evolve as you progress (1). Then, you need a clear area of study, specifying the primary discipline and relevant subfields (2). Next comes the core of the project: the research questions or hypotheses. If the approach is qualitative, formulate these as Research Questions (RQ1, RQ2, etc.); for quantitative methods, use Hypotheses (H1, H2, etc.) (3). In addition to the research questions, you should outline the main objectives that will steer your study. These objectives clarify the purpose behind your questions, helping map out your project’s direction (4). Once your objectives and questions are clear, the next step is methodology: how you plan to find answers. This is the scientific backbone of your proposal, where you can demonstrate rigor and apply relevant theories (5).

The methodology also needs to be feasible—a concept we check under what is called operationalization, which refers to making your ideas realistic and measurable within the scope of the project. Once these elements are in place, you have the bare minimum for a solid research proposal.

Note: “Any science paper is basically just two things”

At its core, any thesis or research project ultimately boils down to two essential elements—a thoughtfully chosen set of questions and precise, well-supported answers. That is the essence of a thesis or any significant research endeavor, for that matter. Mastering the transition between them is a key part of becoming a skilled researcher.

Naturally, there are other components to fill in along the way, but I often view these as supporting details. When I read a thesis, I focus on two aspects initially—the clarity of the questions and the rigor of the answers. If these elements are strong, I’m compelled to keep reading; if they’re lacking, the project loses momentum from the outset. If your question isn’t interesting, why should I continue reading? And if you don’t answer it, why should I bother reading everything in between? If I find gaps in the answers, I immediately recognize that the task hasn’t been fully resolved.

Think of it like a Venn diagram or a solar eclipse: the circle of answers must fully cover the circle of questions. Mathematically, if we denote the area of the circle representing all questions as Q, and the area of the circle representing all answers as A, then the overlap between these two areas, O, must be equal to Q for the thesis to be complete. Then, the job is done.

Then there’s the more detailed version of the research template: one of the most interesting elements is understanding why the student wants to pursue this research—this is the motivation. Of course, we aim to tackle compelling problems, but why specifically this one? Why is it so important that the researcher chose it over others? (6) A timeline is crucial for both the student and the supervisor, helping the student to plan their time and enabling the supervisor to gauge if they’ll stay on track or might need extra time (7). Ethical and legal considerations aren’t just formalities; they require a thorough review to ensure no harm is done, such as by asking insensitive questions or mishandling data. We must comply with GDPR laws, meaning no sensitive data that could directly identify individuals should be collected. This involves going through each stage of the project to assess risks and how they’ll be managed (8). It’s also helpful to share the first solid sources found when reading, with full references, so the supervisor can assess if you’re on the right track. Ideally, these first sources align well with the research problem we’re trying to solve, and they are gathered from good journals (9). Later on, the literature list expands as you progress (Fig. 2.4).

Fig. 2.4

Research proposal template, extended version

2.1.4 Structuring Intellectual Levels: From Domains to Contexts

Of all the questions that may appear in a research proposal, the most challenging for students is understanding the intellectual placement of their problem—that is, identifying the precise area of study from which they’re approaching it. This requires an understanding of classification and division within science.

Broad fields, like Business Studies, can be systematically divided into more focused areas, such as Marketing, and then further into specific subfields like Entrepreneurship. This structured approach, called exhaustive division, ensures that no steps are skipped and every category is accounted for. We often illustrate this with a tree of Porphyry or dichotomous method, a division into two, which traces back to the Greek idea of systematic classification.

For example, if we’re studying “The role of competitive intelligence in enhancing SMEs’ sustainability in African markets,” we can break it down as follows: at the first level, we’re within science broadly; next, we’re in Business Studies, then Marketing, moving down to SMEs, followed by Sustainability, and finally focusing on African markets. This approach helps clarify the academic positioning of the research within the broader landscape of knowledge (Fig. 2.5).

Fig. 2.5

Defining our area of study using the tree of porphyry

We can now label each level in the tree, as in the Table 2.3:

Table 2.3

Types of levels in the problem formulation taken from the project title

Step

Research categorization

Description

Example for “The Role of Competitive Intelligence in Enhancing SMEs’ Sustainability in African Markets”

1

Domain

The broadest academic area, covering a large scope of knowledge

Science

2

Field

A major area within the domain is usually divided by a focus on a specific phenomenon

Social science

3

Meta-discipline

A broad categorization under the field, organizing related disciplines

Business studies

4

Discipline

A recognized area of academic study with specialized methodologies and theories

Marketing

5

Sub-discipline

A focused area within a discipline, with its own specialized theories and research topics

SME studies (small and medium enterprises)

6

Focus area

A specific thematic concern within a sub-discipline

Sustainability

7

Contextual focus

An application of the focus area, often related to geography, culture, or unique circumstances

African markets

8

Theoretical perspective

A specific lens or framework used to interpret and analyze phenomena

The resource-based view (RBV), institutional theory, dynamic capabilities theory, stakeholder theory or network theory

These labels of the research categorization are essential for helping us identify the level at which we’re engaging, making it easier to navigate complex research topics.

Recognizing the hierarchy of fields and disciplines allows us to approach the same problem—like “sustainability in African markets”—from various perspectives, whether through sociology, environmental studies, biology, or another discipline entirely. Each angle offers unique insights and methods, enriching the analysis. This structured vocabulary enables us to clearly distinguish between levels, keeping our focus sharp and directed. Moreover, mastering these distinctions also supports interdisciplinary work, allowing researchers to combine fields seamlessly and effectively.

What makes it difficult is that each discipline does not have a clear idea even of its different levels and frequently mixes them. There are also great overlaps between disciplines or meta-fields.

Other meta-fields that study economic and business issues include Political Economy, which examines how political structures and power dynamics influence economic policies and outcomes. By linking economics with political science, it helps us understand how decisions made by governing bodies impact markets and vice versa. Public Policy and Administration studies how governmental policies affect industries, businesses, and society, focusing on policy design, implementation, and evaluation. This field is vital for analyzing the role of regulation and public sector management in shaping the economic landscape.

Sociology of Organizations takes a sociological approach, exploring how social structures, culture, and power dynamics within organizations affect their functioning and productivity. This field is particularly relevant to understanding organizational behavior and workplace culture. Behavioral Economics and Psychology integrates economics and psychology to investigate how human behavior deviates from traditional economic theories, which often assume rational decision-making. This meta-field sheds light on how biases, emotions, and social factors influence consumer choices and economic outcomes.

Industrial Relations and Labor Studies focuses on employment dynamics, labor markets, and the relationships between employers, employees, and unions. It offers insights into labor policies, wage structures, and workplace conditions by examining labor economics through social and legal lenses. Lastly, Environmental and Sustainability Studies explores the economic and ethical dimensions of sustainable practices, analyzing how industries can mitigate their environmental impacts. This meta-field bridges economics, environmental science, and ethics to inform sustainable development policies and practices, addressing one of the most pressing global challenges today. These meta-fields enrich our understanding by providing multidimensional perspectives on economic and organizational issues.

Then there are the disciplines: Management is broad but still best understood as a discipline within the meta-field of business studies. Like economics, management has developed into its own distinct academic discipline with specific theories, methodologies, and subfields. Within management, subfields like strategic management, organizational behavior, and human resource management reflect specialized approaches and applications. Although management draws on insights from other disciplines—such as psychology, sociology, and economics—it operates as an independent discipline focused on organizational efficiency, leadership, and resource allocation.

Management, however, can also cross into the interdisciplinary realm, particularly in business schools where it intersects with areas like marketing, operations, and finance. The label of discipline for management provides clarity by establishing a structured framework for its study while allowing interdisciplinary exploration. As a discipline, management maintains its own methodologies and academic journals, supporting its status within business studies and strengthening its role in the social sciences as a field with broad practical applications.

In our classification, economics is best viewed as a discipline within the broader meta-field of social science. Unlike a meta-field, which encompasses multiple related areas (like social science, which includes sociology, psychology, and economics), a discipline like economics has a distinct body of theories, methods, and subfields unique to itself. Economics also has specialized branches, such as microeconomics, macroeconomics, and behavioral economics, that support its internal structure and position within social science.

This hierarchical structure of economics mirrors that of other disciplines within the social sciences and aligns with institutional organization in academia, where economics is universally categorized as a discipline. By studying it as a discipline, researchers can maintain clarity on its foundational assumptions and unique approaches while also facilitating interdisciplinary connections. In doing so, economics retains a distinct identity within the social sciences, while also serving as a point of connection for interdisciplinary studies. This alignment allows for clearer intellectual orientation and better-organized research practices within the larger academic framework.

In economics, the various schools of thought are distinct frameworks or approaches within the discipline, but they are not typically considered sub-disciplines in a formal sense. Instead, these schools of thought, such as Keynesian economics, neoclassical economics, Marxist economics, and Austrian economics, represent different theoretical perspectives or paradigms that interpret economic phenomena in unique ways. They each offer contrasting explanations for economic processes, assumptions about human behavior, and policy recommendations.

While sub-disciplines focus on specific applications within economics—like labor economics, international economics, or development economics—the schools of thought are more about philosophical and methodological approaches within these areas. This means that within a sub-discipline, like labor economics, scholars may apply different schools of thought to analyze issues. Each school of thought brings its own assumptions, shaping the interpretation and outcomes of research without altering the overarching focus of the sub-discipline. Thus, schools of thought function much as lenses through which economists interpret the same phenomena, while sub-disciplines categorize the fields of study within the discipline itself.

For the project on “The role of competitive intelligence in enhancing SMEs’ sustainability in African markets” in the example above, we might apply several theoretical perspectives, depending on the focus we want to bring to the research. Some examples could include:

1.

Resource-based view (RBV): This perspective emphasizes that a firm’s sustainable competitive advantage arises from unique resources and capabilities. You could use this perspective to analyze how competitive intelligence serves as a valuable resource for SMEs, enabling them to gain insights into market conditions and competitors, thereby contributing to their sustainability.

2.

Institutional theory: Institutional theory would be relevant if you’re looking to explore how SMEs adapt to, and are influenced by, the institutional environment in African markets. This perspective would highlight how regulatory frameworks, cultural expectations, and business norms impact the role of competitive intelligence in sustaining SMEs.

3.

Dynamic capabilities theory: This perspective focuses on a firm’s ability to adapt, renew, and reconfigure its resources in response to changing market conditions. It’s particularly relevant for studying SMEs in competitive markets. Here, competitive intelligence could be viewed as a dynamic capability, helping SMEs remain resilient and responsive in turbulent economic climates.

4.

Stakeholder theory: If the project intends to explore broader social or environmental sustainability goals, stakeholder theory would allow you to consider the interests of all stakeholders, not just shareholders. Competitive intelligence could be analyzed for how it helps SMEs address the needs and expectations of various stakeholders, such as customers, suppliers, and communities.

5.

Network theory: Network theory could examine how SMEs utilize their networks for competitive intelligence, relying on relationships with other firms, industry groups, or local organizations to gather and share market insights, thereby supporting their sustainability.

The theories provided make up a theoretical framework or perspective through which to analyze the problem, enabling a rich and multifaceted understanding of how competitive intelligence impacts SME sustainability in the African market context. The name of the theoretical perspective can also be included in the title of the project.

The theoretical perspective provided is different from the scientific method description of the project, and so the two should be kept apart. The method description is a step-by-step outline of how the research will be conducted, detailing the specific techniques and tools (like surveys, interviews, or data analysis methods) that will be used to gather and analyze data. While the theoretical perspective frames the research direction, the method description provides the practical plan for carrying out the study within that conceptual framework.

We have used economics and business as examples in this section. Other social sciences have their own fields, disciplines, sub-disciplines, and specialties, where the study of economics or business may sometimes play a role, be treated as a distinct area, or be entirely excluded. As a social scientist and economist working interdisciplinarily, I hold no specific preferences for disciplines, sub-disciplines, or specializations; my focus is solely on the problems at hand. However, once a problem is clearly defined, I do develop preferences for certain methods best suited to addressing it. Additionally, I have distinct preferences regarding philosophical foundations and schools of thought, which, as we will explore later, significantly shape my approach to research. But, more on this later.

Once we are clear about the research categorization from which we are approaching a specific question, we can proceed to the next step: delving into the relevant literature and identifying journals aligned with our chosen research framework.

2.1.5 Strategic Literature Search and Keyword Expansion

We are now ready to start looking for relevant literature. To do that, we need to extract keywords.

To find effective keywords for the literature search on “The role of competitive intelligence in enhancing SMEs’ sustainability in African markets,” used as an example in the template above, start by breaking down the topic into its core components. Begin with “competitive intelligence,” which is central to the research question. Related terms such as “market intelligence,” “business intelligence,” and “competitive analysis” may uncover valuable articles.

This technique is often referred to as synonym expansion or semantic keyword search. It involves identifying and using related terms, synonyms, or alternative phrases to broaden the search scope and capture a wider range of relevant results.

By anticipating various ways, a concept might be labeled we could uncover articles we might otherwise miss if we used only the initial keywords. This approach works well for academic research but is even more common in online product searches since many relevant results may use alternative phrasing. For instance, terms like “business intelligence” or “market analysis” could be used interchangeably when searching for competitive intelligence resources, depending on the study’s focus or regional language preferences. In practice, synonym expansion requires understanding both the main topic and its typical variations to form a comprehensive search strategy, improving the likelihood of gathering a robust, inclusive dataset.

Ideally, academia should ensure that all terms have clear, non-overlapping definitions from the outset. Unfortunately, this is not how the social sciences have evolved. New terms often emerge without their creators conducting thorough analyses of existing terminology. In the older German tradition of the social sciences in the nineteenth century, there was a much stricter emphasis on precise definitions. However, in the current Anglo-Saxon-dominated paradigm, especially after the Second World War, the creation of new terms is often driven by the need to promote ideas in the global marketplace of research, where individual recognition can sometimes take precedence over conceptual clarity. Ideally, someone should systematically review all the terms, eliminating duplicates and those that do not add significant value. However, organizing such an effort is challenging, so we must navigate the current complexities as best we can.

Next, focus on “SMEs” (small and medium enterprises). Because SMEs have specific challenges, try keywords like “small businesses,” “entrepreneurship,” or “business development for SMEs” to explore studies on sustainability specifically relevant to small enterprises.

For “sustainability,” consider other relevant keywords like “sustainable business practices,” “environmental impact,” and “corporate social responsibility (CSR)” to capture the different facets of sustainable development.

Lastly, “African markets” as a contextual focus can be expanded with specific regions or countries in Africa, as well as related terms like “emerging markets,” “African economies,” or “regional market dynamics.” Including these geographic or contextual keywords can help you find studies that address the unique challenges and opportunities within African markets. Combining these terms in various combinations can give a well-rounded literature base, ensuring the research comprehensively addresses all topic components.

You proceed step-by-step, testing keywords to see what yields quality results. Some keywords will turn up articles highly relevant to your topic, while others may lead to fewer useful resources.

Once you have a list of articles, reading the abstracts first is essential to confirm their relevance. Download only those articles that genuinely contribute to your research focus; resist the temptation to gather everything that might vaguely relate. Taking time to evaluate each piece at this stage will save effort later.

For each article, focus on the critical elements: the research questions, findings, and any key theories. At this point, you can usually assume the methodology is sound if the journal is reputable. Make note of any theories that gain or lose strength through the research, as social science theories evolve in credibility with each supporting or contradictory dataset. Record your notes either in an Excel sheet or directly in your main document, and be sure to capture the full citation details in the required journal style or any style of your choosing, but be consistent. For articles with potential impact on your research gap, take note of the implications and suggested future studies described in the paper (Fig. 2.6).

Fig. 2.6

Searching for the literature

We can explain this as a process.

As we are now starting to read journal articles, we are also going to start wondering about the different sections they are divided into and their form.

2.1.6 Sections of a Research Article

When you’ve read a number of academic articles, you’ll notice they generally follow a similar structure and logic: they start with an abstract, which provides a brief summary, and then move into an introduction that frames the research problem. This is typically followed by a section on methodology, explaining how the problem will be addressed, and then the presentation of data and its analysis. Next comes a critical part where the authors revisit the original questions, providing clear answers. This is often followed by a broader discussion that reflects on the findings, assessing which theories were supported or challenged and suggesting directions for future research to help advance the field. Here’s a more detailed breakdown of this structure:

1.

Abstract: A concise summary of the study’s purpose, methodology, key findings, and conclusions. The abstract provides a quick overview, helping readers assess the relevance of the article to their research.

2.

Introduction: This section introduces the research problem, presents background information, and explains the study’s significance. The introduction often includes a literature review, highlighting previous work and positioning the current research within the existing body of knowledge.

3.

Methodology: A detailed explanation of how the research was conducted, including descriptions of the research design, sample, data collection methods, and any analytical tools or procedures used. This section is critical for assessing the reliability and reproducibility of the study.

4.

Results: This section offers the research findings, often including tables, graphs, and statistical analyses. Results are typically reported objectively without interpretation, offering a factual account of what was observed.

5.

Discussion: Here, the authors interpret the findings, comparing them with previous studies and exploring implications, limitations, and potential avenues for future research. The discussion connects the results back to the study’s aims and the broader research context.

6.

Conclusion: A brief recap of the main findings, contributions, and significance of the study. The conclusion often reiterates key points and may propose specific recommendations based on the research outcomes.

References – A comprehensive list of all the sources cited within the article, following a specific citation style. The references allow readers to locate sources, verify claims, and explore further readings on the topic.

We leave writing the abstract until the end, as it is the summary of the paper. Note that many journals have very specific requirements for the abstract, not only in terms of character count but also regarding the topics that need to be included. Next, let’s look at each section in more detail.

2.1.6.1 The Introduction

In my view, a good introduction should clearly define the problem area and aim to convince the reader of the study’s importance. It typically concludes with specific research questions or hypotheses. One effective way to argue for a topic is to use popular sources, like magazine or newspaper articles, as these indicate the topic has current relevance and news value. Another helpful approach is to present some easy-to-understand statistics at this stage, reinforcing the importance of the topic.

The logical structure of the introduction is often deductive: it offers a conclusion (c)—that is, what you plan to undertake—supported by a set of premises (p) that argue for why the conclusion is valid.

To use our example above in a syllogism that captures the structure of a persuasive introduction based on your description:

Premise 1 (p1): The topic of competitive intelligence is frequently covered in current business magazines and news articles, indicating significant relevance for today’s industry trends.

Premise 2 (p2): Studies show that small and medium enterprises (SMEs) in African markets face unique sustainability challenges, with statistics indicating high rates of business closure within 5 years.

Premise 3 (p3): Research on competitive intelligence and sustainable business practices has been limited in the context of African SMEs, suggesting a gap in the literature that warrants further investigation.

Conclusion (c): Therefore, investigating the role of competitive intelligence in enhancing the sustainability of SMEs in African markets is a valuable research endeavor that addresses both a current industry need and a gap in academic literature.

In this example:

Conclusion (c) is what the research will undertake.

Premises (p1, p2, and p3) provide supporting arguments, establishing the relevance, need, and timeliness of the research.

In our example, the introduction includes a literature review, but in most cases, these are separate sections. The literature review typically follows a format like “Andersson (2021) found that…,” presenting a series of relevant studies. After listing the key sources, it’s beneficial to briefly summarize and synthesize the literature, as this establishes a foundation for the analysis by showing how the current study builds on or diverges from existing research. This synthesis often involves identifying common themes, research gaps, and contrasting perspectives, which helps clarify the study’s unique contribution and necessity.

However, a more detailed synthesis or close comparison of findings should be saved for the analysis section, where the study’s results can be directly compared to the literature. By briefly summarizing the literature review and reserving in-depth comparisons for the analysis, the paper maintains a clear structure and keeps each section’s focus consistent.

2.1.6.2 The Methodology

I prefer to conclude the introduction with the research questions (RQs) or hypotheses (Hs) and then go directly into the methodology section, where I explain how I plan to address the research problem. However, it’s essential to consult the author guidelines here, as some journals may require a different structure, such as placing a theory section before the methodology.

The methodology section’s primary goal is to ensure that the chosen method aligns well with the research problem—neither overly complex nor too simplistic. The second priority, in my view, is thoroughness: the methodology should be detailed enough to enable readers to reproduce the study using either the original or similar data. Many journals now encourage researchers to upload raw data, a valuable practice that supports transparency and reproducibility, especially in the social sciences.

This approach ensures that findings are rooted in empirical data and are reproducible, leading to more objective, evidence-based conclusions. Metaphorically speaking, we are now entering the “engine room” of the research. Contrary to popular belief, the methodology doesn’t need to be filled with complex statistical techniques; it simply needs to be appropriate for the research question.

For instance, in social sciences, a simple survey can be an effective tool for gathering data on public opinion or behavior. A well-designed survey may suffice if we’re studying consumer preferences for eco-friendly products. The key is aligning the method with the research question rather than focusing on its complexity.

When using surveys, minimizing biases that could compromise the data is critical. Common issues like leading questions, selection bias, and response bias can distort findings. Ensuring a well-designed survey with neutral wording and a representative sample is more important than methodological complexity, as it ensures data validity and reliability. Pretesting the survey can help identify and address any errors or ambiguities in question-wording.

We aim to collect “good data” or, to continue the metaphor, “clean fuel.” In scientific terms, good data is accurate, reliable, and unbiased, serving as a trustworthy foundation for conclusions, regardless of the specific field of social science.

The primary methodological distinctions within social sciences lie in focus and data collection techniques. Disciplines like sociology and anthropology often emphasize qualitative methods, such as ethnography and fieldwork, to examine groups and cultures. In contrast, psychology and economics rely more often on quantitative methods, like experiments and statistical analysis, to study individual behavior and economic trends. Political science and education frequently use a mix of both, often employing surveys and comparative studies to evaluate systems, policies, and practical outcomes. In the end, it really doesn’t matter what method you choose as long as the data you gather is good.

The table below contains some general suggestions for proper methodologies to be used in different social sciences (Table 2.4).

Table 2.4

Methodological differences between social sciences

Social science

Primary methodology

Example

Key differences

Sociology

Surveys, interviews, ethnography

Studying how social networks influence political beliefs in a community

Focus on groups and societal structures; often uses large-scale data collection like surveys or ethnography

Psychology

Experiments, observational studies, case studies

Testing how car color influences individual emotions or buying behavior

Emphasizes individual behavior and cognitive processes; often relies on controlled experiments and case studies

Political science

Comparative studies, surveys, data analysis

Analyzing voter turnout in different regions during an election

Focus on institutions and large-scale datasets, often comparing different political systems or policies

Economics

Statistical analysis, econometrics, surveys

Investigating how tax incentives affect consumer spending on luxury vehicles

Heavy use of quantitative models and econometrics to analyze market behaviors and economic trends

Anthropology

Fieldwork, ethnography, interviews

Studying the impact of modernization on indigenous vehicle use and mobility

Emphasizes qualitative, immersive fieldwork, focusing on cultural and societal evolution over time

Education

Action research, surveys, case studies

Evaluating how different teaching methods impact students’ understanding of environmental issues in a classroom

Focuses on practical outcomes and intervention effectiveness, often combining qualitative and quantitative methods

When we go deeper into each discipline, we find a variety of methods tailored to their specific focus and goals. One interesting example is economics, which stands out for its reliance on mathematical frameworks compared to most other social sciences. This preference for mathematical modeling, statistical analysis, and theoretical equations reflects the discipline’s aim to quantify and predict complex interactions within markets and societies. Let’s look in more detail to understand how methodological choices shape its approach and differentiate it from other fields.

Economists prefer these methods primarily because they provide rigorous, systematic, and quantifiable ways to analyze complex phenomena. It allows for clear articulation of assumptions, relationships, and outcomes, which enhances transparency and replicability in economic modeling.

Over time, certain methods have become entrenched due to a certain proven ability to solve specific types of problems, such as calculus for marginal analysis or econometrics for data-driven predictions. This preference is not merely traditional; it reflects the accumulated success of these methods in providing insights and enabling forecasts that align with observed data. However, tradition also plays a role, as established frameworks often guide research agendas and teaching, creating a reinforcement cycle (Table 2.5).

Table 2.5

Preferred mathematical branches used in the study of economics

Branch of mathematics

Definition

Purpose in economics

Common applications

Algebra

Study of symbols and rules for manipulating equations

Solve equations and model relationships between variables

Solving supply and demand models, equilibrium analysis, budget constraints

Calculus

Study of change through differentiation and integration

Analyze marginal changes, optimize functions, and study dynamics

Marginal cost/utility analysis, growth models, dynamic systems

Linear algebra

Study of vectors, matrices, and linear equations

Handle multivariable systems and perform matrix operations

Input-output models, econometrics (matrix regression), portfolio optimization

Probability

Study of randomness and likelihood of events

Analyze uncertainty and quantify risks

Risk assessment, expected utility, stochastic processes in finance and insurance

Statistics

Collection, analysis, and interpretation of data

Test hypotheses, infer patterns, and make predictions

Econometrics, forecasting (e.g., GDP, inflation), hypothesis testing

Game theory

Study of strategic decision-making among agents

Analyze interactions between rational decision-makers

Oligopoly competition (Cournot/Bertrand), auctions, bargaining scenarios

Differential equations

Equations involving derivatives to study change over time

Model dynamic systems and temporal economic processes

Solow growth model, business cycles, dynamic adjustment in macroeconomics

Optimization

Techniques for finding the best solution given constraints

Maximize or minimize objectives under constraints

Utility maximization, resource allocation, cost minimization, linear programming

Econometrics

Statistical methods applied to economic data

Estimate relationships, test theories, and forecast trends

Regression analysis, time-series forecasting, panel data analysis

Set theory and logic

Study of sets and formal logical structures

Provide foundations for modeling and defining economic concepts

General equilibrium theory, formalizing preferences, defining Pareto efficiency

Numerical methods

Algorithms for approximating solutions to mathematical problems

Solve complex models that lack analytical solutions

Simulations in macroeconomics, nonlinear equation solving, policy evaluation

For a mathematician, the mathematics used in economics is generally not considered highly advanced or complex. The algebra involved is often at a high school level, while the calculus aligns with material covered in the first year of university for many disciplines. The statistical methods commonly used are foundational, such as those discussed in introductory courses, though econometrics introduces specialized applications of these statistical techniques. Game theory typically relies on relatively simple matrices and decision trees, making it accessible with basic understanding. Set theory, though abstract at first, becomes straightforward once the symbolic language is understood. Econometrics, while more sophisticated, can indeed be seen as an extension of statistics tailored to economic data and its peculiarities.

It’s not as though economists, even the best ones, have always relied on these mathematical methods eighter. For instance, if you read the collected works of John Maynard Keynes, spanning over 30 volumes, you’ll find relatively little use of mathematics—his ideas were primarily expressed through prose (Keynes 1971–1989). Mathematics in economics can be thought of as a language, one way of expressing ideas. Speaking in mathematical terms has certain advantages, such as precision and clarity when modeling relationships, but it also comes with disadvantages—chiefly that it limits participation, as fewer people can understand or critique complex mathematical arguments. The notion that mathematics is just another language was shown by Bertrand Russell, who argued for its utility but also recognized its limitations (Russell, 1903).

We find the same reservation by the brilliant statistician Nicholas Georgescu-Rogen in an essay in honor of Ragnar Frisch:

“Some aspects of its [human society] functioning lend themselves perfectly to mathematical analysis. Yet, when we come to the problem of its evolution, of its mutation into another form, mathematics proves to be too rigid and hence too simple a tool for handling it. Mathematical proofs of future evolutionary changes in any domain should, therefore, be viewed with skepticism, even if, unlike those analysed in this paper, they are logically irreproachable.” (Georgescu-Roegen, 1960, p. 243)

However, Georgescu-Rogen does not fully agree that it’s just another language, as “the analytical process can be accomplished faster than if carried out by ordinary logic alone” (Georgescu-Roegen, 1966, p. 116).

Prose texts are often frustrating for several reasons. First, they tend to discuss ideas without much logical structure, often failing to clearly present their premises and conclusions. In many cases, a straightforward set of syllogisms would be more effective. Second, prose does not excel at providing clear analysis or an overarching view of complex ideas. For this purpose, tables and simple graphical models can often convey information more effectively without necessarily requiring mathematics. However, mathematics has a unique advantage: it forces us to think in a structured and logical way. It is particularly useful for illustrating how changes in certain variables impact others, providing clarity and precision that prose alone often lacks.

At the same time, mathematics is not always essential for addressing every research question. And when it is needed, math is not very advanced, so analytical tools and modeling techniques can often be learned relatively quickly with the right guidance and resources, making them more accessible than they might initially seem. This is not to downplay the importance of the methods dominating much of the economics field but instead emphasizes two key points: these approaches are not as inaccessible as many social scientists might assume, and they are not always indispensable. With proper support, the “languages” of analysis and modeling can be effectively mastered, bridging gaps between disciplines and enhancing research versatility.

For any of the methods listed in the two tables above, there isn’t a single “correct” method, but rather multiple approaches that can help us find the answers we seek. Nonetheless, we can argue that some methods are likely better suited to a given research problem than others. It’s important not to ignore this hierarchy for a given problem, as one method will almost certainly produce more reliable data than another when we take the time to compare them under similar conditions.

Often the choice of method depends on where we intend to publish, as different journals have their standards and implicit preferences. While most journals won’t specify a preferred method outright, examining their published articles often reveals trends. Higher-ranked journals sometimes lean toward quantitative methods, though even these typically involve moderate levels of mathematics in social science research. In contrast to natural sciences like physics, social science journals rarely require complex math; rather, they value the use of appropriate, accessible quantitative techniques.

Editors and reviewers, particularly in social sciences, have varying levels of comfort with complex math, and their preferences often reflect this. The most effective way to gauge a journal’s methodological preferences is by reviewing the types of articles they publish. This can help you align your approach with the expectations of your target journal. As a strategy, you can do as much math as in the examples you read, maybe a snippet more, but not too much. It’s not an ideal way to choose a method, I know, but a practical one.

2.1.6.3 The Literature Review

The placement of the literature review—whether before or after the method section—varies depending on the journal’s style or the field’s conventions, but either approach can work effectively. What’s most important is that the literature review clearly demonstrates the foundation of your study, showing the relevant theories, models, and previous findings that inform your work. This section is where you present the research landscape, positioning your study within the broader field and identifying the specific theories or frameworks you plan to test or build upon.

There are various forms of literature reviews. A comprehensive but not necessarily complete list of review types are listed in the Table 2.6 below:

Table 2.6

Types of literature review

Type of literature review

Description

Purpose

Common protocols

When to apply

Negative sides

Systematic review

A structured, comprehensive search and appraisal of all relevant studies, using predefined inclusion/exclusion criteria

To provide an exhaustive, objective summary of existing evidence while minimizing bias

PRISMA, ROSES, Cochrane.

When needing a rigorous, reproducible synthesis of high-quality research

Time-consuming, requires strict criteria that may exclude relevant studies

Meta-analysis

A statistical technique that aggregates data from multiple studies within a systematic review to measure overall trends

To quantify findings and identify effect sizes across studies, often in medicine, psychology, and social sciences

PRISMA (preferred reporting items for systematic reviews and meta-analyses)

When enough similar studies exist to allow meaningful statistical pooling

Requires methodological consistency across studies, may oversimplify findings

Scoping review

Maps the existing literature on a broad topic, identifying gaps, key concepts, and potential research directions

To determine the scope, range, and nature of research on a topic, often before conducting a systematic review

JBI (Joanna Briggs Institute) methodology

When an emerging or interdisciplinary field needs initial exploration

Lacks depth and rigorous quality assessment

Narrative review

A broad, qualitative summary synthesizing findings from various studies without a strict methodology

To provide an overview of a topic, integrating studies to summarize current understanding and trends

No specific protocol (author-driven approach)

When exploring theories, historical perspectives, or broad academic discussions

Subjective, prone to selection bias, and lacks reproducibility

Non-systematic review

A flexible, informal review without strict inclusion/exclusion criteria; synthesis is based on the reviewer’s discretion

To explore new fields or provide an initial summary where systematic methods may not be feasible

No specific protocol

When literature is too scarce or diverse for a structured approach

Can be selective and subjective, leading to potential bias

Rapid review

A streamlined, accelerated version of a systematic review, using shortcuts to deliver faster insights

To provide timely evidence summaries for urgent policy or decision-making needs

WHO rapid review guide, modified PRISMA

When decision-makers need a quick, evidence-based summary

May lack depth and comprehensive study inclusion

Integrative review

Merges theoretical and empirical literature from diverse methodologies to develop new frameworks or theories

To synthesize knowledge across multiple disciplines and methodological approaches

No fixed protocol, depends on research objectives

When bridging qualitative and quantitative studies to develop conceptual insights

Lacks standardization, making comparisons difficult

Umbrella review

A review of systematic reviews and meta-analyses, summarizing high-level evidence across multiple studies

To aggregate and compare findings from multiple systematic reviews

PRISMA for umbrella reviews

When there are several systematic reviews on a topic, requiring synthesis

Relies on existing systematic reviews, which may have their own biases

Critical review

Evaluates and critiques existing literature, challenging assumptions and highlighting inconsistencies

To re-evaluate dominant theories or methodologies, often taking a conceptual or philosophical approach

No formal protocol (subjective critique)

When questioning existing frameworks and proposing alternative perspectives

Subjective and difficult to replicate

Theoretical review

Examines how theories have been applied in different studies, analyzing their evolution and limitations

To compare, contrast, and refine theoretical frameworks

No fixed protocol, depends on theoretical scope

When analyzing the conceptual landscape of a field

May lack empirical grounding

Bibliometric review

Uses citation analysis and quantitative metrics (e.g., co-citation networks, h-index) to map research impact and trends

To analyze how research fields evolve and which works are most influential

Bibliometric tools (e.g., VOSviewer, CiteSpace, web of science, Scopus)

When identifying influential authors, trends, and research clusters

Focuses on quantitative citation patterns rather than content quality

Global bibliometric review

Uses large-scale citation networks to analyze the structure and evolution of research fields globally

To provide a macro-level understanding of research impact across regions, disciplines, and time

Advanced bibliometric software, machine learning tools

When studying large-scale academic trends and intellectual networks

Computationally intensive, dependent on database coverage, may lack qualitative insights. Requires access to metadata from large-scale databases, which is often difficult to obtain

A systematic review is the most rigorous form of literature review, involving a structured, comprehensive search and appraisal of all relevant studies based on predefined inclusion criteria. It aims to minimize bias and provide an objective synthesis of evidence, making it ideal for research requiring high methodological rigor, though it is time-consuming and may exclude important but non-conforming studies. A meta-analysis takes this a step further by aggregating data from multiple studies to identify overall trends, useful when enough comparable studies exist but demanding statistical consistency across them. A scoping review, on the other hand, is broader, mapping out existing literature on a topic to identify research gaps and key themes, often as a preliminary step before a systematic review, though it lacks in-depth quality assessment.

For a more flexible and qualitative synthesis, a narrative review provides a broad summary of a topic, integrating studies without a strict methodology, useful in historical or theoretical explorations but prone to subjectivity and bias. Similarly, a non-systematic review offers an exploratory overview in emerging fields where structured methods may not be practical, though its lack of selection criteria makes it vulnerable to researcher discretion and bias. A rapid review condenses the systematic review process for quick decision-making, particularly in policy or health research, but sacrifices comprehensiveness for speed.

When bridging qualitative and quantitative approaches, an integrative review synthesizes knowledge across disciplines to develop new frameworks but struggles with standardization. An umbrella review evaluates findings from multiple systematic reviews, aggregating high-level evidence across studies, though it depends on the quality of preexisting reviews. A critical review challenges assumptions in existing literature, re-evaluating theories conceptually, yet its subjective nature makes replication difficult.

A theoretical review examines how theories evolve and are applied, making it valuable for comparing conceptual frameworks, though it may lack empirical grounding. A bibliometric review analyzes research impact and trends through citation analysis, making it ideal for mapping academic influence but often overlooks content quality. Extending this approach, a global bibliometric review applies large-scale citation networks to track disciplinary evolution over time, requiring advanced data access and computational methods, but offering unparalleled insights into academic trends.¹

A strong literature review not only identifies the most influential studies but also positions your research within the broader academic conversation. Rather than merely summarizing existing work, structure your review in a way that logically connects theories, findings, and debates. Organizing studies by themes, methodologies, or theoretical perspectives can help clarify how different contributions shape the field and where your research fits in. Using a reference table, if allowed, can also help synthesize key works efficiently.

To enhance credibility, prioritize peer-reviewed, high-quality sources and foundational texts, ensuring your review is built on a solid academic foundation. Avoid including tangentially related studies that dilute the focus of your discussion. Instead of listing research chronologically, critically analyze key works by discussing their strengths, limitations, and relevance to your study. This analytical approach demonstrates a deeper engagement with the literature and highlights the reasoning behind your research direction.

Identifying gaps in the existing literature is essential—whether they involve unresolved questions, underexplored areas, or contradictions between studies. These gaps help establish the necessity of your research and show how it contributes to advancing knowledge. A well-rounded review should balance recent studies that reflect current developments with classic works that provide historical and theoretical grounding. Engaging deeply with sources ensures that your research does not come across as superficial or limited to a narrow, short-term perspective.

Clearly articulate the theories or models that your study builds upon, testing or refining them in your research. This alignment strengthens the connection between your literature review and research questions, making it easier for readers to see the foundation of your study. By following these principles, your literature review will be both comprehensive and focused, effectively supporting your study’s aims.

With this structured approach in place, the research process moves forward as follows (Fig. 2.7):

Fig. 2.7

How to do the literature review

The working process is straightforward. What can be more challenging and requires some training is not simply understanding what the articles are about but critically evaluating their findings. You need to assess whether the conclusions are genuinely supported by the data and the literature or if the author has overstated the results.

Identifying biases within the article is crucial—what sample was used, and how was it selected? While the actual calculations are generally assumed to be correct, it’s rare to recheck them unless there is a serious suspicion of error simply because it takes much time. In such cases, you may have already discarded the article. If the article is particularly strong, I might also look up the authors to see what other research they have conducted that may be of interest.

By now, you may also have been asking how AI can help with all of this. First, open AI is very new, only 2 years of age as I write this, and it is changing the whole time. AI tomorrow, when you read this, and in the years to come, is going to be very different.

Example: How can AI help with the literature review, and what are the rules for using AI?

Using AI tools like ChatGPT can streamline the literature review process of your scientific paper in several practical ways. First, AI can assist in identifying relevant keywords and search terms based on your research questions, making your database searches more precise and efficient. This can save significant time by narrowing down your search to the most pertinent studies.

Second, AI can quickly summarize key findings from multiple articles, allowing you to grasp essential insights without reading every paper in full. This enhances your ability to review more material in less time while still capturing the core content. Additionally, ChatGPT can help organize your findings by highlighting recurring themes, trends, and gaps in the literature, making it easier to structure your review logically.

What’s more, AI can assist in drafting coherent summaries by synthesizing information from various sources, helping you produce a well-structured and comprehensive literature review. It can also suggest relevant references and additional readings, ensuring that no critical research is overlooked. AI can assist with tasks like formatting, generating tables, and creating diagrams using tools such as LaTeX for typesetting and Mermaid for visualizing data through diagrams. These tools are widely supported today, but it is likely that they will be replaced or supplemented by new technologies and languages in the future.

One valuable function of AI is its ability to fact-check anything you write. While it may not always provide accurate results, it is often reliable in many cases, making it a useful tool for verifying information quickly.

Tomorrow we will see the development of more and more AI agents, advanced software consisting of thousands of built-in prompts that can work semi-autonomously.

However, remember that while AI is a powerful assistant, it’s the researcher’s responsibility to verify all the AI-generated content for accuracy and coherence. Always review the outcomes to ensure they align with the scientific rigor and integrity required for your research. Letting AI write for you means you are transferring the authorship to a machine. That may not be the thing you wanted to do in the first place. We ensure authorship not only by writing all the content ourselves—using AI primarily for grammar and syntax—but also by carefully evaluating and often rejecting the suggestions made by the AI. This is important, as AI-generated language often sounds less human and thus becomes less engaging and relatable. Even if AI can write in all kinds of “voices,” it can still not replicate your way of expressing yourself.

We also need to report when and how we have used AI in a text. For example, this book uses AI primarily for grammar and syntax but also sometimes partly for fact-checking; that is, I will also check for facts in other places, like books, journals, and specialized web pages.

We move now to the next section in the article.

2.1.6.4 Results, Analysis, and Discussion

Some journals combine the sections for Results, Analysis, and Discussion into one, while others separate them. Regardless of the format, it’s crucial not to omit any of these elements.

The Results section is central to the paper, as it offers the findings—what the study actually discovered. This is often the first thing readers examine, looking to understand the strength and certainty of your conclusions. While weak findings may be unavoidable at times, they’re still preferable to overstated results. If the data doesn’t allow for a definitive conclusion, it’s better to be cautious and acknowledge this than to risk overclaiming.

Make the findings easy to see and find in the paper. For instance, if you’re testing hypotheses, clearly indicate whether each hypothesis is Accepted (A) or Rejected (R). If you use Research Questions (RQs), write clearly RQ1, RQ2,…RQn. Avoid ambiguous phrasing that could obscure your answers to the original research questions. Restating the questions briefly before presenting the answers can also help maintain clarity.

The Analysis and Discussion sections serve complementary roles. The analysis section offers presents the data objectively, often using visual aids like tables or graphs to highlight trends without interpreting them. In contrast, the discussion interprets these findings, connecting them to the research questions or hypotheses, the existing literature, and broader implications. This section also addresses the study’s limitations and potential directions for future research.

The implications of the findings, often included in the discussion, should outline the broader consequences of the results if implemented. These implications should aim to be comprehensive and conclude with an overarching summary if they are numerous.

A critical part of the analysis is assessing whether your data supports the theories in your paper. It’s equally important to compare your findings to existing studies, identifying agreements and conflicts.

Supporting a theory strengthens it, while contradictory data weakens it, particularly if other studies yield similar results. Scientific progress typically involves the cycle of supporting, challenging, and sometimes reviving theories as new data emerges.

As researchers, we should approach theories objectively, free from bias. Moreover, a true scientist is open to changing their views when faced with new, credible data. If that sounds difficult, one might consider fields where subjective interpretation plays a larger role, such as political commentary or creative writing (Table 2.7).

Table 2.7

Elements are to be included in the results, analysis, and discussion section

Section

Description

Example (White Cars Study)

Results

Offers the main findings of the study. Readers often look at this first section to gauge the study’s strength. Findings should be clearly stated, avoiding overclaims if the data doesn’t support a definitive conclusion

State if a significant preference for white cars was found, and clarify the level of confidence in these findings based on the data

Clarity in findings

Clearly indicate outcomes, especially when testing hypotheses. Use labels like "Accepted (A)" or "Rejected (R)” for easy identification and avoid ambiguous phrasing. Restate research questions briefly to maintain focus before presenting results

If testing the hypothesis "White is the most popular car colour," indicate findings clearly as Accepted (A) or Rejected (R)

Analysis

Objectively presents data, often using visual aids such as tables and graphs to reveal trends without interpretation. Helps to maintain a neutral tone, allowing readers to understand the raw data before moving to broader discussions

Display a graph showing the frequency of each car color observed, highlighting trends in white cars compared to others

Discussion

Interprets the results, explaining their significance in relation to the research questions and connecting to the broader literature. Includes an exploration of limitations and suggestions for future research directions, allowing the findings to be contextualized

Explain why white may be popular (e.g., aesthetic trends, resale value) and discuss how findings relate to existing car color studies

Implications

Outlines the broader consequences of the findings if implemented or adopted. This should be comprehensive and may conclude with a summary if multiple implications are listed, helping readers see the practical significance of the study’s results

Suggest how car manufacturers might adjust color options based on these trends, influencing marketing and production strategies

Theory evaluation

Assesses whether data supports or contradicts established theories, helping to strengthen or weaken existing concepts based on evidence. Scientific progress relies on this cycle of supporting, challenging, and potentially reviving theories

If findings align with prior studies on color trends, this strengthens theories about consumer preferences in aesthetics and practicality

Objective approach

Emphasizes a neutral approach to data interpretation, free from bias, allowing scientific integrity to guide conclusions. Encourages openness to changing views based on credible new data, maintaining objectivity, and supporting evidence-based conclusions

Highlight any new trends discovered without attaching personal or subjective beliefs, focusing purely on data and established research

Then we come to the last section, the conclusion.

2.1.6.5 The Conclusion

In writing the conclusion, it’s advisable not to introduce any new information that hasn’t already been covered in the paper. This section should be a synthesis of the key points, emphasizing the value of the findings and reinforcing their importance. Additionally, a strong conclusion includes recommendations for future research, which can be particularly valuable for early-career researchers seeking direction. By suggesting areas where more investigation is needed, we contribute to advancing our field and help support our academic colleagues in building upon our work.

Another crucial element is addressing the “so what?” question, which explains the relevance of the findings to practitioners, policymakers, and the general public. This part of the conclusion highlights the broader impact of the research, helping readers understand how the findings might influence real-world applications and potentially benefit society. In essence, this aspect bridges the gap between academic research and practical applications, adding depth to the significance of the study.

Once the conclusion is polished, it’s beneficial to distill its core messages for the abstract. The abstract should be a concise summary of the study’s objectives, key findings, and broader implications—essentially serving as a “conclusion of the conclusion.” Since readers often rely on the abstract to decide whether to read the full paper, making it both clear and impactful is essential.

Clear, direct language is important in the conclusion, as this is where we convey the primary takeaway. Avoid complex jargon and instead focus on making the message accessible. If the study has limitations, briefly acknowledging them here can add transparency, helping readers interpret the findings realistically. Additionally, connecting the conclusion back to the introduction reinforces how the research questions or hypotheses were addressed, creating a sense of closure.

It’s also wise to avoid overgeneralizing the findings. Although it’s tempting to state broad implications, make sure that conclusions are grounded in the data and avoid claims of significance that the findings don’t support. Finally, ending with a thought-provoking statement can leave a lasting impression, encouraging readers to consider the study’s impact or the importance of further research.

Following these principles allows the conclusion to encapsulate the study’s contributions effectively, giving readers a clear, well-rounded understanding of its significance. If we follow these simple rules, we should be ok (Table 2.8).

Table 2.8

Summary of elements to include in the conclusion

Conclusion part

Description

Example (White Cars Study)

Synthesis of key findings

Summarizes main results without adding new information. Reinforces study value by highlighting the findings’ importance

Summarize if data showed a significant preference for white cars, emphasizing why this finding might reflect current consumer trends in car color preferences

Recommendations for future research

Suggests areas for further exploration that could help other researchers expand on the study

Recommend exploring factors influencing car color choices, such as climate, resale value, or cultural associations, to deepen understanding of color preference

Addressing the “so what?” question

Explains the practical relevance of findings for society, practitioners, or policymakers, bridging research and real-world application

Highlight the impact of color preferences on car manufacturers and marketers and suggest potential implications for car design trends and sales strategies

Distilling for the abstract

Condenses core conclusions into a concise, impactful abstract, summarizing objectives, key findings, and broader implications

The abstract summarizes the study’s aim (to assess white as a preferred car color), key finding (significant preference for white), and implications (importance for the industry)

Clear, direct language

Uses accessible language to ensure the main message is easily understood, without jargon or complex phrasing

Use simple language: “Our findings suggest white is currently the most preferred car color, indicating a trend shift in consumer preferences”

Acknowledge study limitations

Briefly note any limitations to provide transparency, helping readers interpret findings realistically

Mention any sample limitations, like geographic scope or demographic representation, that could affect the generalizability of the findings

Avoid overgeneralizing

Ground conclusions in data and avoid overstating findings or making broad claims unsupported by results

Refrain from claiming white will always be the top choice, instead noting the trend as current and possibly influenced by specific factors

Final thought-provoking statement

End with a memorable statement, encouraging readers to reflect on the study’s impact or future research directions

End with a statement like: "As preferences shift, understanding color choices can guide the future of car design and consumer research, potentially across industries”

If we have not done so already, we are now ready to find a journal we can try to publish in, or at least aim for at first.

2.1.7 Finding the Right Journal for Publication

There are several strategies for finding the right journal. Say that we have written a paper in accounting. One way is simply to say we want to publish in a better journal in accounting. We can then identify what these journals are, for example, by relying on well-known journal ranking systems, like the AJG, sometimes referred to as the ABS list (Table 2.9):

Table 2.9

Example of journal ranking, ABS list extraction

ISSN

FIELD

Journal title

Publisher

AJG 2021

AJG 2018

1558–7967

ACCOUNT

Accounting Review

American Accounting Association

4*

4*

1873–6289

ACCOUNT

Accounting, Organizations and Society

Elsevier

4*

4*

1879–1980

ACCOUNT

Journal of Accounting and Economics

Elsevier

4*

4*

1475-679X

ACCOUNT

Journal of Accounting Research

Wiley-Blackwell

4*

4*

1911–3846

ACCOUNT

Contemporary Accounting Research

Wiley-Blackwell

4

4

1573–7136

ACCOUNT

Review of Accounting Studies

Springer Nature

4

4

1467–6281

ACCOUNT

Abacus

Wiley-Blackwell

3

3

2159–4260

ACCOUNT

Accounting and Business Research

Taylor & Francis

3

3

1467–6303

ACCOUNT

Accounting Forum

Elsevier

3

3

1558–7975

ACCOUNT

Accounting Horizons

American Accounting Association

3

3

The list is much longer than this for accounting, but we have listed the journals that have been given 3 and 4 stars only, that is, those that the AJG considers to have the highest scientific quality. (A new list can be found online by the time this book is out).

The Academic Journal Guide (AJG), published by the Chartered Association of Business Schools (CABS), ranks business and management journals on a scale from 1 to 4. A 4 rating* signifies “world-leading” journals known for their originality and rigorous research, such as the Journal of Marketing or Academy of Management Journal. Journals ranked as 4 are internationally recognized as excellent, with examples like the Journal of Business Research. Journals rated 3 are well-regarded internationally but may not have as wide a reach, while 2 and 1 rankings indicate journals with more regional or niche significance or emerging impact.

This ranking system helps academics identify where to publish based on journal quality and impact.

The problem is that there are many kinds of rankings from different types of organizations and that different countries use different rankings. Other ranking systems include (Table 2.10):

Table 2.10

Other rankings of business journals

Ranking system

Criteria used

Rank levels

Description

HQ City

SCImago Journal Rank (SJR) - Scopus

Measures impact based on the number of citations and the prestige of citing journals

Q1 (highest) to Q4 (lowest)

Focuses on the influence of journals through weighted citations and prestige of the citing source

Amsterdam, Netherlands

Journal Impact Factor (JIF) - Web of Science

Measures average number of citations per article over a 2-year period

N/A

A widely used metric in assessing a journal’s influence based on recent citations

Philadelphia, USA

Financial Times 50 (FT50)

Chosen by business school leaders as the top influential journals in business and management

N/A

A list of 50 highly esteemed journals important for research in business schools

London, UK

Australian Business Deans Council (ABDC)

Journals are evaluated by citation metrics and expert reviews

A*, A, B, C

Focuses on business and management research, with A* being the highest quality and globally recognized

Canberra, Australia

Google Scholar Metrics

Uses the h-index to assess both productivity and citation impact

N/A

Covers a broad range of journals with less emphasis on traditional peer-review, including conference papers

Mountain View, USA

Norwegian List

Assesses the quality and international recognition of journals

Level 1, Level 2

Level 2 represents the most prestigious journals with global impact

Oslo, Norway

CiteScore - Scopus

Measures citations over a 4-year period

N/A

Broader than JIF, providing a longer citation window to capture journal influence

Amsterdam, Netherlands

European Science Foundation (ESF)

Expert assessment of journals based on their international reputation

A, B, C

“A” ranks represent top international journals in their respective fields

Strasbourg, France

The most used journal ranking systems today include SCImago Journal Rank (SJR), Journal Impact Factor (JIF), and CiteScore, all of which are widely recognized and cited in academic circles. SJR focuses on the influence of journals through weighted citations, JIF measures a journal’s average citations per article over 2 years, and CiteScore offers a broader view with a 4-year citation window. The Financial Times 50 (FT50) and the Australian Business Deans Council (ABDC) lists are influential in business and management fields, where institutions rely on them to evaluate research quality. These rankings provide benchmarks for assessing the impact and credibility of academic work, guiding researchers and institutions alike.

Another way to choose a journal is to consider which one you’ve cited most frequently in your references, as this journal is likely to align well with your content. I’ve often used this approach; however, from a career perspective, where some universities prioritize publications in high-ranking journals (such as 3- or 4-star journals), this may not always be the best strategy, especially if you find yourself in a niche subfield. Instead, if you’re in an academic environment that follows a specific ranking list, it’s probably wise to tailor your publication choices accordingly.

For instance, at the institution where I am now, we use The Norwegian List. The Norwegian List assigns publication points based on the quality of publication channels, categorized into levels:

Level 1: Standard level for academic quality journals and books, awarding 1 point for journal articles and 0.7 points for book chapters.

Level 2: Higher level for more prestigious journals and publishers, limited to about 20% of publications in each field, awarding 3 points for journal articles and 1 point for book chapters.

Monographs: Academic books receive 5 points at Level 1 and 8 points at Level 2.

The total points you accumulate annually form your research output score, which ideally influences salary increases and career progression—at least in theory.

A third way to choose a journal is to select one at random, which might happen if a researcher is unsure where to publish and stumbles upon a journal that seems suitable.

A fourth approach is to accept an offer from a journal that has solicited the article; this often occurs with less experienced researchers who may not yet be skilled at evaluating journal quality. Those journals that solicit articles often do this in the hope of making money, which may not be the best indication of quality for a scientific journal.

Another common method is to select a journal that you remember hearing about from a colleague or a past encounter. I would not recommend any of these methods.

For one thing, it’s crucial to ensure the journal is not predatory. Predatory journals are designed to seem rigorous but operate primarily as businesses, publishing numerous articles for a fee, often without sufficient peer review. Initially, identifying predatory journals can be challenging, as they may appear credible, with high citation counts and impact factors achieved through sheer volume of publications.

For this reason, the Norwegian Register for Scientific Journals, Series, and Publishers (Norwegian list) has introduced a “Level X” classification to identify journals and publishers that may exhibit questionable practices, including those considered predatory. This initiative aims to involve the research community in evaluating journals operating on the borderland between predatory and reputable practices.

Ideally, we would like to submit to a journal that can give us some to improve our article, but if we get this, it can be difficult to see or know in advance as it also depends on the reviewer. Good reviewers are often allocated by chance. Often the editor will just have to choose among those whom the journal has available for the keywords that show up in your article.

The purpose of peer review is to provide constructive feedback from at least two anonymous reviewers who evaluate your work based solely on its merits. This process can be immensely helpful, especially early in one’s career. However, the quality of peer reviews can vary. One reason is that reviewers are typically unpaid, meaning they often have limited time to devote to these tasks. Because of this, it’s common—but not a rule—that reviewers are not the most senior researchers in a field.

From my experience as an editor for 12 years in a small journal, a typical reviewer is often an experienced author who has previously published with the journal in question, but finding enough of these can be tricky. This is one way that reviewers are selected; some authors even volunteer to review, but editors often politely decline unless the journal needs additional reviewers. Instead, most reviewers are drawn from a pool of regular contributors to the journal, often motivated by a long-standing relationship with the editor and a sense of duty to support the scholarly community.

The current peer review system, however, is largely flawed. Most journals are owned by private, for-profit companies that rely on researchers who volunteer for free as editors, editorial board members, and reviewers. The companies profit in various ways: by charging fees to make articles open access, by reducing costs through unpaid editorial work, and by selling subscriptions to those articles. Therefore, some researchers have opted for Open Access  because their funding agencies require it, not wanting to pay twice for research they finance or simply because they think that scientific results should be free to access for all. So, the system has significant issues, yet it remains dominant. While many open-access journals exist today, building their quality takes time, partly due to established publishers’ first-mover advantage.

The logic goes a bit like this: few researchers want to be the first to publish in open-access journals with little or no impact factor, as writing a single article can take a year or more. If a researcher spends a decade publishing solely in these lower-ranked journals, they will miss out on the promotions they aim for. Established scholars on the editorial boards of high-impact journals face similar considerations—they would risk losing influence and status with key stakeholders if they moved to lower-ranked open-access journals.

An exception might be senior researchers who have already reached the peak of their careers and aren’t actively pursuing further promotions. They might be able to invest their time in open-access journals; however, in my experience, very few do. After reaching the top, many prefer to pursue other interests, like writing books or consulting, which can be quite lucrative. That said, the open-access movement has not given up, and it’s likely that we’ll see renewed efforts to make science freely accessible to everyone—both for those who submit their research and for those who benefit from its findings, especially in poorer countries.

Case: Barriers to Research Access and the Emergence of Sci-Hub

When you live in a poor country or have limited resources but are interested in research and scientific reading, access to journals can be a major barrier, as subscriptions are often prohibitively expensive—even for universities and institutions. Some researchers turn to unofficial methods, even illegal ways, to access these journals, seeing it as justified by the positive impact on scientific knowledge.

In response to this, Alexandra Elbakyan from Kazakhstan founded Sci-Hub in 2011. Sci-Hub is a shadow library website that provides free access to millions of research papers, bypassing publishers’ paywalls. The platform is widely used by scholars worldwide.

Some questions for discussion:

1.

What are your thoughts on Sci-Hub from an ethical standpoint? Do you see it as justified or problematic?

2.

Is there a risk that if everyone begins to view themselves as entitled to free access, fewer people will be willing to pay for articles, potentially undermining the entire scientific publishing system? Why?

These questions normally start a lively debate!

When selecting a journal, take the time to read articles that are like your own. This step reduces the likelihood of receiving a rejection with the common comment that your manuscript is “not within the scope of the journal.” Many rejections happen because authors simply pick a journal that sounds appealing without closely evaluating its focus and requirements.

I cannot stress this enough: journals are like exclusive clubs. If they feel you’re not on the ball—if you haven’t read what they’ve published before or if they don’t see themselves cited in the references at the end of your manuscript—they’re likely to claim that your manuscript is outside of their scope, and that’s it. You might not get any further explanation. So, don’t give them that chance. Spend ample time studying the journal you’re targeting and make the necessary adjustments to your manuscript accordingly. Throwing in an article haphazardly rarely works.

Be prepared for the possibility of submitting to multiple journals along your publication journey. If you receive a rejection—which happens to everyone, even the most experienced researchers—you may need to reassess and consider other journals. This is especially true if your work addresses something new, controversial, or unconventional. In some cases, you may even discover that the initial journals you considered aren’t the right fit, and your paper might ultimately find a home in a different discipline or journal, maybe even one in the natural sciences if your work overlaps significantly. Flexibility and adaptability to adapt your strategy is key to successfully navigating the publication process.

2.1.7.1 Closing Remarks on the First Section

The first section of this chapter provided a streamlined overview of the research process, organized to mirror the structure of a scientific article. This top-down approach introduces readers to the main stages without delving too deeply into specifics, allowing a broad understanding of the research workflow. Dividing the chapter into two parts helps establish a foundational grasp of the process and its organization.

In the upcoming section, we’ll examine the research journey from a different perspective—beginning with operationalization and methodology setup, moving through analysis, and concluding with the documents necessary for submission. Along the way, we’ll explore essential topics and considerations that can shape research outcomes significantly.

2.2 Research Process in Detail

To deepen our exploration of the research process, we’ll now dive into specific topics with more focus and flexibility. Instead of adhering to the chronological structure of a journal article, we’ll tackle essential aspects that influence each stage of research. This allows us to highlight key elements as they arise naturally in the research workflow without the constraints of a strict sequence.

In reality, research rarely unfolds in the same order as a journal article’s layout. For example, when starting a new project to address a research gap, I often begin with “operationalization”—the process of defining and refining key concepts—rather than following the order of article sections. From operationalization, a series of logical steps follow, guiding the development of the research framework and eventually leading to the documents the journal requires for submission.

2.2.1 Operationalizing the Research Framework

Operationalization in research is the process of defining abstract concepts or variables in concrete, measurable terms. This involves taking an idea or theoretical construct and transforming it into a practical, testable format that can be observed or quantified, serving as a crucial control mechanism in research.

The operationalization resembles the methodology but asks broader questions about how and if the project can be carried out. This stage is quick and exploratory, allowing researchers to determine feasibility before committing significant time and resources. Only when it’s clear that the project can be executed do researchers slow down to work through each part in detail.

Effective operationalization involves carefully planning each step to ensure reliable data collection, which includes identifying potential sources of bias and designing methods to minimize or control them. For instance, a concept like “job satisfaction” could be operationalized by developing a survey to measure employee responses to workplace happiness, making the abstract concept tangible and measurable.

Experienced researchers often go through this mental process automatically; as soon as a research problem catches their attention, they instinctively evaluate its feasibility and begin conceptualizing potential methods.

Over time, this becomes second nature, with researchers thinking through the methodological steps as soon as the problem is defined. If a problem appears too complex or impractical, they can quickly move on to more manageable issues, preventing wasted effort on ideas unlikely to yield reliable results.

In short, operationalization is about finding out if something can be done or not, and if yes, how, and about doing that fairly quickly.

2.2.1.1 Choosing the Right Approach

A student will typically start from the wrong end, asking whether they should use qualitative or quantitative methods, or which is “better.” First, the question of what is best depends on the problem at hand and, more importantly, on the nature of the question being asked.

Qualitative methods are best used when the goal is to understand underlying motives, feelings, or perceptions. For example, if you want to explore why some people prefer white cars, you might conduct in-depth interviews, allowing respondents to express nuanced reasons, such as associations with cleanliness, status, or simplicity. This approach can reveal deeper psychological or cultural influences that might not surface in a structured survey.

Quantitative methods, on the other hand, are useful for gathering measurable data from a large sample, giving insight into general preferences. For instance, if the goal is to determine how many people prefer white cars, you would use a survey with a large group, asking straightforward questions about color preference. This approach enables statistical analysis, helping to generalize findings to a broader population and identify trends.

In short, qualitative methods are suited for exploring “why” questions and gaining rich, detailed insights, while quantitative methods are ideal for measuring “how many” or “how much,” providing data that can be statistically tested. Both methods can be complementary; often, researchers start with qualitative research to explore a topic and then use quantitative methods to test broader trends on a larger scale. In other words, it’s not an eighter or question.

Colleagues who favor qualitative methods often argue that underlying motives remain unclear and that the field is still new or emerging, even when this is not always true. On the other hand, those who prefer quantitative methods often proceed as though all relevant variables are already known, treating the task as one of data collection, analysis, and uncovering real causes. In many cases, both perspectives are either partially wrong or exaggerated. Instead, we need an honest assessment of whether the key variables have been identified and their existence widely accepted before embarking on a quantitative approach.

Remember, if you don’t “do quant,” team up with someone who does, and learn it over time. Defining yourself strictly as a “quantitative person” or a “qualitative person” doesn’t make sense, as it limits the range of problems you can solve and the research questions you can explore. While you may have strengths in one method due to training or past work, sticking to only one approach restricts your ability to address a broader set of questions. The basic quantitative skills you need are simple and can be learned quickly, and working across both approaches opens up more opportunities and deepens your research. Limiting yourself to just one method makes research less interesting and limits your ability to explore questions that require statistical evidence, such as identifying trends or generalizable outcomes.

For example, a qualitative approach might excel at exploring the unique reasons people choose white cars, revealing motives like perceptions of cleanliness or aesthetic simplicity. However, a quantitative survey could tell us how many people actually prefer white over other colors across a population, providing insights into trends. Both methods add value, and together they offer a more complete picture: qualitative for the “what” and “why” and quantitative for the “how many.”

Embracing a mixed-methods approach, or at least being open to both qualitative and quantitative methods, enhances our ability to design studies that are adaptable and robust, ultimately allowing for richer, more versatile research. For researchers, versatility in methods is a critical asset, expanding the range of questions they can address effectively.

In our case, we are not only interested in knowing how many customers prefer white cars but also why they do, which is why we will use a combination of both qualitative and quantitative methods. This brings up an important point: while it’s often suggested that students choose one method over the other, in many cases, both approaches can complement each other and provide a more comprehensive understanding of most questions.

The choice between methods shouldn’t be based on personal preferences or comfort with one approach, but rather on what the research problem requires for a thorough investigation. The focus should always be on selecting the methods that best allow us to answer the research question effectively (Table 2.11).

Table 2.11

Choosing between quantitative or qualitative methods

Approach

Techniques

Example use for car preference study

Qualitative

Interviews, case studies

Exploring why customers prefer white cars

Quantitative

Surveys, experiments

Determining how many customers prefer white cars

2.2.1.2 When to Use Hypothesis and When Not to

Another typical question from students is: “Do I have to use hypotheses?”

We typically use a hypothesis in quantitative research when we aim to test a specific prediction or relationship between variables, such as determining if customers prefer white cars more than other colors.

In qualitative research, hypotheses are not used, as the focus is on exploring deeper insights and understanding complex phenomena without preconceived assumptions. Instead, qualitative studies tend to develop theories or themes based on the data collected through interviews or observations. For qualitative methods, we can use Research Questions (RQs) instead. What is important is that the questions are clear and clear to see in the text. Simply writing “RQ” helps with this.

There are several ways to formulate a research question (RQ). One straightforward method is to label it as RQ1: [Your Research Question]. Another approach is to transform it into a hypothesis (H).

For example:

Research Question (RQ1): Do customers prefer white cars over other colors?

Hypothesis (H1): Customers prefer white cars more than other colors.

Note that the Research Question (RQ) is phrased as a question, with a question mark at the end. The hypothesis, on the other hand, is a statement or prediction we put forward to be tested. The result will either confirm the hypothesis, in which case it is accepted (denoted as “A”), or prove it false, in which case it is rejected (denoted as “R”).

We turn a research question into a hypothesis when we want to make a specific, testable prediction about the relationship between variables. A hypothesis provides a clear statement that can be empirically tested through research and data analysis.

We would not always turn a research question (RQ) into a hypothesis (H) in specific cases where the research aims to explore, describe, or understand phenomena rather than test a specific prediction. Here are some instances when formulating a hypothesis may not be applicable:

1.

Exploratory Research.

Example: When little is known about a topic, the research might aim to explore general themes and patterns without preconceived notions.

Case: A study investigating the experiences of refugees adapting to a new country. The research question might be: “What challenges do refugees face when adapting to life in a new country?” This type of research seeks to gather detailed insights and does not start with a testable prediction.

2.

Descriptive Research.

Example: When the goal is to describe the characteristics of a population or phenomenon.

Case: A survey aiming to describe the demographic characteristics of internet users in a specific region. The research question could be: “What are the demographic characteristics of internet users in Region X?” This research is about providing a snapshot and does not involve testing a hypothesis.

3.

Qualitative Research.

Example: When the research focuses on understanding meanings, experiences, or perspectives.

Case: An ethnographic study examining the cultural practices of a remote tribe. The research question might be: “How do cultural practices influence daily life in Tribe Y?” This research aims to gain a deep understanding of cultural context and does not lend itself to hypothesis testing.

4.

Case Studies

Example: When investigating a particular instance or case in depth.

Case: A case study of a successful social media campaign. The research question could be: “What strategies contributed to the success of the social media campaign by Company Z?” This approach is focused on understanding the specifics of the case rather than testing a generalizable hypothesis.

5.

Theoretical Research

Example: When developing or refining theoretical frameworks.

Case: A study aiming to refine the theoretical model of organizational behavior. The research question might be: “How can existing theories of organizational behavior be integrated to better explain employee motivation?” This research focuses on theoretical development rather than empirical testing.

In these instances, the research question remains open-ended to allow for comprehensive exploration and understanding, without the constraints of testing a specific hypothesis (Table 2.12).

Table 2.12

Types of research using research questions instead of hypotheses

Research type

Description

Example

Research Question (RQ)

Exploratory research

Aims to explore general themes and patterns without preconceived notions

Investigating the experiences of refugees adapting to a new country

“What challenges do refugees face when adapting to life in a new country?”

Descriptive research

Describes the characteristics of a population or phenomenon

Survey of demographic characteristics of internet users in a specific region

“What are the demographic characteristics of internet users in Region X?”

Qualitative research

Focuses on understanding meanings, experiences, or perspectives

Ethnographic study examining the cultural practices of a remote tribe

“How do cultural practices influence daily life in Tribe Y?”

Case studies

Investigates a particular instance or case in depth

Case study of a successful social media campaign

“What strategies contributed to the success of the social media campaign by Company Z?”

Theoretical research

Develops or refines theoretical frameworks

Study refining the theoretical model of organizational behavior

“How can existing theories of organizational behavior be integrated to better explain employee motivation?”

So, for each research type presented above in the table, we have:

1.

Exploratory research: Exploratory research involves formulating open-ended questions to gather detailed insights and analyze patterns and themes, such as investigating the challenges refugees face when adapting to a new country.

2.

Descriptive research: Descriptive research aims to formulate questions that describe the characteristics of a population, collecting and summarizing data to provide a snapshot, like understanding the demographic traits of internet users in a specific region.

3.

Qualitative research: Qualitative research focuses on understanding meanings, experiences, or perspectives through in-depth interviews and observations, interpreting these to gain insights, such as exploring how cultural practices influence daily life in a particular tribe.

4.

Case studies: Case studies involve formulating specific questions to investigate a particular instance in-depth, collecting detailed data to analyze specific contexts, like examining the strategies that contributed to the success of a social media campaign.

5.

Theoretical research: Theoretical research develops or refines theoretical frameworks by reviewing and integrating existing theories, aiming to enhance understanding, such as integrating theories of organizational behavior to better explain employee motivation.

In these cases, we typically formulate one or more research questions, labeled as RQ1, RQ2, and so on. The primary goal of our study is to find answers to these questions. If we succeed in answering them, the study is successful; if not, we have fallen short of our main objective. As said before, it’s important to develop a sense of whether these questions are feasible to answer before committing to them. Some questions may be impossible or unrealistic to address given the scope, resources, or current state of knowledge in the field.

For example, if RQ1 is “What are the primary factors influencing voter turnout in national elections?”, this is a realistic question that can be approached through data analysis and literature review. However, a question like “What will be the global political landscape in 50 years?” is very speculative and most likely impossible to answer with any degree of certainty, making it an unrealistic research question.

By carefully selecting research questions that are both meaningful and answerable, we can ensure that our study contributes valuable insights to the field.

In most cases, we will have done some extensive reading before deciding on a research question (RQ). Once we have chosen the RQ, we will do even more reading, but more specialized this time, ensuring that we do not miss any findings related to our study. In other words, we will vacuum-clean all the journal databases. This is a laborious and time-consuming process, not because finding them takes time, but because scientific articles can be difficult to read, at least at first, and can seem boring until you become more familiar with them.

Or, to use a metaphor:

Scientific articles can sometimes feel like that somewhat eccentric uncle—initially dense, hard to follow, and seemingly disconnected from everyday life. Yet, as you spend more time engaging with him, you begin to uncover his value, realizing he holds knowledge and insights that can deepen your understanding. Much like navigating family dynamics, you learn to appreciate his quirks and complexities, recognizing that he contributes to the foundation of your intellectual growth. Over time, you may even come to cherish him, oddities and all. (I hope my nieces Anna Olava and Mari will come to read this 1 day and get a smile out of it).

2.2.1.3 Clarifying the Study Purpose

In the next step, the purpose of the study is determined, which helps guide the research design and methodology. We must first decide whether the study is exploratory, explanatory, descriptive, or causal.

An exploratory study seeks to investigate new areas where little research exists, often without clear hypotheses. A descriptive study aims to describe the characteristics of a population or phenomenon, answering questions like “what” or “who.” An explanatory study looks deeper, aiming to explain the relationships between variables and uncover the “why” or “how” behind an occurrence. Lastly, a causal study aims to identify cause-and-effect relationships between variables, determining whether one event leads to another (Table 2.13).

Table 2.13

Examples of study purpose

Study purpose

Description

Example question

Exploratory

Investigating a new or under-researched area

What are the emerging trends in car color preferences?

Descriptive

Describing characteristics or behaviors

What percentage of customers prefer white cars?

Explanatory

Exploring relationships between variables

Why do customers prefer white cars over other colors?

Causal

Identifying cause-and-effect relationships

Does offering a discount on white cars increase sales?

Once again, we see that it’s often not about choosing just one method but using a combination of methods. We must always keep the reader in mind—if answering multiple questions will help the reader or decision-maker make a better decision, then it’s worth using several approaches. Our readers may be interested in all of these aspects.

However, practical constraints may prevent us from addressing each part equally well, and our study will end up emphasizing one method more than the others. In our case, for instance, the inquiry began in an exploratory manner, so that will be the primary focus. Nonetheless, it’s important to recognize that blending methods often provide a richer understanding.

2.2.1.4 Researcher Interference

In the next step, we decide the level of researcher interference—the degree to which the researcher will actively influence or control the study:

Minimal interference means the researcher plays a passive role, merely observing or collecting data without affecting the participants or environment, such as observing car color preferences without any direct involvement.

Moderate interference involves some level of control or influence, like conducting interviews or surveys where the researcher interacts with participants.

Excessive interference occurs when the researcher fully controls the conditions, such as conducting an experiment where participants’ choices are manipulated, like offering discounts on specific car colors to influence preferences.

The choice of interference level depends on the research question and the type of data needed. If we want to understand natural preferences, minimal interference is ideal. On the other hand, if we aim to test specific hypotheses, higher interference may be necessary (Table 2.14).

Table 2.14

Examples of researcher interference

Level of interference

Description

Example from car color study

Minimal

No direct influence; passive observation

Observing the color of cars parked in different neighborhoods

Moderate

Some interaction or influence

Conducting surveys or interviews to ask customers about preferences

Excessive

Full control or manipulation of variables

Offering discounts on certain car colors to see if it changes preferences

If we want to avoid biases, we strive for minimal interference as it allows us to capture more authentic behavior. Once participants know they are being observed, there is a risk that they will alter their actions, either consciously or unconsciously, which can skew the results.

This is why studying customer preferences through digital marketing is so valuable—people tend to behave more naturally when browsing online, as they often forget that their actions are being tracked. Without the awareness of observation, their choices reflect their true preferences, providing researchers with more reliable data.

Online browsing data can, of course, be utilized across all social sciences, not just marketing. For instance, sociology can leverage this data to study patterns of social behavior, digital interactions, and cultural trends within online communities. Psychology can analyze browsing habits to understand cognitive processes, decision-making, and emotional responses to various stimuli. Political science might use such data to track public opinion, political engagement, or the spread of misinformation. Even economics can benefit, using browsing trends to model consumer preferences, market dynamics, or the impact of digital platforms on economic behavior.

In some research projects though, minimal interference isn’t possible due to the nature of the data collected. For example, in clinical trials testing new medical treatments, researchers often need to monitor patients closely, intervene if health issues arise, and sometimes administer treatments directly. Here, moderate to high interference is necessary for safety and accuracy, even though it may introduce biases such as observer effects.

In education research, when, say, studying a new teaching method, teachers may need to directly engage students with specific tasks, schedules, or tools to observe outcomes. This level of involvement can create bias, as students might perform differently under observation, but it’s often necessary to achieve meaningful insights. Similarly, in behavioral psychology studies involving children, researchers might need to moderate or control the environment to ensure the children interact with experimental setups in specific ways. Such structured interference allows data collection on intended behaviors, though it alters the natural setting.

Field research in environmental science, such as testing the impact of an artificial structure on local ecosystems, also requires moderate interference; researchers often must place equipment or alter habitats to track effects, introducing bias but still providing valuable insights.

Or, in market research or product testing, researchers might actively gather user feedback through surveys or usability tests, where direct interaction biases responses but is essential for understanding user preferences.

In each of these cases, while bias increases, structured interference is needed to gain valuable data and ensure the research objectives are met. In other words, we do the estimation that even though interference levels can be high, the value of the results is still worth it, so we go through with the project as such.

2.2.1.5 Defining the Study Settings

In the next step, we decide whether the study will take place in a contrived (artificial) or non-contrived (natural) setting. A contrived setting involves creating a controlled environment where variables can be manipulated. This setting allows for more precise control but can limit the generalizability of findings, as the environment may not fully reflect real-world conditions. In contrast, a non-contrived setting refers to a natural environment where events occur organically, such as observing customer behavior in an actual car dealership.

While non-contrived settings provide more realistic insights, they offer less control over external factors, making it harder to isolate variables. The choice between these settings depends on the research question and the need for control versus realism. In marketing research, non-contrived settings often provide richer, more applicable data, but contrived settings can be useful for testing specific hypotheses under controlled conditions (Table 2.15).

Table 2.15

Examples of study settings

Study setting

Description

Example from car color study

Contrived (artificial)

A controlled environment designed by the researcher

Setting up a simulated car dealership to observe customer reactions to different car colors

Non-contrived (natural)

A real-world setting where events occur naturally

Observing customer behavior and preferences in an actual car dealership

In our example, an artificial setting would make little sense—it would be like creating a mock-up car dealership to observe how different car colors affect customer choices. Such a setup wouldn’t capture the authentic behavior we aim to study, as customers may act differently in a staged environment compared to a real dealership.

Note: Background for the Car Example in This Book

Some readers may wonder about the recurring car example in this book. Like many people, I’ve always been drawn to cars, and as a result, I’ve chosen this example in the hope that it resonates with readers, particularly students, and is easy to relate to. While I haven’t conducted specific studies on car colors, I’ve been involved in the marketing of cars, for example, a campaign aimed at marketing a particular car model to women. This example, and the story behind it, serves as a practical illustration of how market research and advertising strategies can be used in real-world scenarios.

The campaign was based on a marketing concept known as a “tie-in,” where various partners collaborate to bring added value to the project. In this case, car dealerships in the San Diego area, gyms in affluent neighborhoods with many female clients, and a national charity for heart disease joined forces. The plan involved offering free test drives outside the gyms, with customers receiving a complimentary sweatshirt emblazoned with the slogan “Drive Away Heart Disease.” For every test drive, we also donated $1 to the heart disease charity, creating a win-win-win situation for all parties involved.

The objective was to position the car as appealing to women by highlighting its sleek design and easy handling. However, the strategy didn’t yield the expected results, which might have been due to the car’s underpowered engine despite its sporty appearance. What’s important here is that, even though the campaign didn’t achieve its goal, it provided valuable insights into the intricacies of consumer preferences and marketing strategies in the automotive industry.

The key takeaway I intended to highlight is that using examples you are familiar with—whether it’s cars, or another subject—helps make complex concepts more accessible and relatable. In marketing and market research, real-world examples provide tangible lessons that can be applied to broader theories, making the process easier to understand and implement for both students and professionals.

As researchers, we often get caught up in theories, but it’s crucial that we can quickly relate what we’re discussing to something physical that everyone can understand. Make it a habit to tie abstract ideas to something familiar to your audience. And, of course, it doesn’t always have to be cars!

A contrived environment might make sense for studying customer preferences for cars when we want to isolate specific variables or test controlled scenarios. For instance, if we are testing how different lighting conditions or showroom layouts affect customer preferences for car colors, a controlled setting would allow us to manipulate these factors and observe their impact.

Additionally, it could be useful for testing how different marketing techniques or promotional offers influence customer decisions without external distractions. This level of control is difficult to achieve in a natural dealership setting, making a contrived environment ideal for such experiments.

2.2.1.6 Selecting Research Strategies

Operationalization defines how specific concepts or variables in the study will be measured, making abstract ideas concrete and measurable. Research strategy, by contrast, outlines the broader plan and methodology for addressing the research question, guiding the study’s overall design and approach. This strategy includes decisions about the type of study to be conducted—whether it will be exploratory, descriptive, explanatory, or evaluative—as well as the framework for data collection, analysis, and interpretation.

The methods section focuses specifically on the techniques and procedures used to collect and analyze data. It details the tools, instruments, and protocols employed in the research, such as surveys, interviews, or experiments. The methods also specify the sample size, data sources, and the step-by-step process followed during the study. In contrast, the research strategy provides the larger framework, outlining the rationale behind choosing the research design and how it aligns with the research question and objectives.

So, part of this strategy involves identifying the most suitable data collection methods that align with the research objectives and questions. Common methods include interviews, experiments (testing hypotheses under controlled conditions), surveys (gathering data from a sample of participants), observations (monitoring subjects in natural settings), and case studies (conducting an in-depth analysis of a specific instance or group) (Table 2.16).

Table 2.16

Exploring different data collection methods

Research strategy

Description

Example in car color study

Variables involved

Experiment

Testing hypotheses by manipulating conditions in a controlled environment

Set up a simulated car dealership where customers can choose between different car colors to see how different factors (lighting, price) affect color preferences

 – Dependent: Car color choice

 – Independent: Lighting, price

Survey

Collecting data from a large group of respondents to understand general patterns

Conduct a survey asking 500 participants about their preferred car color and reasons for their choices

 – Dependent: Preferred car color

 – Independent: Demographics (age, gender, etc.)

Observation

Monitoring subjects in natural settings without interference

Observe car purchases in a dealership or on the road over several days to track which car colors are most chosen

 – Dependent: Observed car color purchased

 – Independent: Time of day, weather conditions

Case study

In-depth analysis of a specific individual or group over time. Often through interviews

Conduct a detailed study of a specific car dealership’s sales records to understand color preferences over a 5-year period

 – Dependent: Car colors sold

 – Independent: Dealership promotions, regional preferences

Among these research strategies, experiments remain one of the least utilized methods in the social sciences. Case studies are more common, though few are thorough enough to include detailed teaching notes. Surveys often face issues with biases, while simple observations are sometimes seen as too limited to fully capture the complexity of social phenomena. What truly matters, however, is your ability to defend the research strategy you select, ensuring you have clear, reasoned arguments for why other methods were not chosen (Table 2.17).

Table 2.17

The rational for choosing one method over another

Research method

Reasons for choosing this method

Reasons not to choose other methods

Experiment

 – Provides control over variables to isolate cause and effect

 – May lack ecological validity as artificial settings may not reflect real-world behavior

 – Allows for testing specific hypotheses under controlled conditions

 – Limited generalizability to larger populations or different contexts

 – Produces high internal validity and clear conclusions

Survey

 – Useful for gathering data from a large, diverse group of respondents

 – Does not provide causal relationships or in-depth understanding of individual cases

 – Efficient for understanding broad patterns and preferences

 – Self-reported data may be subject to biases (e.g., social desirability)

 – Economical and relatively easy to administer

Observation

 – Offers insight into actual behavior in natural settings

 – Lack of control over external variables makes it harder to draw causal conclusions

 – Can identify patterns and variables not initially considered

 – Can be time-consuming and difficult to generalize findings

 – Allows for studying real-world dynamics without artificial constraints

Case Study

 – Provides a detailed, contextual understanding of a specific case

 – Limited generalizability due to focus on one case or a small number of cases

 – Ideal for exploring complex phenomena over time in depth

 – May be influenced by researcher bias in interpreting subjective data

 – Useful for generating hypotheses for further research

In our car color study, particularly regarding consumer preference for white cars, each research method offers distinct insights. An experiment would allow for testing how factors like lighting and price influence the preference for white cars under controlled conditions, helping identify cause-and-effect relationships. A survey could gather data from a broader sample, revealing general trends in white car preference among various demographics, though it wouldn’t explain why people prefer or avoid white cars. Observation would provide real-world data on actual car purchases, but it wouldn’t offer the control needed to isolate specific factors that influence white car choices, while a case study would focus on detailed sales trends at a particular dealership, offering valuable historical insights but lacking broader applicability.

So, given the objective of understanding whether consumers prefer white cars, a survey would probably be the most effective method, at least for starts. It allows for gathering data from a broad sample, capturing demographic patterns and preferences, which can provide a clearer understanding of general attitudes toward white cars.

2.2.1.6.1 The Variables

When selecting a research strategy, we consider all types of variables involved, such as dependent variables (the outcome being measured), independent variables (the factors being manipulated or controlled), moderating variables (influencing the relationship between the independent and dependent variables), and mediating variables (explaining the relationship between independent and dependent variables) (Table 2.18).

Table 2.18

Variables and how to measure

Type of variable

Definition/use

How to measure

Example

Additional information

Dependent variable

The variable that is being tested or measured in an experiment (the outcome)

Measured using appropriate instruments or scales based on the nature of the variable (e.g., surveys, tests)

Exam scores in an educational study

Changes based on manipulation of the independent variable

Independent variable

The variable that is manipulated or controlled to observe its effect on the dependent variable

Directly manipulated by the researcher (e.g., time spent studying, type of intervention)

Hours spent studying vs. exam performance

There can be multiple independent variables in a study (factors)

Control variable

Variables that are kept constant to ensure that the results are due to the independent variable only

Kept constant through experimental design (e.g., controlling temperature, age, background)

Age of participants in a medical study

Helps to ensure internal validity by eliminating confounding effects

Moderating variable

A variable that affects the strength or direction of the relationship between independent and dependent variables

Measured alongside independent and dependent variables to analyze interaction effects

Gender affecting the relationship between job satisfaction and performance

Helps refine understanding of the relationship between variables by introducing a third factor

Mediating variable

A variable that explains the mechanism through which the independent variable affects the dependent variable

Measured to see if it carries the effect from the independent to the dependent variable

Stress mediating the relationship between workload and health outcomes

Shows the “why” or mechanism behind a relationship

Confounding variable

An extraneous variable that influences both the independent and dependent variables, potentially misleading results

Identified and controlled or measured, often through statistical methods or randomization

Socioeconomic status in health studies

Can cause spurious relationships if not controlled. Often addressed using statistical controls or design

Extraneous variable

Any variable other than the independent variable that may affect the dependent variable unintentionally

Attempt to control or randomize to avoid its influence

Background noise during a memory test

Could introduce error or noise into the experiment, thus reducing validity

Latent variable

Variables that are not directly observed but inferred from other variables (often used in social sciences)

Measured through indirect methods like factor analysis, using multiple observed indicators

Intelligence measured by test scores

Commonly used in psychological and social science research to measure abstract concepts (e.g., happiness)

Categorical variable

Variables that can take on a limited, fixed number of possible values, often categories

Measured by assigning categories or labels, can be nominal or ordinal

Gender (male/female), Likert scale responses

Statistical tests like Chi-square and ANOVA are used for categorical variables

Continuous variable

Variables that can take any value within a range and are often measured on interval or ratio scales

Measured using precise instruments or scales (e.g., meters, kilograms, time)

Height, weight, temperature, income

Suitable for parametric statistical tests like t-tests and regression analysis

Binary/dichotomous variable

A categorical variable that has only two possible values

Measured using yes/no, true/false, or other binary options

Yes/No response, presence/absence of a trait

Can be analyzed using logistic regression when acting as a dependent variable

The table is a guide for the reader on how to identify and understand how to measure and handle different types of variables in their studies, ensuring proper data collection and analysis techniques.

Dependent variables are the outcomes being measured in a study, with their values influenced by changes in independent variables. Independent variables, in turn, are those that researchers manipulate or control to observe their impact on the dependent variables. Moderating and mediating variables add depth to the analysis by refining the relationships between independent and dependent variables, helping to explain or enhance these connections. To ensure validity and reliability, control, confounding, and extraneous variables need to be carefully managed and accounted for.

Additionally, variables can be classified as categorical or continuous, depending on whether they fall into distinct categories or vary along a continuum, shaping how data is structured and analyzed in the research.

We construct figurative explanations in the social sciences in the form of models that clearly define variables and their relationships to ensure clarity in our studies. These models typically include independent, dependent, and control variables, along with potential moderating or mediating factors to refine the analysis. In addition to defining the variables, researchers integrate hypotheses within the model, outlining the expected relationships and effects to be tested. The table shows the different models used (Table 2.19).

Table 2.19

Terms used to describe models that show variables and hypotheses in a research paper

Term

Description

When to use

Conceptual model

A visual representation of the relationships between key variables in a study, often showing independent, dependent, moderating, and mediating variables

Use when developing a new research framework or explaining how variables interact. Often found in social sciences, business, and psychology

Theoretical framework

A broader structure that connects the study’s variables to existing theories, explaining why the proposed relationships make sense

Use when situating your study within established theories to justify hypotheses and methodology

Research model

A structured diagram that explicitly outlines hypotheses and relationships between variables, guiding empirical testing

Use in empirical studies where hypotheses are tested with data. Common in quantitative research

Hypothesized model

A model showing the expected relationships between variables before conducting statistical analysis

Use when defining the specific hypotheses in a study, often in papers using regression or SEM analysis

Structural model

A statistical representation of relationships between observed and latent variables, typically used in structural equation modeling (SEM)

Use in advanced statistical analysis, particularly when testing indirect relationships and latent constructs

Including such models in research papers helps readers quickly understand the study’s framework, making it easier to identify methodological strengths and weaknesses. By presenting a structured model, we enhance transparency, allowing for more effective peer review and reducing the risk of overlooked errors in study design. An example of a research model may look like this (Fig. 2.8):

Fig. 2.8

Example of research model

This model is for a paper on the impacts of leadership style, organizational design, and HRM practices on knowledge hiding, but the idea remains the same for all quantitative social science research papers (Oubrich et al., 2021).

Similarly, distinguishing between categorical and continuous variables is essential for accurate data analysis when designing surveys. In the car survey, categorical variables might include characteristics such as gender or age group, where respondents fall into distinct categories. On the other hand, continuous variables could involve aspects like income or the number of cars owned, which can vary along a continuum. Understanding the difference between these types of variables helps researchers determine how to collect, structure, and analyze the data, leading to more precise insights into consumer preferences for white cars.

2.2.1.6.2 Choosing Measurement Scales

Next, we look at what scales to use. Learning about scales in science is essential because they determine how we measure and interpret data, ensuring that the results are accurate and meaningful.

Selecting the appropriate scale (nominal, ordinal, interval, or ratio) helps researchers choose the right statistical methods and draw valid conclusions from their data (Table 2.20):

Table 2.20

What kind of scales to use

Type of scale

Description

How to use

Example in car color preference

Advantages

Disadvantages

Nominal scale

Categories without a specific order; simply labels or names

Assign categories to the options without any ranking

Car colors: white, black, red, blue, etc

Simple to collect and categorize data

Limited in analysis; no inherent order or ranking

Ordinal scale

Categories with a clear, ordered ranking but without equal intervals between them

Rank preferences in order, but differences between ranks are not meaningful

Car color preference ranking: Rank colors from most to least preferred (e.g., 1 = white, 2 = black, etc.)

Captures preference order

Cannot measure exact differences between ranks

Interval scale

Numeric scale with equal intervals between values, but no true zero point

Use a scale with equal differences between points; zero doesn’t mean “no preference”

Rating scale: On a scale of 1–7, how much do you like white cars? (1 = dislike, 7 = like)

Allows for advanced analysis (e.g., mean, standard deviation)

No true zero; only relative differences are meaningful

Ratio scale

Like an interval scale but with a true zero point, allowing for a meaningful “none” or absence of value

Use for questions that involve quantities or measurements with a true zero

Number of white cars owned: How many white cars do you own? (0, 1, 2, etc.)

Allows for the most sophisticated statistical analyses

Requires true zero value and direct measurement

Likert scale

A type of ordinal scale commonly used to measure attitudes or opinions, typically 5- or 7-point scale

Use for opinion-based questions with ordered responses, often from "Strongly Disagree" to "Strongly Agree

Opinion on white cars: “I prefer white cars” with responses from 1 (Strongly Disagree) to 5 (Strongly Agree)

Easy to implement and analyze; allows for nuance in opinions

Assumes equal intervals between points, which may not always hold

Semantic differential scale

Measures connotative meaning of things along bipolar adjectives

Use opposing adjectives to gauge perception of white cars

White Car Perception: “White cars are 1 = boring, 7 = exciting”

Measures nuanced feelings toward a product or concept

Respondents may not always interpret scales consistently

Binary/dichotomous scale

Offers only two possible responses, usually "Yes" or "No

Use when the question has a simple yes/no or true/false answer

Do you prefer white cars? Yes/No

Quick and easy to analyze

Does not capture degree of preference or nuanced opinions

Guttman scale

Measures cumulative items where agreeing with a statement implies agreeing with earlier statements

Ask a series of questions that build upon each other to find the threshold for preference

Car purchase likelihood: “Would you buy a white car?” followed by “"Would you buy a white car over any other color?”

Can identify strong vs. weak preferences clearly

More complex to design and requires a clear hierarchy of statements

Visual analog scale (VAS)

A continuous scale where respondents mark their preference along a line

Use for measuring preferences on a sliding scale

Preference for white cars: Mark your preference on a line from "Do Not Prefer" to "Strongly Prefer

Provides a more precise measure of intensity

May be harder for respondents to understand and use properly

This table serves as a guide for selecting the appropriate scale based on the type of data we’re collecting and the level of analysis we want to perform:

Nominal and ordinal scales are effective for basic categorizations and ranking items in order. For example, if you ask, “What color car do you prefer?” and provide a list of color options, you’re using a nominal scale. An ordinal scale would allow respondents to rank these preferences, such as rating colors from 1 to 5 in order of preference.

Interval and ratio scales enable more sophisticated analysis by measuring degrees of preference or actual quantities. For instance, asking respondents to rate their preference for white cars on a scale of 1 to 7 provides interval data, where the distance between values has meaning, making it possible to perform arithmetic operations on the data. A ratio scale might ask for the actual number of white cars someone has owned, allowing for meaningful zero points and comparisons between quantities.

Likert and semantic differential scales are widely used in surveys to capture opinions or perceptions. For example, a Likert scale might ask respondents how strongly they agree or disagree with the statement “I find white cars appealing,” while a semantic differential scale could measure their attitudes along a range from “appealing” to “unappealing.”

Binary or dichotomous scales are ideal for simple yes/no questions, such as asking whether respondents prefer white cars or not. This approach works well when the goal is to gather straightforward, categorical data without degrees of preference.

Guttman and visual analog scales offer specialized options for measuring cumulative preferences or the intensity of feelings. A Guttman scale, for example, might ask a series of questions that build on each other to measure progressively stronger preferences for white cars, while a visual analog scale could allow respondents to mark on a continuum how much they like or dislike white cars, giving nuanced insights into intensity of preference.

These different scales each serve unique purposes and provide flexibility in how data is collected and analyzed.

So, in the context of the car survey, selecting the appropriate measurement scale is crucial for analyzing consumer preferences effectively. For example, using a nominal scale could classify the respondents’ preferences into categories such as “prefer white” and “do not prefer white,” which helps in basic data categorization. Alternatively, an ordinal scale could ask participants to rank their preferences for white cars compared to other colors, providing a sense of the strength of their preferences but without specifying exact differences between rankings. For a more detailed analysis, a Likert scale could be used, where participants rate their agreement with statements like “I find white cars appealing,” offering insights into varying levels of preference. Interval or ratio scales could allow for more granular data, like asking respondents to rate their preference on a 1–7 scale (interval) or reporting how many white cars they’ve owned (ratio), enabling more sophisticated statistical analysis.

2.2.1.6.3 Using the Likert Scale

The Likert scale, named after the psychologist Rensis Likert, is commonly used in surveys to gauge respondents’ attitudes or feelings toward a statement.

Note: In memory of Rensis Likert

Rensis Likert, born in Wyoming, spent much of his career at the University of Michigan, where he co-founded the Institute for Social Research in 1949—today, the University of Michigan Institute for Social Research, and apparently the largest academic social research and survey organization in the world with some 300 employees.

Likert also made significant contributions to the field of psychometrics (the theory and techniques of psychological measurement) with the development of open-ended interviewing, a technique designed to gather insights into a person’s thoughts, experiences, and preferences.

Previously, social sciences relied heavily on closed-ended questions to ensure the validity of the coding process. Likert recognized the limitations of this approach and the need to explore unanticipated insights from interviewees.

However, he also understood the potential for open-ended interviews to veer off course, leading to unfocused or irrelevant information. To address this, Likert and his colleagues introduced the funneling technique, a structured approach that keeps interviews open to meaningful comments while maintaining direction. This method begins with broad, open-ended questions and progressively narrows to more specific ones, ensuring a balance between exploration and focus. Today, open-ended interviewing is widely used in research to gain deeper insights into people’s attitudes and perspectives, especially in studies requiring nuanced understanding.

Perhaps it’s just personal preference, but I find it meaningful to know the stories behind the techniques I use; it brings a sense of connection and appreciation. Knowing about the individuals who contributed to the evolution of social sciences makes me respect what we do together as a profession and as a community.

So, whenever I conduct a survey, my thoughts go to Ann Arbor and Prof. Likert.

Odd-numbered Likert scales, such as 5-point or 7-point scales, are preferred because they allow for a neutral middle option, which represents a balanced view or no strong opinion. While 7-point scales provide more nuance, a 5-point scale is often favored for its simplicity, as it can be cognitively easier for respondents to distinguish between the choices. The scale typically ranges from strong agreement to strong disagreement, making it ideal for measuring subjective opinions in social sciences.

Here’s an overview showing the typical labels for a 5-point Likert scale (Table 2.21):

Table 2.21

Likert scale of 5

Score

Denomination

5

Strongly agree

4

Agree

3

Neutral (neither agree nor disagree)

2

Disagree

1

Strongly disagree

With the 7-point Likert scale, we just add two new denominations (Table 2.22):

Table 2.22

Likert scale of 7

Score

Denomination

7

Strongly agree

6

Agree

5

Somewhat agree

4

Neutral (neither agree nor disagree)

3

Somewhat disagree

2

Disagree

1

Strongly disagree

It’s important to stick with these established “phrases” in English or their equivalents in other languages, rather than trying to create your own labels for scales. The risk of doing so is that you may not produce terms that are accurate or consistent in relation to one another.

We see that when asked to choose between “Somewhat Agree” and “Agree,” respondents may struggle to accurately express their feelings, as they might find it difficult to clearly differentiate between these options. This lack of clarity can lead to confusion and less reliable responses. This sets the limit to how nuanced the scale can be.

2.2.1.6.4 Ensuring Measure Quality

To ensure reliable data, we want to not only choose the right scale but also guarantee the accuracy and consistency of the measurements. This brings us to the next important topic: ensuring the goodness of measure, which focuses on validity, reliability, and minimizing bias (Table 2.23).

Table 2.23

Assessing and ensuring the goodness of measure

Decision/aspect

Description

Key questions to ask

Relevant tests/methods

Outcome/goal

Type of measurement scale

Identify if data is nominal, ordinal, interval, or ratio

What type of scale is used to measure the variable?

Determine scale through data analysis or research strategy

Ensures correct use of statistical tests and measures

Face validity

The extent to which a measure appears to measure what it is supposed to

Does the measure seem reasonable for the concept being measured?

Expert judgment, peer review, or pilot testing

Establishes initial content validity and trust in measurement

Construct validity

How well the measurement relates to the theoretical concepts it represents

Does the measure relate to other variables as expected theoretically?

Factor analysis, correlation with related measures

Ensures the measure accurately reflects the concept

Content validity

Degree to which a measure covers all aspects of the concept being measured

Does the measure cover all facets of the concept?

Expert judgment, literature review

Ensures the completeness of the measure

Convergent validity

Degree to which two measures of similar constructs are related

Are two similar constructs yielding similar results?

Correlation analysis, structural equation modeling (SEM)

Shows that the measure is consistent with similar constructs

Discriminant validity

Extent to which a measure does not correlate with dissimilar constructs

Are the measures unrelated to different constructs?

Factor analysis, SEM

Ensures the measure is distinct from unrelated variables

Criterion validity

How well one measure predicts an outcome based on another, related measure

Can the measure predict a related outcome?

Correlation, regression analysis

Confirms the predictive power of the measure

Internal consistency (reliability)

Consistency of the measure within itself (how well items correlate)

Do the items in the measure consistently correlate with each other?

Cronbach’s alpha, Split-half reliability

Ensures the measure is reliable within itself

Test-retest reliability

Stability of a measure over time when repeated under the same conditions.

Does the measure yield the same results over time?

Test-retest correlation, intraclass correlation coefficient.

Ensures reliability across time.

Inter-rater reliability

Degree of agreement between different raters or observers using the measure

Are multiple raters consistently applying the same measure?

Cohen’s kappa, intraclass correlation coefficient

Ensures consistency across different raters or judges

Factor analysis

Identifies the underlying structure of variables within a set of data

Can the measure be broken down into factors that reflect underlying constructs?

Exploratory factor analysis (EFA), confirmatory factor analysis (CFA)

Verifies dimensionality and construct validity of the measure

Error analysis

Assessing the amount and type of error in a measurement

Is there random or systematic error in the measurement?

Analysis of variance (ANOVA), standard error analysis

Reduces bias and enhances measurement accuracy

Sensitivity

The measure’s ability to detect differences when they exist

Can the measure detect small but meaningful differences in the data?

Range analysis, scale refinement

Increases precision in detecting variations in the data

Generalizability

Extent to which results can be applied to different populations or settings

Can the measure be generalized beyond the sample or specific context?

External validity testing, replication studies

Confirms the measure’s broader applicability

When designing, for example, a car color preference study, several key decisions ensure that the research is accurate, consistent, and meaningful. Validity is critical; it ensures that our measure genuinely reflects people’s car color preferences. For example, if we claim to measure preferences for white cars specifically, construct validity requires that our questions accurately capture this concept and do not unintentionally measure a related but distinct concept like brand preference.

Content validity ensures our survey covers the full range of car color options, so we’re not missing any major color categories. Criterion validity might involve comparing our findings with existing sales data on white cars, validating that preferences align with actual purchasing behavior. Finally, face validity assesses whether the survey appears, at a glance, to measure car color preference reasonably.

Reliability ensures that our measures are consistent. Internal consistency, often assessed with Cronbach’s alpha, checks if responses to related questions—like rating various shades of white cars—are cohesive and measure the same preference. Test-retest reliability would involve administering the survey to the same group twice to see if their answers remain consistent over time, while inter-rater reliability might assess consistency in data collection if multiple researchers are involved in observing or categorizing car color preferences in different locations.

Factor analysis helps us examine the structure of our data, especially if we ask multiple questions about car colors, such as preferences for “classic” versus “vibrant” colors or for different car types like sedans versus sports cars. This analysis reveals any underlying factors that may group related preferences, helping us refine our survey to target specific preferences more accurately.

Error analysis and sensitivity ensure our measures are precise and error-free. For instance, if respondents select multiple colors for the same question due to ambiguous wording, this may introduce error, reducing the accuracy of our findings. Sensitivity analysis may involve adjusting questions slightly to test if minor changes affect responses, confirming that our survey measures are robust.

Finally, generalizability ensures that our findings on car color preference extend beyond the specific sample we studied. For example, if we surveyed only college students, generalizability would address whether these preferences are likely to apply to the broader population, including different age groups or geographic regions. Each of these decisions contributes to a study that is both rigorous and useful in understanding car color preferences in a broader context.

This table menu then gives a clear roadmap for assessing and ensuring the goodness of measure when designing and conducting statistical research. We can also express the same idea with a process (Fig. 2.9):

Fig. 2.9

Deciding on goodness of measure

2.2.1.7 Identifying Population and Unit of Analysis

In the next step, we need to clearly define the population to be studied and the unit of analysis. The unit of analysis refers to the “who” or “what” we are studying, such as individuals, groups, organizations, or even entire cultures. This distinction is vital because it shapes the scope of our study and the conclusions we can draw.

For example, if we are interested in understanding car color preferences, our unit of analysis could be individual consumers or groups from different regions. Choosing the right unit of analysis is crucial for ensuring that our research is relevant and accurately addresses the research question. If we only study individuals, we may miss important trends that occur at a group or organizational level. Therefore, carefully identifying the population and unit of analysis helps us design an appropriate methodology and gather meaningful data (Table 2.24).

Table 2.24

Choosing the unit of analysis

Unit of analysis

Example

Description

Individuals

Studying consumer car color preferences

The smallest unit focuses on single entities, such as people or objects, where each observation represents one individual

Groups

Examining trends in car preferences by region

A collection of individuals who share some characteristics, such as geographic location, demographic factors, or common interests

Organizations

Analyzing car color choices across car dealerships

Institutions or businesses, where the focus is on the collective behaviors, strategies, or outcomes of the entire entity

Cultures

Investigating car color trends in different countries

Large-scale societal groups or populations, where the focus is on shared norms, behaviors, or preferences within a cultural context

The approach to unit of analysis varies across different social sciences. In sociology, studying individuals might focus on how social influences or status impacts car color preferences, while examining groups would explore how regional demographics shape collective trends. In political science or economics, analyzing organizations like car dealerships could involve understanding businesses’ strategic decisions based on consumer data. Anthropology or cultural studies would emphasize car color trends across countries, investigating how cultural norms or values affect preferences. Each discipline uses its unique focus to analyze consumer behavior from varying scales, from individuals to entire societies.

In many cases, I can’t help but think that what a company or organization would truly benefit from is a comprehensive view that incorporates multiple angles, not just a single perspective. This highlights the value not only of interdisciplinary studies but also of approaching the social sciences as a unified field. Treating social science as one integrated project allows us to harness its true potential, offering richer insights and more holistic solutions to complex challenges.

2.2.1.8 Time Horizons in Research

Time horizons in research refer to the different temporal perspectives a study can adopt, depending on the nature of the research question and the objectives. Broadly, there are three primary types of time horizons: cross-sectional, multiple cross-sectional, and longitudinal studies:

A cross-sectional study is one that takes a “snapshot” of a population or phenomenon at a single point in time. Researchers often use this method to describe a subject’s current state, such as measuring public opinion on a political issue or assessing the prevalence of certain behaviors. However, cross-sectional studies are limited in their ability to show changes over time or establish causality since they capture just one moment.

In contrast, multiple cross-sectional studies involve collecting data at several points in time, but from different samples. This method allows researchers to observe trends and changes in a population over time, even if they are not following the same individuals. For example, researchers might study consumer behavior every year for a decade, analyzing how preferences evolve without needing to track the same group each time.

Longitudinal studies, however, track the same group of subjects over a prolonged period, offering rich insights into how individuals change and develop. These studies are particularly useful in understanding long-term effects or trends, such as examining how childhood education impacts adult income levels. By following the same individuals, longitudinal studies can help establish cause-and-effect relationships that are impossible to determine from cross-sectional data.

When deciding between these time horizons, researchers must consider the goals of their study. Cross-sectional studies are more practical and resource-efficient, but multiple cross-sectional or longitudinal studies provide deeper insights into trends and causal relationships. However, longitudinal studies are typically more time-consuming and costly, and they may face challenges like participant dropout, which can affect the validity of the results.

Ultimately, the choice between these time horizons depends on the research question, available resources, and the depth of analysis desired.

Let’s look at each of these time horizons in more detail (Table 2.25).

Table 2.25

How to use different time horizons

Aspect/study type

Cross-sectional studies

Multiple cross-sectional studies

Longitudinal studies

Definition

A study that examines data from a population at a single point in time

Studies that collect data from different samples of the same population at different time points

A study that follows the same population over time to observe changes and trends

How to do it

Collect data from a specific population at one time using surveys, interviews, etc.

Collect data from different groups at multiple points in time, using similar methods each time

Track the same individuals or groups over time, collecting data at regular intervals

Advantages

Quick, cost-effective, and provides a snapshot of the population at a specific time

Captures trends over time without the need to follow the same individuals

Captures changes over time, establishing causal relationships and long-term trends

Disadvantages

Cannot track changes over time, and cause-effect relationships are hard to establish

Provides less detailed insights into individual changes as different samples are used

Time-consuming, expensive, and requires long-term commitment to maintain the same sample

Examples of use

A survey on current customer preferences or attitudes at the one-time point

Repeated surveys to measure public opinion on policies at different times (e.g., every 5 years)

A study on individual health outcomes over decades to observe aging effects

Best used for

Examining relationships and trends within a population at a single time

Tracking general population trends across multiple time points without following specific individuals

Understanding long-term effects and changes within the same individuals or groups over time

Data collection method

One-time data collection from the population

Multiple data collection points, but with different samples each time

Repeated data collection from the same sample over an extended period

Time and resources

Requires fewer resources and less time to complete

Requires more time and resources than a single cross-sectional study but less than a longitudinal

Requires significant resources, time, and a stable sample population over the study period

Data analysis

It focuses on relationships at one-time point but does not account for temporal changes

Compares data from different times to track overall trends, not individual changes

Analyzes changes and trends in individual or group behavior over time

As researchers, it’s crucial that we take our time and carefully select the most appropriate study design. To do this, we must evaluate the pros and cons of each method. For example, while a longitudinal study may provide valuable insights over time, it might be too expensive or logistically difficult, especially if it’s not feasible to follow the same sample group more than once.

At the same time, we should ask ourselves whether the study would be scientifically stronger if a different time horizon had been chosen. If so, we must then critically reflect on why we didn’t opt for that approach and whether practical constraints compromised the scientific value of the research.

2.2.1.9 Sampling Techniques

It is said, “A journalist is someone who asks two people; a scientist asks 384.” Both may find something of value, but only the latter follows a scientific process.

The journalist relies on a rule of thumb, often constrained by time and the urgency to report. As a result, their risk of reaching an inaccurate conclusion is greater due to the smaller sample size. In contrast, a scientist uses a larger, statistically significant sample, which reduces the likelihood of error and increases the reliability of the findings. This difference highlights the importance of methodology in scientific inquiry, where precision and rigor is key to drawing valid conclusions.

By carefully selecting the right sampling method, researchers can ensure that their sample truly reflects the population they aim to study, thus enhancing the validity of the results. The choice of sampling design affects not only the reliability of the data but also the broader conclusions that can be drawn. Inappropriate sampling can lead to biased outcomes, making it essential for researchers to carefully evaluate the best approach for their specific research objectives.

There are two primary types of sampling designs: probability sampling and non-probability sampling. Probability sampling ensures that each member of the population has an equal chance of being selected, making it ideal when representativeness is crucial to the research. This method reduces selection bias and is typically used in studies where a high level of generalization is required. On the other hand, non-probability sampling involves selecting participants based on nonrandom criteria, which may introduce bias but is often more practical when random sampling is not feasible. This approach is useful when studying hard-to-reach populations or when quick, convenient data collection is needed.

Below follows a comparison of sampling methods (Table 2.26).

Table 2.26

Types of sampling design and when to use them

Sampling design

Type

When to use

Advantages

Disadvantages

Simple random sampling

Probability

Use when every individual in the population has an equal chance of being selected and a complete list is available

Ensures high representativeness and reduces selection bias

Can be difficult to implement if the population is large or scattered

Assumes the existence of a complete sampling frame

Stratified sampling

Probability

Use when the population is divided into subgroups (strata) and you want to ensure representation from each

Ensures all subgroups are represented and increases precision

Can be complex and time-consuming to divide the population into appropriate strata

Systematic sampling

Probability

Use when the population is large and organized in a list and you want to sample at regular intervals

Simple and quick to execute, especially with a large, ordered population

Can introduce bias if there’s a hidden pattern in the population

Cluster sampling

Probability

Use when the population is geographically dispersed and you want to sample groups (clusters) instead of individuals

Cost-effective and practical for large, geographically spread populations

Can introduce bias if clusters are not homogeneous

Multistage sampling

Probability

Use when dealing with very large populations and a single stage of sampling is impractical

Combines different sampling methods, increasing flexibility

More complex and can reduce accuracy compared to simpler methods

Convenience sampling

Non-probability

Use when the population is hard to access or you need quick, inexpensive data collection

Easy, quick, and inexpensive to implement

High risk of selection bias and unrepresentative results

Quota sampling

Non-probability

Use when you need a specific number (quota) of individuals from different subgroups

Ensures inclusion of specific groups, even when random sampling is not possible

Subject to bias as selection within quotas is not random

Judgmental/purposive sampling

Non-probability

Use when you want to select participants based on specific characteristics or expert knowledge

Useful for focusing on specific individuals with relevant expertise or characteristics

Highly subjective and can lead to biased outcomes

Snowball sampling

Non-probability

Use when studying hard-to-reach populations, where initial participants help recruit others

Useful for accessing hidden or hard-to-reach populations

Can introduce bias as the sample is based on networks, potentially over-representing certain subgroups

Probability sampling is designed to ensure that every individual in the population has an equal chance of being selected, which generally leads to more representative samples. Methods like simple random sampling and stratified sampling work best when the goal is to maximize representativeness, especially when a full list of the population is available. Cluster sampling is particularly useful when dealing with geographically dispersed populations, while multistage sampling can combine different techniques for added flexibility. On the other hand, non-probability sampling is employed when randomness is impractical, but it carries the risk of introducing bias. Approaches such as convenience sampling and quota sampling are helpful for quick data collection, while judgmental sampling and snowball sampling help reach specific, often hard-to-access groups.

The table helps you guide the choice of sampling design based on the study’s objectives, population characteristics, and practical considerations. We can also express this as a process in a figure (Fig. 2.10):

Fig. 2.10

Deciding what sampling techniques to use

2.2.1.9.1 Preparing for the Calculation

We conduct sampling (s) because it is impractical or impossible to survey the entire population (P). Sampling allows us to make inferences about the larger population by studying a representative subset of it. The key is to ensure that the sample is selected in a way that reflects the population’s diversity and characteristics.

Note: How we write statistics

Note here that in statistics, using lowercase and uppercase letters to denote different concepts brings clarity and consistency to data interpretation. Lowercase letters, like s for sample standard deviation, indicate measures based on sample data—a subset of a population used for analysis. Uppercase letters, such as P for population proportion, signify parameters that describe the entire population. This distinction helps researchers and statisticians instantly understand whether a particular statistic or parameter is based on a sample or the broader population.

For example, μ (mu) represents the population mean, while x¯(x-bar) indicates the sample mean, making it clear when we are referring to a calculated average from a smaller, accessible dataset versus an idealized whole.

This convention also supports consistent mathematical notation, allowing formulas and equations to apply universally across studies. Adopting these standardized symbols enhances accuracy and minimizes misinterpretations, which is essential for effective statistical communication.

By using statistical methods, such as calculating confidence intervals and margins of error, we can estimate how closely the sample’s results approximate what would be true for the entire population. If the sample size is appropriate and the selection method is sound, we can confidently generalize findings to the broader group. This process is grounded in probability theory, which helps us measure the reliability of our estimates and the likelihood that they accurately represent the population. However, it is crucial to account for any potential biases that might affect the sample’s representativeness, ensuring that the conclusions drawn are valid.

Let’s return to the example of car colors. Cultural factors clearly influence preferences for certain car colors, meaning that car color choices can vary significantly across different regions. While a study conducted in Sweden may reveal trends specific to that population, it is unlikely that these findings will apply universally to other countries with different cultural norms. However, depending on the degree of cultural similarity, the results might still be relevant to some countries with comparable preferences. Caution is required when generalizing, as what holds true for one cultural context may not translate well to another.

In Japan, smaller compact cars can actually include various shades of purple. In India, white and silver cars are often preferred, but there’s also an appreciation for more vibrant and unique colors. In the United States, and among American car enthusiasts, the Dodge Challenger and Dodge Charger have been offered in “Plum Crazy.” This shows that while purple isn’t a top choice across the board, it has a strong appeal in certain circles. More importantly, we cannot use universal truths about what color car people on this planet want to make conclusions in any one specific market, even though that would be much easier and would make our endeavors more valuable as a science. Put differently, people are different—unlike, say, hydrogen—and we act differently depending on where we are located geographically and culturally. So, we should distinguish between the population of the world (P) and a subset of that population, which, for the convenience of the author of this book—and he hopes you will forgive him for this—is defined as the population of Sweden (P1). In formal logic and mathematical terms, the statement can be expressed as follows:

Define the Population (P)

Let P represent the population, which is the set of all individuals for whom we want the hypothesis to hold true.

P can be defined as P = {x∣ x ∈ Market}, where x is an individual in the market. We can read this as “P is the set of all elements x such that x belongs to the market.” (In the start, I will write how to pronounce the math to make it easier for you to remember).

Subset of the Population (P1)

Let P1 ⊆ P (“P1 is subset of P”) represent a specific subset of the population, where P1 is the group of individuals we are interested in querying or testing.

Ideally, P1 = P, meaning we would like to query every individual in the population, but in practice, P1 might be a proper subset.

Testing the Hypothesis

The goal is to test the hypothesis H on the population P.

Formally, if f(x) is a function that returns true or false depending on whether H holds for individual x, then we want to evaluate f(x) for every x∈P1 ​.

The ideal scenario: ∀x ∈ P, f(x) = True (“For all elements x in the set P, the function f(x) is true.”)

Don’t get put off by the symbols; just learn how to read them. Think of it as a useful language for scientists.

With the help of mathematics, and more specifically with the branch or subfield called statistics, we can calculate if a sample size we chose is representative of the population we want to say something about:

Here’s a simple formula to express that concept:

Sample Size and Population Size

n=Sample Size andN=Population Size

(2.1)

Using a statistical method (e.g., margin of error or confidence interval), we determine if:

Representativeness (“sampling accuracy” or “sample reliability” may also be used)

Representativeness=fn,N,desired confidence level

(2.2)

(“The representativeness is a function of the sample size n, the population size N, and the desired confidence level”.) This indicates that the representativeness of the sample size n relative to the population N can be calculated using statistical formulas, considering the desired confidence level.

In other words, when we have found P, we can calculate the sample size, designed as small “s”. This means that the answer we find when contacting “s” will be representative of “P”.

This is possible through statistical sampling techniques, which ensure that the sample size s is large enough and appropriately selected to accurately reflect the characteristics of the entire population P. By using methods like confidence intervals and margin of error, we can determine how closely the results from s will approximate what would be found if we surveyed the entire P.

The calculation:

To achieve a 95% confidence level with a 5% margin of error, we would need to survey approximately 384 people from Sweden’s population of 350,000 potential car buyers.

Here’s how the calculation works:

Initial sample size calculation (without population adjustment)

n=Z2.p.1−pE2

(2.3)

where:

Z = 1.96 (for a 95% confidence level)

p = 0.5 (population proportion when unknown)

E = 0.05 (margin of error)

n=1.962×0.5×1−0.50.052≈384.16

where

n = 384.16

N = 350,000

Substituting values:

Nadj=384.16×350,000384.16+350,000−1≈383.74

So, we need to survey about 384 people.

When I did the PhD, all of this had to be calculated by hand, step-by-step on paper, and then documented in the dissertation. Today, however, there are online sample size calculators, much like calculators for math, which you can easily find with a quick search. In these tools, you simply enter values for the confidence level, margin of error, and population proportion. With a press of a button, the calculator will provide the required sample size for your population. This saves time and reduces potential errors, making the planning process far more efficient.

While using online calculators is a great time-saver, I still think it’s important for PhD students to understand the calculations behind sample size determination. Gaining this knowledge ensures a deeper grasp of statistical principles, which is essential for interpreting results and making informed decisions in more complex analyses.

Please note that this is the only statistical calculation example provided in this book. Our focus here is on selecting appropriate methods and understanding why they are used, rather than on detailed calculations. For hands-on calculation exercises, we recommend enrolling in an advanced statistics course. Additional resources can be found in Appendix A.

You can also use your favorite AI tool for assistance—just don’t rely too heavily on estimates it provides, such as the number of potential car customers. These numbers, at least in the current versions, are often inaccurate (I have tested them) and may require a bit of manual adjustment to ensure accuracy. However, on actual calculation, it is doing far better (Table 2.27).

Table 2.27

How to set the confidence level, margin of error, and population proportion

Term

Explanation

How to set

Example values

Confidence level

The probability that the true population parameter is within the margin of error of the sample estimate. Higher levels mean greater certainty

Choose based on the importance of certainty for the study. Common choices are 90%, 95%, or 99%, with 95% being typical for most social research

90%, 95%, 99%

Margin of error

The range within which the true value is expected to fall. A smaller margin requires a larger sample size for greater precision

Select based on desired precision. ±5% is standard; ±3% is used for studies needing higher accuracy, like election polling

±5%, ±3%

Population proportion

The estimated percentage of the population with a specific trait or response. Using 50% maximizes sample size if the proportion is unknown

If no estimate exists, use 50% to ensure sufficient representation. For known proportions, use the estimated value to calculate more precisely

50% (default), or an estimated value such as 30%, 70%

A confidence level of 90% means that if the study were repeated multiple times, 90% of the sample estimates would fall within the margin of error of the true population parameter. In practice, this means you’re willing to accept a 10% chance that the true population value lies outside the margin of error around your sample’s result. While a 90% confidence level is somewhat lower than the commonly used 95%, it may be sufficient depending on the study’s requirements for certainty and precision.

A margin of error of ±5% means that the true population value is expected to fall within 5 percentage points above or below the sample estimate, providing a reasonable balance between precision and sample size for general studies. For studies requiring higher accuracy, such as election polls, a smaller margin of error, like ±3%, is often preferred, which increases precision but also requires a larger sample size. Thus, the margin of error you choose depends on how much deviation from the sample estimate is acceptable for the purposes of your study—smaller margins yield more precise estimates but require greater resources.

The population proportion represents the estimated percentage of people in the population who have a specific trait or would give a particular response. When this proportion is unknown, researchers often use 50% as a default because it maximizes the required sample size, ensuring sufficient representation. If a reliable estimate exists—such as 30% or 70%—using this known value can help fine-tune the sample size calculation, often allowing for a smaller sample size with maintained accuracy.

The critical reader will see those factors like “confidence level,” “margin of error,” and “population proportion” do acknowledge that any estimate from a sample might not perfectly match the true population value, but they serve an important purpose: they quantify the degree of uncertainty and provide a structured way to estimate accuracy. While we naturally want the “right” or exact answer, in practice, working with populations—especially large ones—means that collecting data from every single individual is often unrealistic. Given the practical limits of time, resources, and feasibility, these statistical tools help us gauge how close our sample-based estimates are likely to be.

In the induction to this section, we said half-jokingly that scientists are people who ask 384 people. When pressing the “calculate” button on our calculator, the resulting number often suggests a figure around or above 300, even when population sizes vary dramatically. This is due to the law of diminishing returns in sampling. Statistically, once a sample size reaches about 300 to 400, the margin of error stabilizes significantly for a typical confidence level (like 95%) and does not decrease by much even if the population size is much larger.

This occurs because a sample of around 300 already represents the diversity within a population fairly accurately under most conditions, especially in populations with high variance. Beyond this threshold, increasing the sample size yields smaller and smaller gains in precision unless extremely high confidence levels or very narrow margins of error are required. Therefore, many sample size calculators often suggest numbers near 300 as a standard for robust estimation, as it strikes a balance between accuracy and efficiency for generalizable results across diverse population sizes (Table 2.28).

Table 2.28

Salming for different population (P) sizes

Sampling method

P = 10,000

P = 100,000

P = 1,000,000

P = 10,000,000

P = 100,000,000

Simple random sampling

370

383

384

384

384

Stratified sampling

400

420

450

470

500

Systematic sampling

370

383

384

384

384

Cluster sampling

450

500

550

600

650

Multistage sampling

450

500

550

600

650

Convenience sampling

Varies

Varies

Varies

Varies

Varies

Quota sampling

400

420

450

470

500

Judgmental/purposive sampling

Varies

Varies

Varies

Varies

Varies

Snowball sampling

Varies

Varies

Varies

Varies

Varies

The required sample size can vary depending on the sampling design. While simple random sampling generally provides a standard sample size estimate, stratified or cluster sampling designs may need slightly larger samples to ensure accurate representation. Non-probability sampling methods, like convenience or snowball sampling, do not follow these calculations as closely, as they’re typically less focused on generalizability.

If we were to conduct a study that aims to represent all 8 billion people on the planet with a 95% confidence level and a 5% margin of error, we’d only need a sample size of approximately 384 to 400 people to represent the global population.

However, if greater accuracy is required (such as a 1% margin of error), the sample size would increase significantly, requiring around 9600 respondents.

Note that when determining the sample size for global studies, this often means stratifying the sample by continent or country, adjusting for population density to reflect the diverse demographics and ensure accurate, generalizable results.

Example: Conducting a Global Survey on Car Color Preferences: A Practical Approach

Let’s say we wanted to conduct a global survey on whether people worldwide prefer white cars. The first step would be to define the research question clearly: “Do people worldwide prefer white cars?” and perhaps hypothesize, “H0: People prefer white cars.” The next step would be to determine the countries and regions we want to include, ensuring diversity in terms of geography, population density, and economic status. We would want to stratify the sample based on continents, ensuring a mix of developed and developing nations.

When designing a global survey, it is crucial to consider cultural and economic differences that may affect the relevance of the question. In some regions, cars may not be a significant mode of transportation due to factors like limited access, reliance on public transit, or cultural norms. For instance, in densely populated urban areas with extensive public transportation, car ownership might be far less relevant. This would mean that asking about car color preferences in such areas could produce skewed or unrepresentative data. Therefore, when selecting regions or countries for the survey, we should account for these variations to ensure we are asking meaningful questions to the appropriate audiences.

Next, we might decide to divide the sample into regions such as North America, Europe, Asia, Africa, and Latin America, with equal representation from each region based on their population sizes and adjusted for what could be unrepresentative data. We would also need to select a sample size large enough to ensure reliability, calculating how many respondents are needed per country or region based on the population. Additionally, we would need to decide on the method of data collection: online surveys might be most efficient for certain regions, but for others, we may need to rely on local surveyors or partners.

In addition to the logistical challenges of reaching remote areas, one of the biggest difficulties would be ensuring a representative sample. Socioeconomic factors, such as income disparities and car ownership rates, may significantly vary across regions, making it hard to draw meaningful conclusions from areas where cars are not commonly owned. If, for example, we were a car company looking for market opportunities, we might encounter challenges in making accurate inferences for countries where car ownership is less common. Language barriers would also pose a challenge, as the survey would need to be translated into numerous languages to ensure respondents fully understand the questions. However, with easy translation tools, this could be manageable. Furthermore, cultural differences could affect how people perceive the question of car preferences, potentially leading to varied interpretations of the survey, especially in areas where car ownership is not a priority or where transportation habits differ vastly. Many respondents may refuse to answer or might not find the question relevant.

The survey would ask participants a simple question about car color preference, along with demographic questions to control for factors like age, income, and urbanization. To ensure the sample is random and representative, we would also consider factors like internet penetration and access to technology in certain countries. After collecting the data, we would analyze the responses using appropriate statistical methods, such as cross-tabulation, to examine how preferences vary by region. Finally, we would publish our results, reflecting on both the global trends and regional differences in car color preferences.

Could it be done? Absolutely. How long would it take? With resources in different countries and a motivated group of researchers, probably within a few weeks. Would it have value? Likely not, and that’s the springboard here. Car makers would want to know the color preferences of cars in national markets, but would probably exclude a large number of markets in their survey, assuming that, for example, smaller African markets would follow trends in larger African markets.

Car companies would likely make the survey smaller due to cost-benefit calculations, focusing on larger and more economically viable markets where car ownership is higher. While this approach may reduce the scope of the research, it enables them to gather more targeted and actionable data without costing too much money.

Even though most studies on car color preferences, such as those by J.D. Power, are national or regional, there are actually also global surveys conducted by companies like PPG Industries and Sherwin-Williams that track color trends across multiple regions.

2.2.2 Balancing Qualitative and Quantitative Research

As we said before, different problems require different methods, and understanding which method to use in various scenarios is essential for obtaining valid results.

In the social sciences, statistics are used for the following three key reasons:

1.

Quantifying relationships and effects: Statistics allow researchers to quantify relationships between variables and assess the strength and significance of these relationships. This helps in understanding how different factors interact and influence social phenomena. For example, statistical techniques can determine how leadership styles impact employee behavior in organizations.

2.

Making informed inferences: Through the use of inferential statistics, researchers can make generalizations from a sample to a larger population. This enables social scientists to draw conclusions about broader social trends and patterns based on data collected from smaller, representative groups.

3.

Ensuring objectivity and rigor: Statistics provide a systematic and objective framework for analyzing data, which helps in minimizing biases and subjective interpretations. By using statistical methods, social scientists can ensure that their findings are based on empirical evidence and are reproducible, contributing to the credibility and reliability of their research.

In the same way, we can say that statistics is not required in the following two scenarios:

1.

Qualitative research: When the research focus is on exploring in-depth insights, experiences, and perspectives, qualitative methods such as interviews, focus groups, and ethnography are used. These methods aim to understand the meaning and context behind social phenomena, rather than quantifying them. For example, a study exploring the cultural significance of rituals in a specific community may rely solely on qualitative data without the need for statistical analysis.

2.

Theoretical or conceptual studies: When the goal is to develop, critique, or expand theoretical frameworks and concepts, researchers may not need to use statistics. These studies often involve literature reviews, theoretical modeling, and philosophical analysis to advance understanding within the field. For example, a paper proposing a new theoretical model for organizational behavior may rely on existing literature and logical reasoning without involving statistical data.

It could be argued that qualitative methods are often better suited for the initial stages of research, particularly when exploring a new area where the variables and their relationships are not yet well understood. Here’s why:

1.

Exploratory nature of qualitative research: Qualitative methods, such as interviews, focus groups, and ethnography, are well-suited for exploring new areas of study. They allow researchers to gather rich, detailed information about people’s experiences, perceptions, and motivations. This helps in identifying potential variables and generating hypotheses that can be tested later using quantitative methods.

2.

Context and understanding: In new areas of research, qualitative methods provide a deeper understanding of the context and nuances that might not be immediately apparent through quantitative measures. They help in uncovering underlying themes and patterns, which can inform the design of subsequent quantitative studies.

Once the variables are identified and a foundational understanding is established, quantitative methods become helpful for:

1.

Testing hypotheses: Quantitative methods are ideal for testing specific hypotheses about the relationships between variables. They provide a way to measure and analyze data systematically, allowing for generalizations to be made about a larger population based on sample data.

2.

Measuring and quantifying relationships: With a clear understanding of the relevant variables, quantitative methods can be used to quantify the strength and direction of relationships between these variables. This includes using statistical techniques to determine causality, correlations, and the impact of different factors.

In summary, starting with qualitative research is beneficial for exploring and understanding new areas because it allows researchers to gather rich, detailed insights into unfamiliar topics. Once the foundational elements and variables are defined, quantitative research becomes powerful for testing hypotheses and quantifying relationships, providing the statistical rigor needed for broader generalization.

However, a common challenge arises when researchers claim that an area is “new” simply because they are studying it in a slightly different or novel context. This tendency can lead to an overuse of exploratory methods, even when foundational research already exists. By clearly distinguishing between genuinely uncharted areas and contexts that only seem novel, researchers can choose the most suitable approach, making the research process both more efficient and more impactful. Hopefully, AI will be able to help us here by quickly pointing to similar previous studies having been done.

2.2.2.1 Mixed Approaches in Basic and Applied Research

Balancing qualitative and quantitative approaches also relates closely to the distinction between basic and applied research:

Basic research aims to expand fundamental knowledge and theoretical frameworks, often exploring new ideas without immediate practical application. Here, qualitative methods are frequently employed in the early stages, as they allow researchers to explore unknown areas, gather in-depth insights, and identify variables that could form the basis for new theories. For example, qualitative research on cultural significance or social behavior can uncover meaningful patterns, which can then inspire further investigation.

In contrast, applied research uses established theories and methods to address specific, practical problems, often emphasizing quantitative methods to test hypotheses and generate measurable, generalizable findings. Applied research often benefits from quantitative techniques, which provide rigor and replicability, essential for making data-driven decisions and drawing broad inferences. For instance, an applied study in organizational behavior could use quantitative methods to test how specific leadership styles impact employee motivation, producing actionable results for organizations.

Quantitative methods are also central to ensuring objectivity and rigor, critical for applied research aiming at reproducible outcomes that inform policy or practice. However, in practice, both basic and applied research can benefit from a balanced approach. For instance, qualitative exploration might generate hypotheses for applied research, while quantitative findings can refine theories in basic research.

We may say that, moving between qualitative and quantitative approaches aligns with the iterative nature of research, where qualitative exploration in basic research informs quantitative applications in applied settings. This dual approach supports a richer, more nuanced understanding of both theoretical concepts and practical issues.

However, distinguishing between basic and applied research in the social sciences can be extremely challenging, to say the least. Unlike in natural sciences, where basic research often deals with foundational principles and applied research focuses on technology or specific applications, social science research frequently overlaps these categories. Many studies begin with a theoretical question but quickly reveal practical implications, while others are aimed at solving specific problems yet uncover broader theoretical insights along the way.

What’s more, social phenomena are complex and multilayered, meaning that even applied research often requires an understanding of the underlying social structures, theories, and contexts—elements typically associated with basic research. Additionally, societal challenges, such as inequality or behavioral change, are inherently interdisciplinary and involve both understanding fundamental human behavior and finding actionable solutions, blurring the boundaries even further. This interconnectedness makes it difficult to draw a clear line, as social science research tends to evolve dynamically between theoretical exploration and practical application (Table 2.29).

Table 2.29

Criteria for distinguishing between basic and applied research in the social sciences

Criteria

Basic research

Applied research

Purpose of the research

Expands theoretical understanding; answers “why” and “how”

Addresses specific, practical issues; answers “what works”

Scope of impact

Broad, field-focused contribution without immediate application

Problem-specific, aiming for direct relevance to policy, business, or community needs

Stakeholder involvement

Primarily academic audiences, fewer external stakeholders

Often involves collaboration with external stakeholders (e.g., policymakers, businesses)

Type of questions

Explores foundational questions; often uses qualitative or theoretical methods

Tests interventions or measures outcomes; uses quantitative or mixed methods

Methodology

Qualitative or exploratory for theory-building

Quantitative or applied methods focused on practical implementation

Example in social sciences

Study of social trust dynamics within communities for theoretical understanding

Research on effective strategies for enhancing public trust in health communication

Evolution of research

Findings may inform later applied research projects

Often generates theoretical insights, feeding back into basic research

So, if the project aims to expand theoretical knowledge or answer “why” and “how” questions, it’s likely basic research. In contrast, if it seeks to address a specific, real-world problem or answer “what works” to solve that issue, it’s applied research. Additionally, basic research typically targets academic audiences, while applied research often involves stakeholders outside academia, like policymakers or businesses, who are interested in actionable results.

Although the distinction between basic and applied research is not always easy to pin down, striving to differentiate them remains valuable. This effort clarifies the objectives and underlying purpose of the research, helping us to align its direction and intended outcomes. By understanding whether the focus lies in expanding theoretical insights or addressing practical challenges, we can better define our goals and communicate our intentions.

2.2.3 Selecting Statistical Techniques

To select the most appropriate statistical techniques for a given problem, it’s vital to first understand the purpose and function of each method. Gaining a deep comprehension of these techniques allows us to confidently choose the right tool for the specific research question.

In other words, simply learning how to apply each method isn’t enough—you must also understand when and why each technique should be used.

Operationalization begins, as we saw earlier, as soon as we identify the problem to solve, as we start considering which techniques will best address the research question. The table below serves as a guide for selecting the most suitable statistical methods based on the nature of different research questions (Table 2.30).

Table 2.30

List of statistical techniques frequently used

Statistical technique

Purpose

Requirements

Example

Descriptive statistics

Summarize and describe the main features of a dataset

Data should be quantitative; no specific requirements for distribution or sample size

Calculating the mean age of participants in a survey

Inferential statistics

Make inferences about the population based on sample data

Random sampling, normally distributed data (for many tests), appropriate sample size for power

Estimating the average income of a population based on a sample survey

Regression analysis

Examine relationships between variables

Linear relationship between variables, no multicollinearity, homoscedasticity, normally distributed errors

Predicting housing prices based on features like size, location, and age

Correlation analysis

Assess the strength and direction of the relationship between variables

Interval or ratio level data, linear relationship between variables

Assessing the relationship between hours studied and exam scores

ANOVA (analysis of variance)

Compare means among three or more groups

Normally distributed dependent variable, homogeneity of variances, independent observations

Comparing average test scores across three different teaching methods

MANOVA (multivariate ANOVA)

Compare means on multiple dependent variables

Normally distributed dependent variables, homogeneity of variances, no multicollinearity

Comparing psychological and academic outcomes across different therapy types

Chi-square test

Test relationships between categorical variables

Categorical variables, expected frequencies >5 for each cell (for larger samples)

Examining the relationship between gender and voting preference

T-test

Compare the means of two groups

Normally distributed dependent variable, homogeneity of variances, independent observations

Comparing the mean test scores of two different classes

Factor analysis

Identify underlying relationships between variables

Large sample size, linear relationships between variables, adequate correlation matrix

Identifying underlying factors in a survey measuring job satisfaction

Cluster analysis

Group similar observations into clusters

No specific distribution requirements, sufficient sample size for meaningful clusters

Segmenting customers into different groups based on purchasing behavior

Logistic regression

Predict a binary outcome from a set of variables

Binary dependent variable, no multicollinearity, linearity of independent variables and log odds

Predicting whether a student will pass or fail based on study habits and attendance

Multilevel modeling

Analyze data with a hierarchical structure

Hierarchical data structure, independence of observations within clusters, normally distributed residuals

Analyzing student performance with data from multiple schools

Structural equation modeling

Test complex relationships between variables

Large sample size, no multicollinearity, normally distributed errors, good model fit indices

Testing a theoretical model of customer satisfaction and loyalty

Time series analysis

Analyze data collected over time

Sequential data points, stationarity of the series, sufficient number of observations

Analyzing monthly sales data to forecast future sales

Survival analysis

Analyze time until an event occurs

Time-to-event data, right-censoring, proportional hazards (for Cox regression)

Studying the time until relapse in patients undergoing different treatments

The goal of this book is not to walk you through calculation examples for each technique (the “how”)—for that, you should take an advanced course in statistics or look in appendix A for a summary—but rather to explain the “why” and the “what.”

For example, for the following article:

Oubrich, M., Hakmaoui, A., Benhayoun, L., Söilen, K. S., & Abdulkader, B. (2021). Impacts of leadership style, organizational design and HRM practices on knowledge hiding: The indirect roles of organizational justice and competitive work environment. Journal of Business Research, 137, 488–499.

We used the following techniques (Table 2.31):

Table 2.31

Statistical techniques used in Oubrich et al. (2021)

Statistical technique

Purpose

Requirements

Example in the paper

Partial least squares structural equation modeling (PLS-SEM)

Analyze complex relationships and test hypotheses involving multiple variables

Suitable for small to moderate sample sizes, does not require normal distribution, good for exploratory research

Used to test hypotheses about the impacts of leadership style, organizational design, and HRM practices on knowledge hiding

Bootstrapping

Estimate the precision of sample statistics by resampling with replacement

Large number of resamples (e.g., 1000) to estimate the standard error and confidence intervals

Used to test the significance of direct and indirect effects in the structural model

Harman’s single-factor test

Check for common method bias

Exploratory factor analysis to ensure the first factor explains less than 50% of the variance

Used to address common method bias concerns in the study

Variance inflation factor (VIF)

Check for multicollinearity

VIF values should be below 5.0 to indicate no multicollinearity

Used to ensure that multicollinearity does not affect the results

Note: All statistical techniques you will ever need to learn are explained and placed in Appendix A

In our view, the article employed the statistical techniques that were necessary to address the research questions and hypotheses. Specifically, the partial least squares structural equation modeling (PLS-SEM) was used, which is well-suited for this kind of research. PLS-SEM is effective for testing complex relationships between variables, especially in studies with smaller sample sizes or when the research model includes multiple constructs and paths, as was the case in this study on knowledge hiding.

PLS-SEM was appropriate because the study aimed to examine the relationships between various predictors of knowledge hiding and their outcomes. This technique allowed us to assess both the measurement model (validating the constructs) and the structural model (testing the hypothesized relationships), which was critical for addressing the hypotheses.

In addition, bootstrapping was applied to test the significance of the path coefficients, which is an essential step in SEM to determine the robustness of the findings. Bootstrapping allows for more reliable estimates of standard errors and confidence intervals, helping the researchers draw valid conclusions about the relationships between variables.

These techniques were crucial for providing a rigorous and structured approach to analyzing the complex relationships in the study, making them necessary to answer the research questions and test the hypotheses effectively.

But we could also have used these techniques (Table 2.32):

Table 2.32

Additional statistical techniques that could improve the paper Oubrich et al. (2021)

Statistical technique

Purpose

Requirements

Potential improvement to the paper

Confirmatory factor analysis (CFA)

Validate the measurement model and ensure construct validity

Large sample size, multiple indicators per construct, normal distribution of indicators

Could provide stronger evidence for the validity and reliability of the measurement model

Multigroup analysis

Compare model estimates across different groups

Sufficient sample sizes in each group, measurement invariance

Could explore whether the relationships hold across different subgroups, such as industries or geographic regions

Longitudinal analysis

Analyze data collected over multiple time points

Data collected at different time points, methods to handle missing data

Could provide insights into how knowledge hiding behaviors and their predictors change over time

Mediation analysis with control variables

Understand the role of mediators while accounting for potential confounders

Appropriate control variables, robust estimation methods

Could strengthen the findings by accounting for other variables that might influence the results

Hierarchical linear modeling (HLM)

Analyze data with nested structures (e.g., individuals within organizations)

Data with hierarchical structure, larger sample sizes at each level

Could better account for the nested nature of organizational data and provide more accurate estimates

However, the reason we did not use confirmatory factor analysis (CFA) in this study is because the constructs were already validated through exploratory methods or preexisting scales. Additionally, CFA requires a larger sample size and normally distributed data for accurate results, which was not available or necessary for this research.

We did not conduct a multigroup analysis because the sample sizes for different subgroups, such as industries or geographic regions, were likely insufficient. Without large enough samples for each group, this method could lead to unreliable results and would not provide meaningful insights.

Longitudinal analysis was not used because the data in this study were collected at a single point in time, and there was no budget for making this a longer study. Longitudinal studies require multiple data points collected over an extended period, which was not part of this research’s design.

Although mediation analysis with control variables can provide deeper insights by accounting for potential confounders, it was excluded due to a lack of relevant control variables and because the existing analysis already sufficiently explained the relationships in the model.

Finally, hierarchical linear modeling (HLM) was not employed because the study did not collect data with a nested structure, such as individuals within organizations. HLM is only applicable when there is hierarchical data, which does not appear to be the case in this study, as the data focused primarily on individual-level analysis.

So, in conclusion, we did not use any unnecessary statistical techniques, as far as I can judge. The methods chosen, specifically PLS-SEM and bootstrapping, were both appropriate for the research context and objectives. These techniques were necessary to handle the complexity of the relationships between knowledge hiding and its predictors and consequences, given the exploratory nature of the study.

PLS-SEM is particularly suitable for models with multiple constructs, smaller sample sizes, and when the goal is to predict relationships rather than confirm them, which aligns well with the study’s aim. Bootstrapping further reinforced the robustness of the model’s path estimates.

So, given the research context, the techniques used were well-aligned with the study’s goals, and no extraneous methods were employed. The focus on essential methods to validate the model and test the relationships was appropriate, without introducing unnecessary or overly complex statistical techniques.

Now that we have an idea of what statistical method can be used, we will go ahead and think about how we can gather the data needed to do these operations.

2.2.4 Data Collection Methods

Data collection is a crucial part of any research project, yet it’s often underestimated in terms of both complexity and importance. At first glance, it might seem straightforward, but it can quickly become one of the most time-consuming and challenging phases in our research. For example, a student studying consumer behavior may need access to data from specific companies or customer segments, only to find that companies are reluctant to share this information due to privacy concerns or proprietary restrictions. This lack of access can significantly limit the scope and depth of the study.

Moreover, even when data is available, researchers face numerous biases that can skew results and undermine trustworthiness. Selection bias, for instance, might occur if the sample is not representative of the population, such as interviewing only urban consumers when studying nationwide car preferences. Response bias is another common issue, as respondents may answer questions in a way they think is expected, rather than providing honest responses. Additionally, confirmation bias on the researcher’s part can subtly influence the collection process, as they might subconsciously favor data that supports their hypothesis. These issues illustrate why data collection is far from a simple task—it requires careful planning, ethical considerations, and vigilance to ensure that the final data is accurate, comprehensive, and meaningful.

All social sciences are grounded in empirical evidence, meaning they rely on data to support theories and hypotheses, unlike fields like philosophy, where new concepts can be developed and discussed purely in theoretical terms. Without reliable and well-collected data, conclusions in the social sciences cannot be substantiated.

As a result, consumer-based studies are common. Consumers are easier to identify and reach, and it’s generally simpler to gain their cooperation compared to businesses or industry professionals (Table 2.33).

Table 2.33

Ways of gathering data in social science and potential risks

Social science field

Data collection method

Description

Potential risks

Sociology

Surveys and questionnaires

Collects responses from individuals on social behavior, attitudes, and values

Risk of biased sampling, leading or unclear questions, nonresponse bias, or social desirability bias (respondents may answer in a way they think is acceptable)

Psychology

Experiments and observational studies

Controlled experiments in lab settings to test hypotheses, or natural observation of behaviors

Risk of artificial settings (lab environments) leading to unnatural behavior, observer bias, and ethical issues like informed consent

Political science

Comparative studies and public opinion polls

Compares policies, voter behavior, or political institutions across regions or groups; polls public opinion

Risks include sampling bias, response bias, or misinterpretation of comparative results due to different political/cultural contexts

Economics

Econometric analysis of market data

Analyzes statistical and economic data from surveys, censuses, or historical market information

Risk of using outdated data, failure to account for confounding variables, or incorrect model assumptions leading to biased results

Anthropology

Ethnography and fieldwork

Immersive, long-term observation and participation in the community to understand cultural practices

Observer bias, cultural misunderstanding, ethical concerns related to privacy and consent, and the risk of overidentifying with the group being studied

Education

Case studies and action research

Collects qualitative or mixed data from educational settings to assess interventions, teaching methods, or policies

Small sample sizes can limit generalizability, researcher bias, and ethical risks like the Hawthorne effect (participants altering behavior because they know they are being observed)

We will not go into all these methods in more detail beyond what is shown in the table, but we will focus on two of the more common and useful methods: the survey and the controlled experiment:

2.2.4.1 The Surveys

Surveys, whether online or offline, are probably the most important tool for me as a researcher to gather data. Here are some of the key elements to follow when constructing a survey:

1.

Keep the survey focused: The questions in the survey should directly address the research questions (RQs) or hypotheses (Hs), ensuring full coverage but without including unnecessary or unrelated questions. Each question should be purposeful and relevant to the goals of the study.

2.

Use clear and neutral wording: Questions should be worded in a way that is neutral, avoiding any leading language that could bias the responses. Clear and straightforward phrasing ensures that participants fully understand the questions without confusion or misinterpretation.

3.

Limit the survey length: To encourage completion, the survey should be concise and avoid being too long. A shorter survey reduces the risk of respondent fatigue, which can negatively impact the quality of the data.

4.

Communicate the survey duration: At the beginning of the survey, clearly inform participants how long the survey will take to complete. This transparency helps set expectations and makes participants more likely to finish the survey.

5.

Use a 5–7-point Likert scale: For questions measuring attitudes, opinions, or levels of agreement, use a Likert scale with 5 to 7 points. This range strikes a balance between granularity and simplicity. Standardize the wording across the scale (e.g., “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” “Strongly Agree”) for consistency.

6.

Include an open-ended comment option: For each question, provide participants with the option to leave a comment. This allows them to clarify or elaborate on their answers, giving you additional qualitative insights that the Likert scale alone might not capture.

7.

Test the survey: Before distributing the survey widely, test it with a small sample (pilot test) to ensure that the questions are understood, the flow is logical, and the length is manageable. Feedback from the test run can help you refine the survey and fix any issues.

8.

Explain the purpose of the survey: At the start, provide a brief explanation of what the survey is for and why the data is being collected. Be clear about the goals of the research and how the responses will contribute to the study.

9.

Identify the researcher or institution: Clearly state who is conducting the survey and who is responsible for the research. This adds credibility and transparency, helping participants feel more confident in providing their responses.

10.

Maintain ethical standards: Inform participants about confidentiality, how their data will be used, and whether the survey is anonymous. Ensure they are aware of their right to withdraw at any time if they no longer wish to participate.

The actual survey may look something like this:

Example: “Leadership Style, Organizational Design, and Knowledge Hiding in the Workplace”.

Below follows an example of how a survey may look like:

Introduction

Thank you for participating in this survey, which explores the relationships between leadership style, organizational design, and HRM practices in relation to knowledge hiding behaviors in the workplace. The data collected will help us better understand how different factors influence organizational dynamics. This survey will take approximately 10 min to complete. Your responses are completely anonymous, and all data will be kept confidential. You may skip any question you do not wish to answer, and you can withdraw from the survey at any time. The research is conducted by Moldovia University, Department of Business, under the supervision of

Prof. Dr. Hanswurst Sauerbraten.

Section 1: Demographics

1.

What is your current role in the organization?

 (a) Entry-level employee

 (b) Middle management

 (c) Senior management

 (d) Executive

2.

How long have you been with your current organization?

 (a) Less than 1 year

 (b) 1–3 years

 (c) 4–6 years

 (d) More than 6 years

Section 2: Leadership Style

Using the scale below, please indicate how much you agree with the following statements about your immediate supervisor:

3.

My supervisor encourages open communication within the team.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

4.

My supervisor provides constructive feedback to improve performance.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

Section 3: Organizational Design

6.

The organizational structure promotes collaboration between departments.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

Section 4: HRM Practices

6.

The company provides adequate resources and training for my personal development.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

7.

HR policies in this organization are transparent and applied fairly.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

Section 5: Knowledge Hiding

8.

I have observed knowledge hiding behavior (e.g., withholding information) within my team.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

9.

I believe knowledge hiding occurs because of competitive work environments within the organization.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

Section 6: Organizational Justice

10.

I feel that organizational decisions are made fairly and transparently.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

1.

I trust the leadership to make decisions that benefit the entire organization, not just a few individuals.

1.

Strongly Disagree

2.

Disagree

3.

Neutral

4.

Agree

5.

Strongly Agree

[Optional Comment Box]

Closing Section: Thank you for completing this survey. Your responses will help us better understand how leadership, organizational design, and HR practices impact knowledge sharing and behavior in the workplace. If you have any additional comments or suggestions, please feel free to share them in the box below.”

End of Survey.

Before distributing this survey widely, it should be tested with a small sample group to ensure that the questions are clear, the survey length is manageable, and the flow is logical. Feedback should be used to refine any confusing or ineffective elements. The survey should adhere to the rules of being focused on the research questions, using clear and neutral wording, limiting the length, providing Likert scale questions with a range of responses, including open-ended comments, and explaining the purpose and duration of the survey.

By following these guidelines, we can create a well-structured, efficient, and ethical scientific survey that effectively gathers data relevant to your research questions or hypotheses. However, even if we design the perfect survey, the data can still be inaccurate if we target the wrong participants. The key is ensuring that our sample size and selection reflect the population we’re studying. Here is an example I often use in class:

Example: Mapping Drinking Trends and Behaviors

In this exercise, I challenge students to think critically about biases by asking if it’s a good idea to survey people in Sweden about their drinking habits by standing outside a liquor store at 10:00 a.m. on a Tuesday. Would responses from this sample accurately represent the entire population of consumers’ drinking habits in the country? Yes or no? A well-reflected student will recognize that this method introduces several biases.

First, sampling bias is evident—most people are at work or otherwise occupied on a weekday morning, so the people outside a liquor store at that time are unlikely to reflect typical drinking habits. For example, many people may only buy alcohol on weekends, or after work, which would mean their behaviors wouldn’t be captured in a morning survey. Those out at 10:00 a.m. on a weekday may be more likely to have dependency issues or unusual drinking patterns, which would skew results significantly.

Additionally, response bias could affect the accuracy of the responses. People may not want to openly discuss their drinking habits with a stranger, especially in a public setting where they feel judged. They might downplay or exaggerate their habits depending on how comfortable they feel, creating unreliable data. This bias can be exacerbated by social desirability bias, where respondents might alter their answers to align with what they think is socially acceptable.

The first issue we addressed above is called a selection bias. By focusing only on people who visit liquor stores, we automatically exclude non-drinkers and casual drinkers who might only purchase alcohol occasionally or from other sources. This exclusion could lead us to conclusions that inaccurately represent the broader population, emphasizing heavier drinkers over more moderate ones.

Lastly, confirmation bias on the part of the researcher can also play a role. If the surveyor subconsciously believes that alcohol dependency is common, they might interpret responses or selectively include data that reinforces this belief, further skewing the results.

These biases show that if data collection is flawed at the start, no amount of analysis will correct for the misrepresentation. A well-constructed survey must consider time, location, and respondent diversity to produce results that are as close to the truth as possible. We need the full context.

The next time you read a methodology chapter, try to identify similar biases yourself—not only from the information provided on how the data was gathered but also from details that are noticeably absent.

When collecting data in social sciences, we must be vigilant about all potential biases and methodological issues. As described above, sampling bias can skew results if the participants are not representative of the population, while leading questions or response biases can distort survey outcomes. Researchers should be wary of observer bias in observational studies, where personal judgments may influence how data is recorded. Ethical considerations, such as informed consent and privacy, are also critical in fieldwork or case studies. Also, not to forget that the generalizability of findings can be limited when using small or specialized samples, so it’s important to address these limitations clearly before we set out to gather the data.

2.2.4.2 Controlled Experiments

A few words should also be said about controlled experiments, which is another important method for the social sciences, but also why it is not more often used in research (Table 2.34).

Table 2.34

Types of controlled experiments for the social sciences

Social science

Type of experiment

Description

Example

Psychology

Controlled laboratory experiment

In a lab setting, variables are strictly controlled to observe causal effects

Example: A study on memory retention, where participants are divided into groups and exposed to various levels of noise while recalling word lists. The objective is to assess how noise levels impact memory retention, isolating the noise variable for controlled conditions

Sociology

Natural experiment

Analyzes real-world events or policies as “treatment” factors, often without researcher intervention

Example: To study the impact of increased minimum wage, sociologists compare employment rates across regions that have and haven’t raised wages, observing changes as a natural experiment without researcher intervention

Economics

Field experiment

Real-world setting with controlled interventions to analyze behavioral economics

Example: Testing “nudge theory” in savings behavior, where a bank provides reminders to randomly selected customers encouraging monthly savings and then compares savings rates with a control group

Political Science

Survey experiment

Randomly assigned survey questions to assess framing effects on opinions

Example: A study on immigration views where survey participants receive questions framed positively (e.g., benefits of immigration) or negatively (e.g., costs of immigration). Researchers analyze how framing influences responses

Anthropology

Participatory action research (PAR)

Collaborative experiments with community members to develop insights from shared involvement

Example: Anthropologists working in a rural village on crop sustainability practices involve local farmers in testing various methods. Through observation and interviews, they determine the community’s preferred sustainable techniques and social impacts of each

Education

Randomized controlled trial (RCT)

Participants are randomly assigned to groups to test the effectiveness of an educational intervention

Example: Testing a new math curriculum, where schools are randomly assigned to implement either the new curriculum or the standard one. Outcomes in test scores are compared over a school year to measure effectiveness

Public Health

Quasi-experimental design

Research without full random assignment, often due to ethical constraints

Example: Analyzing the effects of a smoking cessation program in schools, comparing rates between schools that voluntarily adopt the program versus those that do not, to infer effectiveness despite nonrandom assignment

Often, the most effective research approaches emerge when multiple methods and strategies are combined. Integrating various methodologies—such as qualitative and quantitative approaches or experimental and case study designs—can provide a more comprehensive perspective on complex research questions.

This mixed-methods approach allows researchers to cross-validate findings, address limitations inherent in individual methods, and capture both broad patterns and in-depth insights. By strategically blending different techniques, researchers can achieve a richer, more nuanced understanding that single methods might miss, enhancing the reliability and applicability of their results. In the box below is an example:

Case: Case Study Meets Laboratory Experiment: Simulating Information Asymmetry in Negotiations

For the PhD thesis, I applied a game-theoretical model, specifically information sets, to design an experiment that combined elements of a controlled laboratory experiment with a case study framework. The experiment aimed to simulate a business negotiation over a water dam turbine, with both parties represented as major industry players, and the negotiation process was influenced by different levels of information asymmetry.

Participants were divided into four groups; each assigned a different information set in game theory:

 • Set 1: Party A (the seller) is aware of a break-in by Party B (the buyer) to search for secret information before negotiations.

 • Set 2: Party A is uninformed about the break-in.

 • Set 3: Party B knows that Party A is informed about the break-in.

 • Set 4: Party A is informed, and Party B knows that Party A knows, creating common knowledge.

The aim of the study was to explore whether and how the presence of covert information influenced the negotiation process, final prices, and post-negotiation dynamics. Additionally, it sought to examine whether behavior and economic results varied across the different information scenarios.

All participants were given the same base case study to prepare for the negotiations. However, each group was provided with additional information relevant to their assigned information set. The results were assessed based on the final negotiation prices, as well as behavioral differences observed during the process. Post-negotiation interviews provided further insights into participants’ decision-making and reasoning.

The key finding was that in Sets 1–3, the presence of covert information had minimal influence on pricing or behavior during the negotiations. However, in Set 4, where Party B (the “thief”) was perceived as vulnerable, Party A participants sometimes felt a moral compulsion to “punish” Party B, resulting in higher prices. This finding supported hypotheses about fairness and moral enforcement in negotiation dynamics, highlighting the importance of ethical considerations in shaping negotiation outcomes.

Conducting the experiment was resource-intensive, spanning 2 years, and due to the complexities and demands of the study, I have not replicated it. The resources required for such an extensive study often make it unfeasible within the constraints of typical academic publishing. I would have been limited to smaller, more focused studies if I had conducted this as part of a compilation thesis. Writing the primary case study alone took 6 months, which is not unusual for detailed cases.

More recently, I’ve been working on another comprehensive case study for executive training, which has taken 4 years alongside other projects so far, further emphasizing how time-consuming thorough full-length case studies with teaching notes can be. Despite the long data gathering and analysis periods, the drafting process of the final case study tends to proceed much more quickly once the findings are clear.

General Questions for Reflection:

1.

What are the key factors to consider when selecting research methods that align with both short-term and long-term career goals in academia?

2.

How can researchers balance innovative or complex methodologies with the practical requirements and time constraints of academic publishing?

3.

In what ways can interdisciplinary approaches enhance a research project, and what challenges might arise when integrating methods from multiple disciplines?

4.

What are the benefits and potential drawbacks of pursuing long-term, resource-intensive research projects early in a research career?

5.

How might the choice between a monograph or a compilation thesis format impact the types of methods a researcher chooses to use?

6.

What strategies can researchers employ to manage extensive data collection and analysis over multi-year projects, and how can this impact the planning of future studies?

7.

How should researchers approach the trade-offs between pursuing ambitious, large-scale studies and focusing on smaller, publishable projects that may be more feasible within academic timelines?

These questions are not easy but are designed to help reflect on the key considerations in selecting research methods, the challenges of balancing innovation and practicality, and the strategic planning necessary to develop a successful research career.

There are a number of different case studies, anything from mini-cases as used as examples in this book to full-length cases with teaching notes (Table 2.35):

Table 2.35

Types of case studies

Type of case study

Definition

Description

Typical focus

Challenges

Mini-case

Short, focused case, often 1–2 pages, usually illustrating a single concept or decision point

Used to introduce or reinforce a specific idea or concept, these cases are brief and straightforward, often designed to prompt discussion without needing extensive background

Small-scale issues, single concepts

Limited depth; less suitable for complex analysis

Illustrative case

Provides an example or illustration of a particular situation or phenomenon without extensive analysis

Serves as an example of a theory or concept in practice, useful for visualizing or explaining real-world applications. These cases are often based on simplified or hypothetical situations rather than complex or large-scale cases

Theoretical applications

Limited analytical depth; may not reflect real-life complexities

Descriptive case

Describes a real-life situation or organization in detail without analysis, often providing data and background information

Used to give an in-depth view of a situation to inform readers; it presents rich details for analysis or teaching, though it may not have teaching notes or explicit decision points

Detailed organizational overview

Can lack analytical focus; extensive data may overwhelm students

Exploratory case

Used as a preliminary study to gather information and establish a foundation for further research

Often used when little prior information exists on a topic. This case type allows for open-ended investigation and may serve as a basis for more focused, follow-up studies

Early-stage research topics

Can be broad; may not lead to clear conclusions

Decision-based case

Presents a specific problem or decision point, often requiring students to analyze options and recommend solutions

Encourages active problem-solving by posing a decision-making scenario for students, who must analyze alternatives and propose strategies. Typically includes background on stakeholders, constraints, and possible outcomes

Strategy, management, or policy issues

Requires well-defined issues; may oversimplify real-world decisions

Full-length case with teaching notes

Comprehensive, multifaceted case study with detailed background, often covering large organizations and including specific teaching objectives and guidance

In-depth, often with extensive background on complex situations, these cases frequently involve large organizations and require substantial access to data and key stakeholders. Teaching notes guide instructors in facilitating learning, making these cases suited for longer, in-depth class discussions

Large organizations, complex problems

Time-consuming to create; requires access to top management

Longitudinal case

Studies a single subject or situation over an extended period to show changes or developments over time

Provides insights into the evolution of strategies, policies, or organizational changes over time. These cases offer a timeline of events, highlighting cause-and-effect relationships in dynamic environments

Organizational change, strategy evolution

Lengthy data collection; challenging to maintain consistent focus

Comparative case

Compares two or more cases to highlight similarities and differences, often to reveal patterns or contrast strategies.

By examining multiple cases side-by-side, this approach illustrates best practices, strategic contrasts, or diverse responses to similar issues, helping students understand how context influences decision-making.

Cross-organizational comparisons

Can be complex to analyze; may require extensive data on each case.

Big case

Detailed and resource-intensive study of large organizations, requiring access to high-level management and extensive data

Big cases focus on major companies or organizations, often involving high-stakes issues or strategic shifts. These cases require significant preparation time and typically feature interviews with top management, detailed financials, and in-depth background research

Large corporations, strategic decisions

High resource and time demands; challenging to obtain access

Compilation theses typically do not include case studies, although they may contain numerous real-life examples. Due to time constraints, these theses often employ a variety of methods across individual articles, making it challenging to conduct a single, in-depth controlled experiment within the standard timeframe. Another reason is also that high-quality scientific journals rarely accept case studies as primary research. Additionally, compilation theses seldom include controlled experiments, for several reasons outlined below (Table 2.36):

Table 2.36

Reason why controlled experiments are not done more often

Reason

Description

Potential solution

Ethical concerns

Experiments often involve sensitive topics like behavior, health, or social issues, raising concerns about participants’ well-being and privacy

Strengthen ethical review processes and involve ethics boards early in research design to ensure participant protection. Implement transparent consent and anonymization protocols

Complexity of variables

Social phenomena often involve numerous interacting variables that are hard to isolate. Controlled experiments require simplification, which may overlook important factors

Use mixed methods, combining controlled experiments with qualitative approaches, and apply statistical techniques to account for variable complexity

Cost and resource demands

Conducting controlled social experiments, especially with large samples or multiple conditions, can be very resource-intensive, demanding significant funding and personnel

Seek collaborations, apply for dedicated grants, and consider simulations or virtual experiments to lower costs

Time constraints

Social science research often requires longitudinal data (studying participants over long periods), which can delay results and create logistical issues

Use accelerated or longitudinal data sampling methods and improve data collection technology. Conduct pilot studies or short-term controlled trials for initial findings

Difficulties in randomization

True randomization, essential for experimental control, can be challenging due to participant availability, interest, and demographic variability, which can affect representativeness

Use stratified sampling or matching techniques to create representative, balanced groups. Involve larger participant pools to ensure randomization across demographics

Influence of social context

Participants’ behavior may change in a laboratory setting compared to real-life situations (e.g., Hawthorne effect), reducing ecological validity

Combine lab experiments with field studies or employ naturalistic observation within the study’s design to simulate real-world environments

Replication challenges

Replicating social experiments is difficult due to unique social contexts, subjective variables, and the dynamic nature of social phenomena, affecting reproducibility

Encourage standardization in methodology, make detailed protocols available, and promote open-access data sharing to facilitate replication efforts

Limited methodological training

Researchers in some social science fields may lack training in experimental design and statistical analysis, reducing their likelihood of conducting controlled experiments

Include experimental design and statistical training in graduate programs, and provide resources like workshops, software tools, and interdisciplinary collaboration opportunities

Participant recruitment issues

Recruiting a diverse sample can be difficult, particularly if experiments require specific demographics or sensitive information. This limits study generalizability

Use online platforms, diverse recruitment methods, and offer incentives. Work with communities and advocacy groups for more inclusive participant recruitment

Funding bias toward non-experimental studies

Funding agencies may prioritize observational or survey-based studies due to their lower costs and wider applicability, limiting resources for experimental research

Advocate for funding specifically earmarked for experimental research in the social sciences, and collaborate with interdisciplinary fields where controlled studies are more common

Controlled experiments bring a level of rigor and clarity to social science research that resembles the methods used in the natural sciences, making it possible to draw more reliable cause-and-effect conclusions. By isolating variables and minimizing interfering factors, controlled experiments can help reveal relationships between social phenomena that observational studies will often overlook. Although ethical concerns may arise more often in these types of methods—especially when studying sensitive topics like health or behavior—strengthened ethical review processes and transparent consent protocols can help ensure participant protection.

Addressing the complexity of variables is challenging, as social phenomena rarely operate in isolation. However, combining controlled experiments with qualitative methods and advanced statistical techniques allows researchers to handle complexity without sacrificing too much control. Resource and cost demands can be reduced by seeking collaborations, applying for specific grants, and even utilizing virtual simulations to lower expenses. Randomization, essential for unbiased results, can be improved by using stratified sampling or matching to create more representative groups, while larger pools of participants enhance demographic balance.

Both experiments and case studies are a part of what we call experimental research methods (Table 2.37):

Table 2.37

Experimental research methods

Term

Definition

Key differences

Common use

Social science applications

Controlled experiments

Studies conducted under highly regulated conditions to isolate variables and test specific hypotheses

General term; emphasizes variable control regardless of setting

Testing theories, causal relationships, and hypothesis-driven research

Economics, psychology, sociology

Laboratory experiments

A subset of controlled experiments conducted in a laboratory setting to create an artificial but controlled environment

Specifically conducted in a laboratory; more artificial than field studies

Isolating variables in experimental settings; studying controlled interventions

Psychology, business studies, education

Experimental simulations

Studies that mimic real-world scenarios in a controlled setting, often using models or hypothetical situations.

Focuses on creating hypothetical or modeled scenarios for testing.

Exploring the impact of modeled real-world situations or conditions.

Business studies, organizational research, political science

In-lab research

Research conducted entirely within a laboratory environment to test hypotheses or simulate conditions.

Limited to laboratory settings; does not extend to natural environments.

Testing behavioral, decision-making, or experimental processes in controlled settings.

Psychology, behavioral economics, organizational behavior

Behavioral experiments

Experiments focused on understanding human or organizational behavior under controlled conditions

Behavior-focused; often includes psychological or social components

Studying human or organizational behavior under specific controlled scenarios

Psychology, sociology, business studies

Field experiments

Studies conducted in natural environments, testing variables in real-world settings to enhance generalizability

Conducted in real-world contexts; less control but more generalizability

Validating results in real-world settings to test external validity

Anthropology, sociology, economics, political science

Case studies

In-depth analyses of specific cases or phenomena, often qualitative, to understand complex contexts

Does not control variables rigorously; exploratory and descriptive

Exploring unique or rare cases; developing theories from specific contexts

Anthropology, sociology, education, political science

Simulation experiments

Simulations designed to replicate real-world conditions for testing theories or training, often computational

Heavily relies on modeling and replication, often for complex or dynamic systems

Understanding complex systems; testing dynamics and theoretical models

Economics, political science, organizational research

Controlled experiments, including laboratory experiments, experimental simulations, and in-lab research, have become increasingly popular as research methods in the social sciences, particularly in fields like business studies. These approaches offer the advantage of creating tightly regulated environments where researchers can isolate variables and rigorously test hypotheses. By applying such experimental designs, scholars aim to bring precision and rigor to the study of human behavior, decision-making, and interactions, often replicating real-world dynamics in structured, artificial settings.

Nevertheless, these types of experimental studies are not universally suitable for all social science inquiries. The controlled or simulated nature of such research can sometimes limit the direct applicability of findings to real-world contexts. To address these challenges, researchers often combine laboratory-based studies with complementary methods, such as field experiments, case studies, or mixed-method approaches, to enhance the robustness and generalizability of their results. This integration ensures a more comprehensive understanding of complex social phenomena.

One common criticism in these types of methods is the likelihood of the Hawthorne effect, where participants may change their behavior simply because they know they are being observed. To address this in my PhD dissertation, I designed the laboratory experiment to resemble a regular course for the thesis, where students were encouraged to perform as realistically and effectively as possible, without knowing they were part of an experiment. This setup helped minimize any behavior changes due to observation alone. Additionally, to protect participant anonymity, individual results were not linked to personal identities in the data, ensuring that responses remained confidential while enhancing the authenticity of the behavior observed.

It would be highly beneficial to see greater adoption of experiments as a methodology in the social sciences, given their potential to yield productive and reliable results—given that biases are carefully managed.

Now that we have gathered the data—whether on paper, online, or organized and transferred into an Excel sheet—the next critical step awaits: preparing for data analysis.

2.2.5 Data Analysis

In this part, we explain how to process and analyze the data gathered, introducing basic statistical tools or qualitative coding methods depending on the chosen approach.

2.2.5.1 Data Preparation

Once the data has been collected, the next step is to prepare it for analysis. This involves several important tasks to ensure the data is clean, organized, and in a format that can be used for statistical analysis if that is what we are planning to use:

1.

Coding and Data Entry

After collecting qualitative or quantitative data, the responses need to be coded. Coding involves translating open-ended responses into numerical values or categories so that they can be analyzed. This step is essential for survey data, where answers might be provided in text form or include a range of options. Clear and consistent coding is critical to avoid errors and ensure that the data accurately reflects the participants’ responses.

2.

Data Entry

Once the data is coded, it must be entered into a software tool for analysis, such as SPSS, Stata, or Excel. Depending on the size of the dataset, this can be a time-consuming process, requiring careful attention to detail to avoid data entry errors. Automated data entry systems or tools can help reduce manual input errors, but in some cases, the process still requires manual verification.

Note that ChatGPT can assist with organizing and cleaning smaller datasets, offering guidance on data entry best practices, and providing basic statistical interpretations. However, it cannot, currently but probably soon to be improved, directly perform advanced statistical analysis like regression, hypothesis testing, or complex data visualizations that require specialized software such as SPSS or Stata. You would still need to rely on dedicated tools like SPSS or Excel for large datasets or more complex analyses.

3.

Editing Data (actual cleaning)

Editing the data is the process of reviewing and cleaning the dataset to ensure accuracy, completeness, and reliability. This includes identifying and correcting errors in data entry, addressing missing values, and resolving inconsistencies in responses. For example, if a survey respondent skipped a question, you might decide whether to exclude that response or use statistical techniques like imputation to handle the missing data. Additionally, it’s important to identify and remove outliers by using statistical methods such as z-scores or the interquartile range (IQR) to detect values that fall far outside the typical range, especially if they distort trends. Checking for obviously false data points, like negative ages or impossible dates, is also essential; these errors can either be corrected based on context or removed from the dataset. Ensuring that the data is as clean as possible is essential for producing reliable results and maintaining the integrity of the analysis.

4.

Data Transformation

Sometimes the raw data needs to be transformed before it can be analyzed. This might involve creating new variables, combining multiple variables into a single index, or converting categorical data into numerical values. For instance, if age is collected in ranges (e.g., 18–24, 25–34), it might be necessary to convert these into a continuous variable depending on the analysis method. Transformation also includes normalizing or standardizing data, if required by the chosen statistical techniques.

These steps form the bridge between data collection and the actual analysis, ensuring that the data is clean, consistent, and ready for meaningful interpretation. We are now ready for data analysis.

2.2.5.2 Analysis Techniques

Data analysis literally means breaking data down into its individual components and examining these parts carefully. It’s a process, not a mystical or automatic operation where you simply input data into a statistical formula and receive answers. Many students mistakenly think of data analysis as a black box filled with complex statistical formulas, but true analysis involves understanding the data, drawing meaningful conclusions, and interpreting patterns.

When we dissect the data, we begin to draw some simple observations such as “there is more of A than B” or “C and D share similarities.” When we dissect the data, the process extends beyond simply identifying counts and similarities. We engage in classification by grouping similar elements into categories to uncover patterns or trends, like in a SWOT analysis. Or if studying consumer preferences, we might classify respondents by age, gender, or geographic region to detect trends that might not be obvious in raw data.

Next, we focus on correlations to see if relationships exist between variables. For example, if we observe that consumers in certain age groups prefer specific products, we can analyze the strength of these correlations to determine if they are statistically significant or simply coincidental.

We remember that correlation does not imply causation. While correlation shows that two variables, A and B, move together (either positively or negatively), it doesn’t mean that one causes the other. Establishing causation requires demonstrating that A directly leads to B, which means ruling out any other factors that might explain why B is changing. For example, if A and B both increase together, there could be an unseen factor, A2, that influences B but hasn’t been measured.

To claim causation, we must eliminate all alternative explanations. This involves rigorous testing, controlled experiments, and critical thinking to ensure that no other variables could be responsible for the changes we observe. In the social sciences, this process is particularly challenging because human behavior is influenced by so many interrelated factors. Therefore, few studies in the social sciences successfully establish causality, as it is challenging to account for all potential variables, and most articles and analyses are constrained by limited scope and length. This makes causal claims both rare and often tentative. Nonetheless, pursuing causality remains important, and it is unfortunate that such attempts are not more common.

Statistics can help us establish causation, but it requires more than just identifying a correlation. In order to use statistics to prove causality, several conditions must be met, and specific statistical methods must be used (Table 2.38).

Table 2.38

How to try to prove causation

Condition/method

Explanation

How it establishes causation

Temporal precedence

A must happen before B in time

Ensures that A is the cause and B is the effect by establishing the time order

Covariation (correlation)

A statistical relationship between A and B (they move together)

Shows that A and B are related, but this alone does not prove causality

Elimination of confounders (regression)

Regression analysis helps control for other variables that could be influencing B

Helps isolate the effect of A on B, ruling out other potential causes

Randomized controlled trials (RCTs)

Randomly assigns participants to treatment/control groups, reducing bias and external influences

Ensures that changes in B are due to A by eliminating external influences

Instrumental variables (IV)

Uses variables related to A but not directly to B, except through A

Helps in observational studies to isolate the causal effect of A on B

Granger causality test

In time-series data, this test checks if A helps predict future values of B

Indicates causality over time by showing if A leads to B across time periods

The table summarizes key methods that can help move from correlation to establishing causality, along with an explanation of how each approach works to isolate and verify cause-and-effect relationships in statistical studies. We can also define this as a working process (Fig. 2.11):

Fig. 2.11

The process for finding causation

In the diagram above, we outline a systematic approach to identifying and verifying causation step by step. Here’s the breakdown:

1.

Identify research question (A): The process begins with forming a research question that defines the relationship you aim to investigate. No statistical techniques are involved at this stage, but the clarity of the question shapes the entire analysis.

2.

Establish correlation (B): In this step, we identify if two variables are related. We use correlation analysis (e.g., Pearson correlation or Spearman’s rank correlation) to determine whether there’s a statistical relationship between variables, but this alone doesn’t confirm causation.

3.

Assess temporal precedence (C): To prove causality, one variable must precede the other in time. Statistical methods such as Granger causality or time series analysis can help determine which variable occurs first and influences the other over time.

4.

Exclude confounding variables (D): We now look for any hidden variables that may be influencing both A and B. Here, methods like multivariate regression or propensity score matching can be used to control for confounding factors that might bias the results.

5.

Regression or instrumental variables (E): To strengthen causal inference, we apply multiple regression analysis or instrumental variable techniques. These statistical models help estimate the effect of one variable on another while controlling for other potential influences.

6.

Randomized controlled trials (RCTs) (F): The most robust method for establishing causality is through randomized controlled trials (RCTs). By randomly assigning participants to different groups (treatment vs. control), we can control for confounders and ensure that the observed effect is due to the intervention itself.

7.

Confirm causation (G): Once all the above steps are completed and statistical techniques applied, we can confidently state whether causation has been established. This final step synthesizes all the prior analyses, proving or disproving a cause-and-effect relationship.

By following this procedure and applying appropriate statistical techniques at each stage, we can systematically investigate and validate causal relationships in research, as far as it will be possible. That is, we will seldom be able to conclude with certainty, but we can arrive at an answer that is much better than just showing that there is a correlation, and that is often good enough.

In the social sciences, very few studies follow the rigorous process necessary to prove causation. Estimates vary, but when I look at 100 quantitative articles, less than 20% of social science articles apply methods like randomized controlled trials (RCTs) or instrumental variables, which are required for robust causal inference with the use of statistics. This low percentage is largely due to the difficulty of conducting controlled experiments in real-world social settings, as well as practical limitations like funding, time, and data access.

Instead, many social science articles rely on correlation or regression analyses, but these methods alone cannot prove causality. Instead, many researchers claim causation based on associative patterns, but without controlling for confounding variables or establishing temporal precedence, such claims are weaker. This means that while a study may suggest a relationship between two variables, it often lacks the empirical rigor needed to prove one variable directly causes another. The lack of true causal testing is a significant issue in the social sciences.

Leaving causation for now, we do data analysis, and we often perform comparative analysis, where we compare different groups or variables to see how they relate. This could involve comparing sales in different regions or testing the effects of an intervention in one group versus another (Table 2.39).

Table 2.39

Types of comparative analysis

Type of comparative analysis

Description

Example

Cross-sectional comparison

Compares data from different groups or segments at a single point in time to identify differences

Comparing sales performance across different regions during a specific quarter

Longitudinal comparison

Analyzes changes over time within the same group to observe trends or patterns

Tracking the impact of a marketing campaign on sales in the same region over a year

Pre- and post-intervention comparison

Examines data before and after an intervention to measure its effect on a group or variable

Comparing customer satisfaction scores before and after implementing a new service

Benchmark comparison

Compares a group’s performance against an established standard or benchmark

Comparing a company’s quarterly revenue growth to the industry average

Paired comparison

Compares matched pairs within a dataset to control for confounding variables

Matching employees by job role to compare productivity with and without new software

Control group comparison

Involves comparing an experimental group with a control group to isolate the effect of a variable

Comparing test scores between students using a new study method and those who are not

Trend analysis

Identifies and compares trends across multiple groups or time periods to find patterns

Comparing customer buying patterns across different seasons for multiple years

Attribute-based comparison

Compares groups based on specific attributes, like age or income level, to find correlations or patterns

Analyzing purchasing behavior of customers segmented by age groups

Further, we look for outliers—data points that deviate significantly from the rest of the dataset. These could either highlight errors in data collection or indicate a meaningful trend that warrants further investigation.

Trend analysis helps us to track changes over time, identifying shifts in behaviors, preferences, or outcomes. This is particularly useful in longitudinal studies where time-dependent data reveal trends and patterns that are key to understanding broader changes in a population.

A simple and fast method to analyze trends is by using Google Trends, a free tool that provides insights into the popularity of search terms over time. It allows researchers to track interest in specific topics across different regions and periods, offering valuable data for pattern recognition and forecasting. By comparing multiple search terms, one can identify emerging trends, seasonal patterns, or shifts in public interest. Additionally, the tool’s ability to filter by category, geography, and time frame makes it versatile for a variety of research contexts. For social science studies, Google Trends can serve as a starting point to formulate hypotheses or corroborate findings with real-world data.

Other useful free software used in science, apart from the much-talked-about Google Scholar, which is a natural go-to place for all scientists, include R and Python for data analysis, statistical computing, and visualization. JASP offers an intuitive platform for statistical analysis, particularly in psychology and social sciences, while QGIS excels in spatial data analysis and mapping. Gephi is invaluable for network analysis, enabling researchers to visualize and interpret complex relationships. For preparing publications, tools like LaTeX via Overleaf are indispensable for formatting scientific documents with precision. Additionally, Zotero and Mendeley simplify managing references and citations, making them vital for collaborative research and literature reviews. There are many more than these, of course, and even these tools may evolve, change names, or be replaced by others in the near future as technology and research continue to advance.

Other forms of analysis conducted may include (Table 2.40):

Table 2.40

Other forms of analysis used

Type of analysis

Description

Common applications

Descriptive analysis

Summarizes and describes the main features of the dataset, using measures like mean, median, and standard deviation

Provides a general overview of data without assessing relationships; used in almost all fields for initial data exploration

Factor analysis

Identifies underlying relationships between variables by grouping them into factors or dimensions

Psychology, social sciences, and market research to identify latent variables or construct themes

Regression analysis

Explores relationships between dependent and independent variables, often for prediction

Economics, medicine, and social sciences; types include linear, logistic, and multivariate regression

Cluster analysis

Groups data points into clusters based on similar characteristics

Market segmentation, biology, and social sciences for identifying natural groupings (e.g., customer segments)

Content analysis

Analyzes textual or qualitative data to identify themes, patterns, or term frequencies

Communications, sociology, and psychology to systematically examine language or document themes

Survival analysis

Examines time-to-event data to study the duration until an event occurs

Medical research, customer retention studies, and risk analysis (e.g., Kaplan-Meier, Cox proportional hazards)

Network analysis

Investigates relationships within networks, focusing on connections among entities

Sociology, biology, and communications to study social, biological, or technological networks

Meta-analysis

Combines findings from multiple studies to derive an overall conclusion or effect size

Medical research, psychology, and education to synthesize evidence across studies

Sensitivity analysis

Tests robustness of results by varying key parameters to observe impact on outcomes

Economics, environmental science, and risk assessment to understand how changes affect model outcomes

Path analysis

Examines causal relationships and maps out direct and indirect effects within a structural model

Psychology, sociology, and organizational studies to understand causal chains and mediator effects

All these techniques make out a list of possible analyses, which becomes a dynamic process that brings to light meaningful insights, which ultimately inform conclusions and drive decision-making.

Once we complete this process, we move into synthesis, which involves piecing the data back together to form a coherent whole. These two processes—analysis (taking apart) and synthesis (bringing together)—are essential to understanding the bigger picture of what we do when we do science.

The concept of separating and reuniting elements finds a reflection in philosophical frameworks, particularly Hegel’s dialectics, which follow the progression of thesis, antithesis, and synthesis. While both processes involve breaking down components and integrating them into a larger whole, Hegel’s philosophical approach is abstract and idealistic, whereas research analysis is grounded in empirical, systematic observation. But, the comparison highlights a useful metaphor, I think.

So, we have

Analysis = to take a part

Synthesis = bring together

Analysis and synthesis are fundamental reasoning approaches in science and problem-solving. Analysis (A) involves breaking down a complex entity (X) into its components ({x1,x2,…,xn}) to understand its structure or function. For example, analyzing a system means identifying its parts (x1,x2,…) and how they interact. Synthesis (S) is the reverse process, combining these components to reconstruct a whole (Y) or form new insights. Together, they often work iteratively: analysis deconstructs (A(X) → {xi}) for clarity, while synthesis reconstructs (S({xi}) → Y) to generate meaning. This interplay enables both understanding and the creation of new knowledge.

It’s not uncommon for social scientists to study two quite diametrically opposed views or perspectives on a problem and arrive at a third, which represents a compromise or synthesis, something in between. If this conclusion is arrived at through rigorous data analysis, then that is acceptable. However, if it stems from the researcher’s desire to appear novel, showcasing unique thinking and reaching a conclusion based primarily on their own reasoning—especially if it is something no one else has discovered by chance—then we should approach it with skepticism.

In Hegelian dialectics, the process of thesis, antithesis, and synthesis parallels the ideas of analysis and synthesis but operates within a dynamic framework of contradictions and resolutions. The thesis represents an initial idea, proposition, or state of affairs, serving as the starting point for examination. When this thesis is critically analyzed, tensions or contradictions often emerge, forming the antithesis—a direct challenge or counterpoint to the original idea. This mirrors the analytical process of breaking down a complex whole into components or identifying conflicting elements within it.

The synthesis then resolves the conflict between thesis and antithesis, creating a new, more advanced understanding or proposition. This corresponds to the reconstructive process in synthesis, where fragmented or opposing components are integrated into a cohesive whole. While analysis and synthesis focus on systematically deconstructing and reconstructing for clarity and insight, Hegel’s framework introduces a dynamic element, highlighting the role of conflict and resolution as driving forces of progress—much like the biological process of adaptation through external stimuli or irritations. Both approaches share the underlying principle of moving from fragmentation or contradiction to unity and understanding, with Hegelian dialectics presenting this as an ongoing, iterative process of intellectual and historical evolution.

Skepticism is essential throughout the entire scientific process anyway, not just in the data analysis part. We must focus on the content of what has been written, rather than on the identity or reputation of the person writing it. Under no circumstances should we allow ourselves to be swayed by the authority or status of the author or any authority. To do so would be to fall into political thinking, which runs counter to the objectivity required in scientific inquiry.

2.2.6 Interpreting Results

This next stage answers the essential question: What does it all mean? For example, a result might be as simple as “13”—a straightforward number—but the key is how we interpret it. What does this number represent in the context of the research question? Is it statistically significant? How does it compare with other studies? Interpretation goes beyond the numbers—it involves understanding the implications, reasoning about why the results came out as they did, and considering the broader context, such as societal, cultural, or theoretical relevance.

To interpret effectively, we need to connect the dots between our data, the research question, and the existing body of knowledge, as well as suggest areas where further research could extend or challenge our findings.

In interpreting research findings, it’s important to recognize that not all results require in-depth interpretation, as some may be quite obvious or self-explanatory. For instance, if a survey shows that 90% of participants prefer online shopping to in-store shopping, the finding is clear and straightforward. However, other findings may be less obvious and require deeper analysis to uncover underlying patterns or explanations. For example, a study showing a weak correlation between employee satisfaction and productivity may need further interpretation to understand the complex factors influencing both variables. Similarly, in social sciences, cultural or contextual factors often play a role in shaping more subtle outcomes, which may not be immediately apparent. Some findings, such as the direct impact of increased study hours on exam performance, are easily interpreted, while others—like the influence of leadership style on long-term organizational behavior—may require a more nuanced understanding.

If respondents rated their preference for white cars on a scale of 1 to 20, and the average score was 13, this could indicate a moderate, somewhat lukewarm preference. This score is slightly above the midpoint, suggesting that while some participants have a positive feeling toward white cars, they are not that enthusiastic. Such a rating might imply that white is a safe or neutral choice for many, but it lacks the strong appeal that might drive a more decisive preference. In practical terms, this could mean that while white cars would not be a top-selling color, they could still hold a steady, reliable place in the market as a broadly acceptable option. Additionally, this average rating could reflect broader societal or cultural trends, where preferences for certain car colors may be influenced by associations with cleanliness, simplicity, or tradition. To better understand this finding, comparing it with other colors’ preference ratings could reveal whether white stands out positively or if it’s simply chosen as a fallback or compromise among more vibrant options.

The key is to differentiate between straightforward conclusions and those that need more contextual exploration to fully grasp their implications.

What we need at this stage is a structured framework to guide us through the various forms of interpretation required for our research (Table 2.41):

Table 2.41

A framework for interpreting results in the social sciences

Step

Guiding questions

Action/approach

Example

1. Address research questions

Does the result directly answer the research question(s)?

Revisit the original research question(s) and examine whether the data provide a clear, direct answer

If the research question is about the effect of social media on consumer behavior, determine if the data shows a direct impact

2. Statistical significance

Are the results statistically significant? Do they indicate a reliable effect?

Examine the p-values, confidence intervals, or effect sizes to determine statistical significance

A p-value below 0.05 could indicate that the findings are statistically significant

3. Comparison with literature

How do these findings compare with existing research or theoretical frameworks?

Cross-reference the results with similar studies, noting whether they align with or contradict existing theories or findings

If previous studies found a positive effect and your study does not, discuss potential reasons for this discrepancy

4. Practical significance

What are the real-world or practical implications of the findings?

Consider the relevance of the findings for policy, business, or society. What does this mean beyond academic interest?

If consumer preferences for eco-friendly cars are confirmed, this could guide marketing strategies for car companies

5. Broader contextual relevance

How do the results fit within the broader societal, cultural, or historical context?

Discuss how societal trends or external events may have influenced the results

Changes in environmental attitudes might explain why eco-friendly preferences are now stronger than in earlier studies

6. Limitations and assumptions

What limitations should be acknowledged in the interpretation of the results?

Reflect on potential biases, unaccounted variables, or any methodological limitations that may affect the interpretation

If the sample size is small or data is regionally restricted, acknowledge how this impacts the generalizability of the findings

7. Suggest future research

What new research questions arise from the findings?

Propose new research directions based on gaps that have emerged during analysis or unexpected findings

If your study finds a regional variation in consumer behavior, suggest further research on cross-regional differences

8. Causal or correlational

Can we identify a cause-and-effect relationship, or is this merely a correlation?

Apply logical and statistical reasoning to distinguish between correlation and causation, using techniques like regression analysis or controlled experiments

If increased social media use is correlated with consumer spending, explore whether there is a causal mechanism or other confounding factors

9. Synthesis of findings

How do all these different elements (data points) come together to tell a cohesive story?

Reassemble the data and analysis into a synthesized whole, interpreting how the different parts relate to each other and the research questions

The findings on consumer behavior, social media use, and environmental attitudes may collectively suggest a broader trend toward eco-conscious marketing strategies

10. Implications for theory

What are the theoretical implications of the findings?

Reflect on how the findings contribute to or challenge existing theories, and whether they require modification or reinforcement of the theoretical framework

If the findings support the theory of planned behavior, highlight how they strengthen or extend its application in a new context

This framework offers a structured approach to interpreting research findings across various dimensions. It guides researchers through essential steps, such as determining whether the results directly address the research questions, evaluating statistical significance, and comparing the findings with existing literature. Additionally, it encourages consideration of practical and societal implications, acknowledgment of study limitations, and exploration of future research possibilities. By following this framework, you, as a researcher, can synthesize your findings into a cohesive narrative while reflecting on the broader theoretical impact.

When this is done, we are ready to tell the world about what we have found. That means it’s time for publication.

2.2.7 Documenting Findings

In this part, we teach you how to structure your findings in a scientific report or paper, ensuring proper documentation of your process, results, and analysis (Table 2.42).

Table 2.42

Types of outlets for research

Publication type

Rigidity of form

Completion status

Time required

Location

Cost

Other factors

Scientific article

Very rigid format (IMRaD: Intro, methods, results, and discussion)

Full and final version required

Long (6 months to 1 year, sometimes more)

No travel needed (online)

Often free unless open access, which can cost $1500–$3500

Requires peer review, lengthy revisions; high academic rigor

Book chapter

Moderately rigid format

Can be in-progress or completed

Medium (months to a year)

No travel needed (online or collaborative)

Often free (publisher funded)

Good for more comprehensive or theoretical work, typically invited by editors

Book

Least rigid (more creative freedom in structure)

Must be fully completed

Very long (1–2 years or more)

No travel needed

Usually free (publisher funded) may even generate some income; self-publishing has high costs

High effort but results in a substantial body of work, sometimes non-peer-reviewed

Conference paper

Moderately rigid format (varies by conference)

In-progress or complete

Short (2–6 months)

Requires travel to the venue

Travel expenses + conference fees

Fast feedback, but papers may not always be peer-reviewed; some publish proceedings

Working paper

Low rigidity (informal)

In-progress

Short (weeks to months)

No travel needed (online)

Free to upload to repositories

Useful for sharing early-stage findings, informal peer feedback

Poster presentation

Very flexible format

In-progress

Short (weeks to months)

Requires travel to the venue

Travel expenses + conference fees

Primarily visual; less formal than full papers, often used to present preliminary data

Technical report

Moderately rigid (focus on methods and results)

Full completion required

Medium (months)

No travel needed (institution or company-funded)

Usually free (institution-funded)

Highly detailed, practical; not peer-reviewed in academic sense, but useful for industry or government applications

White paper

Flexible, semiformal format

Full completion

Medium (months)

No travel needed

Often free (produced by organizations)

Used to influence policy or market decisions, less academic, more practical

So, what options do we have for publication, and what are their characteristics?

Science can also be published in the form of patents, although this is less common in the social sciences compared to fields like engineering or natural sciences.

In the social sciences, peer-reviewed journal articles are the primary mode of publication, as they ensure the research undergoes rigorous evaluation by experts before being published.

Books and book chapters are typically reserved for more comprehensive treatments or popularization of research, often authored by academics later in their careers when they’ve accumulated more experience and insights. Books, especially those from larger publishers, typically undergo peer review to ensure quality and relevance. Authors often engage in close dialogue with the main editor throughout the project, working collaboratively to ensure coherence and reader appeal across all parts of the book. Writing a book also involves coordination with multiple publishing staff responsible for various aspects of the process, from content editing to design and layout. This teamwork extends to the final stages, where production staff prepare the book for printing, ensuring a polished and professional result. Writing books is generally not the primary focus of social science researchers early in their careers, but rather a culmination of a lifetime of research.

In some institutions, presenting a full paper at a conference is mandatory if the university is to cover costs; posters and less formal presentations might not qualify. This policy varies depending on the source of the funding; for example, if you’re using your own research grants, there might be more flexibility. White papers, while used in industry for internal reports or to influence policy, are less common in academic publishing but can still provide a useful format for early-stage ideas or position papers. They often serve as a way to spark discussion on emerging topics.

On the topic of edited books, these often face criticism for being fragmented and poorly coordinated, as different authors contribute separate chapters, which may overlap or be too detached. Unfortunately, edited books are often seen as a quick way to get a book out. However, when edited properly, they can provide an excellent introduction to a new field, offering a range of perspectives in one volume. Still, one can often achieve the same understanding by reading a series of journal articles. You just have to find them yourself first. Both edited and single-authored books are becoming more expensive, as publishers count on limited sales—primarily to libraries—to recoup their costs. Authors usually receive minimal royalties, often in the range of 10–20%, but then researchers typically write books for recognition rather than financial gain.

Many scholars, especially those aiming for promotion, avoid books in favor of journal articles, as articles usually carry more weight in academia. However, established full professors with some additional time often turn to writing books, as they no longer need to focus on advancing their academic careers. In my case, I also see the need for a book to replace the numerous slides and compendiums I create from the course literature. It’s far more efficient and beneficial to consolidate all this material into a single, comprehensive volume.

Some researchers, particularly those who work closely with industry, prefer to write books because their audiences outside of academia tend to appreciate books more than journal articles. This reflects the reality that not only do few people outside academia read scientific papers in full, but a significant number of those within academia also tend to skim abstracts or conclusions rather than engage with the entire paper.

Alarmingly, a large proportion of published papers are never cited, not even once. While this is partly due to the sheer volume of articles being produced, it also points to systemic flaws that prioritize quantity over quality in academic publishing. The pressure to produce more papers, rather than focusing on impactful research, distorts incentives and contributes to this problem. We will explore these issues further in the following chapters, particularly in relation to the broader consequences for academic integrity and progress.

Despite the slow and sometimes frustrating pace of progress through academic articles, these publications are critical to the advancement of knowledge. Each study adds a small, but important, contribution to the broader understanding of society. To borrow Isaac Newton’s famous phrase, “standing on the shoulders of giants,” scientific and societal advancement is driven by the cumulative effect of many small steps. While progress can sometimes feel slow and even frustrating, each incremental contribution builds upon the last, forming the foundation for meaningful change. This steady advancement is vital for improving the societies we inhabit—and who wouldn’t aspire to contribute to a better world?

2.2.7.1 Choosing the Right Journal

We have previously spoken about how to find a scientific journal to publish in so this will only be a summary. We may start by searching for related research on Google Scholar, Web of Science, and Scopus to identify where similar work has been published. We can make note of the journals that frequently appear in our area of interest. Once we have a list of potential journals, we can check the journal’s reputation by using recognized ranking systems such as the Academic Journal Guide (ABS index) for business studies or NSD Norwegian Register for Scientific Journals, Series and Publishers, often known as The Norwegian List, especially popular in Nordic countries.

Review each journal’s aims and scope to ensure your research aligns with their focus. Also, verify the journal’s impact factor and h-index to assess its influence in the field.

A journal’s impact factor measures the average number of citations received by articles published in that journal over a specific period, indicating its influence within the academic community. The h-index reflects both the number of articles a journal has published and the number of citations each article has received, balancing quantity and impact. Together, these metrics help assess the significance and reach of a journal’s contributions to its field.

2.2.7.2 Following Submission Guidelines

When we have found the journal we are interested in, we check the journal’s submission guidelines and peer review process to ensure they are a good fit for our work. We then go to the webpage of the chosen journal and find this information under “For Authors,” “Author Instructions,” or a similar label.

When crafting a “For Authors” section for a scientific journal, the following topics are typically covered (Table 2.43):

Table 2.43

Topics covered by “for authors” on journal’s webpage

Topic

What it means

Examples

Manuscript submission guidelines

Instructions on preparing and submitting a manuscript

Formatting requirements, word limits, submission portals

Scope and focus

Description of the journal’s subject areas and types of articles accepted

Accepts original research, reviews, case studies in fields like biology, chemistry, etc.

Peer review process

Explanation of the peer review process and criteria for acceptance

Double-blind review, review timelines, acceptance criteria

Ethical standards

Guidelines on ethical considerations in research and publishing

Policies on plagiarism, conflicts of interest, IRB approval for human subject research

Data sharing policies

Expectations regarding data availability and sharing

Requirement for data availability statements, use of repositories like Dryad or Zenodo

Author responsibilities

Summary of the responsibilities of the authors

Ensuring data accuracy, acknowledging funding, outlining co-author contributions

Copyright and Open Access

Information on copyright policies and open-access options

Authors retain copyright, creative commons licensing, open access fees

Figures and tables

Requirements for the preparation and submission of figures, tables, and materials

High-resolution images, appropriate captions, supplementary material submission

Revisions and resubmissions

Guidance on addressing reviewer comments and resubmitting a revised manuscript

How to respond to reviewer feedback, resubmission deadlines

Contact information

Details on how to reach the editorial office for assistance

Email addresses, phone numbers, office hours for editorial queries

We need to carefully read through each section of the submission guidelines to understand exactly what’s required, especially when we’re early in our careers and just beginning to submit work. After spending months, or even sometimes years, on an article, it’s natural to feel eager to get it out the door—but rushing through the submission process can lead to mistakes and rejections.

When we overlook details, we risk sending off a weaker submission than our hard work deserves. It’s important to view this step as an integral part of the research process, deserving the same level of attention and care. So, we need to avoid cutting any corners.

2.2.7.3 Submitting the Article

Say we want to publish an article in the Journal of Business Research. We go to the webpage and look for a tab that says, “Authors here,” “Submission,” or something similar. In the case below, it says “Submit your article.” (Fig. 2.12).

Fig. 2.12

How to submit an article on a journal’s webpage

Once we are on the webpage, it asks us to select our role, asking whether we are visiting as reviewers, authors, or in some other capacity. This step helps the system direct us to the appropriate section based on our role, ensuring we can access relevant submission guidelines or reviewer resources accordingly (Fig. 2.13).

Fig. 2.13

Login into the journal’s webpage

The journal’s registration form requires the user to input various types of data. It begins by asking for login credentials, including a preferred username and a password, which must be entered twice following specific rules. The form then collects personal information, such as the user’s title, first and last name, with an option to include a middle name. Additionally, there is a field for indicating academic degrees and a preferred nickname if desired. Contact details are essential, so users are prompted to provide their primary and secondary telephone numbers, including country codes, and an optional fax number. An email address is mandatory, with a suggestion to add more than one to avoid spam filters.

The form encourages users to link their ORCID ID to help track their academic contributions across systems, ensuring recognition and reducing the chance of errors.

Note: What is ORCID ID?

An ORCID ID (Open Researcher and Contributor ID) is a unique, persistent identifier for researchers, designed to streamline the management of publication records and ensure accurate attribution of scholarly work across platforms. One major advantage of using ORCID is that many academic journals and repositories automatically link your publications to your ORCID ID, eliminating the need for manual updates for most of your work.

ORCID is a nonprofit organization headquartered in Bethesda, Maryland, United States, and operates under a mission of fostering global academic collaboration and transparency through its free service.

The organization is funded primarily by membership fees from institutions and universities and is governed by an independent board to safeguard its nonprofit mission. However, with a team of approximately 50 employees, concerns about long-term sustainability arise, particularly as the platform’s role in academic infrastructures continues to expand. Increased demands for digital security and operational scaling may require ORCID to explore additional funding strategies, potentially challenging its current noncommercial focus. While its mission today emphasizes open access and noncommercial goals, there is no guarantee this model will persist indefinitely.

Institutional affiliation is another key piece of information required, including the user’s position, the name of the institution, the department, and a full mailing address comprising street, city, state or province, postal code, and country. The form asks if the user is willing to serve as a reviewer for the journal and provides a simple yes or no option.

Additionally, users are prompted to specify their areas of expertise by selecting from a predefined list or by adding their own keywords. They must choose classifications that match their research focus, which helps the journal assign suitable reviewers. There are also optional fields for secondary contact details, such as a mobile number or an assistant’s contact information. Finally, the form requests the type of address provided, whether it is for work, home, or another purpose, to ensure the contact information is appropriately categorized.

Once we have completed the registration, we come to the part where we upload our manuscript (MS). However, this is not enough; the journal is likely to require a series of other documents as well. The Table 2.44 below provides some examples of what these may be:

Table 2.44

Types of documents to be uploaded to the journal’s webpage

Document

Length

Type

Purpose

Manuscript

Typically 5000–10,000 words

Word document (.doc/.docx) or PDF

Main content of the article, including abstract, introduction, methods, results, and discussion

Cover letter

1–2 pages

Word Document (.doc/.docx) or PDF

Provides a brief introduction to the study, its significance, and its relevance to the journal. Often includes declarations about originality and conflict of interest

Figures/images

Varies (high resolution)

JPEG, TIFF, PNG

Visual representation of data (graphs, charts, photos) used to support research findings

Tables

Varies based on data

Word, Excel, or embedded in the manuscript

Tabular data that cannot be easily included in the main body of the manuscript for clarity or due to formatting constraints

Supplementary data

Varies

PDF, Word, Excel

Additional materials such as datasets, appendices, or extra analyses not included in the main article but useful for review or as supplementary information

Ethics approval letter

1–2 pages

PDF

A document from the relevant ethics committee approving the study, required for research involving human or animal subjects

Author contributions

1 page

Word document (.doc/.docx) or PDF

Details each author’s specific contributions to the research, ensuring proper attribution and responsibility

Conflict of interest form

1 page

PDF or online form

Discloses any potential conflicts of interest related to the research or its publication

Funding information

1 page

Word Document (.doc/.docx) or online form

Lists funding sources that supported the research, ensuring transparency

Copyright transfer agreement

1–2 pages

PDF or scanned document

Transfers the copyright of the article from the authors to the publisher, allowing for legal publication

A word of advice when working in a group: the journal’s webpage account is linked to a single person, and that person bears full responsibility for everything uploaded. While it’s a good idea to divide tasks among group members, never share the login credentials. In my experience, this can lead to issues—if someone uploads something incorrectly, it is the account holder who will be held accountable. Therefore, it is essential to maintain control over the account to avoid any unintended consequences.

2.2.7.4 Writing a Cover Letter

A good cover letter is concise, professional, and tailored to the journal to which the manuscript is being submitted. It should begin by introducing the title of the manuscript and briefly stating its main contribution to the field. The letter should explain why the research fits within the scope of the journal and why it will be of interest to its readership. Authors should highlight the novelty or significance of their findings and how their work advances the current understanding of the topic. It’s important to mention if the manuscript has been reviewed or rejected elsewhere, to ensure transparency.

The cover letter should also state that the manuscript is original, unpublished, and not under consideration by another journal. Any potential conflicts of interest should be disclosed, ensuring compliance with the journal’s ethical guidelines. The authors can mention if specific reviewers are recommended or if there are individuals they wish to exclude due to conflicts. Briefly, the cover letter can acknowledge funding sources or institutional support. Finally, the tone should remain polite and respectful, thanking the editor for considering the manuscript. Be concise, not overly wordy.

2.2.7.5 Providing the Ethics Approval Letter

An ethics approval letter should be clear, formal, and issued by the appropriate ethics review board or committee. It should begin by identifying the title of the research study and the names of the researchers involved. The letter should confirm that the study has been reviewed and approved in accordance with the ethical standards of the institution or committee. The approval number or reference code should be included, allowing the journal to verify the approval if needed.

The letter should mention the type of study (e.g., human or animal subjects, clinical trials) and confirm that participants were treated according to ethical guidelines. It should also indicate that informed consent was obtained from all participants or, if not applicable, explain why consent was not required. If any changes to the study occurred after approval, the letter should specify that these modifications were also approved. The letter must be signed by the chair or authorized representative of the ethics committee. Finally, it should include contact information for the ethics committee, in case the journal needs further clarification.

An example could look something like this:

Example: Institutional Review Board (IRB) Ethics Approval LetterDate: October 17, 2024

To Whom It May Concern,

This letter is to certify that the research study titled “The Impact of Artificial Intelligence on Collective Decision-Making” has been reviewed and approved by the Institutional Review Board (IRB) of Lederhosen Technical University accordance with the ethical guidelines and regulations governing research involving human subjects.

The study, led by Principal Investigator Prof. Dr. Wunderbar von Schnitzel, was reviewed under IRB protocol number 2024-IRB-456N. It was determined that the study meets all ethical requirements, including informed consent from participants and the protection of participant confidentiality.

Any modifications to the study that were proposed during the course of the research were reviewed and subsequently approved by the IRB. Should any further changes arise, they will be promptly submitted for review.

For further information or verification, please contact the IRB office at IRB@LTU.​edu or by phone at (+999-234-567-8901).

Sincerely,

Prof. Dr. Wunderbar von SchnitzelChair, Institutional Review BoardLederhosen Technical University

2.2.7.6 Clarifying Author Contributions

For simplicity’s sake, let’s consider an example from an article where yours truly was a co-author. Typically, the order of authors’ last names reflects the contribution each person made, with the first author usually having contributed the most—unless the names are listed alphabetically.

In this case, however, we can see right away that the names were not arranged alphabetically, indicating a different approach to the order of contributions:

Oubrich, M., Hakmaoui, A., Benhayoun, L., Söilen, K. S., & Abdulkader, B. (2021). Impacts of leadership style, organizational design and HRM practices on knowledge hiding: The indirect roles of organizational justice and competitive work environment. Journal of Business Research, 137, 488–499.

For this article, my assessment of the workload would be as in table. Other members of the group may have their own assessments, but in my experience, significant disagreements are rare. If such disagreements do arise, it’s likely that collaboration with those individuals won’t continue in the future (Table 2.45).

Table 2.45

Assessment of author contributions

Author

Position

Area of responsibility

% of workload

M. Oubrich

Full professor

Conceptualization, theoretical framework development, data collection, and primary manuscript drafting

25%

A. Hakmaoui

Full prof.

Statistical analysis, interpretation of results, literature review, data collection

25%

L. Benhayoun

Associate prof.

Statistical analysis, data coding, interpretation of results, literature review, data collection

25%

K. S. Söilen

Full prof.

Statistical analysis, interpretation of results, literature review, supervising, review, and critical revisions of manuscript, data collection

20%

B. Abdulkader

PhD

Data coding, formatting of tables/figures, manuscript proofreading, data collection

5%

It’s not uncommon that everyone will have to help with data collection. The position of the member may play a role as a factor of power in reality, but it should not be so in an ideal world. Sometimes, the PhD student will do most of the work, especially if it is part of a compilation PhD to be defended, in which case that would even be a prerequisite. You cannot submit a PhD with articles where most of the work is done by others, even though that does, of course, happen.

For this article, I did less work than most of my co-authors, in my opinion. This was largely due to my having a number of other engagements at the same time, including teaching. We know each other from before, as we have attended the same conferences over the years and work in the same area of study. Group members are usually chosen based on factors like their ability to collaborate well and the specific expertise they bring, which complements the rest of the team. If an article project is successful, it is not uncommon to continue working with the same team several times.

However, it’s also very healthy to change team members completely, as you may learn more that way. I have written most of the articles alone, and that I think is a good idea to clearly demonstrate that it was your own work. But working in groups can also be enriching, especially now with Zoom, where meetings can be very effective even when team members are in different countries, as long as time zones align well.

If you consistently work in large teams or always collaborate with others, it can become challenging to fairly assess your specific contributions to the scientific work. Collaborating in pairs is common and can work well, but it becomes problematic if your partner is always listed as the first author, potentially overshadowing your role in the research.

2.2.7.7 Conflict of Interest Disclosure

A conflict of interest (COI) form is crucial for maintaining transparency in academic publishing. It requires authors to disclose any financial, personal, or professional relationships that could influence their research or its interpretation.

The form should include details about funding sources, whether the study sponsor had any involvement in the research design, data collection, or publication decisions. Authors must also declare if they or their immediate family members have financial ties (e.g., stock ownership or consultancy) with companies or organizations that may benefit from the publication’s outcomes.

Nonfinancial conflicts, such as personal relationships or academic competition, should also be disclosed. The form may ask for a statement confirming that there were no conflicts of interest if applicable. Authors should ensure full transparency, as undisclosed conflicts can undermine the credibility of the research. It’s essential to sign and date the form, and in some cases, all co-authors may need to complete individual COI forms.

The example:

Example: “Conflict of Interest Declaration, Date: October 17, 2024

To the Editors of the Journal of Business Research,

We, the undersigned authors, hereby declare that there are no conflicts of interest associated with the manuscript titled, “Impacts of Leadership Style, Organizational Design, and HRM Practices on Knowledge Hiding: The Indirect Roles of Organizational Justice and Competitive Work Environment.”

Funding: This research was funded by the National Science Foundation under grant number NSF-12345-N. The funding agency had no involvement in the study design, data collection, analysis, or decision to publish this manuscript.

Financial Conflicts: Prof. Dr. Günther Kartoffelsalat serves as a consultant for Strudel Systems AG. However, this role had no bearing on the content, analysis, or conclusions presented in this study. None of the other authors have financial interests or relationships that could be perceived as having influenced the work presented in this manuscript.

Nonfinancial Conflicts: We confirm that none of the authors have any nonfinancial conflicts, such as personal or professional relationships that could be seen as competing interests.

Statement of Transparency: We affirm that all aspects of the research were conducted independently and transparently, and we uphold the ethical standards of the journal and the academic community.

Sincerely,

Prof. Dr. Günther Kartoffelsalat

Frankfurter School of Sausage Engineering, Email: Gunther.​Kartoffelsalat@FSSE.​edu” End of letter.

It is important to emphasize that both marital and supervisory relationships must be reported as potential conflicts of interest in academic publications. While these are nonfinancial conflicts, they can still impact the perception of fairness, objectivity, and the distribution of credit among co-authors.

A marriage between co-authors or a boss-subordinate dynamic may influence decisions made during the research process or in the attribution of contributions. Even if no actual bias occurred, transparency in reporting these relationships helps avoid any appearance of undue influence.

Case: Cooperating with your spouse

Interestingly, my wife and I have never collaborated on a scientific project, despite both being professors in the social sciences. While I work in economics and business studies, her focus is on environmental sciences, and although we often discuss our research, we’ve never published together or shared a conference presentation—except once in Seoul, where we presented different papers at the same seminar. We do occasionally review each other’s work, but even that’s rare; I asked for her feedback specifically on the section in Chap. 3 related to research funding for this book, as she has extensive experience securing top-tier grants with over 30 years of experience in writing proposals.

The reason we don’t collaborate, I think, is that we view each other as independent researchers rather than as a scientific team. There’s something about maintaining separate academic identities that feels important—it preserves the sense of individual contribution and avoids any ambiguity about who did what. Plus, it’s refreshing to have our own separate projects; with so much shared in family life, it’s nice to keep something distinctly ‘yours.’ Of course, there are likely other reasons couples choose not to collaborate, but for us, this approach feels right.

That said, cooperation in research between spouses is quite common, especially when both individuals share similar academic interests or professional backgrounds. Working together can foster a supportive environment where ideas are freely exchanged, and different perspectives are integrated. Such collaborations can also enhance productivity, as spouses often understand each other’s work style and can efficiently divide responsibilities.

Issues to be discussed:

This case invites readers to reflect on the benefits and challenges of collaborating professionally with a spouse. One key aspect is the unique dynamic that working closely with a spouse might bring—leveraging each other’s expertise and shared interests to potentially enhance productivity and innovation. However, maintaining professional boundaries can become more challenging in such partnerships, raising concerns about how shared projects might complicate family life and blur the line between personal and professional roles. What do you think about this balance?

Another question to consider is why some couples in academia, even those in complementary fields, might choose not to collaborate. In our case, my wife and I highly value our separate identities as researchers and the independence that comes from pursuing distinct academic achievements. By focusing on individual projects, we allow each other’s careers to flourish independently, fostering a sense of personal accomplishment and avoiding the challenges of constant professional interdependence. What are your thoughts on maintaining this independence?

An interesting angle is the occasional support we provide for each other’s work without formal collaboration, such as offering feedback on grant proposals or sharing insights informally. This “partial collaboration” allows each of us the freedom to pursue our own projects while still benefiting from the perspective of someone who deeply understands the demands of academic work. How does this type of informal support differ from full collaboration, and how might it be beneficial without the complexities of joint authorship or shared projects?

It’s also worth discussing the practical and strategic considerations behind our choice not to collaborate formally. Our fields—economics and environmental science—offer both opportunities and challenges for joint work. Interdisciplinary collaboration could bring fresh insights but might also introduce difficulties in aligning methodologies or navigating publishing and grant-seeking processes. What strategies would you suggest for balancing these opportunities and challenges?

Finally, there’s the question of how discussing collaboration might impact our relationship or future decisions. I admit I haven’t explicitly asked my wife about her perspective on this topic, which raises the issue of whether partners should periodically reassess their approach to professional collaboration as their careers and circumstances evolve. While this is difficult for you to discuss now when your journey has just started, it would be interesting to hear what you have to say in 20–30 years.

Disclosing these relationships allows the journal to assess whether they could have affected the research or publication process. Failing to disclose such conflicts could undermine the credibility of the work and violate ethical standards. Journals generally prefer full transparency to safeguard the integrity of the peer review process. Therefore, just as financial conflicts must be reported, personal and professional relationships like marriage or supervisory roles should be declared to ensure academic honesty and trust.

If a conflict-of-interest statement is not provided or if false claims are made and later discovered, the consequences can be serious. The most immediate action could be retracting the published paper, which can harm both the research and the authors’ reputations. Such misconduct can lead to a significant loss of trust within the academic community, affecting future collaborations and funding opportunities. Institutions or journals may launch ethical investigations, and in severe cases, legal action could follow. The authors involved may also face damage to their professional reputations, potentially impacting career progression or job prospects. Additionally, they might be banned from peer-reviewing or serving on editorial boards of journals. Funding bodies could withdraw financial support for ongoing or future projects if funding bodies are involved. Academic institutions might sometimes take disciplinary action, ranging from warnings to dismissal.

The same goes for any lack of reporting for funding Information when requested or relevant.

2.2.7.8 Understanding Post-Submission

We come now to the end. After we press the submit button for a journal article, the timeline and outcomes can vary, but the process generally follows several key steps.

First, the submission enters the editorial system, where it is assigned to an editor, often within a few days to a week. The editor’s initial task is to conduct a preliminary review, checking whether the manuscript fits the scope of the journal and meets basic standards of quality. This initial stage might result in a desk rejection if the manuscript is deemed unsuitable, and this decision typically happens within a couple of weeks. We then get an email that says our paper has been “rejected.”

If the editor decides the paper has potential, it will be sent for peer review, a process that involves selecting two or more reviewers who are experts in the field. In most cases, when submitted to a quality journal, it will even be “blinded,” which means that the editor will take away the name and any information that can identify the authors before the MS is sent to reviewers.

The time it takes to secure reviewers can vary, and finding appropriate reviewers may take from a week to a month, or more. Sometimes reviews say yes, but forget and even go back on their decisions after many reminders. This means that more time is lost. Once reviewers are secured, they are usually given 4 to 6 weeks to read the manuscript and provide their feedback, but it can also take months.

After the peer reviewers submit their reports, the editor will review the comments and make a decision. The possible outcomes include acceptance (rare on the first round), minor revisions, major revisions, or rejection. For minor revisions, authors typically get a couple of weeks to address the reviewers’ comments, while major revisions might allow a month or more. If revisions are required, the manuscript will often be sent back to the same reviewers for further review.

The reviewers will often have the possibility to send some comments to the authors and other comments to the editor if needed, maybe to explain a bit more his or her reasoning.

The time it takes to complete this cycle can vary widely, but on average, it takes around 3 to 6 months from submission to the final decision. The length of the reviewers’ responses can range from brief, focused comments to detailed feedback that can be several pages long, especially if major revisions are suggested.

Good reviewers provide you with a spreadsheet where they indicate the page and sometimes the line number that their question or comment refers to, along with their concerns. You then get the opportunity to respond to each comment individually.

If no spreadsheet is provided, make it a habit to create one yourself. Include all the comments in the spreadsheet or a table and write your responses next to them. Note that you don’t have to agree with all the comments, but if you disagree, you’ll need strong counterarguments. What’s important is that you address each comment and remain constructive, perhaps a bit humble, especially when disagreeing (Table 2.46).

Table 2.46

Authors’ answers to reviewers

Reviewer comment

Page/line reference

Reviewer concern

Response

1. Clarify the relationship between leadership style and organizational justice

Page 3, line 15

The connection between leadership style and organizational justice is not clearly explained in the introduction. How does leadership style directly impact perceptions of justice?

Thank you for this comment. We agree that the explanation of the link between leadership style and organizational justice can be strengthened. We have added a more detailed explanation on page 4, referencing additional studies that demonstrate how leadership style influences employee perceptions of fairness within organizations

2. Statistical analysis methodology is unclear

Page 7, line 23

The description of the statistical methods used for the mediating variable “organizational justice” is insufficient. Can you provide more details?

We appreciate your feedback. We have expanded the section on statistical methodology (page 8, lines 22–35) to provide a more thorough explanation of the steps taken to measure the mediation effect of organizational justice. Additional details regarding the tools and regression models used are now included

3. Literature on HRM practices could be updated

Page 2, line 30

The literature on HRM practices seems slightly outdated. Have you considered more recent studies from the past 2–3 years?

Thank you for pointing this out. We have updated the literature review to include several recent studies published between 2019 and 2021 (see pages 3–4). These new references enrich our discussion of HRM practices and their role in knowledge hiding and organizational behavior

4. Disagree with the interpretation of competitive work environment

Page 10, line 8

The interpretation of how competitive work environments impact knowledge hiding appears oversimplified. This factor likely interacts with several other elements. Can you re-examine this?

We understand your concern. After revisiting the literature and data, we have revised the interpretation of the competitive work environment’s role. We now discuss on page 11 how it interacts with both leadership style and organizational justice, acknowledging that competitive environments can amplify or mitigate the effects of these factors

5. Missing explanation for sample selection

Page 6, line 10

The explanation for how the sample was chosen for the quantitative analysis is lacking. Can you provide details on why these firms were selected?

We have added more detailed information on sample selection in the methodology section (page 7, lines 11–18). The firms were chosen based on their relevance to the research question, ensuring a diverse range of industries, sizes, and leadership styles to accurately represent the sectors where knowledge hiding behaviors may be more prevalent

If your paper is accepted after revisions, it will enter the production phase, where formatting, proofreading, and other technical adjustments are made before publication. At this stage, the time to online or print publication depends on the journal’s backlog and production process, which can take anywhere from a few weeks to several months. (For books it can take more than half a year!)

In total, from submission to final publication, the entire process can take anywhere from 6 months to a year, and sometimes even longer. My worst example is 3 years, but I have also had an example when it took 2 years, only to be rejected at the end.

Rejections are an inevitable part of being a researcher, so it’s best to get accustomed to them early on. If it’s any consolation, every researcher—no matter how experienced or renowned—faces rejections when submitting articles to prestigious journals or book manuscripts to reputable publishers. In fact, rejection is a crucial and valuable aspect of the scientific process. When carried out properly, it serves as a filter that prevents poorly conducted, uninteresting, or flawed research from reaching the public domain. Rejection also encourages us to refine our work, often pushing us to improve both the clarity and rigor of our arguments. This doesn’t mean that all rejections are justified—or objectively correct—but most are.

What’s more, rejections can be seen as opportunities for intellectual growth. The feedback provided by reviewers can highlight areas we may have overlooked and can guide us toward stronger and more impactful revisions. It’s important to remember that even highly cited papers or groundbreaking research often face multiple rejections before being published. Persistence and a willingness to learn from critiques are key to long-term success in academic publishing. So, while rejections may sting in the short term, they ultimately contribute to elevating the quality of both individual work and the research community as a whole.

Journals often offer early online access to accepted articles before they appear in print, so you might see your article online within a few months after acceptance, but it could take longer for it to appear in the printed issue. Then you have something exciting to look forward to, and by this point, you truly deserve it. In the academic world, few things are as gratifying as seeing your work finally published.

References

Georgescu-Roegen N (1960) Mathematical proofs of the breakdown of capitalism. Econometrica 28(2):225–243. https://​doi.​org/​10.​2307/​1907718Crossref

Georgescu-Roegen N (1966) Analytical economics: issues and problems. Harvard University Press, Cambridge, MACrossref

Keynes JM (1971–1989) The collected writings of John Maynard Keynes, vol 1–30. Cambridge University Press

Oubrich M, Hakmaoui A, Benhayoun L, Söilen KS, Abdulkader B (2021) Impacts of leadership style, organizational design and HRM practices on knowledge hiding: the indirect roles of organizational justice and competitive work environment. Journal of Business Research 137:488–499Crossref

Russell B (1903) The principles of mathematics. Cambridge University Press

Selected Readings

Albert H (1991) Traktat über kritische Vernunft. Mohr Siebeck

American Psychological Association (2020) Publication manual of the American Psychological Association, 7th edn. American Psychological Association

Babbie ER (2021) The practice of social research, 15th edn. Cengage Learning

Bachelard G (2002) La formation de l’esprit scientifique: Contribution à une psychanalyse de la connaissance objective. Presses Universitaires de France

Berthelot J-M (1996) Épistémologie des sciences sociales. Presses Universitaires de France

Bortz J, Döring N (2016) Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler, 5 Auflage edn. Springer

Bryman A (2016) Social research methods, 5th edn. Oxford University Press

Calof J, Söilen KS, Klavans R, Abdulkader B, El Moudni I (2022) Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses. Technological Forecasting and Social Change 178:121561Crossref

Creswell JW, Creswell JD (2018) Research design: qualitative, quantitative, and mixed methods approaches, 5th edn. SAGE Publications

Desrosières A (2008) La politique des grands nombres: Histoire de la raison statistique. La Découverte

Esser H (1999) Soziologie: Allgemeine Grundlagen. Campus Verlag

Flick U (2018) An introduction to qualitative research, 6th edn. SAGE Publications

Fowler FJ (2014) Survey research methods, 5th edn. SAGE Publications

Gerring J (2017) Case study research: principles and practices, 2nd edn. Cambridge University Press

Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R (2011) Survey methodology, 2nd edn. Wiley

Krathwohl DR, Smith NL (2005) How to prepare a dissertation proposal: suggestions for students in education and the social and behavioural sciences. Syracuse University Press

Maxwell JA (2013) Qualitative research design: an interactive approach, 3rd edn. SAGE Publications

Opp K-D (2020) Methoden der empirischen Sozialforschung, 3 Auflage edn. Springer VS

Passeron J-C (2006) Le raisonnement sociologique: Un espace non poppérien de l’argumentation. Albin Michel

Popper KR (2005) The logic of scientific discovery. RoutledgeCrossref

Punch KF (2014) Introduction to social research: quantitative and qualitative approaches, 3rd edn. SAGE Publications

Quivy, R., & Van Campenhoudt, L. (2017). Manuel de recherche en sciences sociales (5 ed.). Dunod.

Saldaña J (2021) The coding manual for qualitative researchers, 4th edn. SAGE Publications

Saunders M, Lewis P, Thornhill A (2019) Research methods for business students, 8th edn. Pearson

Schäfer B, Kuhl R (2019) Einführung in die empirische Sozialforschung, 9 Auflage edn. Beltz Verlag

Yin RK (2018) Case study research and applications: design and methods, 6th edn. SAGE Publications

Footnotes

1

Example of Global Bibliometric Review, see Calof, J., Söilen, K. S., Klavans, R., Abdulkader, B., & El Moudni, I. (2022).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

K. Solberg SöilenThe Researcher's Journeyhttps://doi.org/10.1007/978-3-031-91565-9_3

3. The Academic Ecosystem

Klaus Solberg Söilen1  

(1)

School of Business, Halmstad University, Halmstad, Sweden

Klaus Solberg Söilen

Email: klaus.solberg_soilen@hh.se

If science were solely about developing theories and conducting research, life as a scientist would be much simpler. However, scientists must navigate a complex landscape of academic institutions and organizations, each filled with individuals who, while often well intentioned, have diverse interests and preferences that can support—but even hinder—their work. This environment, which we will explore in detail in this chapter, is what we call the academic ecosystem. In this chapter, we will break down its components and examine how each part affects the work of scientists.

There have been some books and reports on similar or overlapping topics, but to my knowledge, no book attempts to provide a comprehensive perspective covering all parts. For instance, “The Academic Ecosystem: A Perspective on Faculty Roles and Rewards” by Christenson and Leslie examines the roles of faculty members in the academic system, highlighting the rewards and challenges they face.¹ Similarly, “The Academic Book of the Future” edited by Lyons and Rayner explores the shifting nature of academic publishing and its profound impact on the scholarly community.² Another perspective is offered by Roger King in “The University in the Global Age,” where the impact of globalization on universities is critically analyzed, showing how these institutions adapt and function in a rapidly changing world.³ Meanwhile, securing research funding—a critical aspect of the academic ecosystem—is addressed in Aldridge and Derrington’s “The Research Funding Toolkit,” which provides practical guidance for planning and writing successful grant applications.⁴

The academic ecosystem consists of several key components, or elements, that work together to support the advancement of knowledge. In terms of organizations, it includes universities, research institutions, government funding bodies, academic journals, and accreditation agencies. Objects in this ecosystem refer to the physical infrastructure, such as university buildings, research labs, libraries, and technology centers, all of which provide the necessary environment for learning and research. The functions within the ecosystem are defined by academic positions like professors, lecturers, researchers, and supporting personnel such as administrative staff, lab technicians, and IT specialists, who facilitate the smooth operation of academic institutions. Additionally, processes like peer review, grant allocation, and curriculum development play a critical role in maintaining academic standards and ensuring the dissemination of knowledge. These processes serve as the backbone for innovation and continuous improvement in research and education.

The general idea behind having academia in any country in the first place is to drive innovation, foster critical thinking, and contribute to the country’s social and economic development by producing skilled professionals and research that can address societal issues. This role is usually protected and encouraged by laws and regulations, which ensure academic freedom, provide public funding for research, and maintain quality standards in education and research through accreditation and oversight.

Additionally, the academic ecosystem includes students (yes, those), who are integral as they represent both the future workforce and the next generation of scholars. External partners, such as industries and governments, collaborate on research projects, contributing to the ecosystem’s broader impact. These actors can be categorized as “internal” and “external” stakeholders. In this way, current students are considered internals, while alumni, though external after graduation, often maintain a connection to the institution through alumni organizations. These networks keep them engaged and, particularly in the USA with traditions like “homecoming,” sometimes bring them back to contribute to the academic community. This dynamic interaction between internal and external actors ensures that the academic ecosystem remains interconnected and relevant (Table 3.1).

Table 3.1

Components of the academic ecosystem

Aspect

Components/elements

Example

Organizations

Universities, research institutions, government funding agencies, academic journals, accreditation bodies

Example: Helsinki University, Max Planck Institute, National Science Foundation (NSF), Elsevier Journals

Objects (infrastructure)

University buildings, research labs, libraries, technology centers

Example: MIT’s Media Lab, Oxford University’s Bodleian Library, CERN’s particle accelerator facility

Functions

Academic positions (professors, lecturers, researchers), supporting personnel (administrative staff, lab technicians, IT specialists)

Example: Full Professors in physics, PhD students conducting research, university IT and administrative staff

Processes

Peer review, grant allocation, curriculum development, research dissemination, collaboration facilitation

Example: Grant allocation by ERC, peer review process for Nature journal, curriculum development at Harvard

Purpose

To advance knowledge, drive innovation, foster critical thinking, and contribute to social and economic development

Example: Developing new technologies in renewable energy through university-industry collaboration

Laws and regulations

Academic freedom protections, public funding for research, quality control through accreditation and government oversight

Example: U.S. Higher Education Act, EU Horizon 2020 research funding program

Financing

Funding from the state based on student enrollment, research grants from public or private sources, industry collaborations, tuition fees

Example: State funding per student at public universities, ERC research grants, corporate-sponsored research

Stakeholders

Students (future scholars and professionals), external partners (industries, governments collaborating on research)

Example: Collaborative research project between Stanford University and Google on AI advancements

This chapter delves into the various elements of the academic ecosystem, exploring the structure and roles within universities, from the vice-chancellor to professors, and the significance of administration in ensuring smooth operations. It covers the educational journey, discusses university diplomas and their career implications, and highlights the role of research centers. The chapter also examines attending academic conferences and the importance of colloquiums for knowledge exchange.

The logic of the chapter follows the following structure (Fig. 3.1):

Fig. 3.1

Logical structure of this chapter

A key focus is developing a publication strategy, followed by insights on collaborative research models and the challenges of building both research teams and multidisciplinary collaborations. It guides academics on career building, addressing topics like service roles, navigating tenure, creating a research identity, and securing sabbaticals. Additionally, the importance of time management and managing external academic appointments is emphasized.

The role of bibliometrics in academic evaluation is also discussed, followed by strategies for securing research funding and grant writing techniques. Finally, the chapter addresses the place of science in society, including its impact on innovation, policy-making, public engagement, and the contributions of social scientists to policy research.

3.1 University Structure

First, a definition. A university (Swedish: universitet, French: université, German: Universität) typically offers a wide range of undergraduate and postgraduate programs, including research degrees (PhD), and is often focused on both teaching and research. A college (Swedish: högskola, French: école, German: Hochschule) may focus more on undergraduate education and specialized professional programs, with less emphasis on research and fewer postgraduate options as compared to universities.

The university or college is filled with people who perform various functions. Initially, it can be difficult for a new student arriving on campus to understand these differences. For the student, the primary focus is on the program and courses they have been accepted into, how to begin, where the courses will be held (time and place), and obtaining the course material, preferably before the start if that is the instruction. At this initial phase, the student is less concerned with who will be teaching the course or whether the instructor is any good, but that quickly becomes clear after one or two lectures. The focus now is on how to “pass the exam” or, perhaps more importantly, how to get a good grade in the course. So, students attend classes, complete assignments, and try to prepare as best as they can for the exam.

As the student continues to attend campus, they will start to wonder about the different categories of roles, their functions, and, perhaps more importantly, who makes the decisions (Table 3.2).

Table 3.2

Roles, functions, and decision-making at the university

Role/position

Specific functions

Influence

Decision-making powers

Chancellor/President

The Chancellor or President serves as the chief executive officer of the university, responsible for overseeing its overall direction and operational management. They collaborate closely with the board of governors or trustees to shape long-term strategic goals, drive institutional growth, and secure funding. Additionally, the Chancellor/President represents the university at the highest levels in government, corporate, and public forums, ensuring the institution’s interests are well protected and promoted developments

Strong influence on academic priorities, large-scale decisions, external partnerships, and funding allocations. Shapes the university’s public profile

Major decision-maker on large-scale initiatives, budget approvals, and academic priorities. Has final say in university-wide policies

Vice-Chancellors/Pro-Vice-Chancellors

The Vice-Chancellor sets the university’s overall vision and strategy, often in collaboration with a board that includes key figures from outside academia, such as leaders in politics or business. This role also involves representing the university in public forums, government discussions, and corporate partnerships, ensuring that the institution remains aligned with societal and economic developments

High influence over research agendas, interdisciplinary initiatives, and academic programs. Acts as a liaison between the Chancellor and faculties

Power to allocate research funding, implement policies, and enforce institutional strategies across faculties. Decisions affect university-wide academic structures

Deans

Manages faculties or schools (e.g., Faculty of Science, business). Decides on curriculum design, faculty recruitment, and resource distribution for both teaching and research

High influence on resource allocation between teaching and research. Directs faculty and department focus

Power over hiring and promotions, curriculum changes, and securing external funding for departments. Makes decisions on program offerings and course cancellations

Department chairs

Oversees day-to-day operations of academic departments, faculty performance, research proposals, and departmental budgets

Direct influence on departmental faculty workloads, research directions, and faculty-student dynamics

Makes decisions on teaching loads, research projects, and course offerings. Manages departmental budgets and approves research proposals

Faculty

Delivers academic teaching, conducts research, and supervises students. Contributes to knowledge production and departmental reputation

High influence on academic output, curriculum design, and teaching methods. Shapes student learning experiences

Decides on course content, assessments, and student evaluations. Often contributes to departmental policies and research directions

Administrative staff

Handles course registration, maintains academic records, processes exams, and manages logistics for events and operations. Supports faculty and students

Indirect influence on the smooth functioning of the university’s daily operations, facilitating teaching and research processes

Decision-making in operational logistics, such as scheduling classes, processing grades, or managing financial aid for students. Supports institutional processes

HR department

Manages recruitment, promotions, and faculty/staff benefits. Implements institutional employment policies and handles faculty tenure processes

Influence on hiring practices, faculty development, and career progression. Collaborates with leadership on recruitment strategy

Decides on hiring protocols, promotions, and tenure track processes in consultation with department heads and university leadership

Students

Participate in academic programs, attend courses, engage in research (PhD/postgrad), and provide feedback on teaching and services

Moderate influence through course evaluations, student government, and feedback on university services and programs

Decision-making powers through student government and committees, contributing to university policies, activities, and services that affect student life

Student government

Represents student interests in university decision-making processes, organizes student activities, and advocates for changes in student policies

Direct influence on student life policies, student services, and academic support programs. Liaises with university administration to advocate for student needs

Can influence decisions on student services, policies affecting students, and campus activities through representation in university governance

The roles and hierarchy within a university can vary significantly from country to country and even between institutions within the same nation. The table above provides a general overview of these roles, but it’s important to note that the structure is not universal and should be seen as an estimate rather than a strict guide.

For example, in some countries, like Germany, the rector (similar to the president) often holds more academic influence than a chancellor, who may play a more symbolic or ceremonial role. The vice-chancellors or prorectors in Germany typically focus on specific areas, such as research or student affairs, and have considerable influence over those domains.

In France, the university system is more centralized, and the role of the Président (president) is similar to that of the German rector but with more involvement in day-to-day academic operations. French universities also place a strong emphasis on Faculté (faculty), and deans often wield significant power within their respective faculties, particularly in managing budgets and overseeing research priorities.

In the Nordic countries, the rector or vice-chancellor generally serves as the highest academic authority, with a strong focus on maintaining academic freedom and fostering collaboration with industry and government. Nordic institutions often emphasize consensus-building, and faculty members may have more direct input in university governance compared to other systems. For instance, in Sweden and Finland, student unions also play a formal role in university governance, particularly in decisions affecting student welfare and academic programs.

Additionally, some institutions, particularly in Denmark and Norway, have developed flatter hierarchies, where deans and department heads share more responsibility in decision-making, with a greater emphasis on interdisciplinary collaboration. In all these contexts, the roles may differ depending on whether the institution is research-focused or more teaching-oriented.

Ultimately, these examples demonstrate that while general roles such as chancellor, vice-chancellor, and dean exist across various systems, their functions and levels of influence are deeply shaped by national traditions, governance models, and each institution’s unique culture.

Above the vice-chancellor, a governing board oversees the university’s operations. In some countries, the members of these boards are partially or entirely politically appointed, reflecting the influence of political systems on higher education at local, national, or combined levels. The extent of this political involvement varies, with some boards representing broader societal interests, while others align more closely with government agendas. However, these boards are often slow to change direction, even under a new government, as replacing members takes time and typically occurs only when terms expire or vacancies arise. New governments may choose not to interfere with existing boards immediately but rather appoint their preferred members as opportunities emerge. This structure can significantly impact a university’s autonomy, especially when board decisions prioritize political objectives over academic considerations (Table 3.3).

Table 3.3

Degree of political control in governing boards (Support for these claims can be found in OECD (2019), Capano and Piattoni (2021), Klemperer (2021), Fielden (2010), and Lane and Johnstone (2013))

Rank

Country

Degree of political control

Key features of university governance

1

Sweden

High

University boards and vice-chancellors are directly or indirectly appointed by the government, leading to significant political influence

2

France

Moderate

Universities are public institutions with oversight by the state; internal governance includes elections by faculty and staff

3

Germany

Low

Universities are highly autonomous; governance involves elected rectors or presidents, with minimal state interference

4

United Kingdom

Very low

Autonomous governance with councils or boards of governors appointing vice-chancellors based on merit; minimal outside political involvement

5

United States

Minimal

Universities operate independently; boards of trustees or regents appoint leadership. Public institutions may have some state-appointed members

Governing boards of universities and colleges in Sweden typically include representatives from faculty, students, and external stakeholders, reflecting a tradition of inclusivity and public oversight. In France, university boards often consist of faculty members, students, administrative staff, and government-appointed representatives, emphasizing alignment with national education policies. Germany typically features university governing boards that include academic staff, external experts, and government representatives, with a focus on balancing academic freedom and state regulation. In the UK, governing bodies are composed of independent members, faculty representatives, and occasionally student representatives, emphasizing accountability and alignment with institutional goals. In the USA, boards of trustees or regents are usually dominated by external members, often alumni or donors, with a focus on fundraising, governance, and ensuring institutional independence.

This governing system does not eliminate the risk of internal political interference, which, from an ideological perspective and as a threat to science, can be just as problematic. Political oversight, however, is not inherently negative; it can also help educational institutions align more quickly with pressing societal challenges and priorities.

You may wonder why the young and upcoming researcher should have an idea about all of this. Well, for one thing, this environment will soon become their professional home, and secondly, it is very common today not only to change employers within a single country but also to find opportunities in other countries. This is made possible because science is universal, as it should be. This isn’t anything new either—scientists have always traveled to new universities, whether it was Tycho Brahe going to Prague or scholars in medieval times settling in the Latin Quarter in Paris, named so because that’s where the scholars lived (and spoke Latin).

3.1.1 From Vice-Chancellor to Professors

Students are generally aware that there is a position like the vice-chancellor, but they often don’t fully understand what this role entails, except that it’s important. Moreover, while they may notice that teachers and researchers hold different titles, it’s unlikely that they will question or delve into these distinctions—at least, that’s been my experience.

You typically find the president or chancellor at the top of the organizational hierarchy, followed by vice-chancellors or deans, who oversee various faculties or departments. Below them are department chairs, who manage specific academic units, and beneath them are professors, lecturers, and researchers. The academic hierarchy includes full professors, associate professors, assistant professors, adjunct lecturers, and postdoctoral researchers, each with distinct responsibilities and career paths (Table 3.4).

Table 3.4

Qualifications needed for key positions at the university

Position

Qualifications demanded

Typical duration

President/Chancellor

Extensive academic leadership experience, typically a PhD, a strong publication record, administrative expertise

5–10 years, often renewable based on performance

Vice-Chancellor/Dean

PhD, often a full professor, leadership experience, a strong academic and publication record, administrative and budgetary experience

3–7 years, often renewable

Department Chair

PhD, leadership within a department, strong academic and teaching record preferred, usually a full professor

3–5 years, often renewable or rotating

Full Professor

PhD, extensive publication record, strong teaching and research credentials, international recognition

Permanent, typically tenure track

Associate Professor

PhD, strong publication record, teaching and research experience, peer recognition

Permanent, often tenure track

Assistant Professor

PhD, developing publication record, early-stage teaching and research experience

3–6 years, usually tenure track

Adjunct Lecturer

Master’s degree or PhD, teaching experience, often hired on a course-by-course basis

Contract-based, typically one semester or 1 year

Postdoctoral Researcher

PhD, focus on research and publication potential, often a steppingstone to faculty positions

1–3 years, nontenure track, contract-based

The functions in universities across Asia, Africa, and South America are not fundamentally different from those in Europe or North America, but they are shaped by local contexts, resources, and governance models. In many Asian universities, particularly in countries like China, Japan, and India, there is often a strong government influence on higher education, with centralized planning and a focus on technological and economic development. Depending on the country, African universities may prioritize capacity building and social development, often with international partnerships playing a significant role in research funding and policy-making. In South America, universities frequently balance academic freedom with state oversight, with countries like Brazil and Argentina emphasizing public education and research aimed at addressing regional social and economic challenges.

Moreover, the more competitive an institution is, the more likely it is to adhere to international standards and resemble other top universities in different countries. In fact, it cannot be otherwise, as they are all competing for the same researchers and striving to advance new science built on the same principles and processes, we described in the previous chapter.

So, back to the titles.

Academic titles and roles vary significantly across countries, reflecting different educational systems and structures. In Germany, one of the highest titles is “Rektor” or “Präsident,” who leads the university. Below this level, faculties are headed by “Dekans” (Deans), while individual departments are led by “Institutsdirektoren” (Institute Directors). Professorial ranks include “Wissenschaftlicher Mitarbeiter” (Research Assistant), “Juniorprofessor” (Junior Professor, a non-tenured track), and “Professor” (Full Professor), often designated as “W3” (highest pay scale) or “W2” (mid-level). Habilitation, an additional postdoctoral qualification, is often required to become a Full Professor.

In France, the hierarchy begins with the “Président de l’université,” overseeing university operations. Faculties are managed by “Doyens” (Deans) and departments by “Directeurs.” Academic ranks include “Maître de Conférences” (Associate Professor), who may later become a “Professeur des Universités” (Full Professor). Unlike many other countries, in France, the “Agrégation” is a competitive examination often required for higher academic positions, particularly in prestigious institutions. A unique feature of the French system is the “Chargé de cours,” which is akin to an adjunct lecturer.

In the Nordic countries (Sweden, Norway, Denmark, and Finland), academic titles are quite similar across the region. The top leadership role is “Rektor” or “Universitetsrektor” (Rector). Faculties are overseen by “Dekan” (Dean), while departments are led by “Institutsleder” or “Avdelningschef” (Head of Department). Professorial ranks include “professor” (full professor), and the mid-level is “Docent” or “Lektor” (associate professor). A “Lektor” can be tenure track, leading to a full professorship. The term “Adjunkt” is used for lower-level teaching roles, similar to an adjunct lecturer.

In the Nordics, there is a significant emphasis on academic freedom, and positions often involve a balance of research and teaching duties. Nordic universities, especially in Sweden and Finland, use the term “Postdoktor” (postdoctoral researcher) for those focusing on early-career research after completing their PhDs. Permanent professorships are typically awarded after several years of proving competence in both research and teaching.

These differences reflect not only the academic culture of each country but also the requirements for career progression, which can involve different combinations of qualifications, teaching experience, and research achievements.

To simplify things, I like to think of academics as either those with or without a PhD or a “driving license” (internal lingo). Of those with a PhD, there are some who have done some research and become associate professors and others who have done a lot, called full professors. It’s a simplified view, but it’s a starting point.

3.1.2 University Diplomas and Careers

When you attend a university, you’re not just going there to gain knowledge, but also to earn a diploma, which serves as official proof of the skills and expertise you’ve acquired. In fact, in today’s world, where so much can be learned online, some might argue that obtaining a diploma is the primary motivation for many students.

Universities typically offer a range of diplomas, from undergraduate (Bachelor’s degrees) to graduate (Master’s and PhDs). Common disciplines include Business, Engineering, Humanities, Social Sciences, and Natural Sciences, reflecting broad fields of study that attract many students. Exams serve as a formal method for assessing a student’s understanding and mastery of their coursework, ensuring they meet the academic standards required to advance.

Typically, it takes about 3–4 years to complete a bachelor’s degree, 1–2 years for a master’s degree, and 3–6 years for a PhD, depending on whether it’s pursued full-time or part-time. Each course is assigned a specific number of credits, and each degree has requirements for a set number of credits and mandatory courses to be completed. Courses that are not mandatory are referred to as electives.

In Nordic countries, alongside the syllabus, universities often provide a study guide and literature list to help students navigate the course. In Germany, the Modulhandbuch complements the syllabus by outlining learning objectives and examination methods for each module. In France, the programme pédagogique details the course content and emphasizes the competencies students should acquire. Anglo-Saxon countries use course handbooks and learning outcomes to clarify grading criteria and expected achievements. Across these systems, additional documents like examination guidelines and ethical guidelines ensure clarity in teaching and evaluation.

But this is too detailed for many, so let’s return to the diplomas (Table 3.5).

Table 3.5

Diplomas offered at the university, what they contain and mean

Degree

Length

Typical content

Entry requirements

Format of working

Bachelor’s Degree

3–4 years

General education in the chosen field (e.g., business, engineering, humanities). Courses typically include lectures, assignments, group work, and exams

High school diploma or equivalent, with specific subject requirements depending on the program

Structured learning through lectures, tutorials, projects, and written exams. Often includes internships or practical experience

Master’s Degree

1–2 years

Specialized education focusing on deeper knowledge of a particular subject area. Often includes a combination of coursework, research, and a thesis or capstone project

Bachelor’s degree in a related field, with a minimum GPA requirement and sometimes specific test scores in certain countries (e.g., GRE or GMAT)

Combination of independent research, group work, and classroom-based learning. May include internships or practical components

PhD (Doctorate)

3–6 years (varies by country and discipline)

In-depth research in a specialized area, with the main output being a dissertation that contributes new knowledge to the field. Includes original research, publication of findings, and possibly teaching

Master’s degree (in some cases, a Bachelor’s degree may suffice, depending on the country and university policies). Strong academic background and research proposal required

Independent research with close supervision by an academic advisor. May include teaching responsibilities, seminars, and conference presentations. Often flexible in format, with a focus on producing a dissertation

How many diplomas you should pursue depends on your career goals. Some jobs, especially more advanced or leadership roles, often require a master’s degree, though a bachelor’s is sufficient for many positions. MBAs used to be very popular, and they are still so, especially for those aiming for a career in the industry. They are particularly valuable if your bachelor’s degree is in a non-economics field, like engineering, as they provide a strong foundation in business and management skills. A PhD is typically most useful for research roles within universities or research institutions, but it is also highly valued in private industries, such as the pharmaceutical sector.

As some who have studied abroad will surely know, the French and German systems of higher education differ significantly from the Anglo-Saxon model, particularly in how students enter and progress through university-level studies.

In France, the prestigious “Grandes Écoles” are separate from traditional universities and are considered elite institutions that specialize in fields like engineering, business, and public administration. Entry into these Grandes Écoles requires two years of rigorous preparation through “Classes Préparatoires” (known as “prépa”), a highly selective and intensive postsecondary program. After completing the prépa, students must pass competitive entrance exams to gain admission. This system contrasts with the Anglo-Saxon model, where students typically enter universities directly after completing secondary education, based on standardized test scores and high school performance.

German universities also follow a different model. The “Abitur,” a comprehensive secondary school-leaving examination, is required for university entry. In Germany, students select their field of study early, and the university system is more rigid, with less flexibility to switch majors compared to the Anglo-Saxon model. There is also a distinction between “Universitäten” (research-focused institutions) and “Fachhochschulen” (Universities of Applied Sciences), which emphasize practical skills. This differs from the Anglo-Saxon model, where students generally have more freedom to explore various subjects in their bachelor’s degree early and can change majors more easily.

Another key distinction in both the French and German systems is the lack of tuition fees or very low fees, compared to the often-significant costs of attending Anglo-Saxon universities, especially in the USA and UK, which have just gotten higher in the past few years. In both France and Germany, students can access high-quality education without accumulating large amounts of student debt.

What’s more, the French system is more centralized, with the Ministry of Education playing a strong role in determining curricula and academic policies, while Anglo-Saxon systems, particularly in the USA, offer more institutional autonomy. In Germany, universities are largely state-run, but each federal state (Bundesland) has its education policies, leading to regional variations within the country.

In terms of doctoral education, the German “Doktorvater” system, where a PhD student works closely under the guidance of a single professor, or two, is more hierarchical than the more collaborative approach often seen in Anglo-Saxon countries. The “habilitation” process, required to become a professor in Germany, adds an additional layer of qualification after the PhD, which has no direct equivalent in the Anglo-Saxon system.

Overall, these differences highlight the more structured and rigid path to higher education in France and Germany, in contrast to the more flexible and student-centered approaches in Anglo-Saxon countries.

As a researcher at a university, understanding these different education systems is helpful because they shape academic cultures, expectations, and collaboration opportunities. Knowing how students are trained and how institutions operate in countries like France or Germany can help you adapt to international partnerships, student exchanges, or joint research projects. It also enables you to navigate differences in funding, tenure, and academic hierarchies when collaborating across borders. Ultimately, this knowledge enhances your ability to work effectively in a global academic environment.

3.1.3 Role of University Administration

Another essential group of people at the university ensures that everything operates smoothly—this is “the administration.” They handle a wide range of practical tasks that support both students and faculty, making sure the institution functions effectively.

For example, administrative staff assist students with course registration, ensuring they enroll in the correct classes and meet deadlines. They also manage exam logistics, such as distributing exam materials, organizing exam rooms, and processing final grades. Administrative personnel are responsible for maintaining academic records, such as transcripts, and ensuring that students receive their diplomas upon graduation. Beyond academic tasks, they manage financial services like tuition payments, scholarships, and financial aid, and they often serve as the first point of contact for students needing assistance or guidance in navigating university systems. Additionally, they support faculty by coordinating schedules, organizing departmental meetings, and helping with grant applications or research paperwork. Without the administration, the university’s day-to-day operations would quickly become chaotic.

Academic support staff, such as librarians and IT services, play vital roles in the smooth functioning of the university. Understanding this structure helps to navigate both the academic and operational sides of university life, making it easier to access resources and progress in a chosen field of study. Fewer researchers visit physical libraries these days, except perhaps to find a quiet place to study. Instead, the primary functions of the library, such as accessing books or articles, are increasingly handled through IT services, making resources readily available online. This shift allows researchers to access a vast array of materials from anywhere, streamlining the process of gathering information. The role of the library has evolved into a digital hub, providing essential academic content through virtual platforms.

It’s a pity, perhaps, that these magnificent monuments to the written word have now largely become quiet study spaces, especially for those who have spent time in the grand libraries like the Sainte-Geneviève Library or the Bibliothèque de l’École Normale Supérieure (ENS) in Paris or the British Library and Bodleian Libraries at Oxford. These institutions, once bustling with scholars combing through physical collections, now seem to serve more as tranquil refuges as digital access increasingly replaces the need for physical visits (Table 3.6).

Table 3.6

Administrative personnel and their functions

Administrative function

Role/responsibility

How they help

Course Registration Office

Manages student enrollment in courses

Assists students in selecting and registering for courses, ensures they meet prerequisites, and monitors registration deadlines to avoid scheduling conflicts

Exams and Records Office

Organizes exam logistics and maintains academic records

Prepares exam materials, schedules exam rooms, and processes final grades. Provides official transcripts and diplomas to students

Financial Aid & Tuition Office

Manages tuition payments, scholarships, and financial aid

Helps students understand and apply for financial aid, manages billing and tuition payments, and assists with scholarship applications

Admissions Office

Handles applications for enrollment at the university

Guides prospective students through the application process, reviews applications, and communicates acceptance or rejection decisions

Student services

Provides general student support, including academic advising and personal assistance

Serves as a first point of contact for student inquiries, provides guidance on academic policies, and connects students to other resources like counseling

Registrar’s Office

Maintains academic records, including grades, transcripts, and graduation records

Ensures students’ records are up to date, processes graduation applications, and issues official transcripts and diplomas upon graduation

Faculty Support Services

Assists faculty with administrative tasks such as scheduling and research support

Coordinates faculty schedules, helps organize meetings, and assists with research grant applications, providing a smooth workflow for academic staff

IT support

Provides technical support for students and faculty

Assists with software and hardware issues, maintains the university’s online platforms like learning management systems, and ensures smooth access to digital tools

Library services

Provides access to academic resources such as books, journals, and databases

Helps students and faculty with research by providing access to academic databases, organizing workshops on research tools, and offering quiet study spaces

Human resources department

Manages employment matters for university staff and faculty

Handles hiring, payroll, benefits, and employee-related issues, ensuring smooth administrative processes for university employees

Administrative personnel are like the oil in the engine, ensuring that everything runs smoothly so you can focus on what’s most important—you’re learning and gaining knowledge. When the administration works effectively, it allows you to concentrate on your studies without being distracted by logistical issues.

The administrative needs of a master’s student sometimes differ significantly from those of a PhD student, reflecting the varying complexity and scope of their academic journeys. A master’s student primarily focuses on coursework, so their administrative needs revolve around course registration, exam scheduling, and ensuring they meet the credit requirements for their degree. They may also require assistance with securing internships or completing a thesis, but their program is typically more structured and short-term compared to a PhD.

In contrast, a PhD student’s journey is much more research-intensive and self-directed, requiring a different level of administrative support. PhD students often need help with long-term research planning, including assistance in submitting research proposals and securing approvals from ethics committees, even though the PhD student also takes care more of his own needs. If applicable, funding is another area where administrative staff play a crucial role, as PhD students frequently apply for grants, fellowships, and other forms of financial support to conduct their research over multiple years. But these also differ greatly.

When I was a part-time PhD student for about six years in Leipzig, I essentially handled most of these things on my own with the employer. In contrast, when you are a PhD student in the Nordic countries, you are typically considered an employee and receive a salary from the university.

PhD students also work closely with dissertation committees, and administrative help is needed to schedule committee meetings, track progress reports, and coordinate the dissertation defense.

Case: Comparing Global Dissertation Processes: Germany, France, the UK, and the USA as Compared to Sweden

The cornerstone of the dissertation process in Sweden, much like in the medieval period, is the seminar, where the student (the “respondent”) presents their work before an objective and critical “opponent,” who challenges the arguments put forth. The purpose of this exercise is to rigorously test the dissertation’s arguments and ensure they hold up under scrutiny. For this reason, the opponent is typically an external professor from another university. In theory, the opponent should not have close ties to the supervisor to maintain objectivity, but in reality, they often know each other beforehand.

The process of completing a PhD in Sweden involves several key milestones, starting with the formation of a dissertation committee. This committee generally includes the main supervisor, sometimes cosupervisors, an external reviewer or opponent, and faculty members with expertise in the research area. In principle, the committee provides guidance, feedback, and evaluation throughout the PhD journey, but in practice, much of this responsibility falls on the supervisors.

To ensure progress, PhD students present their work at various seminars. Early in the program, a “startseminarium” (primary seminar) may be held to outline the research plan, even though it is seldom called so, followed by a mittseminarium (middle seminar) about halfway or two-thirds through the program, where significant findings are presented. The mittseminarium serves as the first major milestone, as it is often the first time the opponent gives detailed feedback. It is also the stage where any major concerns, such as doubts about the viability of the thesis, can be raised. Detailed feedback is provided here, and the student has until the slutseminarium (final seminar) to address these comments.

By the slutseminarium, the dissertation is nearing completion. This seminar serves as the last opportunity for the opponent to flag any major issues or indicate that the dissertation is not ready. While one might think the final judgment would occur during the public defense, it is rare for a dissertation to fail at that stage; most major issues are addressed beforehand. The slutseminarium allows the opponent to make final comments and ensures the dissertation is ready for submission.

The dissertation itself may take the form of a monograph or a collection of articles with an overarching introduction, known as the kappa (literally “the coat,” as it is supposed to encapsulate the entire thesis). Before the public defense, the dissertation must meet formatting requirements and pass a plagiarism check.

The public defense, or disputation, is a formal event where the candidate defends their work before the committee, the external opponent, and an audience. In reality, however, this event is more ceremonial, as the critical feedback and evaluation have already occurred during and after the mittseminarium and slutseminarium. During the defense, the opponent critiques the dissertation, and the candidate responds to questions from the opponent, committee members, and the audience. After the defense, the committee deliberates privately to decide whether the dissertation meets the requirements for a PhD. Upon approval, the final dissertation is published, often in the university’s repository, and the PhD is officially awarded in a ceremonial event.

PhD dissertations in Germany focus heavily on independent research with minimal structured coursework, and students typically work closely with a single supervisor rather than a formal committee. The final steps involve a public defense (disputation) and sometimes an oral examination (rigorosum), but there is less emphasis on iterative feedback throughout the process. In France, doctoral studies emphasize close mentorship under a single director, and the dissertation defense, known as “soutenance,” is a formal event before an external jury that evaluates both the research and its societal relevance. Unlike other systems, France often integrates a philosophical or broader societal dimension into the dissertation.

In the UK and USA, PhD programs include substantial coursework and qualifying exams before students embark on the dissertation. A committee provides guidance throughout the process, and the defense typically involves only the student and committee members in a private setting. Dissertations are generally cohesive single works rather than compilations of articles. In contrast to Germany and France, UK and US PhD programs place greater emphasis on formal milestones during the research process, including annual reviews and proposal defenses, to ensure progress and alignment.

Here are some questions for reflection:

What national systems, or better yet, what elements from each system, do you think are most advantageous for advancing the quality of science, and why?

What questionable practices or potential flaws do you observe in these processes?

What, in your opinion, is missing from these examples which could have improved the process?

This should be a fun discussion.

This structured process, with its emphasis on seminars, collaborative feedback, and rigorous evaluation, ensures that doctoral research meets high academic standards and is well prepared for scholarly impact.

While a master’s student may attend a few conferences during their program, a PhD student is much more likely to present research regularly, meaning they often rely on administrative staff to handle travel arrangements, reimbursements, and conference logistics. Moreover, PhD students frequently collaborate with external institutions or labs, which requires additional coordination and support from the university’s administrative offices.

Graduation requirements for PhD students are also more complex. They must manage the submission of their dissertation, navigate the defense process, and meet various deadlines for graduation. Administrative staff are vital in helping PhD students track these milestones and ensure they complete all necessary steps to earn their degree. Additionally, while master’s students typically have a single advisor for their thesis, PhD students typically work with a committee of advisors, making the administrative coordination of meetings and communications more involved. In some countries, the institution funds the graduation ceremony, but more commonly, it is organized and paid for by the family of the person defending their PhD. The accompanying dinner is often a memorable occasion. It’s a real celebration, even if it’s just held for a small group.

A fond memory of mine is when my late father, who was not known for spending money on anything unnecessary, rented the historic sections of Auerbachs Keller in Leipzig and hosted a celebratory dinner for everyone who attended the defense that day.

Anyone familiar with Goethe’s Faust may recall that a scene in the play’s first part takes place in this renowned tavern. Goethe, who studied in Leipzig, was likely inspired by his own student years here to set the memorable scene in this historic tavern.

While such traditions are often associated with universities, research itself is not confined to academic faculties—many researchers find their careers unfolding in research centers, either part-time or full-time.

3.1.4 Role of Research Centers

Research centers and institutes play a pivotal role within universities, serving as hubs for specialized, focused research in specific fields. These entities often operate independently from traditional academic departments, allowing for interdisciplinary collaboration and innovation.

Some of these research centers are located on campus, often in separate buildings, with loose cooperation or attachment to a department, but they typically operate financially independently and are responsible for their budgets.

Note: The University Campus

The university campus has roots in Plato’s Academy in Athens, where scholars gathered in open spaces for dialogue. In much the same way, the Stoics derived their name from the “stoa poikile” or “painted porch,” in ancient Athens, a public colonnaded structure where they gathered for philosophical discussions. These peristyles, open spaces framed by columns, were central to Greek civic and intellectual life, providing areas for public discourse, teaching, and reflection:

Ledger: The author as a young student lost among the stoas in Athens, 1989

Plato’s Academy embraced open spaces, blending the natural environment with intellectual exploration and laying the foundation for the concept of communal learning spaces—an influence still visible to visitors in Athens today. The architectural design of peristyles, central to Greek public life, evolved over time into the enclosed courtyards of medieval monasteries, fostering focused study and quiet contemplation. This architectural tradition profoundly influenced the development of academic environments, with institutions like Oxford and Cambridge adapting the courtyard layout to create iconic communal spaces that became hubs of intellectual life.

American campuses later refined this model, integrating purposefully planned collections of buildings that reflect ideals of openness, collaboration, and academic excellence. Thomas Jefferson’s architectural drawings for the University of Virginia exemplify this refinement. Jefferson envisioned an “Academical Village” that reimagined the academic environment by incorporating a central open lawn flanked by pavilions connected by colonnaded walkways. Departing from the enclosed courtyards of earlier institutions, Jefferson’s design emphasized openness and accessibility. Notably, each pavilion housed both classrooms and faculty residences, encouraging close interaction between students and professors—a physical embodiment of collaborative learning. The central lawn, often with a cross-like pathway, provided a communal space for dialogue and intellectual exchange, symbolizing a vibrant academic community.

Jefferson’s vision influenced campus designs worldwide. For instance, my mentor, colleague, and friend, Prof. Dr. Per Jenster (now retired), drew upon Jefferson’s designs when creating a business school in Chengdu, Sichuan Province, bringing the same ideas to Western China. Jefferson’s innovative layout seamlessly integrated natural surroundings with academic structures, reflecting his belief in education as a tool for cultivating enlightened and engaged citizens.

This architectural approach set a new standard for American campus design by prioritizing open, democratic spaces that encourage interaction and the free flow of ideas. It encapsulated the ideals of the young republic, emphasizing progress, enlightenment, and societal betterment through education. This lineage illustrates how the physical design of learning spaces has continually shaped the pursuit of knowledge and philosophical inquiry, linking ancient peristyles to modern university campuses.

In some cases, these centers are entirely off-campus, particularly when they do not offer any teaching or academic programs. Many of them excel at producing research that influences policy or at least disseminating their findings effectively. This is often because they face greater pressure to demonstrate impact.

Should you work for such a research center? It depends. From a research perspective, the quality of work can be as good or even better than in some departments or faculties, but there is typically more responsibility for securing funding. In many cases, if you fail to obtain funding, you may lose your position. This is increasingly common, even in Nordic countries, despite tenure being somewhat easier to achieve compared to Anglo-Saxon countries. Researchers at these centers must often balance research with fundraising efforts, making the environment more competitive and result-driven. However, the independence and focus on applied research can offer rewarding opportunities for those up to the challenge. Salaries are often much the same.

Research centers provide dedicated resources, including laboratories, funding opportunities, and support staff, to facilitate advanced study and experimentation. They also serve as a bridge between academia and industry, fostering partnerships that translate academic research into real-world applications. Institutes often host visiting scholars, postdoctoral researchers, and experts, enhancing the intellectual environment and bringing diverse perspectives like faculties. Many research centers are also involved in securing research grants, helping to finance cutting-edge projects that may not receive funding through other channels. Ultimately, these centers are key to advancing both theoretical knowledge and practical innovations within universities. But, from a career perspective, they are also much riskier, in my experience. Some research centers are also shut down, some quite suddenly.

3.2 Conferences and Publications

If you are beginning research at a PhD program, going to academic conferences will soon become an essential part of your academic life, offering opportunities to share research and engage with others in the field.

It begins with writing a research paper, which is typically submitted to the conference much like submitting to a journal, though the process is usually less rigorous with a higher acceptance rate. Even if your paper isn’t accepted for oral presentation, you might still be able to submit it as a poster or similar format. Once accepted, the next step involves organizing travel arrangements, including flights, hotels, and registration fees.

At the conference, you’ll present your research to peers, but you’ll also spend time listening to others share their work, often across a range of sessions. The informal discussions during breaks are just as important as the presentations, providing chances to network and exchange ideas with colleagues. These discussions can continue into the evenings, as academic conferences often include social activities like dinners at local restaurants, further enhancing the networking aspect. Conferences typically last 2–3 days, with a mix of presentations, workshops, and social events throughout.

When you return home, it’s important to report your travel costs to your institution, often through detailed expense reports, to get reimbursed for the trip. Academic conferences not only advance research but also offer valuable social and professional connections that can last well beyond the event itself. This is sometimes a tedious process, but then you have little choice.

An important aspect to consider when going to academic conferences is preparing strategically for networking opportunities. It’s not just about presenting your research but also about building relationships with peers, potential collaborators, and senior academics in your field. In other words, conferences provide a chance to meet the people behind the articles you’ve studied, bringing those written words to life. It’s also an excellent opportunity to gain informal, insider knowledge about what it truly means to be a researcher—insights that may not be openly discussed or politically correct but are invaluable for navigating the academic landscape. For instance, you might learn how to handle tricky peer reviews, or which journals are more receptive to certain types of research, tips that can be vital for advancing your career.

Before the conference, it’s a good idea to identify individuals whose work aligns with your interests and plan to attend their sessions or approach them during breaks. Avoid deciding on sessions once they’ve already started, as this may cause you to miss key presentations you were eager to see. However, it’s acceptable to make small adjustments once sessions are underway, even switching to a different room if the topic isn’t what you expected, given that there are large audiences. Just be mindful not to leave if it’s too noticeable, as that can come across as disrespectful to the speaker.

When you first attend conferences, it will likely be as a speaker or presenter. However, as you gain more experience and build a reputation—particularly by applying your knowledge to practical industry challenges—you may eventually be invited as a keynote speaker and even receive compensation for it.

Another point worth adding is the value of workshops and panel discussions that often take place at conferences. These are opportunities to engage more deeply with specific topics or methodologies, often providing hands-on experience or insight that can be directly applied to your own research. Attending poster sessions is also important, even if you’re not presenting one yourself, as it allows you to see emerging research trends and talk directly to other researchers in a more informal setting.

Conferences can also be a platform for discussing funding opportunities. Representatives from funding bodies or research councils often attend, and it can be beneficial to understand the current funding priorities in your field. Finally, it’s crucial to follow up with the people you meet. Post-conference communication—whether through emails or social media—can help solidify new connections and lead to collaborative efforts in the future.

There is also an all-important social part of the conference, which usually involves going out together to eat, either formally for all attendees or in smaller groups by invitation. These gatherings can, in some cases, be mixed with official duties, such as assembling a committee for a scientific journal to discuss a special topic or holding a frequently occurring meeting. They can also involve planning future conferences, like deciding who will host next time. If you are a novice, take the chance to connect with other novices, as they could be your future colleagues if they remain in the field. These are also potential writing partners for articles.

Pay attention to where to book your hotel—don’t choose one too far from the venue, or you’ll end up spending unnecessary and costly time commuting. Normally, the conference will suggest a list of hotels, possibly with priority pricing or discounts. Study the map of everything before you land so you know where everything is located and how to get there—local train, bus, or taxi? These are important details to consider before your arrival, not once you get there. After all, you do not want to arrive for your speech all stressed and sweaty.

3.2.1 Colloquiums

There are other forms of meetings, too. A colloquium is an academic gathering where scholars present and discuss research in a more intimate, focused setting compared to larger conferences. Typically, colloquiums are smaller and center around specific topics or themes, fostering deeper discussions and closer interactions among participants. In the social sciences, colloquiums provide a platform for sharing work-in-progress, receiving feedback, and engaging in intellectual exchange. They can be held on campus, at a conference center, include an overnight stay, or simply be conducted online.

In the USA and UK, colloquiums are often department-specific events, where researchers within a discipline or subfield regularly meet to discuss ongoing projects. In France, colloquiums, often referred to as “séminaires,” are important venues for interdisciplinary debate, with a strong emphasis on philosophical and theoretical discussions. In Germany, they tend to be more formal and structured, often integrated into university research institutes or doctoral programs. In Nordic countries, colloquiums emphasize collaboration and collective knowledge production, aligning with their egalitarian academic cultures. Despite these regional differences, colloquiums universally serve as essential spaces for idea development and networking in the social sciences.

Colloquiums differ from larger academic conferences in several ways, and they hold unique significance in an academic’s journey. Size and intimacy are key aspects that set colloquiums apart. Unlike the large gatherings of conferences that can attract hundreds of participants, colloquiums are smaller, more intimate gatherings where scholars working on closely related topics can engage in deeper, more focused discussions. This creates an environment for closer relationships between scholars, often leading to long-term collaborations.

Another notable difference is the focus on specialized topics. While larger conferences cover a broad range of subjects, colloquiums typically center around very specific, niche areas of study, allowing for a more in-depth examination of particular topics or methodologies. This specialization helps researchers sharpen their expertise in their chosen field.

The interactive format of colloquiums is also a major distinction. Unlike larger conferences where presentations are more formal and time-constrained, colloquiums tend to include more time for Q&A, group discussions, and workshops. This interactive setting allows for real-time feedback and more collaborative learning.

In terms of mentorship and networking, colloquiums provide stronger opportunities for building meaningful relationships. Due to their smaller size, they foster closer connections between senior and junior scholars, offering early-career academics the chance to receive guidance and career advice that can be harder to come by in the rushed atmosphere of a large conference.

Colloquiums also offer an opportunity for deeper reflection on research. The intimate and focused nature means participants can engage with each other’s work in a more detailed and constructive way, leading to richer academic conversations. Much of this is because colloquiums are less formal. Additionally, colloquiums are ideal for reputation building in specialized fields, where consistent participation can boost visibility and credibility within a specific academic circle.

In many cases, colloquiums can also more easily lead to academic collaboration and project development. The interactive and focused environment makes it easier to identify potential research partners with similar interests, leading to joint projects, funding proposals, or co-authored papers.

For emerging researchers, colloquiums offer a less formal platform to present preliminary findings. The informal atmosphere encourages early-career academics to share work in progress and receive constructive feedback, making it an ideal stepping stone for building confidence and refining ideas. Did I mention that they are often cheaper as well? In many cases, you don’t need a personal budget for attending, as the costs can be covered by a larger research grant or administrative fund. This makes it even more attractive for young researchers who rely on external funding (Table 3.7).

Table 3.7

Comparing the use of conferences and colloquiums

Aspect

Conferences

Colloquiums

Purpose

Broad exposure to various topics and networking opportunities

In-depth exploration of specific research areas and detailed feedback

Audience

Large, diverse audiences

Smaller, more focused groups of experts and peers

Feedback

General feedback, less detailed due to a larger scale

Detailed, interactive feedback on ongoing work

Networking

Opportunities to meet a wide range of scholars and professionals

Closer connections with experts in a specialized field

Atmosphere

Competitive, formal

Collaborative, informal

Presentation practice

Ideal for showcasing completed work to a broad audience

Ideal for testing and refining research ideas

Example for PhD programs

2 major international conferences each year

Monthly department colloquiums to refine research

Cost consideration

High travel and registration costs

Lower cost, often local or online

Development of skills

Enhances broad research understanding and public speaking skills

Enhances deep research analysis and fosters methodological discussions

Networking focus

Building a wide professional network

Establishing long-term collaborations with focused research peers

Online/virtual option

Sometimes available, but usually still broad in scope

Easily adaptable to online formats, encouraging international participation

Practice for larger events

Helps students gain experience in large academic settings

Acts as a rehearsal platform for upcoming larger conferences

Alignment with research

Helps position research in broader academic discussions

Aligns with specialized feedback for ongoing PhD work

Frequency

Annually or biannually

Monthly or quarterly

Program organization

Organized by external bodies or professional associations

Typically organized within the department or academic institution

Finding the right balance between conferences and colloquiums is essential for the success of PhD programs, both for the organizers and the students. Conferences typically provide broad exposure to a variety of topics, giving PhD candidates a chance to present their research to larger, more diverse audiences.

These events are great for networking, learning about emerging trends in the field, and gaining insight into how their work fits into the bigger academic picture. However, the scale of conferences can make it harder to get in-depth feedback, and the competitive atmosphere may not always be ideal for early-career researchers.

In contrast, colloquiums offer a more focused and intimate setting where PhD students can dive deeper into their specific research areas. Here, the smaller group sizes and specialized topics allow for more meaningful exchanges with experts and peers. Colloquiums are particularly beneficial for receiving detailed feedback on ongoing work, as the discussions are often more interactive and collaborative. Organizers should, therefore, consider including both formats in PhD programs to provide a balanced academic experience.

For example, a PhD program in social sciences could have students attend two major international conferences each year to present their work to broad audiences while also participating in monthly colloquiums within their department to refine their research ideas with specialized input. Moreover, colloquiums could serve as a testing ground for students to practice their presentations before heading to larger conferences. Online colloquiums can also be a cost-effective way to engage with international scholars without the need for extensive travel.

The key for program organizers is to ensure that both conferences and colloquiums are strategically planned, aligning with the student’s research milestones. Some colloquiums could focus on theory development, while others could be geared toward methodological challenges, offering a comprehensive platform for academic growth. Encouraging participation in both types of events ensures that PhD candidates develop their research while also building the professional network necessary for their future careers.

By offering this mix, PhD programs can help students balance the broad exposure and networking of conferences with the detailed feedback and personal connections that colloquiums offer. Both are essential for academic development, but understanding how to leverage each can significantly enhance the doctoral journey.

3.3 Publication Strategy

As soon as a new researcher begins the PhD program, thoughts about how to approach publications will quickly follow. Let’s assume that during the master’s program, you have already learned the basics of conducting research. However, a refresher on scientific methods, often provided through additional methodology courses, is usually a good idea at this stage.

We began Chap. 2 by exploring what science is, starting with how individual interests develop through a series of events—some shaped by personal choices, while others influenced by society and various factors during upbringing. While it’s hard to claim that these interests are chosen with complete free will, as one might choose to buy a car, they nonetheless tend to reflect genuine personal passions.

The common view when developing a publication strategy is that you shouldn’t just follow your interests. Instead, you should choose a field with various quality journals and conferences attached to it. Although this may make an academic career easier, I strongly oppose this approach. I believe that researchers will do much better if they first and foremost follow their interests. There are enough researchers out there, and more are added every year, and many of them are set to learn how the big machinery functions. What science needs more are people who think differently and go in new directions. That, too, is a strategy—it’s just not the one most would recommend (Table 3.8).

Table 3.8

The increase in the number of social scientists and research papers written (See, e.g., Gupta and Govindarajan (2002))

Time period

Number of social scientists (est)

Disciplines (Approx)

Articles produced (per year)

Two generations ago (approx. 1960s)

250

Sociology, anthropology, political science, economics, psychology

100

One generation ago (approx. 1980s–1990s)

400

Sociology, anthropology, political science, economics, psychology, criminology, gender studies

250

Today (2020s)

1,200,000

Sociology, anthropology, political science, economics, psychology, criminology, gender studies, environmental studies, data science, urban studies, public policy

750

Ledger: The data for estimating the growth of social sciences over generations is drawn from a combination of sources, including reports by the American Sociological Association and British Academy for historical trends and National Science Foundation (NSF) and UNESCO for global research estimates. Additional insights come from OECD reports on academic output and publication data from Scopus and Web of Science, which track the number of published articles. Together, these sources provide a broad view of the expanding number of social scientists and publications across disciplines.

The issue is that much of the growth in social scientists over the last two generations has not had a significant positive impact on society. Just as when you invest in rocket engineers, you expect better rockets to take you further; the same should apply to the social sciences. We are not the humanities, where different goals like the enrichment and general education of mankind are always valid arguments. While part of this might hold true for the social sciences as well, it’s not an argument we can rely on for long or press too hard. For the social sciences, this presents a serious issue and, from time to time, leads to significant scrutiny from taxpayers who question the value of what takes place in the ivory tower. This is why I recommend adopting a more daring research strategy, one that truly thinks outside the box—or rather, outside all the boxes. Society needs you to push those boundaries.

As you begin working on problems in your chosen field, you’re often influenced by what others find interesting. Attending conferences allows you to meet the people you’re competing with, learn how they think, and understand what is expected of you. This doesn’t replace the hard work, but it helps you grasp what kind of papers are suitable for specific journals and conferences.

Equally important is knowing what not to write. If you focus too much on fitting into the established norms, you risk losing your passion and creativity, which are crucial for making significant contributions. I’ve seen this many times: People become disillusioned with their field because they didn’t think hard enough about it and ended up doing something they didn’t feel strongly about. After a few years, they change fields, take up administrative posts, or leave academia altogether. I had hoped not to write this, but disillusioned academics are often the rule, not the exception, in the long run.

Another crucial part of your strategy is time management: Always have projects in progress so you’re never without work in “the pipeline”. This means continuously developing new ideas while polishing ongoing research.

Choosing your research partners is also essential. You want collaborators who are knowledgeable and hardworking, though teaming up with senior researchers can sometimes provide valuable guidance, even if they’re not as involved in data collection or analysis. In today’s world of online communication, research groups are formed much more easily, and group work can be done via video conferencing with participants from all over the world. This is a great development that has enriched me tremendously, both personally and professionally. However, it also means that being pushed out of groups is easier—if you don’t perform, you may not be reinvited. To be excluded from a research group online goes fast, sometimes just with a click on the mouse. This is a good thing; it keeps up the pressure to always deliver.

If you find that you frequently write with people from your university, it may be for the wrong reasons—because they were your former boss, your spouse, or someone you want to impress or simply because they happened to be available. These relationships can often be mentally and intellectually draining, offering little in terms of career advancement. Worse, those who initially invite you into their circles might later be the ones who promote you to full professor—a situation that can be tainted by academic politics. Instead, seek out opportunities beyond institutional boundaries and familiar networks.

More on different forms of research collaboration will be discussed in the next section.

Case: Three Remedies for Academic Integrity: Transparency, Competition, and External Evaluations⁵

Academic corruption, like most forms of corruption, thrives in systems where transparency is lacking. The best way to mitigate it is through clear, unbiased evaluations and decision-making processes. For major academic milestones—such as promotions, tenure decisions, or funding allocations—external evaluations should be standard. Crucially, these external evaluators should not be chosen by the individuals or groups under scrutiny but by an independent office, such as Human Resources, to minimize conflicts of interest within a particular faculty.

You will notice that practices vary greatly, even within the same country and across different educational institutions. Some departments maintain full transparency, sharing the entire budget during monthly or bimonthly meetings, while others operate in complete secrecy, with the department head keeping more or less all financial details to themselves. At times, the differences in practice within the same country can be so stark that it feels like stepping into an entirely different world. That is not a good sign.

Meritocracy must remain the cornerstone of academia. Decisions should be based solely on demonstrated excellence and contributions to the field. While efforts to address historical inequities—such as preferential treatment for women or underrepresented groups—are well intentioned, they risk introducing a new form of bias if not implemented carefully. Preferential treatment, no matter how justified by past discrimination, can undermine meritocracy and foster resentment, further complicating the already delicate balance of fairness in academic systems.

Transparency in hiring, promotions, and collaborations ensures that all stakeholders understand the criteria for success. For example, publishing the metrics and rationale behind key decisions builds trust and deters favoritism. Such measures are not merely administrative formalities; they safeguard the integrity of academic institutions. In the long term, systems rooted in transparency and meritocracy benefit not only individuals but also the reputation and output of the entire academic community.

We have come a long way on these points, but there is also far yet to go.

Some questions for you, the reader:

1.

Do you think there are arguments for why any position should not be advertised publicly for all to apply?

2.

Are there any arguments for why a faculty should be allowed to choose its own pool of evaluators?

3.

Would it make sense to have a central office at the national level to appoint evaluators independently of the university in question?

4.

Should we be open about everything, including all budgets, so that every employee can see where all the costs are and where all the money comes from? Are there arguments against this?

Your thoughts.

After completing your PhD, your initial focus should be on publishing articles in high-quality or good journals to build your reputation. Once you become a full professor, you can shift focus to writing books, where your experience gives you a broader perspective, and you may have more time for larger projects.

In rare cases, you might consider writing a book early in your career, especially if you’re teaching a course and can’t find a suitable textbook. As your career progresses, your goal is not just to publish more often but to increase the quality of your work, aiming for articles that are widely cited and enhance your bibliometric scores.

When developing a robust publication strategy, you might also consider diversifying your publication outlets. While it’s essential to target high-impact journals, publishing in specialized or niche journals can give you greater visibility in certain academic communities and lead to meaningful collaborations. Also, consider the difference between international and local journals. Publishing in international journals increases your global recognition, but local journals help establish your authority within regional networks. Additionally, popularizing your research by writing for newspapers or magazines can be useful, but don’t focus too heavily on popularization before you’ve done the hard work—otherwise, it can come across as self-promotion.

Today’s academic world of competencies is tremendously transparent—a positive development. Within minutes, you can figure out how much of an expert someone is in their field by looking up their publications and checking their bibliometrics. If you’re a star, don’t worry—people who matter will see it for themselves. This is the blessing of the meritocratic system, although it’s not popular among all.

Timing and consistency are also key. Rather than focusing solely on big projects, make sure you have a mix of short-term, mid-term, and long-term projects to maintain a steady flow of publications. Collaborations across disciplines can also boost your profile. Working with scholars from other fields broadens your expertise and opens up more opportunities for publication in interdisciplinary journals.

You should also consider publishing in open-access journals. Although the fees can be higher, these journals ensure a broader readership and are more accessible to a global audience. Engaging in special issues or edited volumes is another strategy. While I personally find many edited books to be intellectually incoherent, they can serve as collections of ideas that may be valuable in certain contexts.

Finally, don’t underestimate the value of writing review papers. These are often widely read and cited, as they synthesize existing research and highlight trends in the field. By demonstrating your mastery of the literature, you position yourself as a thought leader, which can further elevate your profile and increase your citations. Maybe you’ll get invited to do a TED talk. If you can publish a practitioner’s perspective in something like Harvard Business Review or MIT Sloan, take it—these are golden opportunities for new connections.

Case studies are another great form of publication, but doing a truly excellent case study, like those at Harvard Business School or the Ivy League, takes careful planning—sometimes years. To write a good case, you need top-level access within the company and industry, and just getting the contacts is a feat. You then must earn the company’s trust; often, you need to sign NDAs (nondisclosure agreements) and start writing drafts—many of which will be rejected in part by the company. It’s a balancing act: You want to tell the world about a business problem, but the company wants to disclose as little as possible, while still enjoying the publicity a good case brings from its use in top business schools.

3.4 Collaborative Research Models

Collaborative research can take many forms, each with its own set of dynamics, benefits, and challenges. Intra-institutional collaborations involve researchers from different departments or faculties within the same university, allowing for relatively smooth coordination but sometimes limiting the scope of available expertise. On the other hand, inter-institutional collaborations bring together researchers from different universities, often leading to more comprehensive perspectives and shared resources, though these can come with greater logistical and administrative hurdles. Another model, industry-academia partnerships, enables the application of academic research to real-world problems in the private sector. These partnerships are precious for fields like engineering, technology, and medicine, though navigating differing goals and timelines between academics and industry professionals can be tricky. Lastly, international collaborations broaden the reach of a project, offering access to diverse funding sources, specialized expertise, and a global perspective, but they often involve complex issues like differing regulations, time zones, and cultural expectations.

Roles and responsibilities within a collaborative research team must be clearly defined to ensure smooth operation and accountability. In some collaborations, leadership is shared through a co-principal investigator (PI) model, where two or more individuals lead the project, each bringing different expertise to the table. In others, there may be a project-based leadership structure where one PI primarily oversees the research but delegates specific tasks such as data collection, analysis, or writing to others in the team. It is important to establish clear decision-making structures and processes for conflict resolution to avoid misunderstandings and potential conflicts, particularly in multi-institutional projects where roles may overlap.

In today’s interconnected world, collaboration tools and platforms play a crucial role in enabling smooth communication and coordination. Cloud-based platforms, such as Google Drive, OneDrive, or specialized databases, allow team members to access and work on documents simultaneously, regardless of geographic location. Project management software like Trello or Asana helps track progress, assign tasks, and set deadlines, which is especially useful for large or geographically dispersed teams. Depending on the collaborative model, teams may opt for regular virtual meetings for ongoing projects, or they might schedule annual workshops or in-person meetings to consolidate findings and plan the next steps (Table 3.9).

Table 3.9

Collaborative research models

Collaboration type

Description

Benefits

Challenges

Intra-institutional collaboration

Researchers from different departments or faculties within the same university

Smooth coordination, shared institutional resources, easier logistics

Limited scope of expertise, constrained to one institution

Inter-institutional collaboration

Researchers from different universities collaborating on a project

Comprehensive perspectives, shared resources, broader expertise

Greater logistical and administrative hurdles, differing institutional policies

Industry-academia partnership

Collaborations between academia and the private sector to address real-world problems

Practical applications of research, valuable for fields like engineering, technology, and medicine

Navigating differing goals and timelines, balancing academic and industry priorities

International collaboration

Collaboration among researchers from different countries

Access to diverse funding sources, global perspectives, specialized expertise

Complex regulations, time zone differences, cultural expectations, logistical difficulties

Different collaborative models require various tools and platforms, each suited to the specific needs of the research team. For instance, cloud-based platforms like Google Drive or OneDrive may be necessary for sharing documents in real time, while more advanced tools like project management software (Trello, Asana) help coordinate tasks and timelines. These tools are not always free, and someone—either the lead institution or through research grants—must cover these expenses. The costs for advanced data-sharing platforms, secure cloud storage, or even licenses for specialized software can add up, especially for international or large-scale collaborations. Therefore, allocating funding for such platforms should be part of the planning process when applying for research grants. It’s essential to ensure all team members have access to the necessary tools to avoid delays and ensure smooth collaboration. Just don’t spend too long time with bickering over the budgets. Try to get straight to the science as soon as possible (Table 3.10).

Table 3.10

Collaboration tools and platforms

Collaboration tools and platforms

Description

Benefits

Challenges

Cloud-based platforms

Tools like Google drive and OneDrive for simultaneous access and collaboration on documents

Facilitates real-time collaboration across locations

Potential security and data privacy concerns

Project management software

Tools like Trello or Asana to track progress, assign tasks, and set deadlines

Helps manage large teams, improves task delegation, tracks progress efficiently

Can be overwhelming if overused or not adopted consistently by all team members

Virtual meetings and workshops

Regular virtual meetings for ongoing coordination or annual workshops for in-person collaboration

Ensures continued communication and planning, consolidates findings effectively

Coordination across time zones, limited personal interaction

Collaboration also brings challenges in terms of ethics and intellectual property (IP). When multiple researchers from different institutions or sectors are involved, it becomes critical to clarify issues surrounding intellectual property rights (IPR). For instance, who owns the data, and how can it be used once the project concludes? Additionally, teams need to develop clear data-sharing agreements to ensure transparency and trust among partners. Ethical considerations, such as ensuring the protection of participant data in cross-border research, also become increasingly important in collaborative settings.

In academic-industry collaborations, particular attention needs to be paid to balancing academic freedom with the commercial interests of private-sector partners. Often, the industry side may prioritize product development or patents, while academic researchers aim for publication. Negotiating these goals early in the collaboration is crucial to avoid conflicts later. Additionally, funding structures for collaborations, whether internal grants, government funds, or private-sector investment, can influence how responsibilities are distributed and what outputs are expected.

International collaborations bring a unique set of challenges, especially regarding regulatory and legal frameworks. Researchers must navigate different funding requirements, data protection laws like the EU’s GDPR, and sometimes even export control regulations when sensitive technologies are involved. Nonetheless, international partnerships provide invaluable access to diverse expertise and resources that would be hard to achieve within a single institution or country.

Finally, to ensure that collaborative research produces meaningful and actionable results, it’s essential to establish regular communication strategies tailored to the type of collaboration. In small intra-institutional projects, informal check-ins may suffice, while larger or international teams might require formal weekly virtual meetings and quarterly in-person workshops to stay on track. However, to organize all of this, you must be flexible; remember, these researchers have plenty of other things to do than sit and plan meetings with you.

Ultimately, a successful collaborative research model depends on mutual trust, clear definitions of roles and responsibilities, and the effective use of tools and technology that facilitate smooth operation across different institutions, sectors, and even countries. It does not mean you work tighter all of the time, but rather that you can find back to each other again later when the opportunity for a new project arises.

This brings us to the next topic.

3.4.1 Building and Sustaining Research Teams

Building and sustaining research teams require selecting the right individuals who can genuinely contribute to the project and part after each project with a feeling that it was worth it.

It’s crucial to find people who are committed, reliable, and able to meet regularly, whether in person or through platforms like Zoom or what-have-you. Consistency in contributing during every meeting is key because it’s easy to be replaced if you don’t pull your weight, especially in virtual settings where the barriers to entry and exit are low. Once you’ve successfully completed a project, either propose a new one to maintain the momentum and strengthen trust within the group or, if it’s time, bid your farewell and move on to the next opportunity.

It’s beneficial to change team members occasionally, as bringing in fresh perspectives can keep the group energized and foster creativity. Teams shouldn’t function like machines; creativity is vital, and sometimes, new ideas take you in different directions that might not suit the current team members’ focus. Another approach is to take a break from a group for a year or two and reconnect later when you have new ideas or projects. This allows flexibility and ensures that relationships are sustained, but not over-committed.

Additionally, rotating roles within the team, such as leadership or research tasks, can prevent burnout and keep everyone engaged. By nurturing these relationships and staying proactive in suggesting new projects, you can ensure long-term success in your research collaborations.

Typically, the person who initiates the project assumes a leading role, as they are the one with the original idea and vision. This individual reaches out to others to collaborate and contribute their expertise. However, if the initiator fails to take on a significant share of the workload, they risk being perceived as opportunistic—someone who builds their career on the efforts of others without contributing meaningfully. Unfortunately, this kind of behavior is not uncommon in academia and research circles. It undermines trust within the team and can damage professional reputations, as real collaboration demands that each member carries their fair share of responsibilities.

Young researchers should not be exploited simply because they are early in their careers and uncertain about their future in academia. At the same time, don’t shy away from the tasks you’re assigned; they are often an opportunity to prove yourself and demonstrate what you’re capable of. This brings me to an important point about recruitment—many PhD candidates and postdocs today are not only evaluated based on their qualifications but are increasingly given specific tasks to solve or even present. I believe this approach is valuable, as it provides a more practical assessment of their abilities.

When you start group work, outline the specific competencies needed within the team, providing examples for each role. Clarify who is responsible for what, ensuring that everyone contributes to data collection since it is often a time-consuming task. Assign one person to handle the submission process and follow up with reviewers. Ensure that the workload is evenly distributed so that no one is carrying too much or too little responsibility. It’s essential that there are no free riders. Team members should complement each other’s skills, filling in gaps rather than overlapping too much. However, there should be enough cross-checking to allow everyone to review and provide critical feedback on each other’s work.

An ideal list of participants and what they do may look something like in Table 3.11.

Table 3.11

Example of a detailed list of roles in a research team

Competence/role

Specific examples

Who does what

Data collection and follow-up

Balancing workload

Addressing gaps and avoiding free-riders

Principal investigator (PI)/team leader

Senior researcher, project leader

Sets research direction, manages overall progress, coordinates with funding bodies

Usually supervises data collection but may collect specialized data

Ensures even distribution of tasks, oversees progress, resolves conflicts

Reviews all sections, provides critical feedback, steps in when gaps arise

Data analysts/atatisticians

Quantitative/statistical experts

Analyze data, develop models, perform statistical analysis

Receive raw data from field researchers, clean and prepare for analysis

Heavy focus on data analysis, but must balance with interpreting results for the broader project

Check each other’s data interpretations to ensure accuracy, verify that models are correct, but don’t duplicate efforts

Field researchers (in reality, often everyone)

Survey collectors, lab experimenters, data gatherers

Collect primary data through surveys, interviews, or lab work

Primary responsibility for data collection, ensuring data integrity and completeness

Often have more intensive periods of work during data collection phases; balance ensured by rotating collection responsibilities

Data checked by team analysts; field researchers should collect efficiently but not take shortcuts

Literature review specialist

Expert in academic literature, can synthesize existing research

Conducts comprehensive literature reviews, writes background sections of the paper

Gathers secondary data and background research

Collects relevant literature, updates the team on current findings, documents sources

Helps others by providing background research but ensures not to overlap with analysis work

Data entry/management

Organizes and stores datasets

Sets up databases, ensures easy access to all team members, ensures data integrity

Enters raw data into central databases, ensures that datasets are accessible and clean

Critical in organizing data flow, needs to stay on top of updates to prevent backlogs

Oversees data quality control, avoids bottlenecks by keeping datasets regularly updated

Writers/editors

Experienced in drafting research papers

Drafts sections of the paper, assembles contributions from all team members

May use contributions from the whole team to create a cohesive narrative

Needs time for writing and multiple rounds of editing, balanced with data interpretation

Writers can’t bear all writing responsibilities; team members need to provide feedback and contribute parts

Review and submission manager

Typically an experienced researcher

Prepares final manuscript, submits to journals, follows up with reviewers

Submits paper, communicates with journal editors, handles reviewer feedback

Oversees final stages of paper submission and review process, needs significant time for revisions

Ensures submission timelines are met, manages responses from reviewers, distributes revision work among team

Project manager (if applicable)

Often overlaps with PI, organizes team tasks

Monitors deadlines, keeps the team on track, manages logistics of meetings

Sets milestones, tracks task completion, ensures data collected on time

Facilitates communication between members to keep the workload manageable

Ensures everyone is pulling their weight, removes free-riders through clear accountability

Peer review feedback providers

Internal team members provide peer reviews

Reviews sections of the paper for coherence and quality

Reviews each other’s work critically to offer improvements

Peer review ensures no one person bears too much workload

Feedback is constructive; gaps are filled by assigning responsible individuals

External collaborators/consultants

External experts offering niche skills (e.g., advanced statistical methods)

Provide specialized input or tools as needed

May not be involved in day-to-day operations but contribute expert data or methodology

Participation is specific and task-oriented, usually for limited periods

External collaborators should not replace internal efforts but complement the team’s expertise

This is an ideal and extended list, though, in practice, research teams are rarely this specialized. In most cases, teams are composed of only a few individuals with a good grasp of mathematics, essential for handling statistics, and at least one person adept at operationalization who can also analyze advanced literature to identify methodological gaps. Occasionally, an expert with specialized skills, such as advanced bibliometrics, might be brought in, but this is not common.

Most teams tend to be small, which can be an advantage, allowing for more flexibility and close collaboration. A more realistic model would reflect a balanced distribution of skills among the members, ensuring that each person’s strengths complement the others, leading to a more cohesive and effective team, as in Table 3.12.

Table 3.12

Example of a practical list of roles in a research team

Role

Responsibility

Task details

Notes on role

Operationalization lead

Charge of operationalizing the research question

Defines key concepts, constructs hypotheses, and develops a framework for analysis

Ensures the research is conceptually sound and can be effectively measured and works closely with the entire team to align goals

Math (stats) person 1

Charge of mathematical analysis and model development

Develops statistical models, conducts quantitative analysis, and interprets data through advanced statistical methods

Ensures accuracy and reliability in data interpretation

Math 2 (reviews)

Co-lead on mathematical analysis

Supports with data analysis, reviews and verifies models

Provides an extra layer of scrutiny to ensure the robustness of results

Data gathering (all members)

All team members, including junior researchers

Collects primary data (surveys, interviews, experiments) and coordinates fieldwork

Collaborative responsibility to spread the time-consuming work evenly

Submission and formatting (PhD student)

Manages formatting, finalizing, and submitting the paper

Ensures the paper adheres to journal guidelines, formats citations, and submits the manuscript

PhD student or junior member handles administrative aspects to support the team

Only the key components are highlighted here, as all members should be able to conduct literature reviews and gather data. Each function within the team should contribute to data collection as needed. For instance, if a sample of 400 responses is required, each team member could be responsible for collecting 100 responses. This ensures that the workload is evenly distributed and everyone is actively involved in the research process. The ability to handle both literature reviews and data gathering is essential for a well-rounded and efficient team.

If there is no math involved and you are writing a purely qualitative paper, it may be sufficient to work alone or with just one co-author. However, it’s always a good idea to get feedback from a colleague or two before submission to ensure clarity and depth.

I write most of my articles alone, simply because it’s more efficient. I don’t need to wait for others, coordinate efforts, or compromise. However, there are times when I really enjoy writing in groups, especially because it offers a great opportunity for learning.

3.4.2 Building Multidisciplinary Teams

Managing multidisciplinary teams is a complex yet rewarding task that involves integrating diverse expertise from various fields, such as combining social sciences with engineering or technology. One of the key challenges is ensuring effective collaboration between team members who may use different terminologies, methodologies, and approaches to research.

There are also cultural differences between, for example, natural scientists, engineers, and social scientists, which can make working together quite different and may take some time to get used to. In my opinion, engineers are often better at jumping straight into solving a problem, while social scientists tend to excel at discussing and analyzing problems—though I recognize this is a generalization. So, when working in highly multidisciplinary groups, you need more patience. You have to sit back and listen, take your time, and let others speak, at least until you understand how they approach their work.

A good team leader must facilitate communication and create an environment where each member’s expertise is recognized and valued. Clear communication is essential, and this often requires building a common understanding and vocabulary that everyone on the team can work with. A common mix in the social sciences involves economists, psychologists, sociologists, and engineers, with engineers often taking on more of the mathematical work due to their expertise in that area. On the other hand, this approach can be too limiting, overly predictable, and less engaging. Many researchers may be unwilling to join a group if their role is confined to a single task while others handle everything else. This doesn’t foster true teamwork and can erode trust within the group.

From the outset, aligning goals and expectations is crucial. Different disciplines may have varied research norms, timelines, or desired outcomes—some may prioritize theoretical work, while others aim for practical applications. The role of the leader is to bridge these differences and ensure that all team members are working toward a unified set of objectives. Misunderstandings can easily arise due to different research approaches, so it’s important to establish regular check-ins, host joint workshops, and encourage open dialogue to keep everyone on the same page.

In most of the projects I work on, where there is no external funding, there isn’t a clear “leader,” which I find refreshing. Instead, we meet as equals. There’s no need for someone to dictate what each person should do; instead, everyone volunteers or suggests tasks they can handle. It’s what I would call spontaneous research groups—when I have an idea for a paper, I think about who could contribute, scribble some ideas down on a piece of paper, and send it as a suggestion to the project. Most of the time, it works, but sometimes the others may want to contribute but can’t due to other commitments. Still, I prefer this model. It’s different when working with junior researchers and students, as they require much more guidance.

Decision-making in multidisciplinary teams should reflect the diversity of the group’s expertise. Leaders must ensure that all disciplines are represented fairly in these processes. One way to achieve this is by adopting a shared governance model, where team members take turns leading different phases of the project, ensuring that no one perspective dominates.

Methodological differences can present another significant challenge. For instance, one discipline may rely on qualitative research while another focuses on quantitative analysis. Bridging these gaps requires thoughtful strategies, such as developing frameworks that incorporate both methods or using mixed methods approaches that capitalize on the strengths of each discipline.

Managing multidisciplinary teams also often involves navigating the complexities of project funding. These teams often have access to a broader range of funding sources due to their varied fields of expertise. However, this also means dealing with differing expectations from funding bodies, requiring the team to carefully manage multiple review criteria and application processes. Successfully obtaining funding can allow for larger-scale, more impactful projects.

In summary, managing a multidisciplinary research team involves a careful balance of integrating diverse expertise, maintaining clear communication, aligning goals, and navigating methodological differences. Don’t overcomplicate things—it’s not that complex. At the core, it’s just a group of skilled people who have found each other and decided to collaborate on solving an interesting problem.

3.5 Academic Career Development

So, you’ve decided to start to build an academic career. How do you do it?

Building an academic career largely depends on your own efforts and dedication. The system is intended to be meritocratic, meaning that as long as you consistently perform well, produce valuable research, and contribute to the academic community, you should naturally progress to the next level, independent of what sex or ethnic group you belong to.

Do not expect too much hand-holding. This is not a nursery, but more like a bird’s nest. At some point, you will need to step to the edge and take the leap. Either you soar or you stumble, but even if your first attempt ends with a bump on the grass below, it’s unlikely to be fatal. Don’t wait for detailed instructions or expect too much guidance, as that can become an excuse to avoid jumping in and getting started. You love knowledge, have learned the rules of science, and off you go.

Reaching certain positions, like becoming a full professor, can take time, especially if your institution already has established figures in that role. There is often little appetite for having many professors in the same area, although clusters can form naturally as talent attracts talent. Sometimes, career growth may be limited by geographical constraints, such as a lack of universities in your area or an unwillingness to relocate. In that case, career opportunities may naturally diminish.

Building an academic career all the way to becoming a full professor is a long and challenging journey. It often takes 10 years or more to work at a university after your PhD and sometimes even longer. During this time, many of us will start families, which may reduce the amount of time available for research at different stages. This is why it’s crucial to establish good habits early on, in the years right after completing your PhD. If you don’t, you might struggle to advance later when you have the time but have forgotten how to do things. It’s not usually due to a lack of desire or intelligence, but rather a lack of consistent academic training and discipline.

What’s most important is to ensure that you are enjoying each stage of your career, whether it’s as a junior researcher, lecturer, or associate professor. Academic progress shouldn’t only be about climbing the ladder; it should also be about nurturing your passion for research and teaching. You may face periods of slower progress, but using that time to focus on developing new research ideas, expanding your network, and publishing high-quality work can help sustain your career. Lastly, teaching and mentoring students can be a rewarding part of academia, giving you opportunities to shape the next generation of scholars.

So, how long does each stage take? Someone may be wondering. A simplified version may look something like this. Each step will also vary depending on the country in question (Table 3.13).

Table 3.13

Stages and positions in an academic career

Academic stage

What is needed to reach this stage

Estimated time to reach

Teaching (without PhD)

A master’s degree, teaching experience, often temporary or adjunct positions

0–2 years after master’s degree

PhD Student

Acceptance into a PhD program, strong academic record, clear research proposal

3–6 years depending on discipline

Assistant Professor

PhD completed, publication record, teaching experience, potential for independent research

1–3 years after PhD

Associate Professor

Proven track record of publications, teaching, obtaining research grants, and peer recognition

3–6 years after assistant professor

Full Professor

Significant contributions to the field, strong publication and citation record, leadership in research

6–10 years after associate professor

Again, there are no uniform rules; these are just general guidelines, and these can vary significantly. Becoming a full professor often depends on whether there is a need for one in your area at your institution. This decision must first be cleared with university management, especially the dean. It’s not useful to have an oversupply of professors in one field while there’s a greater need for expertise in other areas. Therefore, university management often takes a strategic approach when appointing new professors. For you, that, unfortunately, means you may have to wait longer.

Case: Climbing the Greasy Pole in Academia

In theory, the academic system should be meritocratic, meaning you are promoted to associate or full professor based on the sufficient quantity and quality of your publications. However, several factors influence this process.

For one, a dean may resist appointing more professors, as professors are often harder to manage due to their vocal nature and strong opinions on university operations. Secondly, there may already be a chair in your discipline, and the faculty or university might not want more than one chair in each field. Thirdly, you might have to wait for the current chair or professors to retire before a new position opens, which could take years or even decades. You may also make the mistake of asking for an external evaluation of your merits too early. If you are found not to merit the advancement, it may take you many years again before you are even allowed to try again.

Consequently, climbing the academic ladder can be challenging. Some researchers transition to administrative roles, as this often offers an easier path to higher salaries and career advancement. Another option is changing institutions, which can also provide a broader experience and prevent stagnation in unproductive routines.

Five Discussion Questions:

1.

Should universities implement policies to limit administrative obstacles, such as deans blocking promotions, to ensure a more meritocratic system? Why or why not?

2.

Is the tradition of limiting chairs to one per discipline outdated, or does it serve an important purpose in maintaining academic balance?

3.

How might waiting for retirement openings impact the motivation and productivity of mid-career researchers? Could reforms address this issue?

4.

What are the advantages and disadvantages of researchers transitioning into administrative roles to advance their careers?

5.

How does changing institutions influence a researcher’s career growth and academic development? Should institutions encourage more mobility among academics?

The goal of science is not for everyone to become a full professor. A dedicated lecturer can contribute just as much by inspiring students, shaping future researchers, and maintaining high-quality teaching.

When building an academic career, it’s also important to establish a strong network within your field. You should start by reading the work of all the prolific researchers in our field. This will help you get familiar with your academic “tribe,” as we call it. These tribes can be both a blessing and a curse, by the way. They can foster valuable collaboration and support, but they can also become restrictive in their thinking, almost like cults at their worst, where the leading professors demand some sort of “following,” or at least deep respect. To avoid these situations, it’s crucial to bring in new people and new perspectives and keep the group diverse and open-minded. However, this may be a bit of a challenge, especially if you’ve chosen a small niche as your specialty.

Networking at conferences, collaborating on research, and engaging with scholars internationally can open up new opportunities for joint projects or positions at other institutions. Another key aspect is mentorship; finding a mentor early on can provide invaluable guidance in navigating the academic system, securing grants, and advancing through the ranks.

Students and researchers from German-speaking countries are, in my experience, much better at establishing mentorship relationships. This is because they have an ingrained way of viewing the teacher-student relationship as one built on trust, similar to the bond between a father and a son. While you can always ask a teacher or senior researcher to become your mentor, it’s usually best to wait until you know each other better before doing so.

In the academic world, mentorships are generally not paid and are traditionally based on a mutual intellectual connection, trust, and professional development rather than monetary compensation. These relationships are usually voluntary and built on shared academic interests, with senior researchers guiding early-career scholars in their research, career decisions, and academic growth. Emotional bonds and professional respect often underpin these relationships, especially when the mentor takes a personal interest in the mentee’s development.

However, there are some structured, formal mentorship programs, particularly in larger institutions or specific initiatives like fellowships, where a stipend may be included for mentors. But these payments usually cover the mentor’s time and effort as part of a broader academic program, rather than the personal, informal mentorships that are common in academia.

We could also say that there are official mentors and unofficial mentors. Official mentors are when a student asks a senior researcher to be their mentor, and they agree, often with a clear list of expectations or conditions. Unofficial mentors, on the other hand, are when you find a senior researcher whom you can approach whenever you need guidance, and you know that you’re always welcome, time permitting. You never formally ask for this, but it’s understood that you both share that kind of relationship. Of course, this doesn’t mean it’s unlimited in time, but it often remains unstated and may be time-restricted. As humans, we have a unique ability to navigate relationships intuitively, without needing to express everything or formalize it like a contract. Most of my mentorships have been of the unofficial kind, but I also occasionally get asked if I want to become someone’s mentor, often by a student from a Germanic country. I tend to say yes if we’ve had many good class discussions or in the role as a supervisor and if I feel I can offer something valuable to the person—of course, provided that time permits.

3.5.1 Other Key Ingredients for Building a Successful Academic Career

In addition to teaching and research, service to the academic community—such as serving on committees, reviewing for journals, or organizing conferences—is often required for promotion, especially at higher levels. Besides that, it is also excellent academic training.

Securing funding for your research is also crucial, as external grants demonstrate your ability to conduct independent research and attract resources to your institution. If you have a heavy teaching load, you can often somehow compensate for the lack of research funding as your position then will have another form of financing.

Publishing consistently in respected journals is another major part of career building. Quality often outweighs quantity, so it’s important to aim for impactful work that will be cited and recognized.

It’s also worth considering international opportunities, such as postdoctoral positions abroad or participating in global academic collaborations, which can significantly boost your academic profile. Finally, balancing teaching and research is a challenge many academics face, as both are essential for career growth and excelling in both is key to advancing in academia.

3.5.2 Committee Work and Service

As mentioned, service roles and committee work are integral parts of academic life, offering valuable opportunities for career development, networking, and leadership. However, these roles can also be incredibly time-consuming if not managed properly.

The first step to balancing these responsibilities is understanding the scope of service roles. Academics may be asked to serve on various committees within their university, sit on external boards, participate in community outreach, or review articles for academic journals. While these tasks are essential, they can quickly add up, making it important to assess how much time each service commitment will require.

In addition, you may be asked to serve as a representative for the union, or for one of several labor unions if multiple exist in your country. Employers might allocate a few hours for this role, though it rarely covers the actual time commitment. I have taken on this responsibility for several years and found it both useful and rewarding, particularly as it provides insight into employee rights.

I was also the chairman of the professors’ association at my current university for many years. This position was especially valuable as it allowed me to connect with colleagues from other departments. However, it’s a role that can be delicate, as the professors’ association may not always be formally established or fully supported by the institution. While some management teams express support for such associations, they may also prefer to retain control over decision-making, which can limit the association’s influence—particularly at newer institutions that may not have a strong tradition of the old inter pares system. Inter pares is a Latin term meaning “among equals,” referring to a governance system in which all members are considered peers, and leadership rotates or is shared. Primus inter pares means “first among equals,” signifying a leader chosen from the group who holds a position of authority but remains fundamentally equal to the others in terms of status and rights, normally associated with the vice-chancellor. The “first among equals” system, which developed in medieval European universities, emphasized shared governance among professors. It declined with the rise of New Public Management (NPM) in the 1970s, leading to the professionalization and bureaucratization of university leadership.

The inter pares system meant faculty members, particularly professors, would elect one of their own to serve as the head of the institution for a limited time. This system emphasized equality among the faculty, with the leader being considered the first among equals, with no absolute authority over the others. In contrast, the more recent New Public Management (NPM) system emphasizes the role of professional administrators, often led by a professor but seldom a “primus,” more frequently by someone seeking to take on a more political role in society. In this system, leadership and decision-making responsibilities are increasingly delegated to individuals without an academic background, yet they are entrusted with the task of managing the institution. While this system brings some efficiency to university administration, it has also created a greater distance between researchers and the administration. The move toward nonacademic administrators often leads to challenges in understanding the nuances of academic work, which can affect decision-making and the overall functioning of universities.

Exercise: Breaking the Bureaucracy: A Collaborative Leadership Model for Universities

Imagine for a while a university where the leadership is composed of professors who rotate in management roles for limited periods, a modernized version of the “academic leadership rotation” system. While this approach may seem like a throwback to the past, it offers a fresh perspective on governance that could prove more efficient than the current, bureaucratically driven model.

This model focuses on shared responsibilities, with professors taking on leadership roles for one to three years, overseeing key administrative functions like curriculum design, research priorities, and student affairs, before returning to their academic work. By ensuring no individual remains in a leadership role for too long, the system promotes continuous renewal, fresh perspectives, and shared responsibility.

Professors could delegate administrative tasks such as housing, HR, finance, IT services, student services (like career counseling and academic advising), facility management, marketing and communications, procurement, legal services, event management, and research administration to external private actors, ensuring that the university’s day-to-day operations run smoothly without relying on full-time bureaucratic staff. This delegation reduces the administrative burden on faculty, allowing them to focus on what truly matters: teaching, research, and innovation.

Unlike the traditional “inter pares” system of medieval European universities, which focused solely on shared governance and decision-making among faculty members, this modernized approach introduces professional expertise from external actors in nonacademic areas, enabling professors to prioritize academic excellence without being overwhelmed by the administrative load. Furthermore, this model would ensure a more dynamic and adaptable university, with professors managing with a clear and transparent system, guided by a long-term academic-focused plan.

If the university’s vice-chancellor were to deviate from this plan, perhaps due to external pressures, they could easily be replaced, ensuring leadership remains aligned with the institution’s academic priorities. This ensures long-term goals are stable, and professors maintain control over the direction of the university, free from external political forces or shifting administrative trends.

This model fosters fairness and transparency. Professors, who are intimately familiar with the institution’s academic mission, would make decisions prioritizing research and teaching rather than administrative convenience. In contrast to the traditional “inter pares” system, this approach benefits from modern expertise while maintaining strong academic leadership.

In summary, by delegating administrative functions to external private actors and keeping leadership responsibilities within the academic community, universities can enhance their focus on research, reduce bureaucratic overhead, and create a more collaborative governance structure. This modernized academic leadership rotation system offers a way to build a more sustainable, productive, and academically focused university environment.

Others would argue that the concept of rotating leadership among professors may sound appealing but that there are significant challenges that could prevent it from working in practice. First, professors are primarily trained in their academic fields, not in management, which could lead to a lack of effective leadership and decision-making during their term in charge. Second, rotating leadership could result in instability, as frequent changes in management might disrupt long-term projects, create confusion, or lead to inconsistent decision-making. Third, the need to rely on external private actors for administrative functions such as HR and finance could introduce risks, such as a loss of institutional knowledge or inconsistent service. Finally, while professors may have strong academic expertise, they may not possess the administrative experience necessary to efficiently run an entire institution, especially when it comes to navigating complex financial and legal matters.

What do you think?

Discussion Questions:

1.

What are the potential benefits of rotating professors into leadership roles for fixed terms, as opposed to having professional administrators?

2.

Could outsourcing administrative functions like HR, finance, and legal services to external actors lead to a more streamlined and cost-effective system?

3.

How might a leadership structure that focuses solely on academia and minimizes politics impact the university’s overall effectiveness?

4.

Would such a system allow for better continuity in long-term planning, or could it lead to instability?

5.

What challenges would arise in implementing a model like this, and how could they be addressed? Would this model be feasible in different cultural or institutional contexts?

If these questions arise too early in your career as a young researcher, revisit them again in ten years’ time.

Service Roles in Scientific Journals

If we are going to serve in various roles for scientific journals, as most of us likely will—starting by submitting papers as authors and then progressing to reviewer roles and potentially even editor positions—it’s crucial to understand how these journals are managed and operated.

Let’s also take a step back and consider the business models of most privately owned scientific journals first: Essentially, wealthy owners ask you to work for free by serving on the editorial board or as a reviewer. Your colleagues are asked to submit articles to the same journals for free—well, it’s even worse than that—they’re asked to pay to submit their articles. Even journalists get paid for writing. Readers also have to pay heavily to access the articles, and research funders, who may have funded the research, end up paying twice—once to fund the research and again to access the results. It is odd that this system was ever accepted, but somehow, it does function. The reason this system works is that, as researchers, we rely on journals that guarantee a certain level of quality. However, I doubt that the same business model would be successful in any other industry.

It’s difficult to get exact figures for the number of social science journals, but one estimate suggests there are between 8650 and 10,550. Assuming each journal publishes a minimum of three issues per year, which is often a requirement for accreditation (for example, by Scopus) and that each issue contains at least five articles, this results in at least 130,000 articles annually (Table 3.14).⁶

Table 3.14

Estimated number of social science journals and their major owners (Based on data from the National Science Foundation (NSF))

Discipline

Estimated number of peer-reviewed journals

Major publishing companies

Economics

1500–1700

Elsevier, Wiley, Springer, Emerald

Sociology

700–900

Sage, Routledge (Taylor & Francis), Springer

Political Science

400–500

Cambridge University Press, Oxford University Press, Routledge

Psychology

2500–3000

APA, Elsevier, Wiley, Springer

Anthropology

250–350

Wiley, Routledge, Cambridge University Press

Education

1200–1500

Springer, Sage, Taylor & Francis

Law

500–600

Oxford University Press, Cambridge University Press, Sage

Business and Management

1100–1300

Elsevier, Emerald, Springer, Wiley

Geography

300–400

Elsevier, Wiley, Routledge

Communication

200–300

Sage, Routledge, Wiley

Ledger: The table is based on data from Ulrich’s Periodicals Directory, which offers detailed information on peer-reviewed academic journals. It also uses figures from Scopus and Web of Science, which track indexed journals across disciplines. Additionally, publisher websites (e.g., Elsevier, Springer, Wiley) provide insight into the number of journals they publish in various subject areas.

It’s not as if everything is fine and dandy in this jungle of journals. According to Van Noorden (2017) in Nature, it is estimated that 20% to 50% of academic articles in certain fields go uncited, even when including self-citations, which themselves account for an estimated 10–30% of total citations. These numbers are only estimates but highlight the issue of research visibility and impact.

There are some valid reasons for self-citations in academic work, but they are limited. In niche or emerging fields, researchers often have fewer peers to cite, making self-citations necessary to build on their own earlier work. Additionally, when a scholar is leading research or pioneering new areas, they may need to cite their foundational studies to maintain coherence in their arguments. Editors or researchers with an authoritative position in a specific journal might also cite their previous work to ensure continuity or reinforce their expertise in the topic. Finally, in highly specialized fields, it is common to develop interconnected research that requires frequent self-citations for the sake of clarity and development of the topic. But otherwise, it is considered that they should be kept to a minimum.

So, the academic publishing market, or the publishing business model, is far from ideal, leading to the rise of alternative suggestions over the past two decades, such as open-access journals and open-source software platforms to support them. These initiatives are commendable, and I have participated in many, but they face significant challenges. One of the main issues is that existing journals have already established dominance in various disciplines and subfields, making it difficult for new entrants to gain traction. This is mainly because established journals have higher impact factors, and early-career researchers often aim to publish in higher-indexed journals for better career prospects. As a result, newer journals, despite their innovative and accessible models, often—though not always—struggle to achieve the same visibility and influence in the academic world.

More established researchers, who have little to lose by switching to open-access journals, often hesitate to do so. This is largely because they have built their careers within traditional private journals and see no immediate personal benefit in making the switch. Additionally, some seasoned academics become less focused on writing articles, preferring to concentrate on books or other forms of scholarship. These trends contribute to the fact that the open-access movement in academia faces an uphill struggle. However, as the saying goes, “Rome wasn’t built in a day.” The culture of open access may take time to gain full traction, but science has time on its side, and change is likely to continue evolving in favor of more accessible knowledge sharing.

Concluding Remarks on Service Roles in Academia

So, in conclusion, the importance of service in career development should not be underestimated. Participating in service roles can help academics become more visible within their institutions and fields, providing networking opportunities and the chance to take on leadership responsibilities. Some universities place a high value on service contributions when making decisions about promotions or tenure. Beyond institutional recognition, service roles can also open doors to collaborations and partnerships that might not arise through research or teaching alone.

However, not all service roles are equally valuable, and academics need to learn how to choose the right service roles. It’s important to select tasks that align with personal and professional interests, whether those involve research goals, networking, or enhancing one’s presence within the university. Saying yes to every request can be detrimental, so it’s equally important to feel comfortable saying no to tasks that don’t align with long-term objectives. Prioritizing service roles that directly benefit one’s research or teaching can help academics stay focused on their overarching career goals.

In this game, managing time for service commitments is critical. Service work, if not carefully controlled, can overshadow research and teaching responsibilities. To manage this balance, academics should limit the number of committees they participate in or take on roles with defined time commitments. Setting boundaries around service roles—by delegating smaller tasks to others or by agreeing on a clear scope of involvement upfront—can prevent them from overtaking core academic duties.

However, in my experience, the real thief of time is the barrage of meaningless administrative meetings or endless discussions about how to start the research you intend to do. Many colleagues seem overly fond of these prolonged processes, and some of them feel uncomfortable with the actual science itself, which may be related. As a result, these meetings become a substitute for action for many, allowing them to avoid diving into writing that book or that paper that has been waiting for such a long time in the desk drawer. Instead, the focus shifts to endlessly talking about what they plan to do, which would probably shock most who work in the private sector. This problem is sometimes even more pronounced in research institutes, where a lot of time is spent applying for funding and, once received, reporting back on how the money was used. In my opinion, this takes a lot of the fun out of doing science, and we need better systems.

To a large extent, researchers are being rewarded for their skill in writing and managing research grant applications rather than for conducting outstanding research itself. While securing funding is undeniably important, excelling in this area does not necessarily equate to producing high-quality research. Unfortunately, administrative bodies often place greater value on obtaining research grants than on the actual impact or quality of the research produced. As a result, the system has shifted focus from its original purpose—promoting scientific excellence—to prioritizing financial outcomes instead.

There are better ways of doing this. For example, funds could be automatically allocated to those with a proven track record of producing quality research. This process need not be complicated, as publication outputs, LinkedIn profiles, and other professional platforms already provide the necessary data for evaluation. The senior researcher, receiving the funding through this meritocratic system, could then allocate portions to emerging researchers, ensuring equitable distribution across both established and new scholars. I believe such a system would be much more efficient, promoting continuity while driving innovation in research.

Beyond funding allocation, academics should also consider delegating and sharing roles with colleagues. Collaborative service work not only spreads the workload but also makes large responsibilities more manageable. Instead of carrying the burden of committee tasks alone, sharing them with peers can increase both productivity and engagement.

Finally, balancing service work with research and teaching requires careful planning, but the benefits are significant. Engaging effectively in service roles can raise an academic’s profile and open doors to career advancement while still contributing meaningfully to the broader academic community. The key is to manage these commitments in a way that doesn’t overshadow the essential focus on research and teaching.

3.5.3 Tenure and Career Growth

If you’re a businessman, you aim for an IPO—that’s when the big money comes in. For an academic, the equivalent is securing a tenure position. Once you have tenure, that’s when you can finally relax, or so many think. At least it gives some security. Unfortunately, the university administrators know this too, so they try to wait to give you one as they are afraid you will be less productive.

Just like employees in other fields, researchers and academics seek steady, secure positions, often referred to as tenured positions. The tenure system varies greatly depending on the country. For instance, securing a tenured position in the USA is significantly more difficult compared to Norway. In countries like Norway, obtaining tenure as a teacher often only requires a PhD, completion of pedagogical training, and securing a full-time position at a university. In these cases, you may be on a probationary period for the first six months, but after that, the position becomes tenured automatically.

However, this is not the case for PhD students or non-PhD staff and many postdoc positions nowadays, who usually remain on temporary contracts. A postdoctoral position, or postdoc, is a temporary research role taken by individuals who have completed their PhD and are gaining further specialized experience in their field before seeking permanent academic or research positions. In some organizations, particularly research institutions, researchers may be at risk of losing their positions if they fail to secure sufficient research funding. On the other hand, tenured positions in academic departments are often protected by union agreements, offering greater job security. It’s crucial to understand the specific tenure rules in each country, as there are no universal guidelines that apply to all.

Country-specific tenure systems: Different countries have distinct tenure processes. In the USA, tenure follows a strict, time-bound track system where faculty must demonstrate progress over a set period. In contrast, many European countries require researchers to apply for permanent positions rather than offering automatic tenure after a certain number of years. The criteria for evaluation also differ, with some countries placing a greater emphasis on teaching performance, while others prioritize research output. This national variance significantly affects the pathway to tenure.

Steps to achieve tenure: The general journey to achieving tenure typically involves several key steps. The researcher needs to build a robust publication record, secure research grants, and demonstrate excellence in teaching. Additionally, contributions to service roles, such as committee work, are often required. Achieving a balance between these demands over several years is critical for tenure track faculty in most countries, at least in the Western world.

So, given this long and tedious process, it is no surprise that there are a number of challenges in the tenure process. Many researchers face the pressure to publish in high-impact journals, which can lead to work-life balance issues. Rejection, whether in publications or grant applications, is another common obstacle. Moreover, institutional politics or shifting university priorities may affect one’s chances of obtaining tenure, creating further uncertainty.

For those who finally make it, obtaining tenure provides job security and grants academic freedom, enabling faculty to take greater risks in their research. The long-term benefits of tenure include increased influence within the academic community and the opportunity to take on leadership roles within the university or professional organizations.

In summary, tenure systems vary by country and institution, but regardless of location, achieving tenure is a significant milestone in an academic career, offering both security and freedom to pursue new bold ideas or just to take it a bit easier.

Most researchers I have met are not primarily loyal to their university but rather to their field of study and to colleagues they respect within their department. However, since universities are not abundant, most researchers will have to relocate if they want to change jobs. As a result, researchers are, at least in principle, highly mobile and willing to move to another institution that offers better opportunities, whether in salary, funding, or research environment. In many ways, this is a healthy dynamic, as it allows for intellectual cross-pollination, brings fresh perspectives to universities, and helps researchers broaden their experience across different academic cultures. However, changing universities can be a slow and complex process, often taking a year or longer, as academic hiring cycles, grant dependencies, and relocation logistics create significant delays. Once a researcher becomes a full professor, mobility becomes even more challenging, as there are fewer equivalent positions available, making the search for a new professorship longer and more uncertain. That said, the academic community remains highly interconnected, as researchers regularly collaborate across institutions, meet at conferences, and review each other’s work, forming one shared intellectual network rather than being bound to a single university.

3.5.4 Creating a Research Identity

As you start as a researcher, you need to begin thinking about where you want to leave your mark. We cannot excel in everything, and the days when one could present themselves as a polymath have become less accepted. Just claiming to have two main research areas can raise eyebrows today, especially if they are quite distinct. “So, what is it then? Are you an expert in X... or Y?” a colleague might ask, as if trying to grapple with an unresolvable issue. Therefore, it is essential to specialize in something—not necessarily a single problem, but an area or, at most, two areas. Some succeed in many areas, such as Herbert Simon (economics, cognitive psychology, artificial intelligence, decision theory, organizational theory) or John von Neumann (mathematics, physics, computer science, economics, game theory, statistics), but they are not many. It’s an advantage if these areas that you eventually focus on are related in some way and belong to the same discipline. Once you expand to three areas, it quickly becomes overwhelming to many.

So, creating a unique research identity is essential for any academic aiming to make a lasting impact in their field. Developing a niche is often the first step, and this involves identifying a specific area of interest where you can establish yourself as an expert. Just make sure you enjoy that area first and take your time when selecting one. Early in your career, you may need to decide whether to focus deeply on a narrow subject or to embrace interdisciplinary research. Both approaches have their merits. Specializing in a niche allows you to fill gaps in the literature and become an authority on a particular subject, whereas interdisciplinary work can position you as a connector between different fields, offering unique perspectives.

In my experience, claiming expertise in interdisciplinarity alone is not a true specialization. To be credible, you must first have a deep understanding of at least two distinct disciplines; otherwise, your interdisciplinarity lacks substance. Interdisciplinarity by itself is a method, not a discipline or subfield. I often see this mistake—people stating that their specialty is simply “interdisciplinarity” without explaining what the specific components of that interdisciplinarity are or how they have achieved expertise in each of those areas.

My own modest PhD and subsequent research illustrate this problem. The thesis was interdisciplinary in the sense that it brought together economics, business, and some philosophy, all distinct disciplines, but I did not get very far into either of the directions in the thesis itself. Additionally, it covered various areas, or subfields, such as negotiations, international business, and information sets. What made it interdisciplinary was not the inclusion of multiple subfields but the way I tried to integrate and apply knowledge from each discipline. Interdisciplinarity requires more than just blending fields; it demands a deep engagement with each discipline involved, which is often overlooked and difficult to succeed with, in my experience. As you progress in your career and accumulate more knowledge across different fields, it becomes easier to integrate and engage with multiple disciplines, as your understanding of their nuances deepens.

Building thought leadership requires going beyond simply publishing articles. To be recognized as a leader among researchers, you must actively engage with the academic community through conference presentations, invited talks, and contributing to high-profile review papers. First, it’s essential to contribute genuinely new ideas to the field rather than simply repackaging older ones. This is a persistent issue in the social sciences, particularly in recent decades. Researchers often focus intensely on reading the latest articles, creating the impression that innovative thought is happening at a faster rate than it truly is. New ideas are often treated like commodities, “sold” on the academic market, but the unfortunate truth is that many of these ideas are just recycled versions of older concepts. Few researchers recognize this, largely because the emphasis on recent literature causes them to overlook historical foundations. This is the curse of neglecting history—without a solid understanding of the field’s past, it’s easy to mistake slightly rebranded ideas for groundbreaking innovation.

These new ideas need to be not only clear and coherent but also offer a fresh perspective on how to approach a problem. This is, of course, a tall order. For many researchers, the focus is on making incremental contributions to the existing body of knowledge—a modest but valuable step forward. Attempting to transform these smaller contributions into a grand narrative that positions you as a thought leader can be daunting. It requires a leap from simply advancing knowledge to redefining how others see and approach the field, and that’s where the challenge lies. Thought leadership is not built on small advances alone but on a bold vision that inspires others to think differently.

Now, how can we achieve this?

Participation in editorial boards or advisory panels can raise your visibility, as it associates your name with key decision-making processes within your discipline. These roles also provide opportunities to shape the direction of future research in your field, further cementing your status as a thought leader.

Branding yourself as a researcher is equally important in the digital age. Strategic networking, both in person and online, helps reinforce your research identity. Engaging with scholars on social media platforms like Twitter or LinkedIn, sharing your publications, and offering commentary on current trends in your field can help keep your work in the spotlight. Few things are more refreshing than starting your social media channel, like a YouTube channel. It acts as a litmus test to see if your ideas and communication skills resonate with others. It provides instant feedback on whether your content engages, educates, or entertains and offers a great way to assess how effectively your message connects with your audience. Through viewer reactions and comments, you get real-time insights, allowing you to fine-tune your approach and see if your ideas have a broader appeal beyond academic or professional circles.

Your publications and presentations should have a consistent theme or central thread, which helps others easily recognize your contributions to the field and remember your expertise. That is, when they see you, they should associate you with something, a discipline and a subfield at the least.

As you progress in your career, mentoring younger scholars, such as PhD students or junior colleagues, becomes a key part of establishing a legacy. By fostering the next generation of researchers, you not only help develop the field but also extend your influence. Forming a research group where your students or mentees can collaborate strengthens your identity as a thought leader, as the work they produce under your guidance directly contributes to your own research trajectory.

Example: Legacies

Legacies can take many forms. One example comes from a former teacher at HEC in France, Joseph Le Bihan, who, upon retiring, transformed his modest home in Brittany (his mother’s farm) into a thriving conference center. I recently had the privilege of attending a conference there in honor of his 94th birthday, and the venue was alive with scholars, former students, colleagues, and a large contingent from the general public.

Le Bihan’s legacy is not defined solely by his research but by the vibrant academic community he nurtured through his lectures and teachings. His lectures in geopolitics were legendary, often going on for hours after they were supposed to end. He filled the halls with exotic guest lectures from industry and the public sector, and when he was all done, he could sometimes go and eat dinner with whoever had not gone home.

His impact stems from his ability to inspire others as a knowledgeable academic and an extraordinary communicator. His passion for the subjects he teaches radiates through his work, creating a unique and enduring connection with those fortunate enough to learn from him. Joseph, who is still with us, was—is—truly unique as both a teacher and a researcher. He will undoubtedly leave a significant legacy, and that is well deserved.

Cultivating a research identity is not an overnight process, but rather a continuous effort that requires a combination of publishing, public engagement, and strategic networking. This is something that hardly anyone achieves as young researchers, but it is precisely during those early years that you lay the foundations for your future identity. It’s a process that’s hard to imagine, let alone plan for, and often feels more like an unexpected bloom from a cherry tree you didn’t even notice was there. As with any long-term endeavor, the growth is slow and not immediately apparent, but the efforts you make during this time are crucial for later successes.

Your research focus should evolve over time, reflecting both the developments in your field and your own intellectual growth. Staying flexible while maintaining a clear academic identity allows you to adapt to new challenges without losing the core of your expertise. Ultimately, your research identity will be defined not by the number of four-star articles or your h-index, but by the meaningful contributions you make to advancing knowledge, how you share that knowledge with the broader community, and the legacy you build through mentorship and collaboration. It’s about the impact you have, the relationships you foster, and the way your work resonates beyond the confines of academic metrics. As long as you can preserve your health, these enduring elements will define your true academic legacy.

3.5.5 Sabbaticals

Once you have secured a tenured position and have established some recognition in your field, it’s a good time to start considering how to obtain a sabbatical year. This period allows you to dedicate more focused time to a research project or perhaps even to write a book, without the usual pressures of teaching or administrative responsibilities.

A sabbatical is a period of leave granted to academics, usually for the purpose of research, writing, or professional development. Sabbaticals differ greatly across countries and institutions, with some offering fully paid sabbaticals, while others provide partial funding or unpaid leave. In some countries, like the United States, sabbaticals are more commonly built into a faculty member’s contract, usually every seven years, while in other regions like Europe, the system may be more flexible or tied to external funding opportunities. To secure a sabbatical, it’s important to plan carefully and align your proposed project with both personal goals and institutional priorities. Universities often look for clear research plans that show the potential for significant scholarly contribution or impact.

To enhance your chances of being granted a sabbatical, it’s essential to prepare a detailed proposal. This should include your research objectives, timelines, and expected outcomes, whether it’s completing a book or advancing a long-term research project. Aligning your proposal with the university’s strategic goals, such as expanding its research profile or fostering interdisciplinary collaboration, can further strengthen your case. Additionally, securing external funding can be crucial, particularly in institutions where sabbaticals are only partially funded. Fellowships or research grants can support your time away and cover associated costs.

Sabbaticals are invaluable for academics as they provide uninterrupted time to focus on research without the day-to-day demands of teaching and administrative work. This allowed extra time often leads to breakthroughs in long-term projects and allows researchers to explore new methodologies or interdisciplinary work. For example, many scholars use sabbaticals to visit other universities or research institutions, building new networks and collaborations. These experiences can lead to new opportunities and a broader understanding of global academic trends.

Another critical aspect of research productivity during a sabbatical is securing research grants. Large grants, often from national or international funding bodies, provide the financial support necessary for ambitious projects. Writing successful grant proposals is a skill in itself, requiring careful attention to the criteria and expectations of the funding organization. Networking plays an essential role here, as collaboration with established researchers can make a proposal more competitive.

Balancing research grants with teaching responsibilities is a common challenge. In many cases, securing a grant allows for teaching buyouts, reducing a faculty member’s teaching load to focus on research. This is especially helpful when planning multi-year projects or writing books, where uninterrupted time is necessary. However, managing both teaching and research obligations while applying for grants can be daunting, requiring excellent time management skills.

In some unfortunate cases, academics may face what can be called an unwilling sabbatical or time-out, where external factors force them into a break from their usual duties. This could be due to institutional budget cuts, administrative leave, conflicts, or health reasons. Unlike a voluntary sabbatical, these breaks can disrupt professional momentum and create uncertainty about future career prospects. In such cases, academics may struggle to maintain productivity and financial stability during the imposed leave, but they can also be opportunities to engage in new research projects.

3.5.6 Time Management

So, how do you become a good researcher? Is it those who are very smart who succeed?

In my own experience, this is not the case. What is needed is what the Germans call “Sitzfleisch”—the ability to sit still for hours and work. It’s about what you do during those hours, and whether you’re able to work effectively.

Today, efficiency also includes working with AI tools while ensuring that whatever comes out is still your responsibility to verify. For years, we’ve used grammar programs that help us save time by constructing better sentences. Now, we also have tools that make our ideas clearer, much like having a research assistant at hand. This isn’t all that different from when the calculator came into use, freeing us from manual math operations with pencil and paper. With AI, we now get help in systematizing our thoughts, and the time saved from this is significant for all kinds of intellectual work. It will revolutionize the way we do science, and we’ve only just begun.

Most successful scholars I’ve met haven’t struck me as being extraordinarily smart but, first of all, as well organized and disciplined. That is, they are deliberate about how they spend those hours, avoiding distractions and focusing directly on the science. That’s why courses in scientific method are so important—knowing how to do science should become your second nature. Only by internalizing this will you be able to fend off all the other activities that try to steal your time. The wonderful thing about books on science is that you do not necessarily depend on having a good supervisor; you can educate yourself.

Balancing the Triad: Teaching, Research, and Service in Academic Life

Balancing teaching, research, and service is one of the greatest challenges academics face. The amount of time dedicated to each area often depends on personal interests as well as the specifics of the position and contract. For instance, many professors have positions that allocate 50% or more of their time to research, while others may focus heavily on teaching, particularly at institutions where teaching excellence is prioritized. The balance between these roles must be carefully managed to avoid burnout and ensure productivity.

Burnout is a genuine concern, not just among PhD students, though they may voice it more openly. It’s equally prevalent among colleagues who have gradually stepped back from active research, contributing only occasionally to papers. This can stem from various factors: personal choices, shifting interests, increased focus on family responsibilities, or even disillusionment with the role of being a researcher. Administrative pressures, which often seem more self-serving than supportive, can also contribute to this disengagement.

I deeply respect the challenges involved in sustaining a decades-long career in academia. It’s not uncommon to encounter colleagues who approach the job with minimal enthusiasm, focusing solely on their teaching hours and leaving as soon as possible. While less visible than traditional burnout, this disengagement can also be seen as a form of burnout—one that reflects the difficulty of maintaining motivation and passion over the long term.

Avoiding long hours spent commuting is a significant advantage, and working from home has been a blessing for many researchers. As we have seen, the productivity of researchers can be measured by their output, so the notion that they aren’t working hard at home is easily dispelled by the amount of effort required to produce a quality article or case study. If commuting is unavoidable, using that time wisely—such as by listening to educational podcasts—can be beneficial.

Case: Finding Your Quiet Moments and Places to Work

For years, I’ve made it a habit to work on trains. Much of my research is actually written during these trips, like this book. On a typical day, when I’m not working from home, I head straight to a quiet compartment on the train and dive right in. With 2.5 hours each way, it becomes a full workday of solitude, just me and my thoughts—a fantastic setup, especially since I’m a big fan of trains and train stations.

In contrast, I have a much more complicated relationship with airports and planes, which I find to be a cocktail of stress and discomfort. But short train rides, say 30 minutes, can be a challenge for focus—knowing you’ll be getting off soon makes it hard to settle in. Some researchers prefer the office for the concentration it offers, escaping the distractions of home. At the end of the day, every researcher has to figure out their own ideal work environment—those precious, quiet moments where focus can truly thrive.

Be aware of anything that steals your time and destroys your concentration. Stop attending meaningless meetings if you can (although, at the beginning, this can be difficult). Remember why you are there: It’s to teach students and conduct research. Nothing else. Everything else is a “thief,” with one major exception. Most of us are also obligated, or find it beneficial, to collaborate with the surrounding society, which is what we sometimes refer to as “the third mission.” This concept is encapsulated in the “Triple Helix model,” where industry, government, and academia work together to drive innovation and societal progress.

Here are some questions for reflection:

1.

Where do you usually sit and work, and why have you chosen that particular time and place?

2.

If you could choose anywhere else to sit and work, where would it be and why?

3.

How do you think the time of day and the location you work impact the quality of your work?

4.

How long do you find it necessary to work for it to feel worthwhile? Is there such a thing as “too short” a session? Where do you think the limit lies?

5.

How much time would you like to spend on “the third mission” and why?

Take your time to answer each question, if you can.

Teaching requires a significant amount of preparation, engagement with students, and grading, which can sometimes make it hard to find time for research. The solution is often in time management, setting dedicated hours or days for research to ensure that teaching does not completely take over. For those who enjoy research more, it’s important to remember that effective teaching can provide new insights and inspiration for research. You can also involve your master’s students in your research, creating opportunities for them to think critically and collaborate with you. This approach works particularly well if you’re teaching scientific methods or courses that require students to produce research papers or projects, as it allows students to gain practical experience while contributing to real-world studies. Such collaboration can be mutually beneficial—students develop their skills and understanding, while you gain fresh perspectives and support for your research.

However, this practice is not without its ethical considerations. It’s essential to ensure that students’ contributions are appropriately acknowledged and that their participation is voluntary and based on genuine interest. Additionally, in recent years, stricter regulations and demands for transparency in academia have made this process more challenging. For example, concerns about power dynamics, conflicts of interest, and the proper delineation of credit and authorship must be carefully navigated.

Institutions often now require clear documentation of the scope of student involvement and formal ethics reviews for collaborative projects. While these safeguards are necessary to maintain integrity, they can add administrative burdens and deter researchers from engaging students in meaningful ways. Balancing these ethical and practical concerns is key to fostering a collaborative and enriching academic environment.

Time management in academia is crucial in that process, for balancing the many responsibilities that come with a career in teaching and research. One of the first steps in managing time effectively is setting clear priorities. Academics often juggle various tasks such as research, teaching, administrative duties, and service to the institution. Identifying which tasks are most urgent or contribute the most to long-term career goals is important. For instance, meeting grant proposal deadlines or finishing a research article may take precedence over smaller, less impactful tasks. Flexibility is also important, as new responsibilities can arise unexpectedly, requiring quick shifts in focus.

A helpful strategy for managing time is structured scheduling. By using techniques like block scheduling, academics can allocate specific times of the day for focused activities. For example, mornings might be reserved for research and writing, while afternoons can be dedicated to preparing lectures or meeting with students. This method helps maintain productivity by avoiding the constant switching of tasks. Tools such as digital calendars, task managers, or time-tracking apps can provide structure and allow academics to track progress over time.

Case: How to Set Your Working Routine

This case is related to the previous one on time and place, but here, we are more interested in the overall routine. I will start with an example first, the one I know best, my own.

I divide the day into two parts: before and after lunch. During lunch, I actually don’t eat (but that’s another story). Instead, I take a 30-minute nap after taking the dog (a Westie, for those who know the breed) for a walk, if I am at home. This gives me some fresh air, and I feel more rested. I usually finish around 16:00 and then sometimes even work out for an hour before we have dinner. This gives me about 6–7 hours a day to be productive, if you ignore the usual interruptions that come with “real life”—you know, like buying food, picking up at school, and the occasional existential crisis.

Sometimes, I also work in the evenings if I get too excited, like right now while trying to complete this book. Before I was married, I used to work most weekends too, but I stopped doing that once I started my own family and became a full professor. I don’t think it’s right to sacrifice everything for work; there are other things in life too.

When I start my morning, I first check the meetings scheduled for the day and then focus on answering any necessary emails. Once that’s done, I dive into my research projects, starting with the one that has the highest priority. If I’m teaching, I head to the university or conduct online seminars and supervision, which is a great time saver for both me and the students.

If I nap through lunch, I can usually keep going until midnight. But if I skip it, I sometimes find myself yawning after dinner—especially if I’ve been out and the trains have turned into a chaotic mess of delays and frustration.

Here are some discussion questions:

1.

How do you think dividing the day into distinct parts, as described above, might benefit productivity? How would you do it yourself and why?

2.

What are the potential benefits and drawbacks of working in the evenings or on weekends, especially in an academic or research career?

3.

How can one find a balance between productivity and personal life? Any ideas?

4.

In what ways might short breaks, like a mid-day nap or a walk, influence one’s focus and productivity over the course of the day? Do you think it’s important to incorporate these kinds of breaks into a daily routine?

5.

Given the shift to online seminars and supervision, what are the advantages and challenges of online versus in-person academic interactions for both professors and students?

6.

How does this routine prioritize different types of tasks, such as meetings, emails, research, and teaching? Are there any changes you might make to improve efficiency or work-life balance?

These questions are meant to encourage you to think critically about routines, work-life balance, and productivity strategies in the context of an academic career.

One of the ongoing challenges in academia is balancing long-term and short-term projects. Short-term tasks like grading, answering emails, or preparing for upcoming classes often consume much of the day, leaving little time for the more significant long-term goals such as writing research articles in a large group or applying for grants. To avoid this, it is helpful to set weekly or monthly goals that ensure incremental progress on long-term projects. By breaking down larger tasks into smaller, manageable actions, long-term goals can remain on track even amid daily responsibilities.

Case: The Realities of Collaborative Case Study Research: Time, Patience, and Persistence

I’ve been part of a team working on a case study for four years now. Just getting the NDAs signed took a full year. We conducted eight interviews, which only took a few months, but in between, we held countless meetings with the company, mainly to test ideas and get a sense of what they wanted. Sometimes, they’d change their minds about the content, while we’d explain what we needed for a solid case.

A lot of time went into simply understanding each other’s priorities. One team member eventually dropped out, citing time constraints, I guess, which is understandable. The companies often accept these cooperations to get good publicity. We can make some compromises, but not many—after all, we’re doing this for science.

For what is only a 3000–5000-word study, it’s been a significant time investment. Most of the time goes into planning, coordinating meetings, exchanging drafts, and, above all, waiting—plus the ongoing discussions about what to include or exclude.

If the content weren’t so compelling, I likely would have given up long ago, but now we’re almost finished. Along the way, you also build friendships, often without even realizing it, which may even give you consultancy work, if you are lucky.

Anyway, deciding whether to join a research team or drop out is like juggling a thousand flaming torches—it’s a constant balancing act where you weigh the time you invest against the hope of some sort of outcome. If you’re in a research group that’s secured funding, it’s even worse—because now you’re likely stuck for the long haul, enduring inefficiencies and personal squabbles. Especially if you’re the one in charge of the budget. If someone drops out or doesn’t deliver, guess who gets to pick up the slack? Yep, you.

Here are some student discussion questions:

1.

What are the challenges of balancing scientific rigor with a company’s interest in publicity, and how might this affect the content and credibility of a case study?

2.

How does the long timeline for producing this case study highlight the importance of patience and persistence in collaborative research projects? What strategies might help researchers manage these extended projects?

3.

What role does relationship-building play in achieving successful collaboration? How do you do it?

4.

Given the time-intensive nature of such a case study, what factors should researchers consider when deciding to embark on similar projects in the future?

5.

What are the potential benefits and drawbacks of researchers forming friendships with industry partners or team members throughout a project? How could these relationships impact the research process and outcomes?

6.

Reflecting on the dropout of a team member, how can research teams ensure that all members remain engaged and committed over a lengthy project?

These questions can help you think about managing collaboration, balancing scientific objectives with external interests, and building professional relationships within research projects.

Delegation and collaboration can also significantly ease the workload. Many academics hesitate to delegate, but sharing responsibilities with teaching assistants or research assistants can free up valuable time. Additionally, collaborating with co-authors on research projects not only distributes the workload but can also lead to more innovative outcomes. Effective collaboration and delegation can greatly enhance overall productivity while fostering a supportive academic environment. Far from all delegation’s work, but some do, and then, it is worth it. Most of the time, however, you must be prepared to do everything yourself.

Managing time in academia is about striking the right balance between immediate demands and long-term goals while maintaining a healthy work-life balance. With the right strategies, academics can navigate the complexities of their careers more efficiently. It’s all about starting to build healthy working habits.

3.5.7 External Appointments

If our schedules are already full, it can be difficult to find time for external academic appointments, even if we want to. However, the additional salary can be an attractive incentive.

External academic appointments offer both opportunities and challenges for academics who seek to expand their professional horizons. Holding part-time positions at multiple institutions can provide additional income, foster new research collaborations, and broaden an academic’s network. Just be aware not to break any agreements with your primary employer when taking on external work.

In the Nordic countries, for example, it is generally acceptable to work up to 20% at another institution, but you still need to report this every year. Some professors manage multiple employments at different institutions, carefully arranging their leave percentages at each place to ensure their combined workload remains within allowable limits. While this strategy can maximize opportunities and income, it often comes with significant stress and logistical challenges. Balancing responsibilities across institutions can lead to overcommitment, leaving less time for research, teaching, and personal well-being. Furthermore, this approach carries risks, as exceeding permissible limits or failing to meet expectations at any institution could jeopardize professional relationships and career stability. It’s a delicate balancing act that requires careful planning and a realistic assessment of one’s capacity.

The reporting requirement also applies if you work with companies or engage in consulting on the side. Many professors, especially full professors, are often called upon to serve as evaluators for academic positions at other universities, educational programs, or even government bodies conducting official investigations, often to inform policy decisions. These external roles, whether in academia, the public sector, or industry, typically come with compensation—sometimes quite substantial. For instance, professors in the UK or Germany may be asked to serve on committees evaluating research funding applications, while those in France might contribute to the design of national education strategies. In some cases, professors also participate in corporate board memberships or consult on industry projects, which can be highly lucrative. Regardless of the opportunity, it’s essential to maintain transparency with your primary institution to avoid any potential conflicts of interest or breaches of employment contracts.

These roles often allow for the sharing of expertise across different academic environments, contributing to more diverse teaching experiences and research projects. However, again, balancing multiple commitments requires careful time management and strategic planning to ensure that all obligations are met without compromising the quality of work at any one institution.

So, a key aspect to consider when holding external appointments is the reporting obligations to your primary institution. Universities often have strict policies to ensure transparency and avoid conflicts of interest. It’s essential to disclose all external roles to maintain institutional trust and comply with guidelines that govern secondary employment. Many institutions require faculty to report external engagements to ensure there is no overlap or conflict with their primary duties, such as teaching, research, or service to the home institution. This also protects against any potential legal or ethical issues that could arise from holding multiple roles.

Balancing teaching, research, and service responsibilities across different universities is one of the most significant challenges for academics with external appointments. Managing these obligations effectively requires a clear understanding of the workload at each institution and the ability to prioritize tasks. Academics need to ensure that they do not overextend themselves by taking on too much, which can lead to burnout or a decline in the quality of work. Institutions may also have different expectations for teaching loads, administrative duties, and research outputs, making it necessary to adjust one’s work schedule accordingly.

We may ask, is it a bad idea to have a large part of the faculty engage in consulting instead of focusing solely on research? Well, it depends. While conducting academic research is essential for advancing knowledge and ensuring the institution’s reputation, consulting can bring significant advantages. Consulting with industry often leads to better, more applied research projects by aligning theoretical work with practical challenges. Additionally, faculty who consult can bring up-to-date industry knowledge and real-world examples into the classroom, making teaching more relevant and engaging for students.

At many institutions, it’s the vice-chancellor or senior leadership who sets the tone for what is encouraged or allowed. They influence the hiring process by deciding whether to prioritize faculty who excel in pure research or those with strong industry ties. For example, when I started teaching some 25 years ago, at Copenhagen Business School under Finn-Junge Jensen, there were many “consultant types” on the faculty. I appreciated this because it made the school feel more connected to real-world business challenges. You could smell the business in the halls. Over time, as leadership changed, the emphasis shifted toward more traditional “academic types,” and today, CBS is recognized for its robust scientific output more than ever. However, whether it has become a better business school because of this shift is debatable.

Institutions like Harvard Business School and INSEAD are well known for encouraging a balance, where faculty actively consult but also contribute significantly to academic research. This model ensures that cutting-edge industry practices are integrated into research and teaching. Ultimately, the right balance depends on the institution’s mission and how it aligns with the needs of its students and faculty. Too much focus on either consulting or research can result in missed opportunities to create a well-rounded academic environment.

It’s important to follow institutional guidelines on external work to avoid conflicts of interest. Each university typically has policies in place to prevent faculty members from overcommitting or engaging in activities that could conflict with their primary responsibilities. These guidelines help safeguard both the academic and the institution by ensuring that external roles do not interfere with core duties. Academics must navigate these rules carefully, balancing their professional growth with the commitments they’ve made to their primary employer.

In summary, while external academic appointments offer valuable opportunities for professional growth, they also demand careful management of time, obligations, and institutional relationships. Academics must be transparent with their primary institution, mindful of potential conflicts of interest, and diligent in balancing their commitments. By doing so, they can make the most of these external opportunities without compromising their core academic duties.

The bottom line is that universities shouldn’t be afraid to allow their staff to engage with industry on the side. In my experience, while there might be a reduction in the number of traditional paper outputs, this is more than compensated for by creating more dynamic and practical research opportunities. Faculty involvement in industry often brings real-world problems back to the academic setting, making research more relevant and impactful: ultimately leading to more innovative outcomes that can benefit both academia and industry alike. This is also beneficial for students.

There’s a simple test for this: Next time you visit a university, particularly one with a program focused on industry, such as engineering or business, take a walk through its halls. Pay attention to whether the space is buzzing with companies setting up stands and promoting job opportunities. If the halls are empty, it’s often a sign that the researchers in that department have too little consultancy work or engagement with industry, that the administration is neglecting existing relationships that their staff have, or various reasons.

3.6 Role of Bibliometrics

We have touched on the topic of bibliometrics several times thus far in the book, but we have not done any comparisons with different systems yet.

Bibliometrics is the quantitative study of scholarly publications, primarily used to evaluate the impact and productivity of academic research. It involves measuring various aspects such as the number of articles published, citation counts, and journal rankings to assess the influence of researchers, journals, or institutions.

For example, citation indexes like Google Scholar and Web of Science track how often a research paper is cited, which helps determine its impact within the field. We can call this the measurement of scholar quality. One common metric is the h-index, which reflects both the productivity and citation impact of a researcher. Additionally, journal impact factors measure how frequently articles in a particular journal are cited, serving as an indicator of a journal’s influence in the academic community (Table 3.15).

Table 3.15

Example of websites that offer bibliometrics

Website

Measurement focus

Owner

Google Scholar

Tracks citations, h-index, i10-index, number of articles published, and citations across multiple disciplines, including books and conference papers

Google LLC

Web of Science

Measures citation impact, h-index, journal impact factor and provides in-depth citation data across highly curated scientific journals

Clarivate Analytics

Scopus

Citation tracking, h-index, author profiles, journal impact metrics, and research performance analytics across disciplines

Elsevier

Microsoft Academic

Measures citations, researcher impact, and academic collaboration networks, focusing on journal articles, books, and conference papers

Microsoft Corporation

Dimensions

Offers citation analysis, research funding data, and collaboration mapping, with a focus on connecting research impact to funding sources

Digital Science

ResearchGate

Tracks article views, citations, and collaboration networks; emphasizes research visibility and peer review interactions among researchers

ResearchGate GmbH

Altmetric

Tracks social media mentions, policy document citations, and media coverage to measure the broader public and societal impact of research

Digital Science

PubMed

Citation counts and article visibility focused on biomedical and life sciences publications, often linked with funding agency metrics

U.S. National Library of Medicine (NLM)

Crossref

Measures citation linking and DOIs for research articles to track article usage and citation networks

Crossref (nonprofit)

Most researchers tend to have a preferred go-to platform for tracking their bibliometric data. Google Scholar is favored by some due to its ease of access and its comprehensive listing of an author’s works, including books, conference papers, and journal articles. Meanwhile, Web of Science and Scopus, two leading competitors in the field, primarily focus on measuring citations within the journals they index. However, both have had to expand their coverage in recent years to remain competitive with Google Scholar, which casts a wider net by including a broader range of sources. Ideally, bibliometrics should aim for exclusivity, highlighting only high-quality research, but this can become problematic when major works are excluded because they appear in journals not indexed by a particular database.

In addition to citation platforms, there are also tools that assess journal quality. Scholar quality, to some extent, is a function of journal quality. Some systems, like the Norwegian List, assign points to journals based on their quality but do not calculate individual researchers’ scores, requiring scholars to do the math themselves. There are also specialized tools for ranking journals in specific fields. For example, The Harzing Journal Quality List offers a comprehensive ranking across disciplines like economics, finance, accounting, and marketing and is often used by researchers to assess where to publish. These rankings help researchers focus on high-impact outlets, but each tool has its own limitations in coverage and calculation.

So, essentially, we have various platforms and services designed to help researchers evaluate the quality of journals and research outputs (Table 3.16).

Table 3.16

Services that assess journals and outputs

Website/service

What it measures

Owner

Description

Google Scholar

Citation counts, h-index, i10-index

Google

Provides a broad overview of citations and includes a wide range of content such as journal articles, theses, and books

Web of Science

Citation counts, journal impact factor (JIF), h-index

Clarivate Analytics

Tracks citations from high-quality, peer-reviewed journals in its indexed database. It also provides Journal Impact Factor (JIF)

Scopus

Citation counts, h-index, journal ranking (CiteScore)

Elsevier

Offers detailed citation analysis with coverage of a wide array of disciplines and journals

Journal Citation Reports

Journal Impact Factor (JIF)

Clarivate Analytics

Provides impact factor rankings and citation data for academic journals based on Web of Science data

Harzing’s Publish or Perish

Citation counts, h-index, g-index, age-weighted citation rate

Anne-Wil Harzing (independent)

Software that uses data from Google Scholar and other sources to provide citation analysis and researcher metrics

The Norwegian Register for Scientific Journals, Series, and Publishers (Norwegian List)

Journal ranking (levels 1 and 2)

Norwegian Directorate for Higher Education and Skills

Ranks journals based on quality, used by Norwegian universities to assess research outputs for funding purposes

Eigenfactor

Influence of journals based on citation network

Eigenfactor.​org

Measures the importance of a journal within the scholarly community by accounting for where citations come from

The Harzing Journal Quality List

Journal rankings across multiple fields

Anne-Wil Harzing (independent)

Provides a compilation of journal rankings from multiple sources for specific disciplines, including business and economics

CiteScore

Citation counts, journal ranking

Elsevier

Similar to JIF, CiteScore ranks journals based on citations from Scopus data

SciMago Journal Rank (SJR)

Journal influence (weighted citations)

SCImago Research Group

Measures the impact and prestige of journals based on Scopus data, considering citations and the source of those citations

You may wonder why we need bibliometrics at all. Why not simply read the research for what it is? Some would argue that bibliometrics is necessary because not all research is of equal quality or impact, and tools like citation counts and journal rankings help distinguish more influential work. But isn’t it the reader’s responsibility to judge the quality rather than relying on machines to do that? Others say that knowing how often a paper has been cited reflects its impact—and that’s a fair argument, but it doesn’t always tell the whole story. For example, some articles gain citations simply because they are featured in popular outlets like Harvard Business Review (HBR), which isn’t necessarily a measure of scientific rigor, but more of popularity.

To take an extreme example, as a researcher, I could have hundreds of thousands of followers on LinkedIn or other social media platforms (though, I can only wish). However, the number of followers alone doesn’t necessarily indicate that I have anything meaningful to contribute or that I’ve advanced knowledge in any substantial way. Popularity on social media, or even being on TV once, doesn’t equate to genuine research impact. It could simply mean that I’ve found a way to capture attention, whether through sensational headlines, engaging content, or perhaps a viral moment. However, true academic value comes from the depth of the research, the insights gained, and the ability to contribute something new and valuable to the field—not merely from being widely recognized or well followed. This suggests that “impact” should not be measured solely by popularity or visibility, but by the meaningful contributions and advancements made in the field through rigorous research.

There are also cases where poorly written or methodologically flawed articles get cited a lot due to sensational topics, or because they’re required reading for widespread courses. We sometimes fall into the trap of assuming that high citation counts automatically indicate brilliance. What if the scholar in question was a co-author who did a minimum of work on a widely used textbook that inflated their citation numbers? Does that reflect their individual expertise or contribution? Hardly.

Bibliometrics, while helpful in offering a quick overview of research influence, comes with many biases. High citation numbers can skew perceptions, leading us to assume quality where it may not exist. The context of a scholar’s contributions—such as whether they are the first or sole author—matters just as much as the raw numbers. Ultimately, bibliometrics is a two-edged sword: a useful tool, but not one to replace careful reading and nuanced judgment of the actual content. Overreliance on these metrics, as is common today, risks overlooking the true depth and quality of scholarly work.

In summary, bibliometrics has become a central tool in evaluating academic performance, but it is also a source of significant pressure and controversy. One of the core questions is what exactly to measure: Should we focus on the number of articles a scholar publishes, the prestige of the journals in which they appear, or the number of citations those articles receive?

The issue with relying solely on bibliometrics is that it tends to reduce a researcher’s contribution to a set of numerical indicators. A scholar with 14 articles in prestigious journals and 5000 citations might seem like an obvious candidate for promotion, but the context behind these numbers is crucial. Again, what if most of those citations stem from a single popular paper? What if they were written 10 years ago and there have been little since? Or what if the scholar wasn’t the first author on any of them? Metrics like these can offer a snapshot of academic impact but don’t tell the whole story of a scholar’s intellectual contribution. Besides, it’s easy to game the system by focusing on quantity over quality or strategically publishing in high-citation fields without necessarily advancing knowledge in significant ways.

We need to move away from the current trend in recruitment, where reviewers simply tally the number of four-star articles: “1, 2, 3... 12 articles. Yes, we can make him a professor,” and consider the job done. I wish this were an exaggeration, but unfortunately, it often reflects the reality of the process.

3.6.1 Metrics in Academia

Many problematic practices are driven by the pressure to “publish or perish.” One issue is authorship inflation, where researchers add names of colleagues who made little or no contribution to the work, inflating their publication records. Another related issue is citation padding, which occurs when authors deliberately cite their own work or collaborate with others in mutual citation agreements, artificially boosting citation counts. This behavior can distort the true impact of research. Then, there’s the problem of citation cartels, where groups of researchers or journals mutually cite each other to increase their bibliometric measures.

Journal impact factors can also lead to skewed incentives; researchers may prioritize publishing in high-impact journals rather than focusing on the quality of their work, sometimes choosing less innovative but safer topics.

Predatory journals represent another major problem, exploiting the open-access model by charging high fees to publish low-quality or unverified research without proper peer review. Meanwhile, delays in peer review create frustration, with papers sometimes taking years to be published, hindering the timely dissemination of new findings.

Moreover, unequal access to prestigious journals affects researchers in less affluent institutions or countries, who may struggle with high submission fees or subscription costs. Metrics obsession—relying heavily on h-indexes, citation counts, and impact factors—may overshadow the intrinsic value of the research itself. This creates an academic environment where quantity over quality becomes the norm, leading to fragmentation of research into multiple smaller papers instead of more substantive work.

To move forward from the current challenges of the academic publishing system, we may instead need to embrace a range of alternatives. One idea is to revitalize oral presentations and other live forms of peer engagement, reducing the sole reliance on written papers. This would make it harder to rely on AI or other shortcuts to produce content that hasn’t been rigorously developed. Such presentations could be peer-reviewed in real time, allowing for a more dynamic exchange of ideas.

Another approach is to reform the peer review process itself. Speeding up peer review, offering better incentives to reviewers, and perhaps even using open peer review systems could reduce bottlenecks and increase transparency. Additionally, the open-access model could be further improved. By eliminating paywalls for high-quality journals, we might reduce the appeal of predatory journals and allow a broader range of scholars to participate in global academic discussions.

We could also consider de-emphasizing bibliometric indicators like citation counts and impact factors in career evaluations. Instead, a more qualitative approach, focused on peer assessments and the practical implications of research, could offer a better-rounded understanding of an academic’s contributions.

In tandem, publications with an interdisciplinary focus should be encouraged. Such journals would promote innovative, cross-field work, rather than siloing research into safe, formulaic topics that are more likely to get accepted into prestigious journals. Or maybe creating rewards for long-term, high-risk research could incentivize scholars to pursue more meaningful and impactful projects, rather than simply focusing on short-term outputs.

These strategies represent a broad effort to rethink what constitutes success in academia and how to foster genuine innovation and integrity in research. One potential solution is to place less emphasis on bibliometrics overall and instead pursue more holistic evaluations of each researcher’s contributions. However, this introduces a challenge: Holistic evaluations can quickly become subjective, relying on opinions rather than standardized metrics. In this sense, academia faces a true dilemma, akin to being “damned if you do and damned if you don’t.” While bibliometrics may oversimplify a researcher’s impact, subjective evaluations may introduce biases and inconsistencies, leaving no perfect solution.

So, what do I do? Well, I try to balance both approaches. I use bibliometrics as a starting point, but I also make a conscious effort to look beyond it. Metrics like citation counts and h-index provide valuable data, but they can’t capture the full depth or context of a researcher’s contributions. I try to dive deeper into the quality of the work itself (reading)—whether the ideas presented are innovative or if the research has broader social or policy implications. By combining both quantitative and qualitative evaluations, you can form a more complete picture of someone’s academic impact.

3.6.2 Beyond Metrics: Assessing Real Impact

Bibliometrics, while useful, fall short of capturing the broader influence of research beyond academia. Contributions to industry, public policy, and societal conversations often do not appear in citation counts but remain crucial to the overall impact of a scholar’s work.

Bibliometric tools like the h-index and impact factors serve as a baseline for measuring academic influence, but they should not be the only factor considered. A true evaluation of a scholar’s work requires incorporating peer reviews, interdisciplinary collaboration, and real-world applications.

The limitations of citation indexes, such as self-citations and citation circles (ethical side of citation cartels), highlight the potential for bias in measuring true academic impact. Additionally, impact factors, while important for ranking journals, can be manipulated by strategies like prioritizing review articles, which naturally attract more citations. These measures vary greatly between disciplines, with fields like medicine seeing significantly higher citation rates than humanities, making cross-discipline comparison difficult.

Career advancement increasingly relies on bibliometric scores, but overemphasis on these can result in a focus on quantity over quality. For early-career researchers, building citation counts is critical, whereas more established scholars may focus on high-impact, transformative projects. However, balancing both remains a challenge. International collaborations and altmetrics—which track mentions in policy documents, social media, and news outlets—offer a broader view of a scholar’s contributions and societal influence, complementing traditional citation-based evaluations.

For me, scholars focusing on humans and society should also prioritize writing books. Unlike articles, which tend to address narrow issues in brief formats, books demand a more comprehensive understanding of an entire field. Crafting a book requires deep reading, thorough knowledge, and the ability to synthesize a wide range of material into a cohesive narrative. Traditionally, the quality of a scholarly book reflected the depth of the author’s engagement with diverse sources.

However, with the advent of AI, this landscape is also evolving. AI tools can now generate entire texts from just a few prompts, raising questions about the future of authorship and originality. It’s also often recognizable by its somewhat boring, common-sense approach to writing. My expectation is that this shift will place greater emphasis on the practical utility and unique contributions of scholarly books.

In the social sciences, we tend to think of ourselves as scientists conducting experiments in controlled environments, much like natural scientists. But this is a misguided notion. We are immersed in complex, interconnected social systems, as thinkers like Niklas Luhmann or Kenneth Boulding have pointed out. To truly grasp any social phenomenon, you need to see its interconnections with broader contexts, and for that, you should be well read across disciplines.

Regrettably, today’s academic systems often fail to encourage young researchers to engage deeply with books or cultivate broad, in-depth knowledge as was common a generation or two ago. The emphasis now tends to be on producing and consuming small-scale, empirical studies—sometimes limited to reviewing 25 recent articles and replicating something similar. While this has its place in the research process, such a narrow focus rarely leads to groundbreaking discoveries in the social sciences.⁷

Case: Book Cultures vs. Article Cultures in the Social Sciences

There are noticeable national and institutional differences in how books and articles are valued in the communication of ideas within the social sciences. For example, French academia traditionally places a higher value on books compared to many Anglo-Saxon universities, where journal articles dominate as the primary medium of academic output. While the Anglo-Saxon academic world produces a mass of popular books on various topics—easily found at any newsstand—it tends to lack the same emphasis on deeper, broader intellectual works that offer both perspective and depth. In my opinion, Germany, in contrast, seems to occupy a middle ground between these two approaches, valuing both articles and intellectually rigorous books.

Interestingly, certain universities also develop their own internal cultures that favor books over articles or at least value them equally. One such example is Lund University in Sweden, where books are more appreciated than at many other universities. The reasons for this emphasis on books might be historical, given its strong connections to the broader European academic tradition. Unlike newer institutions, which have adopted the more article-focused Anglo-American model, Lund has retained a balance between these forms of scholarly communication. That said, this observation is based on personal experience rather than empirical evidence, but it does raise intriguing questions about the expectations placed on scholars and the evolving role of books in academia.

Ideally, it shouldn’t matter what medium you use to produce your science—whether it’s carved into stone tablets or written on the frosting of a cake. But in reality, we tend to favor short articles for testing hypotheses based on empirical data, doing literature reviews, or presenting concise discussion papers. And then there are books, which are perfect for showcasing that you truly master an entire area or discipline. We also write books for students who need them for their courses, for practitioners, or for both. Writing a book when you’re young can be a challenge; it requires a big-picture perspective, and let’s face it, when you’re just starting out, you’re still figuring out what the big picture even looks like. But as you gain experience, writing books becomes expected, almost like a rite of passage—proving you’re not just cranking out small statistical tests of data, but you actually know your field (and, preferably, you’re at least as good at it as your peers).

Now, let’s hear what you think or, better even, how you reason around these questions:

Questions for Discussion:

1.

Should scholars who have published widely in a field be required or encouraged to produce a book that synthesizes their research? Why or why not?

2.

How should books be evaluated and rewarded compared to articles in terms of academic merit, career progression, and scholarly impact?

3.

What are the advantages and disadvantages of prioritizing books over articles, beyond the obvious point that articles typically present current research findings?

4.

How do institutional and national academic cultures shape the value placed on books versus articles, and what might be the implications for cross-cultural scholarly collaboration?

5.

Do you think the dominance of article-driven academic cultures in many parts of the world has led to a loss of intellectual depth or breadth in the social sciences? Why or why not?

Without sounding overly nostalgic or conservative, I believe we should re-emphasize the value of producing rich, insightful works and ensure that evaluations of social science scholars prioritize substance over superficial outputs. This shift could foster the deeper, more creative insights urgently needed in today’s academic landscape. Additionally, we need to prioritize dialogue and exchange of ideas, rather than simply producing a steady stream of articles. Universities should aim to create vibrant intellectual communities that champion free speech and openness to ideas, cultivating an environment where true innovation can flourish.

Visiting universities today often feels like stepping into a bureaucratic government agency, where you can’t quite tell if anything meaningful is happening and where intellectual discourse seems increasingly rare. Instead, the focus is on processes—having endless meetings, being part of task forces, sitting on the board of the “Strategic Committee for Advancing Bureaucratic Synergy,” checking the right boxes, and filling in the necessary forms. In many cases, we’ve nearly strangled whatever was left of the vibrant intellectual discussions that once thrived.

3.7 Securing Research Funding⁸

Research funding refers to the financial resources provided to support the advancement of knowledge across our various disciplines. These funds are typically allocated by a variety of sources, including government agencies, private foundations, corporations, and nonprofit organizations.

The idea is that research funding is used to cover the costs associated with conducting research, such as personnel salaries, laboratory equipment, travel, publication fees, and data collection. Researchers go through competitive grant processes, where they submit proposals outlining their projects, goals, and the expected impact of their work. It is not easy to get. Success rates with ERC (European Research Council) are 5–10%. In contrast, agencies like the National Science Foundation (NSF) in the USA tend to have higher success rates, often ranging from 10–30%, depending on the specific program.

Securing research funding is often a crucial part of academic life, especially at institutions where external financing is the primary source for research activities. Some universities, particularly those with fewer students and smaller programs, require researchers to secure their own funding, as they cannot finance research with student fees alone. In such environments, it’s often a “succeed or disappear” scenario, where failure to secure funding can lead to losing one’s position or at least some of one’s reputation. In other places, researchers who are also heavily involved in teaching often face less pressure to obtain external funding, at least in theory. However, the level of pressure can also vary significantly depending on the financial strategy for the organization. Some universities have become dependent on the research grants they got in the past to grow the organizations, and now, they need to keep it up. Or said differently, once researchers and institutions become reliant on external funding, it is difficult to reverse that dependence.

We need to bear in mind that there’s a significant difference in reliance on external grants across various faculties. Medicine and engineering departments can be up to 75% dependent on external funding, while the humanities and social sciences typically require far less external support for their research.⁹

Applying for grants is not just about securing financial support; it’s also about establishing a reputation for your research group or university. Successful grants bring visibility and credibility, further attracting collaboration and future funding opportunities. It also often gives you a higher salary, as what you do in practice is to fund yourself. Thus, researchers often spend a significant amount of time writing grant proposals, refining their projects to fit the priorities of funding agencies, and navigating the bureaucratic requirements of the application process. It can take months to complete, and when the deadline approaches, you may find yourself even working at night. Funding agencies also develop their priorities based on input from researchers.

At the same time, we must not lose sight of the fact that the ultimate purpose of all our efforts is to produce high-quality research.

Grants are not, and should not be, viewed as an end in themselves; they are merely a means to achieve that goal. Unfortunately, sometimes administrators forget this critical point, focusing more on managing budgets and securing funding than on the research outcomes themselves. When the emphasis shifts too far toward financial metrics and away from intellectual contributions, it risks distorting the true mission of academia. The primary goal should always remain the creation of impactful, meaningful research. At the same time, getting grants is a competitive process, ensuring that the best proposals win. If grants were simply distributed, it would increase the risk of favoritism and the resurgence of old boys’ clubs, many fear.

Let’s look at some examples of research grants (Table 3.17).

Table 3.17

Examples of research grants

Grant type

Size (in euros)

People involved

Duration

Examples

Individual research grants

€20,000–€100,000

1–2 researchers

1–3 years

Marie Skłodowska-curie actions (European Commission): Provides funding for postdoctoral researchers to pursue individual research projects across Europe

Project-based grants

€100,000–€1,500,000

3–10 researchers

5 years

ERC starting Grants (European Research Council): Supports early-career researchers with innovative projects, funding teams of up to 3–10 researchers

Collaborative grants

€500,000–€12,000,000

10–100+ researchers

3–6 years

Horizon Europe collaborative projects: Encourages international collaboration in all kinds of areas involving large teams from multiple countries

Infrastructure grants

€1,000,000–€10,000,000+

10–50+ researchers

5–10 years or more

ESFRI (European strategy forum on research infrastructures): Funds large-scale research infrastructure, including labs and facilities, benefiting many researchers

Corporate/industry grants

€50,000–€1,000,000

Varies (2–10 researchers)

1–3 years

Siemens research Grants: Corporate-funded grants to support applied research, particularly in engineering or technology

Governmental research grants

€100,000–€10,000,000+

5–30 researchers

2–10 years

Swedish Research Council (Vetenskapsrådet): Provides funding for basic research projects across various disciplines, supporting national research priorities

Non-governmental organization (NGO) grants

€10,000–€500,000

1–5 researchers

1–3 years

Bill & Melinda Gates Foundation: Supports global health research, often involving small teams working on specific health challenges in developing countries

Fellowships

€10,000–€250,000

1 researcher

1–2 years

Alexander von Humboldt Foundation: Funds individual researchers for postdoctoral studies or academic stays in Germany

Innovation and technology grants

€100,000–€2,000,000

3–15 researchers

2–5 years

EIC accelerator (European innovation council): Supports startups, SMEs, and researchers to bring innovative technologies to market, involving interdisciplinary teams

Ledger: The amounts and time frames in the table above are based on a combination of personal experience and information from various websites. They should be regarded as estimates and may vary significantly

Securing research funding is an essential aspect of academic work, and understanding the different types of research funding (grant types) is crucial. There are several categories of funding sources available, including governmental grants, corporate sponsorships, foundation/NGO funding, and international consortiums. Each funding source comes with its benefits and challenges, and its relevance often depends on the specific discipline of research. For example, government grants are reliable but highly competitive, while corporate sponsorships may be more suited to applied sciences or technology fields, where industry partnerships can offer mutual benefits but also come at a cost.

There is a clear hierarchy when it comes to research grants, with some being more prestigious and competitive than others. This hierarchy is often shaped by national contexts, but some grants, such as those offered by international bodies like the European Research Council (ERC) or the National Institutes of Health (NIH) in the USA, carry global recognition. The prestige of a grant is typically tied to the reputation of the funding body, the amount of funding offered, and the perceived impact of the supported research.

Additionally, academic prizes—such as the Nobel Prize or the MacArthur Fellowship—differ from grants, as they are typically awarded through nominations rather than applications, offering both financial rewards and significant academic prestige.

Funding opportunities also vary by career stage. Early-career researchers often focus on fellowships and seed grants to kickstart their research, while mid-career researchers might look for larger, interdisciplinary projects or collaborative grants. For senior researchers, grants for large-scale infrastructure or leadership in major research initiatives are more common. As academics progress through their careers, funding priorities and the types of grants available tend to shift, often requiring a more strategic approach to funding applications.

Case: The Wallenberg Foundations and Research Excellence in Sweden

In Sweden, the most prestigious grant a researcher can receive is arguably the Wallenberg Scholar grant, widely regarded as the ultimate recognition of research excellence. This highly coveted grant is awarded for a period of five years and provides substantial financial support to enable cutting-edge research across various disciplines. The grant period can be extended for additional five-year terms if the researcher demonstrates that the funds have been effectively utilized and the research has yielded significant results. The funding is provided by the Knut and Alice Wallenberg Foundation (KAW), Sweden’s most prominent private research financiers.

The foundation was established by Knut Agathon Wallenberg (1853–1938), a Swedish banker, and his Norwegian wife, Alice Wallenberg (1858–1956). Over the years, KAW has become a cornerstone of Swedish academic and scientific advancement, supporting everything from basic research to groundbreaking technological innovation. Together with the Marcus and Amalia Wallenberg Foundation (MAW) and the Marianne and Marcus Wallenberg Foundation (MMW), these foundations collectively represent Sweden’s largest private financiers of research. Their funding extends to a wide range of scientific, educational, and societal projects, creating opportunities for long-term advancements in multiple disciplines.

The Wallenberg Scholar grant does not just provide financial resources; it also carries immense prestige within Swedish academia. Receiving this grant can significantly bolster a researcher’s career, opening doors to international collaborations and creating a ripple effect in their field. The Wallenberg family’s longstanding commitment to advancing Swedish research underscores the importance of private investment in complementing public funding for science and education.

Questions for Discussion:

1.

What are the most prestigious grants in your country? Why?

2.

How does the prestige and funding of grants like the Wallenberg Scholar grant influence the direction and priorities of academic research in general, do you think?

3.

Do you think reliance on private foundations, like the Wallenberg Foundations, is a sustainable model for advancing scientific research? Why or why not?

4.

What are the potential benefits and challenges of private foundations playing a major role in funding research compared to public funding models?

Discipline-specific funding plays a key role in the academic world. Fields such as biomedical research benefit from significant corporate or pharmaceutical grants, while humanities researchers may have to rely on smaller fellowships or government support. Understanding where to find these discipline-specific resources is essential for successful grant applications. Additionally, there are various types of grants, including individual vs. team-based grants, short-term vs. long-term funding, and project-specific vs. unrestricted funding. Matching research goals with the right type of grant is key to ensuring the success and longevity of the project.

3.7.1 Funding Allocation in Research Teams

Applying for a grant doesn’t necessarily mean that the funds will go directly to the lead researcher. In many cases, the lead researcher only allocates a small portion of the funding to herself, while the majority is directed toward covering the salaries of a larger team, including PhD students, postdoctoral researchers, and junior academics working under their supervision. This structure can lead to somewhat of a “mother goose” scenario, where senior researchers spend significant time applying for funding not only for their research but also to secure salaries for those in their teams.

Additionally, research teams are often composed of multiple members, each submitting various grant applications and securing fractional positions for one another. This phenomenon where research teams consist of multiple members, each submitting grant applications and securing fractional positions for one another, can be referred to as “reciprocal funding allocation” or “collaborative grant hedging.” The idea behind this strategy is that researchers hedge their bets by working on multiple grants within a team, ensuring that even if some applications are unsuccessful, others might secure funding to sustain the group’s research efforts. It reflects the collaborative and sometimes strategic nature of securing research funds in academia, with each member contributing to the financial sustainability of the team. It helps spread the risk by ensuring that if one member doesn’t receive funding, others might, keeping the team collectively afloat.

However, this system can also lead to complications. In some cases, researchers may end up with more funding than they can feasibly manage or deliver on. We can call this phenomenon “overfunding inefficiency.” It refers to situations where researchers secure more funding than they can feasibly manage, leading to logistical challenges in delivering on all project commitments. This can result in the return of unspent funds to the granting body, highlighting the complexity of balancing multiple projects and the potential pitfalls of overextending research teams.

The entire process of managing research funds can become a complex administrative burden, consuming more time than the actual research, even though the difference is greatly depending on the country. For example, EU research grants tend to demand more administration. Leading researchers apply for funding not only for themselves but for their teams, which often includes junior researchers, postdocs, and PhD students. However, when excess funds are secured—more than what the team can feasibly use—those funds must be returned, sometimes with detailed explanations. The reporting requirements on how the money was spent are extensive, adding another layer of bureaucracy. This diverts attention away from the core research tasks.

Another common funding system is the formula funding model where most of the research and operational funding for faculties or departments comes directly from the state, based on measurable criteria such as student enrollment and graduation rates. In this model, institutions receive funding proportionate to the number of students they attract and successfully graduate. This approach ensures steady funding for faculties with a strong educational focus, such as those in the humanities or social sciences, even if they receive less from competitive research grants. While this system incentivizes institutions to focus on performance and efficiency, it has also been criticized for potentially prioritizing quantity over quality in education. For instance, the pressure to graduate more students might lead to compromised academic standards or reduced emphasis on rigorous research.

A portion of these state funds can be allocated internally for research purposes, allowing faculties to support the work of their staff without the burden of applying for external funding. This type of system is particularly common in Nordic countries such as Sweden and Denmark, where universities receive baseline funding from the government tied to student performance. In Sweden, for instance, universities receive a mix of “direct appropriations” for both teaching and research based on performance indicators, with a portion of funding also tied to research quality and outputs. In this model, universities have flexibility in how they allocate funds, allowing some of the resources that come from education-related funding to be redirected toward research. This system helps reduce administrative burden, as internal research funds do not typically require the same level of detailed reporting or competition that external grants do.

In Germany, a similar system exists where state funding, based on student numbers, forms the backbone of university budgets, though additional funds may come from competitive research grants. Universities can channel a portion of their general budget toward internal research activities, allowing faculties to support research without external grants. However, in many cases, there are still incentives for researchers to seek competitive funding to enhance their research profile.

France follows a similar model, where universities receive state funding based on performance indicators, including graduation rates and research output. A portion of this funding is often allocated to research, especially in departments with fewer opportunities for external grants.

In contrast, countries like the United States tend to place a heavier emphasis on competitive grant funding, with less state support linked to student numbers and performance. Faculty members are more reliant on external funding sources, such as the National Science Foundation (NSF) or private grants, to support their research.

This alternative system, which blends state funding with performance metrics, allows universities in many countries to fund research more internally, reducing reliance on external competitive grants and the associated administrative load. However, the research produced within this system may be more institutionally focused and may vary in scope compared to projects funded through international competitive grants like those from the EU or major national agencies.

The inefficiencies in research management stem from the growing complexity of the grant system. While research grants are intended to support innovation, they now come with extensive administrative requirements, including proposal writing, financial justifications, and detailed reporting. These tasks add a significant burden on researchers, taking away time that could be spent on actual research.

One factor behind this inefficiency, which is often overlooked, is the increasing number of administrators and civil servants involved in overseeing the grant process. Their role in managing funds, ensuring compliance, and maintaining the system creates additional layers of bureaucracy, which, in the worst case, serve their interests. This logic describes how administrative systems, not only in academia and research, grow increasingly complex due to the vested interests of those employed within them.

3.7.2 Third-Party Funding Sources: Industry, NGOs, and International Partnerships

When seeking funding, many researchers turn to third-party funding sources like industry partnerships, NGOs, and international organizations. Industry-academia collaborations are a powerful way to secure funding, particularly in fields where private companies, such as Siemens or Google, are eager to support innovative research in exchange for access to cutting-edge insights.

For academics in the global health or environmental sustainability sectors, organizations like the Bill & Melinda Gates Foundation or the Wellcome Trust offer opportunities to align research agendas with the mission of nonprofits.

International organizations like the European Commission or the World Health Organization (WHO) often provide grants for large, cross-country collaborations, particularly through initiatives such as Horizon Europe, which funds interdisciplinary research on a global scale. The rise of public-private partnerships (PPP) is also significant, with both governments and private companies co-funding important research projects. These partnerships often come with complex requirements, but they offer substantial funding for large-scale initiatives.

When cooperating with third parties, it is often not the financial aspect that holds the most value for researchers, but rather access to unique data. For instance, partnering with a company like SAP or a hospital system provides researchers with specialized datasets that are otherwise inaccessible. This data can be far more valuable than a direct monetary contribution, as it offers a foundation for conducting cutting-edge research.

Additionally, not immediately requesting funding can make it easier to initiate collaborations with third parties. This approach removes financial pressure from the initial discussion, making the partnership more attractive and fostering trust. For example, researchers who first approach a company for access to their proprietary data rather than for funding often find it easier to enter collaborative projects. If the partnership proves fruitful, the company may later be more willing to finance future research initiatives, recognizing the value of the collaboration. This method can be a strategic first step that opens doors for long-term cooperation.

While collaborating with companies can provide valuable resources and data for researchers, accepting funding from corporate partners also raises important ethical concerns. One of the primary issues is the potential for conflicts of interest, where the company’s financial support could unintentionally or intentionally influence the research outcomes. As researchers, we must ensure that our work remains objective and not skewed to favor the interests of the company, as this would compromise scientific integrity. Transparency is, in other words, essential, with researchers openly disclosing any financial relationships and potential biases to both the public and their academic institutions. Additionally, corporate funding can create pressure for favorable results, which can challenge the independence of the research process. To safeguard academic freedom, clear agreements should be established that allow researchers to publish their findings without interference, regardless of the results. Finally, academic institutions should have robust oversight mechanisms in place to ensure that corporate funding does not undermine the ethical standards of research. In the past, it was not uncommon for research on the hazards of tobacco to be financed by tobacco companies. You don’t want to face these kinds of revelations later in your career.

Companies are generally more willing to invest in a project once they have seen its worth, and by focusing on mutual interests first—like access to data or expertise—researchers can build the foundation for future financial support.

Co-funding is another issue. When applying for research funding, especially from certain funding agencies, it’s important to consider the role of co-funding and the participation of societal actors like companies or nonacademic institutions. Many agencies, such as the European Union’s Horizon Europe program, require the involvement of nonacademic partners, which can include companies or public sector organizations, to ensure that the research has practical applications. These partners may be asked to provide monetary contributions or in-kind support, such as access to data, equipment, or expertise. This kind of collaboration strengthens the research proposal by demonstrating its relevance to real-world challenges and increasing the chances of success.

Case: The Pros and Cons of Research Collaboration with Companies

As a researcher, one of the key decisions you face is whether to engage in projects with companies and what that collaboration truly means. Collaborating with industry can provide numerous benefits, such as access to unique data, equipment, and environments, as well as the opportunity to align your research with practical, real-world problems. However, these benefits come with challenges that must be carefully considered.

Collaborations between academia and industry can take many forms, but they often involve co-funding, where both the academic institution and the business sector contribute to the financing of the project. This co-funding model ensures that both parties have a vested interest in the project’s success. On the one hand, these partnerships allow researchers to tackle questions that have immediate industry relevance, and they can lead to more impactful, applied results. On the other hand, researchers may find themselves navigating the complexities of balancing academic integrity and industry pressures, where the lines between unbiased, fundamental research, and commercially driven goals can blur.

One example of such collaboration I have worked with a few times in Sweden is the Knowledge Foundation (KK-Stiftelsen), which supports research projects that bring together universities and businesses. Through this model, both parties co-design and co-implement projects, often with companies contributing equally to the project’s funding. This mutually beneficial partnership means researchers gain access to cutting-edge industry data, while companies benefit from insights and research outcomes that improve their operations and employee skills. But the question remains: Does this type of collaboration enhance or limit academic independence?

The underlying goal of these collaborations is to foster innovation and strengthen competitiveness. By aligning academic research with industry needs, researchers are better able to solve real-world problems, but this also means that the academic freedom to pursue any research direction without constraints is compromised. As such, researchers must weigh the pros and cons of industry collaboration carefully—while it offers practical benefits, it also presents ethical dilemmas and potential conflicts of interest.

For researchers considering engaging with businesses, the key is to ensure that their work remains scientifically rigorous while still meeting the practical demands of industry. Finding a balance that allows for high-quality, applicable research without sacrificing independence or academic values is crucial. And for those who take the plunge, the rewards—access to resources, relevant data, and tangible real-world impacts—can make it a valuable endeavor.

Ultimately, the decision of whether to work with industry or not depends on your values as a researcher, the field you are working in, and the specific project at hand. Should you prioritize academic purity or embrace the potential to shape innovation within the business world?

Below are five critical questions for discussion:

1.

Balancing Priorities: How can academia ensure that co-produced projects maintain academic rigor and scientific independence while addressing the immediate needs of the business sector? In other words, how do we avoid these becoming mere subsidized consultancy projects?

2.

Inclusivity of Small Enterprises: What are the benefits for large companies if the project mainly consists of smaller businesses? How do you make this mix work?

3.

Equity in Benefits: How can we ensure that the benefits of co-produced research are distributed equitably between academia and the business sector, rather than disproportionately favoring one party?

4.

Ethical Concerns: How should conflicts of interest be managed in co-produced projects to prevent undue influence from business actors on research outcomes or educational content?

All of these partnerships also come with additional requirements, as funding agencies often demand accountability for how resources are used. Companies, for instance, might request monitoring rights to ensure that their contributions are being utilized effectively, which could add complexity to managing the research project. Researchers need to balance the expectations of their academic work with the interests of their nonacademic partners. Clear agreements and defined roles from the start can help prevent potential conflicts and ensure that both the research objectives and the contributions of societal actors are fully aligned.

3.7.3 Grant Writing Techniques

Grant writing techniques are basically about how to write applications. It refers to the specific strategies and methods used to craft compelling and successful grant proposals (Table 3.18).

Table 3.18

Grant writing techniques

Technique name

Description

Example

Understand the funder’s priorities

Research the mission and funding priorities of the organization offering the grant. Align with the nature of the call

Tailoring a proposal to a foundation that focuses on environmental sustainability by emphasizing how your project aligns with their goals

Clear objectives and specific goals

Clearly define the problem your project will address. Use specific, measurable objectives and explain the significance of your work

Stating that the project will reduce carbon emissions by 20% in a specific region within two years

Strong introduction

Start with a compelling introduction that highlights the urgency or importance of the issue your project addresses

Introducing a project on renewable energy by explaining the immediate need to reduce reliance on fossil fuels

Well-organized structure

Ensure the proposal is easy to follow with clear sections, headings, and subheadings, including problem statement, and goals

Organizing sections for a research project on climate change with clearly labeled headings like “problem statement,” “goals,” and “timeline”

Methodology

Be specific about how you plan to achieve the project’s goals, providing step-by-step details and a plan for measuring success

Providing a detailed plan for how a new technology will be tested in real-world conditions and evaluated for effectiveness

Realistic budget

Provide a realistic budget with justifiable expenses that match the scope of the project without inflating costs

Listing all necessary expenses, such as equipment and personnel, and showing how each cost directly supports the project’s goals

Emphasize impact

Explain how the project will have a broader impact on the field or community and align with the funder’s mission for long-term benefits

Explaining how the project will reduce pollution in urban areas and how it will continue to provide benefits after the funding period ends

Follow the guidelines

Carefully follow submission guidelines, such as page limits or formatting, to avoid disqualification from the process

Following exact formatting guidelines, such as 12-point font and a ten-page limit, to meet the requirements set by the funding agency

Use data and evidence

Support claims with relevant data, statistics, or previous research to show that the project is grounded in reality and likely to succeed

Including statistical data on the effectiveness of similar projects to show that your project is likely to succeed based on past evidence

Collaborate and include stakeholders

Highlight any collaboration with other teams or stakeholders, which can enhance the appeal of the proposal

Working with a local government and a university to show broad support and collaboration in addressing a regional problem

Strong conclusion

Summarize the key points of the proposal in a persuasive conclusion that reinforces why the project is essential

Reinforcing that the project will solve a critical issue in energy consumption and meet long-term environmental goals

Revise and get feedback

Revise the proposal for clarity and precision and seek feedback from experienced colleagues or mentors

Some funders like that projects have a reference group or an advisory board, some require that projects are executed in ways that avoid environmental impacts

Asking a colleague who has received grants before to review your proposal for clarity and suggestions for improvement

These techniques are designed to persuade funding organizations to provide financial support for a project or initiative. They involve various aspects of proposal development, such as clearly articulating the problem to be addressed, outlining measurable objectives, providing a detailed project plan, demonstrating the potential impact, justifying the budget, and aligning the proposal with the funder’s priorities and guidelines. Effective grant writing techniques focus on clear communication, structured organization, and evidence-based reasoning to increase the likelihood of receiving funding.

Grant writing is a critical skill for researchers seeking funding to support their projects. One of the most important aspects of successful grant writing is understanding funding agency priorities. Each funding body has specific objectives, often tied to societal needs. Therefore, it’s essential to thoroughly research the agency’s priorities before crafting your proposal.

Funding agencies regularly issue calls for proposals, which are specific invitations to submit research projects for funding. Some calls are open year-round, allowing continuous submissions, while others are announced at set intervals, such as once a year. Many agencies, like Formas in Sweden, offer all three types of calls: continuous submissions, annual calls, and special one-off calls for specific research areas. Researchers need to be aware of these timelines to ensure their proposals are submitted within the appropriate window. Calls typically outline the funding priorities, eligibility criteria, and evaluation process for the proposals. Spend time reading about the grant and the grant givers online.

Tailoring the content to meet funders’ needs is crucial, as proposals that align closely with these goals are more likely to succeed. For instance, analyzing past funding calls can provide insights into what the agency values, whether it’s innovative solutions to global health problems or groundbreaking developments in artificial intelligence. Ask colleagues for advice who have gotten a grant from the same funding calls before. Maybe they have some last-minute tips on what to do or, sometimes more helpful, not to do.

A well-structured grant proposal is key to securing funding. The length of a research proposal varies significantly depending on the funding agency and the specific call. Each agency has its own guidelines and structure, and these requirements can change from one call to another, even within the same organization. Some agencies may ask for short proposals of a few pages for initial screening, followed by longer, detailed applications if shortlisted. Others may require a full-length proposal from the start, often ranging from 10 to 30 pages. It’s essential for researchers to carefully review the specific requirements for each call to ensure their proposal meets the length and formatting guidelines.

Regardless of the length, it often begins with a compelling executive summary or abstract that concisely captures the essence and significance of the project. Since this is often the first section reviewers read, it must be persuasive. Next, the research plan or methodology should be clearly outlined, detailing the approach to conducting the research and explaining why these methods are the most appropriate for achieving the desired outcomes. A budget justification is equally important, where you explain how the requested funds will be allocated. It is essential that the budget is realistic and aligned with the project’s scope and timeline. Don’t forget to emphasize the project’s impact and relevance, linking your research to broader societal needs or global challenges, which demonstrates the importance of your project beyond academia.

However, there are common mistakes that researchers should avoid in grant writing. These include setting unclear or overly broad objectives, proposing timelines that are too ambitious, or submitting unrealistic budgets. Additionally, it’s crucial to address the specific evaluation criteria set by the funding agency right from the start. Reviewers expect proposals to directly address these criteria, so ensuring that your application meets their expectations is critical.

The evaluation of research applications typically follows a structured and transparent process set by the funding agency. Applications are usually reviewed by a panel of expert peer reviewers, who assess the scientific quality, originality, and potential impact of the proposed research. The evaluation criteria often include relevance to the call, the qualifications of the research team, the feasibility of the proposed methodology, and the expected outcomes. Many agencies also consider the societal impact or applicability of the research, especially if the funding involves public or industry partners. After the peer review, applications are often scored or ranked, and a final decision is made based on these scores and available funding. Some agencies, such as the European Research Council (ERC), use a two-stage evaluation process, with a first screening round followed by a more in-depth review for shortlisted proposals. The evaluation process also frequently involves conflict of interest checks to ensure fairness and impartiality. You may also be required to do interviews, sometimes videoconferences to explain our idea.

Another key aspect is demonstrating the feasibility of your project. Reviewers must be confident that the research can be completed within the proposed timeframe and budget. To achieve this, include pilot data, a clear timeline, and evidence that your team has the necessary resources and expertise. Without these, even the most innovative ideas may be dismissed due to doubts about their practicality.

When writing your proposal, consider the reviewer’s perspective. Depending on the funding agency and the call, not all reviewers will be specialists in your field, so your writing must be accessible to both experts and nonexperts. For example, you may get a reviewer who is not at all in your own field but in a related field that deals with the same kinds of problems. This even means that an applicant in the natural sciences can get a reviewer from the social sciences and vice versa, especially if you are doing interdisciplinary research. Avoid jargon and explain complex ideas simply. Clear, precise language will resonate with a wider range of reviewers, increasing your chances of success. Do not assume that the meaning of terms is universally understood. It is better to be overly explicit.

It is also helpful to highlight past successes. If you have received grants before or completed impactful research, mention these achievements in your proposal. Demonstrating a track record of success boosts reviewers’ confidence in your ability to deliver on the proposed project.

When submitting a research proposal, attaching a CV and reference list is usually required to showcase your qualifications and previous work. These documents are essential for demonstrating your expertise and track record, which can boost the reviewers’ confidence in your ability to carry out the proposed project. Sometimes, the CV can be general, covering a broad overview of your academic and professional achievements. However, it’s often advisable to tailor your CV to the specific call, highlighting experiences, publications, and skills that are most relevant to the research being proposed. The reference list should also be adjusted accordingly, focusing on publications and previous research projects that align with the call’s goals. If you’ve received grants or completed impactful projects in the past, be sure to emphasize the successes you’ve had. Highlighting your research impact and prior funding achievements will further strengthen your proposal by showing you have the experience to deliver meaningful results.

Finally, handling rejection is a natural part of the grant-writing process. If your proposal is rejected, use the reviewer feedback to improve it, if you get any feedback, that is. Most agencies don’t provide feedback. Revising and resubmitting a stronger version of your proposal demonstrate resilience and a commitment to refining your ideas—assuming, of course, that the initial idea was sound—which can ultimately lead to success. Remember, grant writing is a process of continuous improvement, and persistence is key to achieving long-term success.

3.7.4 Using AI Technology in Grant Writing

Applicants dedicate a significant amount of time refining the language of our grant proposals to achieve a polished, professional tone. Over the past few years, advancements in grammar tools have made this task easier. For example, software like Grammarly or language functions embedded in word processors have significantly improved the quality of writing. Additionally, in the last two years, AI-driven tools such as ChatGPT or specialized AI writing assistants have made formulating sentences more efficient, helping to reduce the burden of the writing process. Today, we are witnessing the next step in research support: AI agents are being utilized to assist with grant writing. These tools can help researchers draft proposals, refine language, and even tailor submissions to meet specific funding criteria. While this streamlines the process and saves time, it also raises questions about originality, fairness, and the evolving role of human creativity in securing research funding.

With AI agents, a new challenge has emerged: ensuring that our writing does not seem like it was produced by a machine. Many readers today are highly sensitive to writing that feels automated or formulaic, which can undermine the credibility of the proposal. Some reviewers equate this with a form of intellectual dishonesty, or even plagiarism, especially if the ideas appear unoriginal or disconnected from the author’s voice.

In practice, many authors now dedicate significant time to making their grant proposals sound as natural and human as possible, especially when they’ve used AI tools in the drafting process. It’s common for researchers to revisit and reshape AI-generated text, softening overly formal or mechanical phrases to reflect their unique voice.

For example, instead of leaving a generic sentence like, “Our team is committed to groundbreaking innovation,” they might rewrite it with a personal touch: “Through this project, we really hope to redefine how energy efficiency is approached in real-world applications.”

Researchers also tend to tweak AI-generated templates to better match their specific goals. AI might suggest broad statements like, “This project will revolutionize the field,” but experienced writers will often modify such claims to sound more precise and grounded, but also careful, saying instead, “This initiative may represent a significant step in advancing sustainable energy practices.” This process involves stripping away clichés and overstatements to produce a more thoughtful and credible proposal.

Another thing researchers do is pay close attention to the rhythm and variation in their writing. AI can produce text that feels flat or repetitive, so people naturally adjust by mixing sentence lengths and structures to keep the narrative engaging. A machine might generate, “The project will lower costs. It will also increase efficiency,” but a researcher might rewrite it to flow more naturally: “By simultaneously reducing costs and improving efficiency, we think this project offers long-term benefits for both industry and society.”

Enthusiasm is another hallmark of human writing that researchers consciously work into their proposals. Rather than sticking to sterile statements like, “We aim to improve environmental outcomes,” authors often include expressions of genuine passion, such as, “This project is driven by a deep commitment to a cleaner, more sustainable future.” While humor is sometimes considered, it’s generally used sparingly in grant writing due to its mixed reception.

Finally, it’s standard practice to thoroughly review and revise text after using AI to ensure it accurately reflects the author’s intentions and ideas. Researchers often test their proposals using AI-detection tools to verify the writing feels authentic and doesn’t flag as overly machine-generated. If any section feels robotic or detached, they go back and rewrite, ensuring the final submission represents not just their goals but also their voice.

This careful blending of technology and personal effort is becoming a standard in academic and professional writing, not just for grant applications but also for articles and books. While this shift may seem concerning to some, it also offers a potential silver lining: It allows humans to focus on more meaningful aspects of research, such as ensuring its real-world relevance and impact. After all, as machines become increasingly capable of producing polished, professional text, our role might shift toward prioritizing substance over style, making sure that the work we do truly matters.

It’s akin to the advent of the calculator—no one is impressed anymore by your ability to perform complex calculations (unless you do it in your head). What truly matters is how you interpret and apply the results to solve meaningful problems or advance understanding.

3.8 Science in Society

What role does science play in our society, and what purpose does research serve? These are important questions with many potential answers, but as researchers, we need to consider them carefully. The investment from both state and private sectors in research reflects what society expects to gain: reliable, useful knowledge that can improve our world. If research fails to provide this, it misses its purpose.

In recent years, there has been a tendency for researchers to emphasize groundbreaking results, largely due to the pressure to secure funding. This approach raises an essential question: What does society truly gain from work in the social sciences? While natural sciences and engineering often drive many visible advancements, the contribution of social sciences can seem less clear. This raises the challenge of defining what social sciences uniquely offer to justify continued investment.

The social sciences emerged strongly after the Second World War, helping to shape new institutions and inform policy in times of peace and growth. But as global instability has increased in recent decades, we must ask again how social sciences contribute in this new context. Their value may not be as immediately measurable as technological breakthroughs, but they provide critical insights into human behavior, social structures, economics, and governance. Without these insights, it would be harder to address issues like inequality, cultural shifts, or political instability effectively.

Fields like economics, psychology, and sociology inform policies that address real social issues, offering perspectives that allow for a more thoughtful application of scientific and technological advances. Although their impact may not be as visible or immediate as that of engineering, social sciences contribute in subtle yet long-lasting ways.

This slower impact can be challenging for researchers, particularly in a field where the value of contributions isn’t always clear. It’s essential for social scientists to believe in the value of their work while also recognizing its limitations. Social sciences and humanities complement each other, with the humanities exploring the meaning behind human experiences and social sciences focusing on practical impacts. All these fields collectively contribute to a holistic understanding of society and its improvement. However, the challenge lies in the time it takes, the reality that many new articles offer limited value, and the cycles of progress often followed by stagnation or even regression. Witnessing these fluctuations can make it difficult to maintain faith in the process.

Science doesn’t exist in a vacuum; it’s deeply connected to social, economic, and political systems. Universities and research institutions are the centers of scientific inquiry, but their output is designed to influence society broadly. This is a dialogue between academia and society that should include all fields, as each has its unique role in driving progress.

3.8.1 Driving Innovation and Technological Advancement

One of the primary roles of science is to drive innovation. The research conducted in universities and laboratories often leads to technological advancements, medical breakthroughs, and new methodologies that can be applied across industries. For instance, collaborations between academia and the private sector frequently result in the commercialization of new technologies or the development of improved industrial processes.

In short, Europe needs more companies like ASML—they need to build their own tech giants, similar to those in the USA, which have been profiting so handsomely. They also need to revitalize their factories and industries to ensure they don’t get outcompeted in critical sectors like car production. But what role do the social sciences play in this? Do they contribute to innovation merely by writing about innovation, as they have been doing for the past two decades? There is no clear correlation between the number of papers on innovation in the social sciences and actual technological breakthroughs—in fact, sometimes it seems the opposite is true. Instead, European research must reposition itself toward a clearer purpose, one that is more aligned with the practical and economic needs of industry.

Meanwhile, Asia has increasingly become a competitive force, thanks to large-scale investments in higher education and research. Countries like China, South Korea, and Singapore have poured resources into building world-class universities and research institutes, helping their industries rise rapidly in global markets. This is a model that Europe used to master and that they could learn from again, investing in the nexus between academia and industry, fostering innovation in a more direct and impactful way. Europe has the potential to leverage its academic strengths to fuel its technological rise, but it will require a more focused, strategic approach that connects research with real-world applications.

The social sciences are not a “side project” to this edifice but lie at its heart. They are essential to understanding and addressing the complex societal challenges that drive innovation and economic progress. By studying human behavior, societal structures, and cultural dynamics, the social sciences provide the insights needed to inform policies, guide technological development, and ensure that advancements are inclusive and sustainable.

If we in the social sciences are to make a meaningful contribution to innovation, we need to focus on understanding and enhancing the environments where innovation takes place. This involves studying how policies, organizational cultures, and social networks promote creativity and risk-taking. Crucially, social scientists should work more closely with actual innovators. By analyzing specific barriers to innovation within industries, social scientists can help design effective frameworks for collaboration between businesses, governments, and educational institutions. Too much production today on innovation takes an outside view of innovation, describing it from the outside, how it is, or how some people think it should be. There is a significant group of social scientists who confuse being critical with simply dismantling existing institutions through deconstruction, without offering constructive alternatives in their place. While the vital concern for the environment has become a focus, at times it veers toward critiquing the wealth that has been accumulated, rather than fostering solutions for a more sustainable future. Much of this critique stems from a misunderstood interpretation of critical theory (Frankfurt School ideology), which, although valuable in many contexts, may inadvertently distance us from our objectives and make us less competitive, rather than more so.

While interdisciplinarity could be part of the solution, it’s important to recognize that simply discussing problems—or worse, focusing on small fragments of problems without understanding the bigger picture—doesn’t suffice and may even be more harmful than helpful. The focus should be on addressing real-world challenges with practical, actionable solutions that drive progress.

In other words, we all perform better when faced with clear and compelling problems to solve—much like during the Space Race, when the Russians took the lead, prompting the Americans to focus all their efforts on putting the first man on the moon, a goal they ultimately achieved. This mindset and determination should also inspire us in the social sciences. There is no shortage of challenging and unresolved issues waiting for solutions. If a particular problem proves too difficult to address effectively, it’s better to redirect efforts toward one that is both achievable and impactful.

Only by following this path can the social sciences contribute to genuine technological and societal advancements. The alternative will inevitably result in our decline and diminishing relevance.

3.8.2 Fostering Critical Thinking and Societal Awareness

Any good idea emerges through critical thinking, which involves examining what hasn’t worked and understanding why. From there, we need to make the necessary changes.

Unfortunately, the social sciences often fall into the trap of simply continuing to do more of what they are already doing, as if the sheer volume of articles and other outputs—rather than a shift in direction—will lead to progress. This is, of course, an illusion. We must be willing to be highly critical, not just of others’ work but also of our own—“to kill our darlings.”

True progress comes from challenging the status quo, and criticism of an idea is not the same as an attack on the person behind it. A good researcher combines the ability to produce science with the capacity to critically assess both new and established ideas. One is meaningless without the other.

Example: How I Teach Critical Thinking

When I teach the scientific method, I emphasize critical thinking because I see that most students know what it is in theory but struggle with how to apply it in practice.

My pedagogical approach often goes something like this: Critical thinking is cognitively demanding, as it requires you to listen carefully to what is being said or written, while simultaneously asking yourself, “Why might this be wrong?” This sparks a mental process where you actively look for counterarguments. The challenge is that, as a listener, the speaker often doesn’t pause, so you’re engaged in an ongoing effort to find flaws while the lecture continues—this can be mentally exhausting and requires long-term training. We have to both listen and think at the same time. True critical thinking means understanding the content while continuously generating counterpoints, and this process continues, often for as long as the speaker speaks. It becomes even more challenging when we prepare to respond by raising our hands, as at this point, we keep rehearsing what we should say, missing much of the content being discussed in the meantime.

It’s because critical thinking is so demanding, and we are not naturally equipped for it that we must practice it intensively. This is one of the most important skills we teach students at our universities. Once we identify a counterargument, the next step is to speak up and ask questions, for example, by using the Socratic method: “But isn’t it true that...?” If we’re too afraid to question, critical thinking will never take place—it will simply be left to others, not ourselves. The entire concept of learning in a group is to nurture each other’s thinking, to challenge and grow together. Being a scientist requires bravery and the courage to stand up for your ideas. Few of us are capable of this in theory, and even fewer in practice. The alternative is to bury our heads in the sand, like an ostrich, but that certainly doesn’t make for a strong foundation as a scientist.

Because critical thinking is so mentally exhausting and difficult, we have to practice it intensively. This is, at the same time, one of the most important abilities we teach students at our universities. In the USA, student groups often set up debate clubs to practice engaging actively in discussions. It’s excellent training. Above all, we should not assume that we already know how to do this or that it’s somehow natural for us. On the contrary, we seem more inclined to follow than to oppose, which is dangerous and leaves us vulnerable to manipulation.

We need to cultivate an environment where critique is valued and encouraged, where researchers are not only producers of knowledge but also constant questioners of that knowledge. This is the only way real innovation, in both thought and practice, will occur.

We learn about critical thinking through a lifetime of education, and ideally, this process should begin early and deepen over time. For example, by studying formal logic, we develop the ability to assess arguments structurally, understanding not only whether something is true but whether it logically follows. Learning the history of philosophy exposes us to centuries of intellectual debate, teaching us how ideas have evolved and how to critique them within their contexts. Courses in statistics are essential because they teach us how to interpret data and avoid common pitfalls in reasoning, such as false correlations or biased sampling. A solid course in scientific methods is also crucial, as it provides the foundation for understanding how to design, conduct, and evaluate research in a rigorous and systematic way.

This brings us back to the purpose of writing this book: building intellectual bridges.

3.8.2.1 Foundations of Critical Thinking in Research: Building Intellectual Bridges

The motivation for this book lies in the conviction that a comprehensive understanding of what it means to be a researcher is crucial for advanced education, particularly at the PhD level. Students would benefit from courses that encourage them to synthesize knowledge from diverse disciplines, such as logic, philosophy, and statistical reasoning, as this would better prepare them for the multifaceted demands of research careers. An interdisciplinary foundation enables PhD candidates to produce and critically assess research, fostering both depth and breadth in their academic development.

This book aims to address that need by providing an overview of research methodology, philosophy, and critical thinking within a single framework. It is intended as a resource for doctoral-level courses, helping future researchers build the intellectual foundation needed to devise innovative solutions and contribute meaningfully to their fields. However, the material is also accessible to a broader audience, including those outside of academia.

The content extends beyond traditional scientific methods courses, exploring the philosophical foundations of research, interdisciplinary synthesis, and the analytical tools necessary for rigorous critique. A course based on this material could be titled “Foundations of Scientific Inquiry: Methods and Perspectives” or “Scientific Methodology and Critical Thinking in Research.” It would complement existing scientific methods courses by focusing on intellectual and analytical skills, as opposed to purely procedural methodologies. This course could provide PhD students with a solid grounding in formal logic, philosophical inquiry, advanced statistical reasoning, and scientific critique, equipping them to challenge assumptions, build coherent arguments, and navigate complex research environments.

Elements of this approach have been incorporated in courses I’ve taught, such as “Advanced Methods in Scientific Research” at Halmstad University. Unfortunately, the course was discontinued due to the trend toward one-year master’s programs, which often do not have room for comprehensive methods courses. Nevertheless, the need for such a course remains, particularly at the doctoral level, where it could be introduced early in a student’s academic journey.

This course would not only prepare students to produce high-quality research but also to evaluate their own work and the work of others critically. Through practical exercises and in-depth discussions, students would apply these concepts to real-world scenarios, fostering a mindset of continuous inquiry and intellectual growth. By prioritizing critical thinking, the course would help researchers navigate the academic landscape with confidence and adaptability.

Ultimately, this approach strengthens both individual researchers and the academic community by promoting interdisciplinary understanding and intellectual rigor. It prepares researchers to face the evolving challenges of academia and make meaningful contributions to their fields.

The central question remains: How do we implement this vision, and what assumptions must we challenge to make it a reality?

3.8.3 Academic Freedom and Ethics

No scientific progress is possible in the social sciences without the freedom to say and write what one believes—this is the essence of freedom of expression. The notion of freedom of expression can be traced back to Enlightenment thinkers, notably John Locke and Voltaire, who argued for the protection of individual rights against government interference. Locke’s “Two Treatises of Government” (1689) emphasized the importance of freedom of thought and expression as essential to individual liberty and the pursuit of truth. Voltaire, in his “Philosophical Dictionary” (1764), famously defended freedom of speech with the quote:

I disapprove of what you say, but I will defend to the death your right to say it.

This philosophical foundation laid the groundwork for later developments in freedom of expression, particularly in legal contexts, such as in the First Amendment of the US Constitution.¹⁰

In my view, this should be considered a fundamental human right. Even if we disagree with what is being said, we must defend the right of individuals to express their views freely.

In legal terms, the right to freedom of expression is protected under international human rights law, notably in Article 19 of the Universal Declaration of Human Rights (1948), which states that “everyone has the right to freedom of opinion and expression.” This principle is echoed in regional human rights frameworks, such as the European Convention on Human Rights (ECHR), which under Article 10 guarantees the right to freedom of expression, subject only to necessary restrictions in a democratic society (Council of Europe 1950). Furthermore, the US Constitution’s First Amendment unequivocally protects free speech, stating that “Congress shall make no law... abridging the freedom of speech, or of the press” (U.S. Congress 1791).

Legally, the restriction of free expression must be justified by compelling interests, such as national security, public order, or the prevention of hate speech, but these restrictions are subject to strict scrutiny in democratic societies.

Unfortunately, the current trend in Europe is moving in the wrong direction, toward increased censorship and surveillance, which is deeply concerning. However, we have seen similar developments in the past, and we have managed to get back on the right track.

In academia, we refer to this assumption for doing science as academic freedom, and it is crucial for the advancement of knowledge. Academics must be free to research whatever they choose, using scientifically valid methods. They should remain independent, not only of external influences, whether from politicians, advocacy groups, or other outside pressures, but also from inside pressure, from the university’s administration and leadership. Unfortunately, here we still have a long way to go.

In certain countries, such as Sweden, professors are afforded strong protections, similar to judges and police chiefs. When you become a full professor, it is considered crucial for the advancement of science that you are free to present your research findings, regardless of whether they are popular or align with current government policies. There should be no external pressure to silence or remove professors because of their research—this includes pressure from any party, even from the vice-chancellor of your own university. Academic freedom must be protected to ensure that knowledge and truth can flourish without fear of retribution or censorship. To guarantee these rights, a professor’s dismissal is not decided by university leadership but by a special tribunal in Stockholm known as the State Responsibility Board (Statens Ansvarsnämnd, SAN). This safeguard is a wise measure, designed to protect academic integrity. However, even with these protections in place, professors are increasingly facing threats, making these mechanisms more necessary than ever.¹¹

In Germany, academic staff are protected by laws that ensure dismissals can only occur under specific circumstances, with oversight from independent legal bodies. In the United States, the tenure system is designed to protect academic freedom, where tenured professors cannot be dismissed without just cause and a formal review process. In the United Kingdom, higher education institutions follow strict guidelines laid out by employment laws and independent review boards to prevent unjust termination of academic staff. France also has strong protections for academic freedom, with dismissals subject to legal scrutiny to prevent political or external influence. Despite these protections, academics in various countries are still facing increasing pressure, making these safeguards more important than ever. The need for such protections is growing when external forces, including political and financial interests, continue to challenge the independence of academic research.

Researchers and academics have faced attempts to suppress their work many times throughout our 1000-year history—or 2000 years if we count from the classical Greeks. One of the most serious periods of suppression occurred during what we now call the Medieval period. It’s referred to as such for a reason: It was a time of intellectual darkness, especially when compared to the flourishing sciences of ancient Greece and Rome. Although some historians today argue that the Middle Ages were not as bleak as once thought and that there were indeed pockets of progress—which here was—the broader context was one of stagnation and decline. This was in stark contrast to the classical period when inquiry and reason thrived. Of course, this does not mean that everything was better during the classic period. For the average person, life was much more difficult, even for scientists.

The Renaissance was aptly named so because it marked the “rebirth” of reason and a rediscovery of knowledge. During this period, intellectual freedom began to flourish again, albeit still limited. Much of early scientific thought was preserved and developed in monasteries, as the Church was both a custodian of knowledge and, paradoxically, one of its greatest suppressors. The Enlightenment, however, was a turning point when the Church’s grip on intellectual pursuits began to weaken. This shift allowed reason and scientific inquiry to take center stage, which eventually led to the Industrial Revolution—a period that created the tremendous wealth and technological progress we all see around us today (but also many of its problems with pollution).

There have been other times when ideology has influenced academic discourse, such as during the 1968 protests, when political perspectives occasionally overshadowed evidence-based thinking. This movement, which began in Paris and spread across the West, emphasized ideology alongside scientific reasoning. Today, similar dynamics can be observed in what is often termed “woke culture,” “cancel culture,” or certain diversity, equity, and inclusion (DEI) initiatives, where political ideologies occasionally influence university environments to the extent that they may overshadow the primary focus on advancing science. Unlike past political movements—such as the “68 Rebellion”—these influences originate from within academia itself, with students increasingly backed by faculty and administrators.¹² This does not mean that the values promoted in these movements are not good, right, or necessary; rather, the issue arises when these values are imposed in ways that overshadow or constrain the scientific method. The concern is not with the principles themselves, but with how their enforcement can stifle open inquiry and critical thinking, which are essential for academic progress.

Despite all of these challenges, science has always endured, and it will likely continue to do so, simply in the end, because man can see that science leads to a general improvement in our societies. The contributions of scientific inquiry are too vital to be disregarded, and history demonstrates its irreplaceable role in societal progress. When nations fall behind in science and technology, they often redirect their focus to catch up as others surge ahead. For the Western world today, this could mean recognizing the impressive strides many Asian countries are making in areas such as engineering, mathematics, and physics. Over time, the intrinsic value of science will remain evident, encouraging the Western world to reassess its priorities and place science above political agendas. I am not saying this because I believe that Asian, African, or any other regional values are inherently better than European ones; on the contrary, I think it’s important to recognize the complexities and nuances of each cultural context.

There are other reasons to be optimistic, too. The regulatory frameworks established by governments protect academic freedom and maintain quality standards. Accreditation agencies ensure that universities and research institutions adhere to these standards, thereby maintaining public trust in the scientific process. Moreover, laws and regulations play a key role in promoting ethical research practices and ensuring that scientific progress benefits all members of society, not just select groups. This governance is crucial in maintaining a balance between academic independence and accountability to society.

3.8.4 Science and Public Policy

While politicians should not decide what science should be conducted at universities, scientists do accept jobs from the public sector to provide advice on certain issues and make policy recommendations (Table 3.19).

Table 3.19

Examples of public policy area and types of decisions scientists help with

Public policy area

Type of decisions

Examples of suggestions

Environmental policy

Regulations on pollution, climate change mitigation, conservation efforts

Scientists advising governments on carbon reduction targets and biodiversity protection

Health policy

Healthcare system reforms, public health campaigns, vaccination strategies

Public health experts recommending vaccination campaigns or disease prevention strategies

Education policy

Curriculum development, funding allocation, teacher training initiatives

Education researchers proposing updates to curricula to improve STEM education

Economic policy

Tax policies, labor market regulations, economic stimulus packages

Economists advising on tax reforms or stimulus packages to address economic downturns

Urban planning

Zoning laws, infrastructure development, housing strategies

Urban planners recommending sustainable city designs and affordable housing projects

Energy policy

Renewable energy adoption, energy efficiency measures, fossil fuel reduction

Energy experts suggesting transitions to solar or wind power and reducing coal dependency

Social policy

Welfare programs, pension systems, unemployment benefits

Sociologists offering insights on improving welfare programs to reduce poverty

Transportation policy

Public transport systems, road safety measures, infrastructure investments

Transportation engineers suggesting improvements to public transit systems or road safety

The reason scientists assist with these types of decisions is not only because much of the expertise and knowledge resides with them in most areas of society but also because, in our representative democracies, elected officials often lack the specialized knowledge required to make informed decisions.

Politicians are tasked with making critical choices on topics like climate change, public health, or technology regulation, but few have the scientific background needed to fully understand the complexities of these issues. As a result, they heavily rely on input from scientists and civil servants to provide the in-depth knowledge necessary to make well-informed policy decisions.

Case: Policy Decisions and Scientific Advice During the COVID-19 Pandemic

During the COVID-19 pandemic, governments depended on epidemiologists and public health experts to guide decisions on lockdowns, vaccinations, and public health measures. Similarly, when making decisions on energy policy, elected officials often rely on environmental scientists and engineers to understand the feasibility of transitioning to renewable energy sources. This reliance on scientific expertise ensures that policies are based on the best available evidence, even when the decision-makers themselves lack technical expertise.

Of course, that scientists give suggestions for decisions does not mean that politicians always follow them. Statistics on the extent to which politicians follow the advice of scientists before making decisions are not always straightforward. It varies significantly by country, issue, and political context. However, there are several studies and examples that shed light on this relationship.

In the United States, a 2014 Pew Research Center survey found that while 88% of scientists believed the benefits of vaccines outweighed the risks, only 68% of the public and a smaller percentage of politicians shared this view. This gap between scientific consensus and political decisions was also evident during debates on climate change, where many policymakers did not fully adopt the scientific recommendations on carbon reduction and renewable energy. This inconsistency illustrates that while scientific advice is available, political agendas and public opinion often influence decision-making.

In contrast, Germany, many would argue, has been a model for integrating scientific advice into policy, particularly in climate change and public health. The German government’s commitment to the “Energiewende” (energy transition) reflects close adherence to environmental scientists’ recommendations on reducing dependence on fossil fuels and increasing the use of renewable energy. Similarly, during the COVID-19 pandemic, Chancellor Angela Merkel, who has a scientific background, heavily relied on virologists and epidemiologists to shape Germany’s response, and the government was praised for basing its policies on scientific advice.

In New Zealand, Prime Minister Jacinda Ardern followed the advice of public health experts during the pandemic, leading to one of the most successful COVID-19 responses globally. Ardern frequently mentioned how decisions were “driven by science,” including early lockdowns and strict border controls, which were critical in curbing the spread of the virus.

In contrast, Brazil under President Jair Bolsonaro provided a stark example of political leadership dismissing scientific advice. Bolsonaro’s administration downplayed the severity of COVID-19 and ignored public health recommendations, resulting in one of the highest death tolls from the pandemic. This was despite widespread scientific consensus on the necessary health measures.

Sweden offers an interesting middle ground. The Swedish government followed the advice of its chief epidemiologist, Anders Tegnell, during the pandemic by opting for a more relaxed strategy without stringent lockdowns. Although controversial, this approach showed how governments sometimes rely heavily on one key scientific advisor, but the decisions may still diverge from international scientific recommendations.

In the UK, Boris Johnson’s government initially delayed lockdowns during the early phase of the pandemic, reportedly against scientific advice. However, as the severity of the pandemic became clearer, they shifted to follow the guidance of SAGE (Scientific Advisory Group for Emergencies), leading to stricter measures.

Globally, scientific advice tends to be more consistently followed in environmental policy in countries like Finland and Norway, where governments have prioritized sustainability and the recommendations of environmental scientists, particularly in areas such as deforestation, climate change, and biodiversity conservation.

In China, scientific input plays a key role in shaping technological and environmental policies, though the authoritarian nature of the government also means that scientific advice can be politically controlled, especially when it conflicts with economic goals.

Of course, just because scientists give advice, it doesn’t necessarily mean that the advice is always sound from the start. Often, one particular scientist or a small group is tasked with preparing a report, and their views may not be shared by the broader scientific community—in fact, they rarely are. The example of lockdowns during COVID-19 illustrates the clear differences in how countries reacted based on the opinions of their respective scientists. After the pandemic, there has naturally been discussion about which country’s scientists were most correct. In this case, Anders Tegnell from Sweden received considerable support for not imposing a full lockdown, in contrast to the completely opposite approach taken by China. However, even in Sweden, opinions remain divided, with some agreeing with Tegnell’s approach and others believing a stricter response would have been better. This highlights how scientific advice can differ significantly, even among experts, and how the outcomes of such advice are often subject to ongoing debate.

Here are some questions for discussion:

1.

To what extent should policymakers prioritize scientific advice over public opinion or political agendas when making decisions, and how can the balance between these factors be effectively managed?

2.

How do differences in national governance systems (e.g., democratic vs. authoritarian) influence the extent to which scientific advice is followed, and what are the implications for public trust in science?

3.

What mechanisms can be implemented to ensure the diversity of scientific perspectives is considered, rather than relying on the opinions of a single scientist or a small group, as seen in some cases during the COVID-19 pandemic?

4.

How can governments address the challenges of communicating complex scientific recommendations to the public in a way that builds trust and minimizes misinformation?

5.

Given the variations in how countries responded to the pandemic, what lessons can be drawn about the effectiveness of integrating scientific advice into policy, and how should these lessons inform future crises such as climate change?

There are also differing opinions on other issues, of course, like the “green transition,” and particularly regarding the move away from nuclear energy. It’s becoming less clear whether abandoning nuclear energy was a wise choice, especially considering that the risks associated with nuclear power may have been exaggerated—given the very low number of accidents and fatalities at these plants. New generations of nuclear reactors are much safer and more efficient.¹³ But again, the role of science is not to dictate policy to politicians; rather, it is to present the facts clearly and objectively, allowing them to make informed decisions.

The problem with halting research in certain areas, such as nuclear energy, is that the knowledge base in that field can quickly disappear within a country. For instance, Sweden has recently reconsidered its stance on nuclear energy, realizing it needs to significantly increase energy production and that nuclear power must be an option. However, the challenge is that many Swedish researchers no longer have the expertise to build nuclear facilities, despite the country being a global leader in the 1950s and 1960s. Of the 12 nuclear reactors Sweden once had, six have been shut down, and the remaining ones require maintenance, often sourced from abroad.

Now, Sweden must rely on external assistance from countries like South Korea, the United States, China, Russia, or France to build new facilities. While South Korea has strong scientific expertise, the distance is a challenge, and France, although geographically close, is known for high costs, making the next reactors in Sweden potentially very expensive to build. What’s more, regarding the broader green transition, many of the recommendations did not adequately consider the economic consequences, often focusing solely on what would be best for the environment under ideal conditions. For example, wind turbines may struggle to be cost-effective over their lifetime and produce less energy when it is cold, and solar panels are not a viable solution for countries near the polar circle. In this context, it’s uncertain whether some of the advice provided by scientists for the green transition was fully sound or sufficiently practical, at least not at the present moment. The problem here is that politicians have often commissioned research that supports their interests while almost suppressing studies that challenge or contradict their views.

To avoid these kinds of mistakes in the future, it is essential that science is left free and to ensure a broader, interdisciplinary approach in decision-making that includes not only environmental and technical experts but also economists and social scientists to assess the full impact of recommendations. Politicians must be cautious not to “order” desired results through research grants. In the long run, this undermines the credibility of the entire educational institution. Rigorous peer review and open debate within the scientific community can help identify potential gaps or flaws in proposed solutions before they are implemented. Governments should maintain long-term investment in research across diverse fields to prevent knowledge loss, ensuring expertise remains available when needed. Continuous education for policymakers on scientific advancements can help bridge the gap between technical knowledge and practical decision-making. Independent advisory panels, with diverse representation, can offer more balanced insights, minimizing the risk of biased or incomplete advice. Finally, regular post-implementation reviews of policies can assess the outcomes and provide learning opportunities for future decision-making.

These examples demonstrate that while scientific advice is often sought, political factors, economic interests, and public opinion can heavily influence whether and to what extent politicians follow this advice. The relationship between science and policy is complex and shaped by many factors beyond the evidence itself. It will never be perfect, of course, but we as a community of researchers must strive to be as scientific and nonpolitical as possible. This attitude should be in our DNA as researchers.

Political ideas are an inherent part of studying political science, but even in this field, they should be examined objectively, much like a selection of ideas on a menu, rather than from the subjective standpoint of a chosen ideology. While political ideologies have their place in society, academic institutions should maintain a neutral stance, ensuring that research is focused on understanding and analysis, not advocacy for a particular agenda.

To wrap up, while politicians should not dictate the direction of scientific inquiry at universities, scientists can, and often do, provide valuable insights by accepting roles that allow them to advise the public sector and offer policy recommendations. Scientists cannot, of course, control how their knowledge is ultimately used in society, but they can conduct research that highlights the implications of how their findings are applied. For instance, research can be used as a reminder of the potential dangers of political extremism, helping to ensure that scientific knowledge serves the greater good, rather than being exploited for ideological purposes. By staying true to their academic integrity and objectivity, scientists can in this way contribute to more balanced and informed decision-making.

3.8.5 Science, Politics, and Systemic Bias

Science, particularly the humanities and social sciences, plays a vital role in shaping the cultural and intellectual development of society. These fields offer critical insights into human behavior, culture, history, and ethics, helping us reflect on our values and make informed decisions about the future.

The contributions of social scientists provide essential context for addressing complex issues, guiding the public in understanding ethical dilemmas and societal challenges. For example, the ethical debates surrounding artificial intelligence or genetic engineering are deeply influenced by disciplines like philosophy, law, and sociology, which examine the implications of these technologies on society’s values and future direction. These interdisciplinary insights are crucial for developing a well-rounded perspective on the ethical dimensions of scientific advancement (Table 3.20).

Table 3.20

Examples of how scientists shape cultural and intellectual development

Activity

Description

Risks

Writing debate articles in the press

Scientists contribute articles to newspapers or magazines, offering opinions on cultural or intellectual topics

Risk of appearing biased or overly political, which can damage public trust in the scientist’s objectivity

Starting podcasts

Scientists host podcasts to discuss scientific, cultural, or intellectual ideas with a wide audience

Potential for cherry-picking topics that align with personal beliefs, leading to a perception of partiality

Creating YouTube channels

Scientists use YouTube to reach the public with educational content, often simplifying complex topics

Oversimplification of topics might lead to misinterpretation, and the platform can incentivize sensationalism

Giving public speeches

Scientists speak at public events or conferences, addressing societal, cultural, or intellectual issues

Public speeches can sometimes blur the line between scientific discourse and advocacy, leading to perceived bias

Engaging in social media debates

Scientists interact with the public on platforms like twitter or Facebook, discussing cultural or societal issues

Risk of engaging in polarizing or heated debates that can tarnish the scientist’s professional reputation

Writing books for a general audience

Scientists write books aimed at a broader public, sharing insights on science and its role in society

The challenge of balancing accessibility with rigor can lead to misunderstandings or accusations of bias

Participating in documentaries

Scientists collaborate with media producers to bring scientific ideas to the screen, reaching a wide audience

Potential for selective editing that distorts the message or frames it in a politically charged context

Hosting public lectures

Scientists hold lectures open to the public, aiming to educate and inform on current research or societal topics

The content may be skewed by personal beliefs, influencing the audience’s perception of objectivity

The role of science, particularly in the social sciences and humanities, in shaping cultural and intellectual development is both significant and complex. One of the challenges arises from the fact that culture is subjective and diverse, varying across individuals and groups. While social scientists—many of whom may lean toward center-left perspectives, particularly in Western societies—are often called upon to contribute to discussions on cultural and ethical issues, there is a potential for their personal values to influence their recommendations (systemic bias). “Systemic bias” typically refers to biases that are embedded within the structures or systems of an organization or society. This could inadvertently shape societal norms and ethics in ways that do not fully align with the broader population’s diverse perspectives.

For example, discussions on individualism versus collectivism or debates on topics such as artificial intelligence and genetic engineering might be influenced by the personal beliefs of researchers, rather than objective analysis. When social scientists advocate for policies or cultural changes that reflect their personal political or ethical leanings, it risks blurring the line between scientific inquiry and personal advocacy. Maintaining the neutrality that should underpin scientific research is crucial, as failure to do so may lead to one-sided cultural narratives that do not reflect the full range of societal views.

Moreover, when scientists are perceived as promoting particular ideological agendas, it can erode public trust in science, especially among those who feel their views are not represented. In fields like ethics or sociology, where research often intersects with policy and public opinion, it is essential for scientists to be aware of their biases and make a conscious effort to separate personal values from their professional research. This approach helps ensure their contributions are inclusive and reflect society’s diversity. Failing to address this risk could diminish public support for the social sciences, potentially leading to funding cuts, as seen in instances where public funding has been questioned.

In my experience, it’s generally not a positive sign if a social scientist’s political affiliations are readily apparent. This often suggests that their work may be influenced by personal biases, which can undermine the objectivity central to effective social science. A model of professionalism, like that of my friend Prof. Mats Alvesson, who engages thoughtfully with ideas across the political spectrum while maintaining an impartial stance, exemplifies the ideal balance social scientists should strive for. Honestly, despite knowing him personally, I’ve never been able to pinpoint his political stance, and that’s exactly the way it should be. This neutrality is a testament to the ideal of intellectual impartiality. Aspiring social scientists should aim to cultivate a similar nonpolitical approach, ensuring impartiality and intellectual integrity in both their research and personal expressions.

References

Aagaard K, Schneider JW (2019) Research funding and disciplinary performance: a study of the allocation of research funds in Denmark. MPRA Paper No. 88931. https://​mpra.​ub.​uni-muenchen.​de/​88931/​1/​MPRA_​paper_​88931.​pdf

Aldridge J, Derrington AM (2012) The research funding toolkit: how to plan and write successful grant applications. Sage

Brembs B, Button K, Munafò M (2013) Deep impact: unintended consequences of journal rank. Front Hum Neurosci 7(291):1–12. https://​doi.​org/​10.​3389/​fnhum.​2013.​00291Crossref

Brenner R (2022) Protests of 1968: the politics of memory or the memory of politics. Academia.​edu. https://​www.​academia.​edu/​109072222/​Protests_​of_​1968_​The_​Politics_​of_​Memory_​or_​the_​Memory_​of_​Politics. Retrieved 21 November 2024

Bretag T (2016) Handbook of academic integrity. Springer. https://​doi.​org/​10.​1007/​978-981-287-079-7

Capano G, Piattoni S (2021) University governance in Europe: patterns of state oversight and institutional autonomy. Policy Soc 36(1):67–83. https://​academic.​oup.​com/​policyandsociety​/​article/​36/​1/​67/​6401404

Chapman DW, Lindner SA (2016) Degrees of integrity: the threat of corruption in higher education. Stud High Educ 41(2):247–268. https://​doi.​org/​10.​1080/​03075079.​2014.​927854Crossref

Christenson SL, Leslie DW (1989) The academic ecosystem: a perspective on faculty roles and rewards. ASHE-ERIC Higher Education Reports

Council of Europe (1950) European Convention on Human Rights. Council of Europe. https://​www.​echr.​coe.​int/​Documents/​Convention_​ENG.​pdf

Eaton SE (2021) Academic integrity during COVID-19: reflections from the University of Calgary. Int Stud Educ Admin 48(1):80–85. https://​prism.​ucalgary.​ca/​handle/​1880/​112293

Fielden J (2010) Global trends in university governance. Springer. https://​link.​springer.​com/​chapter/​10.​1007/​978-3-030-83465-4_​5

Gupta AK, Govindarajan V (2002) Cultivating a global mindset. Acad Manage exec 16(1):116–126. https://​doi.​org/​10.​5465/​ame.​2002.​6640211

Jongbloed B, Vossensteyn H, van Vught F, Westerheijden DF (2018) Transparency in higher education: the emergence of a new perspective on higher education governance. In: Curaj A, Deca L, Pricopie R (eds) European higher education area: the impact of past and future policies. Springer, pp 315–330. https://​doi.​org/​10.​1007/​978-3-319-77407-7_​27Crossref

King R (2004) The university in the global age. Palgrave Macmillan

King’s College London (2023) Woke, cancel culture, and white privilege: how the UK’s culture war debate is evolving. King’s College London News. https://​www.​kcl.​ac.​uk/​news/​woke-cancel-culture-and-white-privilege-how-the-uks-culture-war-debate-is-evolving. Retrieved 21 November 2024

Klemperer T (2021) German universities: balancing autonomy and public accountability. Policy Soc 36(1):67–83. https://​academic.​oup.​com/​policyandsociety​/​article/​36/​1/​67/​6401404

Lane JE, Johnstone DB (2013) Higher education systems 3.0: harnessing systemness, delivering performance. ERIC. https://​files.​eric.​ed.​gov/​fulltext/​ED574089.​pdfCrossref

Larivière V, Haustein S, Mongeon P (2015) The decline in the prominence of books in scholarly communication. PLoS One 10(11):e0127505. https://​doi.​org/​10.​1371/​journal.​pone.​0127505Crossref

Locatelli G, Mancini M, Todeschini N (2013) Generation IV nuclear reactors: current status and future prospects. Energy Policy 61:1503–1520. https://​doi.​org/​10.​1016/​j.​enpol.​2013.​06.​101Crossref

Locke J (1689) Two treatises of government. Awnsham Churchill

Lyons RE, Rayner SJ (eds) (2015) The academic book of the future. Palgrave Macmillan

Mingers J, Leydesdorff L (2015) A review of theory and practice in scientometrics. Eur J Oper Res 246(1):1–19. https://​doi.​org/​10.​1016/​j.​ejor.​2015.​04.​002Crossref

OECD (2019) Universities, the state, and the market: changing patterns of university governance in Sweden and beyond. OECD iLibrary. https://​www.​oecd-ilibrary.​org/​education/​universities-the-state-and-the-market-changing-patterns-of-university-governance-in-sweden-and-beyond

Qvist SA, Brook BW (2015) Potential for worldwide displacement of fossil-fuel electricity by nuclear energy in three decades based on extrapolation of regional deployment data. PLoS One 10(5):e0124074. https://​doi.​org/​10.​1371/​journal.​pone.​0124074Crossref

Statens Ansvarsnämnd (n.d.) State responsibility board: about us. https://​statensansvarsna​mnd.​se. Retrieved 21 November 2024

Stensaker B, Harvey L (2011) Accountability in higher education: global perspectives on trust and power. Routledge. https://​doi.​org/​10.​4324/​9780203818268Crossref

U.S. Congress (1791) The first amendment to the United States Constitution. https://​www.​archives.​gov/​exhibits/​american_​originals/​billofrights.​html

Van Noorden R (2017) The scientists who get credit for others’ work. Nature 543(7644):12–14.

Voltaire (1764) Philosophical dictionary. https://​www.​gutenberg.​org/​ebooks/​12319

Wikipedia contributors (n.d.) Publish or perish. In: Wikipedia. https://​en.​wikipedia.​org/​wiki/​Publish_​or_​perish. Retrieved November 2023

Selected Readings

Albert H (1991) Traktat über kritische Vernunft. Mohr Siebeck

Bachelard G (2002) La formation de l’esprit scientifique: Contribution à une psychanalyse de la connaissance objective. Presses Universitaires de FranceCrossref

Berthelot J-M (1996) Épistémologie des sciences sociales. Presses Universitaires de France

Bortz J, Döring N (2016) Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler, 5. Auflage. Springer

Creswell JW, Creswell JD (2018) Research design: qualitative, quantitative, and mixed methods approaches, 5th edn. Sage

Desrosières A (2008) La politique des grands nombres: Histoire de la raison statistique. La Découverte

Esser H (1999) Soziologie: Allgemeine Grundlagen. Campus Verlag

Kuhn TS (2012) The structure of scientific revolutions (50th anniversary ed.). University of Chicago Press

Maxwell JA (2013) Qualitative research design: an interactive approach, 3rd edn. Sage

Opp K-D (2020) Methoden der empirischen Sozialforschung, 3. Auflage. Springer VS

Passeron J-C (2006) Le raisonnement sociologique: Un espace non poppérien de l’argumentation. Albin Michel

Popper KR (2005) The logic of scientific discovery. RoutledgeCrossref

Quivy R, Van Campenhoudt L (2017) Manuel de recherche en sciences sociales, 5e édn. Dunod

Saldaña J (2021) The coding manual for qualitative researchers, 4th edn. Sage

Saunders M, Lewis P, Thornhill A (2019) Research methods for business students, 8th edn. Pearson

Schäfer B, Kuhl R (2019) Einführung in die empirische Sozialforschung, 9. Auflage. Beltz Verlag

Schimank U (2005) Die Governance-Perspektive: Analytisches Potenzial und weiterführende Fragen. In: Schimank U, Windolf P (eds) Governance und Märkte in der Hochschulpolitik. VS Verlag für Sozialwissenschaften, pp 45–84

Stephan P (2012) How economics shapes science. Harvard University PressCrossref

Van Raan AFJ (2005) Measuring science: basic principles and application of advanced bibliometrics. Springer

Weingart P, Taubert N (2017) Science, technology, and innovation culture: cultural and organizational influences on innovation. Routledge

Footnotes

1

Christenson and Leslie (1989).

2

Lyons and Rayner (2015).

3

King (2004).

4

Aldridge and Derrington (2012).

5

For more detailed contributions on individual topics, see Jongbloed et al. (2018), Chapman and Lindner (2016), Bretag (2016), Eaton (2021) and Stensaker and Harvey (2011).

6

According to the National Science Foundation (NSF), approximately 166,000 social science articles are published annually.

7

For more on this, see Larivière et al. (2015), Mingers and Leydesdorff (2015), Brembs et al. (2013) and Wikipedia contributors. (n.d.). Publish or perish.

8

Thanks to Oksana Mont for comments in this section.

9

See Aagaard and Schneider (2019).

10

U.S. Congress (1791).

11

Statens Ansvarsnämnd (n.d.).

12

See Brenner (2022) and King’s College London (2023).

13

For a broader discussion, see Qvist and Brook (2015) and Locatelli et al. (2013).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

K. Solberg SöilenThe Researcher's Journeyhttps://doi.org/10.1007/978-3-031-91565-9_4

4. Positioning in Science: Foundations, Perspectives, and Schools of Thought

Klaus Solberg Söilen1  

(1)

School of Business, Halmstad University, Halmstad, Sweden

Klaus Solberg Söilen

Email: klaus.solberg_soilen@hh.se

Chapter 4 delves into the core elements that shape a researcher’s identity, exploring how our academic journey is built upon the literature we engage with, the methods we choose, and the philosophical perspectives we embrace. These components guide us in deciding which methodologies we use, the philosophies we claim to follow, and, importantly, why we choose them. As we advance through these stages, our understanding of the scientific landscape deepens, and we align ourselves with specific schools of thought that mold both our approach to research and our professional identity. Consistency and credibility in these choices are essential, requiring thoughtful study and reflection if we are to convincingly share our perspectives with others.

This chapter illustrates how these elements are interconnected, each step guiding and reshaping the next, as we try to find our place within the broader research ecosystem. By tracing these connections—from early influences in scientific literature to the choices we make in methodology and philosophy—we gain a clearer view of what defines us as researchers.

This understanding not only solidifies our scientific stance but also enables us to engage more thoughtfully with the academic community, first of all in our own writings and research.

Embracing this intellectual journey helps us to become more self-aware and adaptable, empowering us to make better contributions to our field. Ultimately, the aim of this chapter is to provide a framework that allows you to navigate the complex web of ideas, methods, and schools with confidence and purpose.

I call the journey map that helps us keep each of the elements presented in this chapter a part but at the same time interconnected for the scholarly positioning framework (Fig. 4.1).

Fig. 4.1

The scholarly positioning framework

The diagram shows how the chapter’s core elements—foundations, perspectives, and schools of thought—interconnect and contribute to understanding research positioning.

4.1 Foundations of Scientific Inquiry

The purpose of this first section is to set you on a journey through the history of science and philosophy, enabling you to define your philosophical foundations as a researcher. It’s not to persuade you that wherever I ended up, or whatever I think about science, is the right perspective.

Settling on a scientific perspective is not just a matter of personal preference—it is closely tied to certain philosophical schools of thought. Each scientific perspective often aligns with a particular philosophy, which in turn influences how we approach research, interpret data, and draw conclusions. This intertwining of philosophy and science makes it essential to not only understand the different scientific methods, as explained in Chap. 2, but also to get an overview of the philosophical schools that underpin them.

By doing so, you’ll gain insight into how philosophical ideas shape scientific inquiry and can better determine which approach resonates with your own beliefs and research style. In this way, this section is meant to serve as both an invitation to engage with the foundations of science and a guide to navigating the complex landscape of scientific perspectives.

In summary, the aim here is to illustrate the connection between what we read about science as we grow up (1), the methods we eventually adopt in our research (2), the philosophical perspectives these shape within us (3), and, ultimately, the schools of thought we find ourselves aligned with (4). These processes are deeply intertwined, each influencing and reshaping the others over time. Understanding this intellectual journey helps clarify your identity as a researcher. By recognizing how your reading, methods, and philosophical inclinations connect, you can better define your scientific stance.

This intellectual self-awareness not only grounds your work but also strengthens your contributions to your chosen field. It also offers you the clarity to engage more confidently with other scholars and schools of thought (Fig. 4.2).

Fig. 4.2

The intellectual journey of a scholar

This is not a one-way process but a continuous cycle of influences and adjustments. Over time, you will likely find yourself aligning with a particular philosophical perspective or school of thought, yet small shifts within that category will occur as you continue reading, reflecting, and working. Occasionally, you might even break away from previous positions—a natural part of intellectual growth, not a failure. A true failure would be ceasing to read, think, and question, especially while you’re an active researcher.

I wish there were a completely neutral, objective way to guide you, but since I am the one writing this book, I can only present it from the perspective I know—my own. That said, I’ve made every effort to showcase the many alternatives and diverse schools of thought available to you, so you can find what resonates most with your own approach. My aim is for this book to be valuable across the social sciences, regardless of which philosophy or framework feels most aligned with your interests and ideas.

I suspect that many researchers go through their careers without reflecting much on which perspective they adhere to. Some of us inherit our views from PhD supervisors or adopt those common at our workplaces, and that’s natural, but it’s not the same as consciously choosing. I want you to choose from a broad spectrum of perspectives. Most importantly, I want you to make that choice intentionally because even if you don’t and continue as a researcher without making it, you will write—consciously or unconsciously—from a certain perspective, and other well-read researchers will see what that perspective is.

To help you on your journey, I thought I’d share my own academic path through the books, journals, and even TV programs that have influenced me along the way. I hope this offers some guidance and inspiration for your own development. By making the academic process a bit less mysterious, I want to help you find useful resources, encourage critical thinking, and create a sense of community based on the shared experiences and intellectual exchanges that shape our growth as researchers.

Exercise: A Metacognitive Reflection Task

After reading the next section, “4.1.1 The Literature that Shapes Us: My Example,” or maybe better after reading the entire book, reflect on the books that have influenced you so far.

1.

What were the first significant books you read, particularly in science?

2.

How have they shaped your thinking and perspective, do you think?

3.

Considering your future aspirations, begin compiling a list of books you want to read in the coming years (approx. 1 hour, if done as a class exercise).

4.

Exchange your lists and stories with other students, and discuss your choices, presenting arguments for why you selected certain books and readings (30 min).

5.

Based on new considerations, update your list accordingly, keeping in mind that your reading list is a living document—always open to changes, not a fixed selection.

6.

Revisit your reading list every 6 months or so. How is it progressing? Have your priorities or interests shifted? Adjust as needed.

Metacognitive Reflection Task

I do not claim to be an expert in pedagogics—I simply enjoy teaching and conducting research—but this exercise can be described as a metacognitive reflection task designed to encourage self-directed learning and critical self-assessment of intellectual influences. It incorporates experiential learning (Kolb’s learning cycle), prompting you to analyze your past reading experiences and use these insights to shape your future learning goals. Additionally, it aligns with constructivist pedagogy, as you construct meaning by connecting prior knowledge with your evolving academic and professional interests.

Why This Exercise Matters

Reflecting on past reading experiences and planning future intellectual pursuits are powerful ways to cultivate lifelong learning. By identifying the books that have shaped your thinking so far and setting goals for future reading, you develop habits of continuous, independent learning that extend beyond formal education. This proactive approach can hopefully help nurture your curiosity and adaptability further, qualities that are essential for success in any academic or professional field.

Moreover, this exercise enhances self-awareness and critical thinking. When you examine your intellectual influences, you become more conscious of the perspectives and frameworks shaping your understanding. This reflection allows you to recognize patterns in your knowledge, identify potential biases, and pinpoint areas for further exploration, hopefully making you an even more discerning thinker.

Additionally, creating a structured reading plan promotes goal-oriented learning. By aligning your reading choices with your aspirations, you ensure that you engage with literature that is relevant to your academic and professional growth. Rather than passively encountering new ideas, you take an active role in shaping your intellectual development, equipping yourself with the knowledge and insights needed for your work as a researcher—or so the thinking goes.

4.1.1 The Literature that Shapes us: My Example

We learn through multiple channels, including reading books and journals, engaging in discussions, observing others, and hands-on experiences. The extent to which we remember and apply this knowledge depends on reflection, cognitive processing, and active engagement with the material.

I do not claim to be exceptionally gifted or particularly smart, and my academic career cannot be described as fulfilling for anyone other than myself, the students I’ve had (hopefully), and a handful of colleagues who have become very close over the years. This story is shared solely through my own experience:

My interest in science began around the age of 12. While my best friend would borrow mostly detective stories from the public library in the city where I grew up, I was drawn to books on history, geography, and science. We were like sponges at that age, absorbing as much knowledge as we could. We’d walk home—a 45-minjourney—discussing everything we were reading, all while saving bus money and lugging home heavy bags filled with more books than we could possibly read.

I started to learn to read through comics before I was in the first years of school. While most other kids were reading Donald Duck, I was absorbed in Silver Arrow, a Belgian series set in the American Old West. This series portrayed the Native American perspective and their struggle to survive within an unjust world order, and I’d like to believe it instilled in me a sense of justice from a young age. Likewise, much of my sense of humor was shaped by the works of Goscinny, like Asterix and Lucky Luke, which brought a lighthearted wit which I try to hold on to. My dark humor, however, came much later and was largely an English trait shaped by a generation raised on Monty Python and similar influences.

In high school, my interests expanded to include social sciences, particularly economics and political issues. This was sparked by a single teacher who saw further than the others, someone who thought on a larger scale. He wasn’t necessarily smarter than the other teachers, I think, but he spoke freely about the world in a way that challenged us to think for ourselves, and we liked him. He spoke to us as equals, genuinely interested in our thoughts, rather than adopting the tone of a teacher talking down to his students. Later, I would come to understand that this was all a part of the study pedagogy, but at the time, I guess we simply saw him as friendly. In my experience, you cannot be a good teacher if you don’t like your pupils.

It wasn’t until university that I developed a true appreciation for fiction. Many of the people I met later, especially during my time in Paris, were much more familiar with the literary classics than I was, and I realized that I had a lot of catching up to do. An almost frenetic period of reading began. My love for fiction peaked in my 30 s, but as my work as an economist and researcher advanced, my reading focus shifted toward social sciences and philosophy. Philosophy, especially in its original German and French, required a great deal of dedication—not least in mastering the languages. I spent considerable time learning these languages to fully engage with the literature I wanted to understand. Italian, I started learning simply because I love the culture, and I still struggle to read anything more advanced than a newspaper.

The interest in research articles, despite their backward form, eventually dawned on me, however slowly. Although academic articles initially seemed narrow and tedious, I soon realized their value as the primary means through which scientists communicate their findings.

But before that, while growing up, I remember we had a Norwegian translation of Bernal’s Science in History.¹ This wasn’t unusual, as the book was widely printed and commonly found in secondhand bookstores. My father, who didn’t own many books and preferred borrowing from the library, must have seen Bernal’s volumes as essential reading—likely why he made an exception for them. Given their wide circulation and continued presence in used bookstores today, I imagine the Norwegian translation had a significant influence on science enthusiasts in Norway throughout the 1970s, 1980s, and 1990s:

Bernal, J. D. (1954). Science in history (A. Bull Njå, Trans.). Oslo, Norway: Tiden Norsk Forlag.

John Desmond Bernal (1901–1971) was an Irish scientist. He was pivotal in advancing X-ray crystallography in molecular biology, wrote extensively on the history of science, and mentored notable scientists, including Dorothy Hodgkin, who later won the Nobel Prize in Chemistry. They say he could have gotten the prize too, but that he was keener on sharing what he knew. A noble trait in a scientist.

We had other books as well when I grew up—some classics, of course, and an encyclopedia, which was very expensive and had to be handled with great care (wash your hands first)—but also a set of yellow books, which at the time I didn’t understand. I later recognized them by their distinct yellow color and the symbol of a chess piece on the cover. As someone who loved chess but wasn’t particularly good at it, I came to realize these were Springer mathematics books, which I would come to respect greatly, not so much because I could understand them but because I knew how much they meant for my father, as an engineer. He also had some other books from his university days, most notably.

Kreyszig, E. (1962). Advanced Engineering Mathematics. John Wiley & Sons.

Erwin Otto Kreyszig (1922–2008) was a German who had moved to Canada. From that time on, I started to get the feeling that North America had gained tremendously from European immigration, also before and during the Second World War. Brain drain, they call it. Later, I discovered how many of these scholars came from Eastern Europe, among them favorites, like Georgescu-Roegen, Morgenstern, and von Neumann:

von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behaviour. Princeton University Press.

When I first discovered game theory at university, I thought it was simply magic. A beautiful new logic for human social behavior. However, I must admit, I haven’t used it much since, other than for illustrating decision trees. First, it often assumes rational actors with complete information, which doesn’t align well with real-world human behavior, where biases, emotions, and incomplete knowledge play a significant role. Second, social interactions are rarely as clear-cut or isolated as game-theoretic models suggest; they are interconnected and influenced by broader cultural, historical, and institutional factors that are hard to quantify. Additionally, game theory tends to focus on short-term strategies and outcomes, while societal changes often unfold over longer periods, influenced by evolving norms and collective behavior. Also, the simplifications required to model social situations typically overlook unique, context-specific factors, making game theory an imperfect tool for understanding the full complexity of society. But none of this is Neumann’s fault. I am still fascinated by its invention and view it a bit like a secret language for decision-making.

I remember that my father often spoke about his math books by Hütte. He didn’t have them anymore by the time I grew up—perhaps they were lost somewhere, I don’t know. Later in life, I found a used set in a bookstore and bought them for him again. I could tell they were special. I think it was the enormous technical details and the comprehensive tables that he treasured, yes, admired. When he became ill and moved into a small room on the first floor, those books were among the few items he chose to bring, occupying a shelf otherwise filled with medical supplies:

Hütte – Des Ingenieurs Taschenbuch. (1857). Des Ingenieurs Taschenbuch. Springer-Verlag.

For him, these engineering books were like literal treasures, and he handled them as reverently as a religious person would handle the most sacred texts, but funny enough, he didn’t need to own them. He was fine with borrowing at the public library, for the most part. There was also

Fäntzel-Clairmont. (1912). Die Praxis des modernen Maschinenbaues. C. A. Weller, Berlin.

The beautiful green two-volume set was long considered the bible of machine production for engineers, filled with illustrations of machines that could thrill any young boy, much like a set of LEGOs, I imagine. Although I was never interested in building machinery myself, I loved drawing buildings, and I tried to be meticulous about it. Perhaps some of this inspiration also came from a large book on architecture we had with an image of the Reims Cathedral on the cover, though I can’t recall its name. For a long time, I thought I might become an architect—until I realized that, to make a living, architects actually have to design buildings that others want, not what you want to draw. So, that was it. I have only continued making small drawings for my own use since, in the house and around the garden.

None of my high school teachers managed to awaken any curiosity in the sciences for me. I’m not entirely sure why, but perhaps it was because they taught each subject without passion, as though it were a fixed system to memorize, without explaining what it was all for. Maybe also it was because I matured late intellectually. We studied math, biology, physics, and chemistry, but it was presented in such a dull way, I recall, just something to get through, so I never found any of it particularly interesting. Math was made even worse by the constant shuffle of substitute teachers.

It wasn’t until university that I began to appreciate math, thanks to a wonderful teacher from Eastern Europe by the name of Sladek. Later, when I got my first dog, I called him after him—I’m not sure if that’s necessarily seen as a compliment, but I assure you (and his relatives) it was intended as one. I also took statistics, first with a teacher who wasn’t very invested, but then again with his opposite, and got a liking for it. As I went to a liberal arts university, I had to take a class in computer science which sparked my early interest in digitalization and information. Though I was, first of all, studying economics and business, most of my courses weren’t particularly inspiring. However, I discovered excellent teachers in the political science department, leading me to pursue a double major. At this time, I had no idea I would be drawn to science. I guess I thought I would get a job as an economist or in business.

Few books from this period truly stand out. Most textbooks, even those used at the university level, lacked engagement and depth and, I must say, were completely void of any human touch, an ingredient which exists in so many older classical books. They were mostly “dry.” A simple test is that I rarely found myself reading them a second time, and they never found a cherished place in my heart, like certain history or fiction books. Now, they sit in a cold corner of the loft with other seldom-read material and archived items. In a way, I find that a bit sad, as the subjects themselves are often interesting.

As a university undergraduate student, I soon discovered that cheap pocketbooks were everywhere, making it easy to indulge in reading. One such book was Conant’s Modern Science and Modern Man (Conant, 1952).

Conant, J. B. (1952). Modern science and modern man. Columbia University Press.

The book primarily addressed the dangers of nuclear weapons and other existential threats brought about by scientific progress, such as chemical warfare. However, it failed to inspire, as it presented science in an oversimplified and technical way. James Bryant Conant himself, despite being a significant figure—Harvard’s president and the first US Ambassador to West Germany—was not able to make science engaging, at least not to me. But there would soon be other encounters.

I think the breakthrough came with Isaac Asimov’s books, which are also available in pocket format at an affordable price. Isaac Asimov was a very productive author, a biochemist, and one of the most influential science fiction writers of the twentieth century. Born in 1920 in Russia and raised in the United States, Asimov made significant contributions to popular science and science fiction literature. In my mind, he may have been one of the greatest science educators of the twentieth century, often writing at least one book per year and sometimes even more. His ability to explain complex scientific concepts made him a key figure in popularizing science, not only in the USA but throughout the Western world. I devoured every Asimov book I could get my hands on, including his nonscience works like the one on Shakespeare. I must admit I avoided everything he wrote on science fiction, but that was due to the genre. I have always found reality thrilling enough.

The publisher Penguin was a blessing for all students when I was younger—and still should be today—offering plenty of excellent pocketbooks at very affordable prices. Pelican Books, an imprint of Penguin, distinguished itself by focusing on accessible, high-quality nonfiction on a wide range of topics, aiming to educate the general public in areas like science, history, and philosophy. At the time, I picked up practically anything they did on science. These were mostly classics, and they were all excellent. After all, that was the reason they had been reprinted as pocket editions—it was a no-brainer. For statistics, I liked

Moroney, M. J. (1951). Facts from Figures. Penguin Books.

This is a layman’s introduction to statistics with plenty of great examples. Or I would attempt more advanced books like

Savage, L. J. (1972). The Foundations of Statistics. Dover Publications.

The original edition was published in 1954. Leonard J. Savage (1917–1971) made significant contributions to the field of statistics through his work on decision theory and subjective probability. His Foundations of Statistics (1954) introduced the concept of “personal probability,” which argued that probabilities are subjective and based on an individual’s beliefs rather than objective frequencies. Savage also developed the “Savage axioms,” which laid the foundation for modern Bayesian decision theory, influencing how statisticians think about uncertainty and decision-making under conditions of risk and ambiguity.

Easy books on statistics are often the best, I find. At university, we had

Wright, R. L. D. (1976). Understanding Statistics. Harcourt Publishers.

We had others as well, but they were what I’d consider non-pedagogical. Over time, I’ve realized that when things seem complicated—whether in books or in speech—it’s often because the person explaining them isn’t fully at home with the topic. Experts can communicate clearly, presenting ideas in a simple, straightforward manner, I find, unless they become nervous. The best pedagogy, then, is for the teacher to truly immerse themselves in the subject they’re teaching. When you understand something deeply, you can easily get to the heart of the matter and return to it even if you stray off course for a while. This not only shows clarity but also makes the content far more engaging for the reader.

When we start reading about science, and not just math, we encounter the problem of semantics. Semantics is the study of meaning in language, focusing on how words, phrases, and sentences convey meaning and how that meaning is interpreted in different contexts. So, I picked up a cheap pocketbook on this topic too:

Leech, G. N. (1981). Semantics: The Study of Meaning (2nd ed.). Penguin Books.

Geoffrey Leech (1936–2014) was a renowned English linguist specializing in English grammar, corpus linguistics, stylistics, pragmatics, and semantics. He authored over 30 books and numerous papers, with his focus on semantics peaking before 1980, after which his interest shifted to pragmatics.

His influential work Semantics (1974, 1981) categorized seven types of meaning: conceptual, connotative, social, affective, reflected, collocative, and thematic. These categories describe how meaning is derived from language structure, context, idiomatic usage, and rhetorical choices.

There are many older books on science and science-related topics that are great too. For example, I consider Aristotle not only to be the father of science but, maybe more importantly, still enjoyable to read. As I started working and could earn some of my own money, I acquired the ten-volume set of his works:

Ross, W. D., & Smith, J. A. (Eds.). (1912). The works of Aristotle translated into English under the editorship of W.D. Ross (Vol. 5). Clarendon Press.

In these books, he lays the groundwork for many disciplines, though he built on the work of his teachers, especially Plato.

The big shift in my reading came when I moved to Paris as a young man, first to learn the language (I had taken some evening classes at UCLA) and then to qualify for a good MBA program and read for the GMAT test. Paris is a young scholar’s dream. I still remember the enthusiasm with which I would take the elevator all the way to the top floor of bookstore Gibert Joseph on Boulevard St. Michel. There, rows upon rows of pocketbooks on every imaginable topic awaited—a true Eldorado of delights wrapped in paper. Their used books were inexpensive, and they used a color-coded dot system on the books to show their condition—certain colors indicated heavily used books, which were even cheaper. This was a big help when money was tight, which was always the case.

Of course, there was also the famous Shakespeare & Company, but it was hard to find anything specific there as their shelf system was somewhat chaotic. Instead, the libraries became my second home. For me, it was the Bibliothèque Publique d’Information (BPI) inside the Centre Pompidou—a newer library with easy access to books and even music CDs, unlike the older libraries where you had to request everything from behind a desk.

In my early 20 s, I was an avid reader, exploring books from various fields. At the time, I couldn’t afford to buy many, but as I got older, I was able to build my collection, and those books have remained with me ever since. They’ve become like friends, in a way. Sometimes, I take them out and almost talk to them casually, flipping through their pages.

I’m not going to list all the books that influenced me as a researcher today—as that would take many pages—but if I, as a young man were to begin with some of the Greek and Latin classics related to science, I would certainly include the following (Table 4.1):

Table 4.1

Classic Greek and Roman books related to science

Author

Title

Year published

Description of content

Hesiod

Works and days

Ca. 700 BCE

A didactic poem providing instructions on agriculture, ethics, and economics from a mythological and practical standpoint

Thucydides

History of the Peloponnesian war

Ca. 400 BCE

A detailed historical account of the war between Athens and Sparta, emphasizing empirical evidence and human motivations

Herodotus

Histories

Ca. 425 BCE

The first known historical account of the ancient world, covering the Persian wars and vast geographical, cultural, and scientific knowledge

Xenophon

Oeconomicus

Ca. 362 BCE

A Socratic dialogue on household management and economics, one of the first known treatises on economics

Plato

Timaeus

Ca. 360 BCE

A dialogue on cosmology and natural philosophy, offering explanations for the creation of the universe, the nature of matter, and the soul

Aristotle

Meteorology

Ca. 350 BCE

A work on weather, astronomy, and natural phenomena, attempting to explain the natural world systematically

Aristotle

Nicomachean ethics

Ca. 350 BCE

A foundational text in ethics, discussing the nature of virtue, happiness, and the good life in a systematic and rational manner

Euclid

Elements

Ca. 300 BCE

A comprehensive treatise on geometry and mathematics, one of the most influential works in the history of science and mathematics

Archimedes

On the equilibrium of planes

Ca. 250 BCE

A foundational work on the principles of mechanics and hydrostatics, introducing the law of the lever

Eratosthenes

Geographika

Ca. 240 BCE

A pioneering work on geography, including the first accurate calculation of the earth’s circumference

Apollonius of Perga

Conics

Ca. 200 BCE

A treatise on geometry, particularly on conic sections, laying the groundwork for later developments in mathematics and astronomy

Hipparchus

Commentary on the Phaenomena of Eudoxus and Aratus

Ca. 150 BCE

An astronomical work correcting the earlier star catalog and contributing to the understanding of the motion of celestial bodies

Lucretius

De Rerum Natura (on the nature of things)

Ca. 50 BCE

A Latin poem explaining epicurean physics, including atomic theory, the nature of the soul, and natural phenomena

Vitruvius

De Architectura

Ca. 15 BCE

A ten-book treatise on architecture, engineering, and city planning, including discussions on materials and physics

Plutarch

Parallel lives (lives of the Noble Grecians and romans)

Ca. 100 CE

Biographies of famous Greek and Roman figures, offering moral lessons and insights into leadership, ethics, and politics

Ptolemy

Almagest

Ca. 150 CE

A comprehensive treatise on astronomy, presenting the geocentric model of the universe, influential for over a thousand years

Galen

De Usu Partium

Ca. 170 CE

A foundational text in anatomy and medicine, describing the functions of various parts of the human body

Ptolemy

Geographia

Ca. 150 CE

A compilation of geographical knowledge, including maps, that became influential in both the ancient and medieval worlds

These are not outdated books, as someone will have you think, but content that still speaks to us, if we only let it. To give one example: When I briefly worked as a math teacher when returning from France to start a job as an accountant in Oslo, I found that Euclid’s Elements was still a more effective geometry textbook than the modern alternatives they were using:

Euclid. (1956). The thirteen books of Euclid's Elements (T. L. Heath, Trans.). Dover Publications. (Original work published ca. 300 BCE)

I think it’s fair to say that the clarity and simplicity of Euclid’s explanations made it easier for the pupils to grasp geometric concepts compared to the overly “pedagogical” texts in their standard textbooks. I recall how I even wrote a few articles in a local school magazine to make that point.

Lucretius’ De Rerum Natura is still a pleasure to read, as is Vitruvius’ De Architectura. New books are not always better. It’s a pity, really, that we so systematically tend to fill our bookshelves with newer titles instead of considering all books—old and new—on the same footing. In some ways, this seems unavoidable, as publishers will always prioritize promoting new titles, and there may not be any used bookstores available in your area.

My passion for antique booksellers and used bookstores could fill an entire chapter. Over the years, many of the owners of these stores have become friends, some spanning decades. While most booksellers are merchants primarily focused on sales, there are a select few who share a genuine love for learning and are eager to engage in thoughtful conversations, which makes buying books there even more enjoyable. One such friend is Lars Jannedal of Ystad Antikvariat, whose passion for knowledge always makes visiting his shop a delightful experience.

For many years, before I started a family, we would gather in the backroom of his bookstore every Saturday with older researchers like the Viking historian Mats G. Larsson and the poet Magnus Andersson. Lars, who must be one of the biggest Anglophiles in the country, jokingly referred to us as “stray cats” and dubbed his establishment “The Stray Tearooms,” as it was customary to be offered a cup of tea upon arrival. Conversations in those days ranged widely but also centered around science. As a special treat, we were given the first look at the latest arrivals—sometimes even before they were purchased by the original seller, but just turned in for inspection. For me, there has always been an inseparable connection between the passion for reading books and visiting bookstores, and I can’t imagine it being any other way. However, I have noticed—to my big surprise, I must admit—that very few of my colleagues share this same interest, and many of those who do collect rare books are less interested in the content. I always found that strange.

Looking back to history for a moment, as we emerged from the Dark Ages and entered the Renaissance—so named because, during this period, scientific progress had been relatively stagnant—Tycho Brahe became one of the early luminaries of science. He may have attended the Latin school in the city of Ystad—back when this area was still part of Denmark—and his renowned observatory, Uraniborg, was located on Ven, now a popular tourist destination in the region.

I use Brahe in my courses on scientific methods as an example of someone who excelled at one thing above all: observations, gathering precise data using little more than his eyes, and some simple homemade instruments. A turning point for science came when he was forced into exile and moved to Prague and met the young German mathematician, Johannes Kepler. Kepler was good at one thing, too, mathematics, and he used Brahe’s meticulous data to develop his groundbreaking laws of planetary motion, demonstrating the powerful synergy in science that occurs when precise data is combined with insightful analysis.

There is a statue of the two in Prague symbolizing their collaboration, though their personal relationship was complex, particularly, it seems, due to Brahe’s reluctance to share his data with Kepler (Fig. 4.3).

Fig. 4.3

Brahe, Kepler, … and the author as a young scholar

Alongside Prague, the University of Kraków was an early center of learning, where Copernicus began his studies. One reason for this curiosity among monarchs in Eastern Europe was due to the influence of specific individuals. In Poland, King Casimir III the Great founded the University of Kraków in 1364, seeing it as a means to enhance his kingdom’s prestige and intellectual strength. By establishing this institution, he aimed to bring Poland closer to the knowledge and advancements flourishing in Western Europe. Later, Queen Jadwiga and King Władysław II Jagiełło would revive and expand the university, enabling it to attract scholars from across Europe and turning it into a significant academic hub.

Similarly, in Prague, Emperor Charles IV of the Holy Roman Empire established Charles University in 1348, the first university in Central Europe. Charles IV wanted to elevate the status of his capital and provide an intellectual foundation for his realm. His vision encouraged a flow of scholars, theologians, and legal experts to Prague, shaping the city into a cultural and educational center. Emperor Rudolf II, much later, continued this tradition of intellectual patronage, bringing luminaries like Tycho Brahe and Johannes Kepler to his court.

By fostering these institutions and supporting such figures, the monarchs of Eastern Europe cultivated environments that welcomed scientific inquiry and intellectual exchange. These early efforts would shape both Kraków and Prague into pivotal centers of learning, influencing generations of scholars and thinkers, but then came change, and other universities took the forefront. For example, the University of Leipzig was founded in 1409, following the expulsion of German students from Prague.

While the examples of Oxford, Cambridge, and Paris are well known, the Netherlands became a leading center of learning indirectly, we may say, because of its fight for independence from Spain in the late sixteenth century. The logic goes like this: Countries striving for freedom are often more inclined to grant freedom to their universities, and with that academic freedom often comes great scientific achievement. For instance, Leiden University, founded in 1575, became a refuge for scholars facing persecution, particularly those targeted by the Catholic Church. The Netherlands thus exemplified how freedom of expression can foster scientific progress—a lesson with continued relevance today, as issues of intolerance and censorship again challenge open discourse.

Of course, the flourishing of knowledge in northern Europe would not have been possible without the keen interest in learning held by dukes and minor rulers in Italy, who actively sought out the ancient texts that had been lost in Europe but preserved in countries across the sea.

During my university years, I developed a passion for the Italian Renaissance, particularly the intersection of art and science. On a Grand Tour to Italy and Greece during my student years, I had the chance to visit some of the castles where these long-forgotten works had been reintroduced to Europe, brought back by traders and merchants after centuries of absence. One such place was the castle of Urbino, home of the Montefeltro family. It was also the Medici family in Florence, the Este family in Ferrara, the Gonzaga family in Mantua, and the warrior-like Sforza family in Milan. I visited each of their palaces to get a sense of what it might have been like to collect these books, to see how this passion influenced their way of living, the houses they built, and the social lives they led, maybe as a consequence of this newly acquired learning.

These rediscovered texts, though costly, were instrumental in reviving European intellectual life, laying the groundwork for the Enlightenment, which eventually led to the Great Age of Industrialization and, eventually, our modern standard of living. A people, country, or culture that forgets this lesson will soon find itself in decline.

Of course, knowledge was also embedded in the physical objects that surrounded Renaissance Italians, such as the Roman ruins. A notable example is Cardinal d’Este, who lived in Tivoli, south of Rome, and likely studied the Villa Adriana to understand how the Romans built their aqueducts and water systems. This ancient engineering knowledge inspired him to design his own garden nearly 1500 years later. I also became a collector of books on Pompeii, as that city served as a model for much of what was built during the Renaissance.

A classic book on early Italian centers of culture is

Burckhardt, J. (1860). The civilization of the Renaissance in Italy.

But also.

Ruskin, J. (1851–1853). The stones of Venice (Vols. 1–3). Smith, Elder & Co.

Books don’t have a monopoly on learning. You can also learn from TV programs and series—today from pods and vlogs—almost as well as from books, I would say.

One series that had a profound influence on a whole generation was Civilisation by Kenneth Clark, which first aired in 1969 and was shown many times in various parts of the world. You have to remember that this was during a time when there were far fewer programs on TV, so the series had a significant impact:

Clark, K. (1969). Civilisation: A personal view. BBC.

Carl Sagan’s Cosmos had a profound impact on my thinking too, much like it did for countless others. I’ve watched the series more than once and revisited the companion book just as often. Sagan’s ability to convey the wonder of science, combined with his awareness of its social role, exemplified what it means to be a true scientist. More importantly, alongside Darwin’s revolutionary ideas on evolution, Cosmos led me to seriously consider how evolutionary theory might also be applied to the social sciences, opening my mind to the ways natural selection could inform our understanding of human society and behavior:

Sagan, C. (1980). Cosmos: A Personal Voyage. PBS.

It’s hard to overstate his dedication to inspiring interest in science among people all over the world. For this merit, his portrait stands in our reference library.

Example: Formative Books and Ideas During My PhD Studies

During my PhD studies in Leipzig, I became fascinated by all things German, in the way that young men can become completely absorbed in their passions—primarily the philosophy, but also the development of economics in the mid-1800s. I was equally intrigued by the complex and sometimes contentious history of the relationship between biology and the social sciences. It became clear to me pretty early that the social sciences had distanced themselves from biology too soon and, more importantly, for the wrong reasons.

The University of Leipzig, long a leading institution for the study of economics, gained prominence partly through Wilhelm Roscher, whose textbook was the standard reference for generations worldwide, much like the textbooks of Paul Samuelson after the war, first published in 1946:

Roscher, W. (1878). Principles of Political Economy (N. F. A. Sprague, Trans.). Holt & Williams. (Original work published 1854).

Originally.

Roscher, W. (1854). System der Volkswirthschaft: Ein Hand- und Lesebuch für Geschäftsmänner und Studierende.

Compare that to.

Samuelson, P. A. (1948). Economics: An introductory analysis. McGraw-Hill.

When you compare them, it’s almost strange to think they’re writing on the same subject. Roscher, with his focus on historical and cultural contexts, advocates for an evolutionary and interdisciplinary approach to understanding economic systems. Samuelson, by contrast, takes a mathematical and systematic approach, blending Keynesian and classical theories to create a structured, model-driven view of economics.

Leipzig was a significant center for the development of economics as a science, particularly in the late nineteenth and early twentieth centuries when scholars began to ground economic theory in evolutionary and biological principles. Wilhelm Roscher, with his pioneering work on the historical method for the study of economics, was instrumental in establishing Leipzig’s reputation around the world. Everyone used his writings and influenced early American economists like Torstein Veblen, who even spent some time in Germany in 1891. Roscher argued that economics should consider historical and cultural development, and his “historical school” of thought influenced generations of economists who explained how economic systems evolved over time. Veblen began his career at the University of Chicago before it became the center of neoclassical economics. The institution was still heavily influenced by German historical and institutional economics during his tenure, attempting to understand and adapt European approaches to the American context. In this tradition, Veblen wrote his classic:

Veblen, T. (1899). The theory of the leisure class: An economic study of institutions. Macmillan.

Another key figure at Leipzig was Karl Bücher, a professor of economics who further expanded on evolutionary ideas by examining the development of labor and trade from primitive to modern societies:

Bücher, K. (1893). Die Entstehung der Volkswirtschaft: Vorträge und Versuche [The Origin of the National Economy: Lectures and Experiments].

Translated almost a decade later.

Bücher, K. (1901). Industrial evolution (S. Morley Wickett, Trans.). Henry Holt.

Bücher’s studies on the stages of economic development brought attention to the ways in which economies grow and adapt, aligning with evolutionary theories in biology. His work helped solidify Leipzig as a hub for economists interested in the intersection of social systems and biological processes using the historical method. It was the criticism of this method that led to the so-called Methodenstreit.

The critics of this method, led by Gustav Schmoller and the German Historical School, argued that economic behavior could not be understood without considering historical and cultural contexts, emphasizing empirical and inductive approaches. In contrast, Carl Menger and the Austrian School contended that such methods lacked scientific rigor, advocating for universal laws derived from individual behavior and logical deduction.

Schmoller, G. (1900). Grundriß der allgemeinen Volkswirtschaftslehre [Outline of General Economics]. Leipzig: Duncker & Humblot.

Comprehensive translations of his major works, such as Grundriß der allgemeinen Volkswirtschaftslehre, have not been widely available in English.

Menger, C. (1883). Untersuchungen über die Methode der Sozialwissenschaften und der politischen Ökonomie insbesondere [Investigations into the Method of the Social Sciences with Special Reference to Economics, translated in 1985]. Leipzig: Duncker & Humblot.

Based on this progress within the study of economics and business, the Handelshochschule Leipzig (HHL) was established in 1898 as one of the earliest in the world. Its new building was constructed close to the main university buildings on Ritterstraße, just a few hundred meters from Leipzig University’s main campus, where it still stands today but has another function:

The original main building of the Handelshochschule Leipzig (HHL) (author’s photo).

Max Weber, although later affiliated with Heidelberg, also studied at Leipzig and was influenced by its intellectual environment. He introduced concepts of social evolution into economic and sociological analyses, later developing his own theories on rationalization and the role of cultural values in economic systems. Other professors, such as Lujo Brentano, also contributed to this tradition by emphasizing social and cooperative aspects of economic growth, linking economic evolution with the well-being of workers.

Leipzig fostered an economic school distinct from the increasingly mathematical approaches seen in centers like Lausanne, Vienna, and Paris. Influenced by scholars like Wilhelm Roscher and Karl Bücher, Leipzig’s approach focused on historical, adaptive, and evolutionary perspectives, emphasizing real-world applications over abstract modeling. This perspective laid the groundwork for interdisciplinary studies, linking economics with sociology, anthropology, and history. Leipzig’s emphasis on understanding economic systems as dynamic and evolving rather than fixed structures contributed significantly to the German Historical School’s influence and inspired future developments in institutional and evolutionary economics across Europe.

Thorstein Veblen brought ideas influenced by the German Historical School, where he had studied, to the United States. At the time, the study of economics in the USA was still in its formative stages, and Veblen quickly gained prominence, particularly at the newly formed University of Chicago. However, as the University of Chicago grew in stature and influence, it quickly moved away from evolutionary and institutional approaches to economics, and Veblen’s tenure was short-lived. Over time, Chicago became the leading bastion of Neoclassical economics, establishing a paradigm that continues to dominate its academic and ideological framework to this day.

Veblen, T. (1899). The theory of the leisure class: An economic study of institutions. Macmillan.

The tradition of evolutionary theory in the social sciences diminished after World War II, as Anglo-American standards set new ideological, methodological, and philosophical expectations. The integration of biology and the social sciences, once an essential approach in economics and business studies, came to be seen as problematic, especially as concepts like “Drang nach Osten” and “Lebensraum” had cast a shadow on certain interdisciplinary methods. While critiques of the historical method were not new—having already emerged in institutions like the University of Vienna through the Austrian School of Economics—this earlier opposition was unrelated to the political climate of the twentieth century.

In the postwar era, the social sciences gravitated toward a new paradigm, increasingly modeled after physics. This approach emphasized quantitative rigor, mathematical modeling, and objective measurement, steering the field away from historical and evolutionary perspectives. This quantitative shift has persisted to the present day, though more recently, the social sciences have seen a revival of historical, cultural, and even evolutionary frameworks as alternatives or supplements to strictly quantitative models.

Schumpeter sought to advance the concepts of evolutionary economics but was unable to develop them fully. His ideas were left in broad strokes, particularly in his posthumously published book History of Economic Analysis, which was completed by his wife, Elizabeth Boody Schumpeter.

Nicholas Georgescu-Roegen did make significant strides in the same direction, most notably through his works Analytical Economics and The Entropy Law and the Economic Process. Despite initially continuing along Schumpeter’s path at Harvard, Georgescu-Roegen faced resistance from the economic establishment. His work diverged fundamentally from the dominant Neoclassical paradigm, leading to his departure from Harvard, difficulty in obtaining a post with any of the more established schools of economics, and his eventual tenure at Vanderbilt University, where he continued his work but without much outside support or interest.

Schumpeter, J. A. (1954). History of economic analysis. Oxford University Press.

Georgescu-Roegen, N. (1966). Analytical economics: Issues and problems. Harvard University Press.

Georgescu-Roegen, N. (1971). The entropy law and the economic process. Harvard University Press.

The second time the idea of evolution was to resurface in connection with human behavior was not within the social sciences, but in biology with the development of sociobiology founded in large by the American biologist Edward Osborn Wilson:

Wilson, E.O. (1975). Sociobiology: The New Synthesis. Harvard University Press.

Other leading figures in this field include Irenäus Eibl-Eibesfeldt, George C. Williams, Richard Dawkins, W.D. Hamilton, and Niko Tinbergen, one of the founders of ethology and the brother of renowned economist Jan Tinbergen, who received the Nobel Prize for his contributions to econometrics.

In his next book, Wilson wanted to “close the gap” between general sociobiology and evolutionary theory:

Wilson, E. O. (1978). On Human Nature. Harvard University Press.

Wilson summarizes: “the brain is a product of evolution. Human behaviour (…) is the circuitous technique by which human genetic material has been and will be kept intact: morality has no other demonstrable ultimate function” (p. 167). Here, Wilson breaks with two of the leading moral philosophers of his time, John Rawls (A Theory of Justice, 1971), who opposes meritocracy, but even Robert Nozick (Anarchy, State, and Utopia, 1974), who presents a libertarian counterargument to Rawls, focusing on individual rights rather than collective welfare. While Nozick’s views differ from evolutionary approaches, his primary critique is of state-imposed structures, not necessarily of evolutionary views on morality.

Sociobiologists think that, as humans, we are genetically programmed to survive and thrive. Individual survival—and indeed collective survival—depends largely on how effectively we manage the resources we can access. From this, we establish a series of strategies designed to secure our well-being, strategies that necessarily account for the reactions and cooperation of others. These foundational principles lie at the heart of the social sciences seen from an evolutionary perspective, forming the basis upon which further premises are built. It essentially aligns with the tradition set forth by Charles Darwin but is still considered controversial among a larger group of social scientists. That it should be this way is, of course, a completely different (and normative) question, which sociobiologists may argue lies within the study of the humanities and moral philosophy, not the social sciences.

Many disagree with Wilson and the sociobiologists. Cultural anthropologists, for instance, often emphasize that behavior is primarily molded by cultural factors rather than biological programming. Similarly, sociologists and social constructivists argue that societal norms and values are products of social environments, not evolutionary biology. Humanists and critical theorists caution against biological determinism, stressing the role of historical, economic, and political influences on human choices. Feminist scholars often critique evolutionary frameworks for reinforcing traditional gender roles, instead advocating for the cultural learning of behaviors. Postmodern thinkers also question the idea of universal truths, including evolutionary explanations, viewing knowledge as inherently relative. These perspectives share a concern that biological explanations could simplify or overlook the complexity of human social life, which is richly varied and deeply influenced by culture and social context.

This is where the debate about “nature versus nurture” stands today. While evolutionary perspectives have gained traction, especially in fields like evolutionary psychology, behavioral ecology, and some branches of sociology and anthropology, they are still met with substantial critique in mainstream social sciences. A majority of scholars in cultural anthropology, sociology, and related fields continue to emphasize the importance of cultural, environmental, and social influences over biological determinism.

Sometimes scholars who adhere to evolutionary theory in the social sciences do not call themselves evolutionary economists or claim that they study evolutionary economics, as there are many interpretations of how to apply Darwin’s ideas. Instead, it is common to refer to this perspective as “the evolutionary approach,” given the varying degrees of application.

Interest in evolutionary economics saw a resurgence in Europe during the 1980s, marked by the establishment of the International Joseph A. Schumpeter Society in 1986 and the launch of the Journal of Evolutionary Economics in 1991. However, scholars in the field struggled to coalesce around a unified theoretical framework, leaving evolutionary economics a vibrant yet fragmented area of study.

A neoclassical economist might argue that while evolution is an interesting concept, it primarily affects human behavior over the long run and has little relevance to the day-to-day decisions that are the focus of most economics. They would emphasize the need to address real, practical problems in the present and justify the assumption of a static world because it allows for the development of mathematical models that are both usable and predictive. Furthermore, they might question the practicality of evolutionary economics by asking: If it is so valuable, why has it not produced any well-functioning mathematical models to assist in solving real-world problems?

For years, I’ve been unable to revisit the ideas that shaped my PhD student years primarily because I’ve spent most of my time conducting research in microeconomics, focusing on business studies and management, rather than in macroeconomics. However, there is a manuscript sitting in a folder on the laptop that explores how to make evolutionary economics work. Maybe I will attempt it next.

It was also during my PhD studies that I stumbled upon a small but impactful book on formal, or classical, logic, which transformed the way I approach reasoning. Authored by a master’s student, Stanley Williams, at Oxford in 1919—a period when master’s students often produced remarkable works (and that’s intended as encouragement, not critique):

Williams, S. (1919). Principles of Logic. T.C. & E.C. Jack Ltd.

I found this book more or less by accident, likely during one of my visits to some bookstore. The book represents not only the best explanation of syllogisms and argument forms I’ve come across, but also has an exceptional introduction to scientific thinking, as it also covers hypothesis formation. To think that this was what all students in Europe learned for centuries, even during the Middle Ages. This was, in a sense, common knowledge at the time. I was both impressed by it and dismayed by its absence in today’s schools, so much so that I translated it into Norwegian one summer in Paris, hoping it might one day find a place in high school curricula.²

Many argue that formal logic is outdated, having been replaced by symbolic logic and more modern approaches. However, there’s no contradiction between the two; they complement each other, and both should be taught. Formal logic lays the groundwork for clear, structured thinking, while symbolic logic introduces more abstract, technical reasoning. Mastering both provides a deeper and more versatile understanding of logical reasoning. The notion that formal logic is less useful today is not only incorrect but a dangerous misconception—it prevents young students from studying syllogisms at the profound level that has been taught since the Middle Ages and before that on a simpler level, even in ancient Greece, of course.

Symbolic logic is useful for scientists, too, because it allows them to precisely represent and analyze logical arguments and relationships, making complex reasoning and problem-solving more systematic and rigorous. I probably picked this book up largely because of the name of the author:

Carroll, L. (1896). Symbolic logic: Part I, Elementary. Macmillan.

Carroll Lewis’s book was important, just like my favorite book on logic by Stanley, as it applies formal logic to everyday reasoning, thus bridging the gap between abstract mathematical logic and practical thinking.

Carroll also made some contributions as a scientist in his field, introducing the use of diagrams (commonly known as Carroll diagrams) to simplify logical propositions and syllogisms, making it easier for students to visualize relationships between sets. Lastly, his approach emphasized the importance of systematic thinking in solving logical problems, influencing the way symbolic logic was taught and understood in educational contexts.

Another book I picked up during my university years was.

Snow, C. P. (1961). Science and government. Harvard University Press.

I recall that my father had read Snow and treasured him. In Science and Government, C.P. Snow explores the complex relationship between scientific research and government policymaking, highlighting how political objectives can sometimes conflict with scientific advice. He revisits the “Two Cultures” debate, emphasizing the gap between scientists and nonscientists, especially among policymakers, and how this can lead to flawed decisions. Using historical case studies from World War II, Snow shows how scientific advice was either followed or ignored during crucial moments, such as the development of radar and the atomic bomb. He also reflects on the ethical responsibilities of scientists, stressing their role in ensuring their work is used responsibly in governmental decision-making. We have a similar debate about AI today.

Snow was, in many ways, a larger-than-life figure in the intellectual circles of postwar Europe. He belonged to an older generation that insisted on weaving together culture, literature, and science, refusing to let these fields drift apart—a sentiment Richard Dawkins echoes in the introduction to his autobiography, seeing himself as part of the same tradition here as Snow:

Dawkins, R. (2015). Brief candle in the dark: My life in science. Bantam Press.

In Brief Candle in the Dark, Dawkins reflects on his career and the themes of interconnectedness and exploration that have shaped his scholarship. This journey began of course with his groundbreaking book, The Selfish Gene:

Dawkins, R. (1976). The selfish gene. Oxford University Press.

In The Selfish Gene, Dawkins presents the idea that DNA is the fundamental unit of life, existing primarily to replicate itself. He suggests that organisms, including humans, serve as “survival machines” or vessels for these genes, which “travel” through generations, like passengers on a spaceship, to ensure their continuity into the future.

The version I have of Snow’s book is from the Mentor Book series by the New American Library (NAL). This publisher continues to spread affordable editions of classic literature, history, and science to the general public, with a mission to democratize knowledge by making high-quality publications accessible to everyone, especially those without formal education. Other similar publishers, like Dover Publications and Modern Library, share this goal by offering inexpensive reprints of out-of-print works and world literature. Pelican Books, an imprint of Penguin Books, also focuses on educating the public through affordable nonfiction titles on topics like sociology and science. These publishers play a crucial role in making learning accessible to everyone. In this way, they have been tremendously valuable for the post-WWII generations, supporting education and knowledge dissemination.

One book which I think that all PhD students should read, also because it’s very thin and can be read in no time, is

Weber, M. (1994). Science as a Vocation (H. H. Gerth & C. Wright Mills, Trans.). In H. H. Gerth & C. Wright Mills (Eds.), From Max Weber: Essays in Sociology (pp. 129-156). Routledge. (Original work published 1919)

Max Weber’s Wissenschaft als Beruf (Science as a Vocation) addresses the role of science in modern society, emphasizing that science is not only a career but also a calling, requiring dedication and perseverance. He argues that scientific work should be pursued for its intrinsic value, not for material or personal gain, and stresses the importance of rationality and objectivity in scientific inquiry. These ideas have resonated strongly with many scholars, particularly over the past decade and a half, as academic freedom has faced growing threats from new waves of political interference.

Academic freedom is the right of researchers—once they have earned their qualifications, typically with a PhD—to determine what is important to study. Even UNESCO supports academic freedom through the “Recommendation concerning the Status of Higher-Education Teaching Personnel,” adopted in 1997. This document outlines the rights and responsibilities of higher education teaching personnel, emphasizing the importance of academic freedom and institutional autonomy.

We assume that social scientists are motivated not by political agendas but by a genuine desire to improve society and, hopefully, also by a love for humanity. While this ideal may not always be realized, it remains the best framework for thinking about our work as researchers, even if we are just students writing a research paper at the end of our bachelor’s degree. As such, researchers are somewhat like modern-day knights, I like to think, embarking on the noble quest for truth for the benefit of all mankind.

We should have the freedom to pursue topics we find personally interesting. This can be challenging, as determining what is important to investigate often depends on one’s experience and perspective, which may be limited at the outset. Therefore, guidance from a supervisor can be valuable. At the same time, external pressures—such as from politicians or interest groups—can influence research direction, pushing it toward issues that serve particular agendas. This is problematic for several reasons. Research should remain independent and focused on advancing knowledge for the benefit of all humanity, rather than serving the interests of a single company or special group. This is why academic freedom is essential—it safeguards the integrity of scientific inquiry and ensures the health of the entire academic ecosystem.

Weber also reflects on the limitations of science, observing that while science can explain the “how” of phenomena, it cannot address the deeper “why” questions related to meaning and values. He argues that science offers no absolute truths, only provisional insights that are continually open to revision. In many ways, the social sciences are still shaped by this understanding, grappling with the complexities of meaning and the limits of empirical knowledge. However, that is ok, for as long as we are making at least some progress.

Another book on science that influenced me was Hermann von Helmholtz’s Science and Culture, brilliantly edited and translated by David Cahan:

Cahan, D. (1995). Hermann von Helmholtz: Science and culture. The University of Chicago Press.

David Cahan’s book explores the life and work of Hermann von Helmholtz, a pioneering nineteenth-century German scientist whose contributions spanned fields such as physiology, physics, and philosophy. The book examines how Helmholtz’s scientific discoveries influenced broader cultural and intellectual movements, highlighting his role in shaping modern scientific thought. The book has also been translated into German.

I have also appreciated some books on pedagogy, such as

Gombrich, L. (1994). Tributes: Interpreters of our cultural tradition. Phaidon Press. E.H.

Gombrich, a renowned art historian, pays homage here to various influential figures in the humanities, such as G.E. Lessing, G.F. Hegel, and Sigmund Freud. The book reflects Gombrich’s deep appreciation for the intellectual traditions that have shaped our cultural landscape.

Related to books in pedagogics, I have also enjoyed books on Bildung—or general education in English even more. The classic book in Germany for generations was

Herder, J. G. (2002). Herder on social and political culture (F. M. Barnard, Ed. & Trans.). Cambridge University Press. (Original work published 1793).

As could be expected in an older book, it has a strong Christian influence. So, a modern, shorter, and popular book on the subject is

Schwanitz, D. (1999). Bildung: Alles, was man wissen muss. Goldmann Verlag.

I could not find an English translation of this book. Books on Bildung or general education emphasize the holistic development of individuals, integrating knowledge from various disciplines, including science, to cultivate critical thinking, ethical judgment, and intellectual curiosity. They teach us about science not just as a body of knowledge, but as a process of inquiry and a tool for understanding the world, encouraging a balanced view that includes cultural and moral insights alongside empirical discovery.

My favorite book for many years in this category is

Jaeger, W. (1939). Paideia: The Ideals of Greek Culture (3 Vols.). Oxford University Press.

Werner Jaeger was a German classicist and scholar, one of many brilliant minds Germany lost to the United States due to the rise of Nazism, which led to one of the most significant brain drains in history—catastrophic for Germany and much of Europe but highly beneficial for the countries that received these scholars, like the USA. Jaeger contributed greatly to classical studies in his new academic home, bringing a wealth of knowledge and a deep commitment to the humanistic tradition.

Interestingly, the beautiful Loeb Classical Library series also has roots that trace back to German scholarship. Before James Loeb founded the series in 1911, German editions—especially those from the Bibliotheca Teubneriana in Leipzig—had already established a rigorous standard for translating Greek and Latin classics.

Jaeger emigrated to the United States in 1936 and was promptly appointed as a full professor at the University of Chicago, where he served from 1936 to 1939. In 1939, he accepted a position at Harvard University as a University Professor and director of the Institute for Classical Studies, roles he maintained until his death in 1961.

I have also read a fair amount of religious books. While I often express criticism toward religion, as a scientist, and enjoy much of Dawkins’s criticism, I am, per definition, always open to changing my views when presented with evidence, which, of course, includes questions of religion or faith. It’s simply that no facts—within any religion—have ever convinced me otherwise. At least I can say I have explored and continue to explore, though not as intensively as I once did. When I was about 13, it struck me as odd that a person’s birthplace seemed to determine their beliefs more than the actual teachings of those beliefs. “What does geography have to do with religion?” I remember having thought. Even now, I think it’s a compelling counterargument to the idea that any one faith holds absolute truth.

Culturally, I identify with a religion, but I don’t feel a longing to believe in one. That is a big difference. However, I greatly enjoy exploring religious texts, particularly the Norse sagas and ancient Scandinavian mythology, which I find both fascinating and enriching. In this sense, I would likely be considered more of an agnostic, a religious skeptic, or a scientific materialist, but I also think that these stands are personal and unimportant for the researchers.

A researcher is free to hold any personal beliefs in their free time, as long as they do not allow their faith to influence the process of seeking new truths. There is, to my knowledge, no evidence to suggest that a Muslim or a Jew, or anyone of any faith, is any less capable as a scientist due to their religious beliefs.

For similar reasons, I am entirely without political affiliations and have never had any, despite the fact that my family has a mix of staunch communists on my father’s side and social democrats on my mother’s. I had friends who grew up in both liberal and conservative families, but for all practical purposes, I had no clear idea what that actually meant until I started high school. For the purpose of doing science, personal beliefs should not matter. Just as we do not bring religion into our work, we should also keep political beliefs separate. In fact, we should be much stricter in ensuring that this separation is maintained.

Karl Marx was an integral part of the intellectual foundation I grew up with, and I still consider him one of the greatest economists and a highly influential thinker—but only for Das Kapital, not for the political works he co-authored with Engels:

Marx, K. (1867). Das Kapital: Kritik der politischen Ökonomie (Vol. 1). Verlag von Otto Meissner.

Or, in English

Marx, K. (1867). Capital: A critique of political economy (S. Moore & E. Aveling, Trans.). Charles H. Kerr & Co. (Original work published 1867).

When it comes to great Marxists, I have a particular fondness for Alexander Herzen, not because of his political convictions, but rather for his vast general education and literary brilliance:

Herzen, A. (1968). My past and thoughts (C. Garnett, Trans.). Chatto & Windus. (Original work published 1852-1868).

As a sign of deep intellectual infinity, I placed flowers on his grave in Nice the last time we were there. My father’s favorite was the poet Heinrich Heine, a romantic figure from his youth that I could never fully understand or appreciate, likely due to the generational difference. It’s similar to how his generation felt about Lord Byron and Shelley, I guess, finding them a bit off. However, in the works of Herzen, we could connect.

There’s one more thing to say about Marxism. Growing up in Norway in the 1970s and 1980s, I found that many of the keenest minds around were Marxists, with a few notable exceptions, such as the philosopher Hans Skjervheim. Even in my high school—far from the intellectual debates in the capital—Marxist ideas were clearly shaping much of the discourse, a lingering influence from the early days of the labor movement. It was something you absorbed almost intuitively in that era. One of the best newspapers at the time was Klassekampen (The Class Struggle), which had its own publishing house and played a significant role in national debates. Among its contributors, the philosopher Jon Elster rose to prominence, eventually becoming a leading scholar in the United States. My father, a regional head of public road administration appointed by the king, had been a Marxist-Leninist in his student years. However, professional expectations required him to keep his personal beliefs separate from his work, so he was careful to do so. For me, Marxism was my first real gateway into the intellectual world—not because it convinced me of its ideology, but because it introduced me to the life of the mind.

Early on, the identification was more with humanism, in the tradition of early Enlightenment figures like Erasmus of Rotterdam and Michel de Montaigne. Montaigne, in particular, stood out as a guiding influence. His statue outside the Sorbonne remains a symbol of intellectual connection, with countless visitors placing their hand on the shoe that protrudes—its bronze surface polished bright by generations of touch.

Erasmus, D. (1509). In Praise of Folly (M. A. Screech, Trans.). Penguin Classics. (Original work published 1509).

And

Montaigne, M. de. (1580). Essais. Bordeaux: Simon Millanges.

The only intellectual community I was part of growing up was the Norwegian Humanist Association (Human-Etisk Forbund), where young people gathered to discuss religion and philosophy. Humanism emphasizes the value and agency of human beings, both individually and collectively, often prioritizing reason, ethics, and justice over religious or supernatural beliefs. Humanists typically advocate for a secular approach to life, focusing on human welfare, education, culture, and personal responsibility. Over the years, colleagues have referred to me as a “Renaissance man,” though it’s not a label I use for myself.

I am, above all, a meritocrat, and I think you learn to be one when you study business, as it’s much about competition and the rewards of work, but also when you start to compete with other researchers to become a full professor. Thus, another book I’ve naturally come to refer to for decades now is The Rise of the Meritocracy by Michael Young:

Young, M. (1958). The rise of the meritocracy: 1870–2033: An essay on education and society. Thames and Hudson.

Young’s book is a satirical sociological critique that explores the concept of meritocracy, where individuals are rewarded based on talent and achievement rather than social class or privilege. He warns that such a system, though seemingly fair, can lead to a rigid class structure where those at the top justify their dominance and those at the bottom are seen as deserving of their failure. In this fictional future society, inequality deepens, as merit becomes an exclusive marker of status, breeding resentment and social unrest. The book is a cautionary tale, highlighting the dangers of unchecked meritocratic ideals.

Young is critical of meritocracy, a point I must admit I mostly missed during my first reading at an early age, perhaps also because I devoured the book too quickly. However, this became clearer after a second reading. I frequently refer to this book because he coined the term and because I view meritocracy as the least flawed way to organize society—much like how democracy is often considered the least flawed political system, for example, by W. Churchill. Our university systems are built on meritocracy and must be so to function. However, critics of meritocracy also draw on Young’s work, and they are justified in doing so.

For a more entertaining and modern introduction to the history of science, I recommend John Gribbin’s History of Western Science, 1543–2001 (Gribbin, 2006), which covers key developments in science in an accessible manner. Gribbin, a prolific British science writer and astrophysicist, is not a formal scholar but has made science understandable to a wide audience:

Gribbin, J. (2006). The history of Western science, 1543–2001. Harper Perennial.

Gribbin’s book, however, does not address the social sciences. We must recall that the social sciences are still not even regarded as “real” science for many natural scientists. However, social sciences do aim to follow the same fundamental principles established in the natural sciences. At least that has been the philosophy.

What is the relevance, we may ask, of philosophy in understanding science and research? I will attempt to demonstrate this here in this chapter, as it relates to what is known as the scientific perspective or, more specifically, the recurring question among scholars: “What scientific perspective do you adhere to?” In many ways, this also implies, “What philosophical schools do you adhere to?” These are big questions.

The journey from classic works in the philosophy of science to schools of thought begins with foundational texts in the philosophy of science. The first two books on this list have already been mentioned, but here is a longer list of recommended readings (Table 4.2):

Table 4.2

Major books in philosophy important for the development of science

Author

Time lived

Major work

Year published

Why it was important

Plato

428/427–348/347 BCE

The republic

c. 380 BCE

Discussed the role of reason and ideal forms, introducing dialectical methods of inquiry

Aristotle

384–322 BCE

Organon (set of six works)

c. 350 BCE

Laid the foundations of formal logic and syllogism, essential for scientific reasoning

René Descartes

1596–1650

Discourse on the method

1637

Introduced methodological skepticism and deductive reasoning to scientific inquiry

Francis bacon

1561–1626

Novum organum

1620

Advocated for the empirical method, emphasizing inductive reasoning in science

David Hume

1711–1776

An enquiry concerning human understanding

1748

Examined causality and the limits of human knowledge, challenging empiricist views

Immanuel Kant

1724–1804

Critique of pure reason

1781

Analyzed how human perception shapes knowledge and the limits of pure reason in science

Karl popper

1902–1994

The logic of scientific discovery

1934

Introduced the concept of falsifiability as a criterion for scientific theories

Thomas Kuhn

1922–1996

The structure of scientific revolutions

1962

Developed the concept of paradigm shifts, influencing the philosophy of science

John Stuart mill

1806–1873

A system of logic

1843

Explored logic and scientific reasoning, contributing to the understanding of causality

Isaac Newton

1642–1727

Philosophiæ Naturalis principia Mathematica

1687

Formulated the laws of motion and universal gravitation, foundational to modern physics

Galileo Galilei

1564–1642

Dialogue concerning the two chief world systems

1632

Advocated for the Copernican system, using observational evidence to support heliocentrism

Bertrand Russell

1872–1970

The problems of philosophy

1912

Discussed the nature of knowledge and the relationship between empirical evidence and logic

Albert Einstein

1879–1955

Relativity: The special and the general theory

1916

Revolutionized the understanding of space, time, and gravity, shifting the scientific paradigm

Pierre Duhem

1861–1916

The aim and structure of physical theory

1906

Argued that scientific theories are not confirmed by single experiments but by systematic coherence

Henri Poincaré

1854–1912

Science and hypothesis

1902

Explored the role of hypothesis in scientific discovery and the limits of scientific certainty

I hesitate to include Kuhn on this list of influential scientific thinkers, as his contribution strikes me as somewhat intuitive and simplified. His concept of “paradigm shifts” is commonly used to describe significant changes in science, though today many use the term to mean any major change, perhaps because it sounds impactful. While there have certainly been true paradigm shifts throughout history, the term has lost some of its original weight. That said, I do believe we are witnessing a genuine paradigm shift now with the rise of AI.

In the list above, we should also include Nicolaus Copernicus, who published On the Revolutions of the Heavenly Spheres in 1543:

Copernicus, N. (1543). On the revolutions of the heavenly spheres. (A. M. Duncan, Trans.). Prometheus Books.

The book introduced the heliocentric theory, changing humanity’s view of the cosmos. In other words, he was the first to not only suspect that the Earth was not the center of the universe but also show why and dared to speak up about it, which was very risky.

You may think that all scholars today dare to speak about their findings, but that is not entirely true. Many researchers hesitate because they feel or know their findings do not align with popular opinion, and they fear standing out or becoming unpopular, even today. This view can be referred to as “political correctness.” We know that this is an issue because we frequently do surveys that show it.³

Another scientist who delayed publishing out of fear of the repercussions of his ideas was one of the most important scientists of all time, Charles Darwin:

Darwin, C. (1859). On the origin of species by means of natural selection. John Murray.

In his 1859 book On the Origin of Species, Darwin laid the foundation for evolutionary biology with his theory of natural selection. In terms of its implications for the social sciences, I consider him the most important scientist of all time.

What is remarkable is that, even today, the social sciences have yet to fully embrace his ideas. There persists an irrational belief that evolution applies to other animals but not to humans, as if we are somehow exempt due to our intelligence (“sapiens”) or the concept of a soul (“Christianity”). This has led us to follow other frameworks, like in much of economics, borrowing from physics—the study of inanimate matter—to understand human behavior.

When it comes to science, I think it’s essential not only to read good books on the history of science, but, above all, to engage directly with the original sources. Many of these classic texts are far from the dry, technical works you may think; they are often vivid, personal accounts of observations, readings, journeys to gather data, and the conclusions drawn from them.

Here are some of the science books I’ve gathered over the years, which I still reference from time to time and would like to recommend to you:

1.

Archimedes. (1897). The Works of Archimedes (T.L. Heath, Ed.). Cambridge University Press.

2.

Bertalanffy, L. von. (1952). Problems of Life: An Evaluation of Modern Biological and Scientific Thought. Harper & Brothers.

3.

Buffon, G. L. L. (1749–1788). Histoire naturelle, générale et particulière. Imprimerie Royale. (Natural History, General and Particular).

4.

Brahe, T. (1588). Mapping the Heavens. Multiple publishers for various translations.

5.

Copernicus, N. (1543). De revolutionibus orbium coelestium (On the Revolutions of the Heavenly Spheres). Johannes Petreius.

6.

Einstein, A. (1916). Relativitätstheorie: Die Spezielle und die Allgemeine Relativitätstheorie. Vieweg+Teubner Verlag. (The Special and General Theory of Relativity).

7.

Cuvier, G. (1821–1824). Recherches sur les ossements fossiles de quadrupèdes. Deterville. (Research on Fossil Bones of Quadrupeds).

8.

Darwin, C. (1859). On the Origin of Species. John Murray.

9.

Darwin, C. (1839). The Voyage of the Beagle. Henry Colburn. (Resa kring jorden).

10.

Descartes, R. (1966). The Secret Notebook. (Translation by Multiple Publishers).

11.

Descartes, R. (1637). Discourse on the Method and Meditations. Cambridge University Press.

12.

Descartes, R. (1637). The Geometry. Cambridge University Press.

13.

Galilei, G. (1638). Dialogues Concerning Two New Sciences. Elzevir.

14.

Gould, S. J. (1993). The Science and Humanism of Stephen Jay Gould. Harvard University Press.

15.

Hawking, S. (1988). A Brief History of Time. Bantam Books. (Univers uten grenser).

16.

Hippocrates. (1950). Hippocratic Writings (G. E. R. Lloyd, Ed.). Penguin Books.

17.

Humboldt, A. von. (1808). Ansichten der Natur. Cotta’schen Verlagsbuchhandlung. (Views of Nature).

18.

Kepler, J. (1619). Harmonices Mundi (Harmonies of the World). Linz.

19.

Lamarck, J. B. (1809). Philosophie Zoologique. Dentu. (Zoological Philosophy).

20.

Crane, N. (2002). Mercator: The Man Who Mapped the Planet. Orion Publishing Group.

21.

Newton, I. (1687). Philosophiæ Naturalis Principia Mathematica (The Principia). Royal Society.

22.

Watson, J. D. (1968). The Double Helix: A Personal Account of the Discovery of the Structure of DNA. Atheneum.

23.

Wilson, E. O. (1978). On Human Nature. Harvard University Press.

Right now, I’m reading Der berühmte Herr Leibniz: Eine Biographie by E. C. Hirsch (2007). Leibniz was an extraordinary scientist from Leipzig who attended the Alte Nicolai Schule, the same school that Richard Wagner (1813–1883) would later attend.

Leibnitz’s statue stands in the main courtyard of the university building, right in the heart of the city, next to the new social sciences building that now also houses, among others, the Faculty of Economics and Management Science. He was remarkable not only for his contributions to mathematics but also for his curiosity and eagerness to engage with scholars across Europe. He traveled widely, meeting many of his time’s great scientists, such as Christiaan Huygens in the Netherlands and others in Paris and London.

He also sought to make a name for himself in the European courts, though many of these relationships ended in disappointment—perhaps inevitable, as the nobility of seventeenth-century Europe were not exactly intellectuals. He died alone and largely forgotten, but his contributions have been re-recognized and celebrated in the years since.

What I take away from reading about Leibniz is that researchers shouldn’t try to be opportunistic or seek to amass a fortune outside of academia. Instead, they should focus on what they know and be content with that. If fame comes, then let it come. For most parts, we are more like a colony of ants, happily going about our work—one task at a time, building something meaningful, layer by layer, with no grand expectation other than the satisfaction of the job itself. But again, that’s just my opinion.

At this point, I should make it clear that I am not all glued to older books and authors but read many of the popular science books you see on bestseller lists. At the same time, I must admit that I find most of them somewhat superficial, repetitive, and speculative—often appearing to be written more for the author’s popularity than for genuine intellectual contribution. Often, they introduce a few ideas—that are interesting but seldom new—and overextend them without offering broader or deeper insights. There are, of course, exceptions, but I won’t name any here, as I’d rather encourage you, as PhD students, to spend your time on works that offer more substantial value. It’s much easier to appreciate what is good in new books on science when you have a basic understanding of what has been written before, so

Becoming your best as a researcher is choosing the right books that will develop you as an intellectual, not merely entertain you.

Consider Elon Musk, arguably one of the most successful entrepreneurs in history, regardless of what you may think of him personally or of his political convictions. From an early age, he was an avid reader, famously devouring his family’s entire encyclopedia. Ever since, he has felt a natural and almost instinctive attraction to books, much on science, but also science fiction. The takeaway? Make books your lifelong companions. While you can certainly learn from people, the depth and breadth of knowledge gained from reading far surpasses casual conversations. True, we will not remember every detail of what we read, but the impressions and insights shape our thinking in profound ways.

I read plenty of nonscience books too. In fact, science books make up only a part of my reading. Much of my scientific knowledge comes from other sources, like newspapers such as The Economist, the occasional novel, or through my YouTube or pod subscriptions, where a wealth of outstanding researchers provides updates on a wide range of topics daily. It’s an incredible way to stay informed and dive into cutting-edge findings across disciplines.

As this personal journey through the “Bücher Landes”—the land of books—draws to a close, I hope to have shown that reading, like research, is a continuous process. It is not just about the enjoyment of learning history or gaining insight into the societies we live in today; it also helps refine your scientific perspective and connect you with scholars who have arrived at similar conclusions. In many ways, it functions as an ongoing literature review—or even a meta-review—that situates your work within a broader intellectual framework and deepens your understanding of the scientific landscape.

Our reading shapes how we perceive the nature of science, steers us toward certain philosophical traditions, influences the methods we choose, and ultimately determines what and where we publish (Fig. 4.4).

Fig. 4.4

Scholarly development cycles

With each cycle of reading, our epistemological understanding may evolve, ideally leading to an adjusted philosophical perspective and a refined approach to scientific methods. This, in turn, might influence our choice of publication outlets, the school of thought you align with, and the academic tribe and conferences we engage with, potentially shifting our position within the scholarly landscape.

However, in reality, most researchers, in my opinion, find themselves gravitating back to familiar patterns—reading within the same frameworks, reinforcing existing epistemologies, and staying within their established schools of thought. While the cycle offers growth and transformation opportunities, it also highlights the challenges of breaking free from intellectual comfort zones.

As we strive for intellectual growth, we often find ourselves returning to familiar frameworks and assumptions that provide structure to our inquiries, help keep research manageable, and make sense of the complexities of human behavior. While these foundational assumptions are essential for building coherent methodologies, they also constrain our openness to alternative approaches and limit the scope of new perspectives.

When these assumptions prove unrealistic, they risk undermining the real-world relevance of our work. At the same time, once we are convinced of a particular scientific approach—and can justify our reasoning—our commitment to it may solidify into a belief in its inherent superiority. Over time, these assumptions become so deeply embedded in our understanding of what constitutes “good science” that they shape not only our research choices but also the very way we evaluate knowledge itself.

4.1.2 Methods and Assumptions

In the social sciences, we accept certain assumptions as part of a trade-off between achieving realism and obtaining practical, actionable results. These assumptions create a framework that shapes how we approach inquiry, but they also limit the scope of acceptable methodologies, effectively excluding alternative ways of doing science.

Once a scientific approach is defined, we often reinforce our commitment to it, believing it to be the most rigorous and valid path forward. This commitment can solidify into a sense of methodological superiority, where the assumptions we started with become deeply embedded in our view of what constitutes “good science.” Thus, rigor in science is not just about strict adherence to methods but also about how the foundational assumptions we choose shape the boundaries of our inquiry and our convictions.

Reading both scientific literature and fiction shapes the methodologies we adopt as researchers, as it introduces us to a range of approaches, techniques, and frameworks. It goes beyond simply honing our quantitative or qualitative skills; engaging with foundational texts in the philosophy of science deepens our understanding of the assumptions embedded in different methodologies. This exposure helps us critically examine and choose the assumptions we are willing to accept in our own research, enriching our investigations with a more nuanced awareness of methodological foundations.

Defining a scientific perspective should be about identifying which groups of methods you think are best suited for solving a particular problem. Of course, sometimes one group of methods is best for one type of problem, and another group of methods may be more effective for others. For example, if we are studying power relationships between social classes or between the sexes, perhaps critical theory, part of what we call the Frankfurt School, will be more productive.

On the other hand, if we are predominantly studying marketing and how customers react to different products and services using surveys, this could fall under the empirical-positivist or empiricist scientific perspective. This approach emphasizes the collection of observable, measurable data through experiments or surveys to derive conclusions, often using statistical analysis. The empirical-positivist perspective is grounded in the belief that knowledge is best obtained through sensory experience and empirical evidence, which is why it is commonly used in market research, where customer behavior and preferences are quantified and analyzed.

In psychology, phenomenological approaches are often used to understand subjective experiences. For instance, a psychologist studying grief may conduct in-depth interviews to explore the personal and emotional dimensions of loss. Rather than focusing on measurable outcomes, the psychologist seeks to understand the lived experience of grief, aiming for a deep, qualitative insight that would be difficult to capture through standardized surveys or experiments.

Sociologists may use ethnographic methods to study community dynamics or cultural practices. For example, a sociologist might immerse themselves in a specific community to observe and document daily rituals and interactions. By engaging in participant observation, they gain a richer understanding of social norms, relationships, and values within the group—an approach that prioritizes depth and context over quantifiable data.

In political science, discourse analysis is a valuable tool for examining political ideologies and rhetoric. A political scientist might analyze speeches, policy documents, and media coverage to understand how specific language frames issues like immigration or healthcare. By studying language use and underlying narratives, discourse analysis provides insights into how political ideas are shaped and communicated, often revealing biases and power dynamics.

In economics, historical analysis can offer insights into economic systems and patterns without relying on empirical-positivist methods. For example, an economic historian might examine archival documents, newspapers, and legislative records to trace the evolution of labor laws over time. This approach allows economists to understand how past events and social movements have influenced economic policies, helping to explain current structures.

In education, grounded theory is often used to develop theories based on observed data rather than testing predefined hypotheses. An educational researcher might conduct interviews with teachers to explore how they adapt their methods in diverse classrooms. Through iterative data collection and analysis, grounded theory allows for new theories to emerge organically from the research setting itself, offering insights into complex educational practices that standardized tests may not reveal (Table 4.3).

Table 4.3

Examples of methods used in the social sciences

Method

Description

Example

Field

Ethnography

In-depth study of people and cultures through immersion, often involving participant observation

A sociologist immerses in a community to document daily rituals and interactions, providing insight into social norms and values within the group

Sociology

Discourse analysis

Analyzes language, rhetoric, and underlying narratives to reveal ideologies, biases, and power dynamics

A political scientist examines speeches and policy documents to understand how language frames issues like immigration or healthcare

Political science

Historical analysis

Uses historical documents and archives to trace patterns and understand developments over time

An economic historian studies archival records and legislative documents to trace the evolution of labor laws and their impact on current economic policies

Economics

Grounded theory

Develops theories directly from data through iterative data collection and analysis, without predefined hypotheses

An educational researcher conducts interviews with teachers, allowing new theories to emerge about adaptive teaching methods in diverse classrooms

Education

Case study

In-depth examination of a single case, event, or organization to understand complex phenomena in real-world contexts

A business researcher studies a company’s response to a crisis to gain insights into decision-making and crisis management practices

Business/management

Content analysis

Systematic coding and interpretation of text or media to identify themes, patterns, and trends

A media researcher analyzes news articles over a decade to study the changing portrayal of climate change in the media

Media studies

Survey research

Collection of standardized data from a large group to identify trends, attitudes, or behaviors across a population

A sociologist conducts surveys to examine public attitudes toward social policies or community issues

Sociology

Experimental method

Controlled testing to observe cause-and-effect relationships, typically involving an independent and dependent variable

A psychologist runs a controlled experiment to study the effect of sleep deprivation on cognitive performance

Psychology

Phenomenology

Focuses on individuals’ lived experiences to understand how they perceive and interpret events or conditions

A researcher interviews patients with chronic illness to understand how they perceive their experiences and challenges

Psychology/healthcare

Comparative analysis

Examines similarities and differences across cases to identify patterns or causal mechanisms

A political scientist compares democratic processes in different countries to understand the factors that strengthen or weaken democracy

Political science

In each of these examples, empirical and nonempirical-positivist methods provide a depth of understanding by focusing on context, interpretation, and meaning, often uncovering insights that quantitative methods may overlook.

It took me a long time to figure out which approach to the social sciences I found most productive for my own studies. I also tend to shift between different perspectives depending on what problems I am trying to solve.

You could, of course, develop your own unique scientific perspective—how one perceives, interprets, or approaches science—but it’s likely that someone else has already thought through a similar idea more thoroughly. Additionally, it can be challenging to persuade others to accept a completely new perspective, and this might isolate you and your research. So, it’s much about reading and choosing, at least early in your career.

Less curious colleagues often just choose a scientific perspective because it sounds good or because someone they admire has chosen it. While this is not a good reason, it’s far from uncommon.

We’ve already noted that what makes the social sciences fascinating yet extremely challenging is their inherent complexity. In practice, this complexity requires us to narrow down the scope of the phenomena we want to study until it becomes manageable. This narrowing inevitably means excluding certain elements. Put differently, we make certain assumptions—things we take as given, knowing they are not absolute truths, but necessary simplifications to make our research feasible.

For example, if we cannot assume that people are at least somewhat rational and do not act purely at random, it becomes very difficult to make any predictions about human behavior. Similarly, if we can’t assume that people make choices freely and are instead largely shaped by their genetic makeup, then perhaps that should be the focus if we aim to predict behavior effectively. These assumptions allow us to focus on meaningful questions within the constraints of practical research. The problem is that these assumptions also need to be realistic.

4.1.3 Finding Rigor in Complexity

In the social sciences, key assumptions are often made to build and validate theories. Two central assumptions are rationality and free will.

Rationality assumes that humans assess the options available to them and typically choose the one that aligns best with their self-interest, moral convictions, or both. This concept of rational choice posits that, by evaluating potential outcomes, people generally act in ways that they believe will maximize their satisfaction or achieve their goals.

The second critical assumption presented here as an example is free will, the notion that individuals make choices because they have the agency to do so, rather than being compelled by biological determinism or external programming, such as DNA-based instincts purely aimed at survival. The assumption of free will supports the idea that people are autonomous agents capable of making decisions independent of predetermining factors.

Together, these assumptions are foundational to many theories in economics, psychology, and sociology. They enable researchers to model and predict human behavior while also acknowledging that these simplified assumptions may not capture the full complexity of human motivations and constraints.

Recent research has raised questions about both the assumptions of rationality and free will in social sciences:

1.

Rationality: Studies in behavioral economics and psychology, especially by researchers like Daniel Kahneman and Amos Tversky, have shown that people often act in ways that contradict traditional models of rational choice. Cognitive biases, such as confirmation bias and the availability heuristic, lead people to make decisions that are not always in their best interest or logically sound. For example, Kahneman and Tversky’s prospect theory highlights that individuals evaluate potential losses and gains differently, leading to decisions that can appear irrational by classical economic standards. This suggests that while people can be rational, their decision-making processes are more complex and influenced by emotions and cognitive limitations:

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185

2.

Free Will: Neuroscientific research has also challenged the assumption of free will. Experiments by neuroscientists like Benjamin Libet and more recent findings using fMRI technology suggest that many decisions are made subconsciously before individuals are consciously aware of them. Libet’s studies in the 1980s showed that neural activity associated with decision-making occurs before people consciously decide to act. This finding has sparked debate about the extent to which human actions are consciously chosen or biologically determined, with some researchers suggesting that our sense of free will may be more of an illusion than a concrete reality:

Libet, B., Gleason, C. A., Wright, E. W., & Pearl, D. K. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential): The unconscious initiation of a freely voluntary act. Brain, 106(3), 623–642. https://doi.org/10.1093/brain/106.3.623

3.

Contextual Decision-Making: The work of Richard Thaler in “Nudge” theory also shows that environmental cues and “choice architecture” influence decision-making, suggesting that rationality is not only a personal trait but also highly context-dependent. Decisions vary significantly based on social influences, the way options are presented, and situational stressors, further complicating the assumption that people act purely from rational self-interest:

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.

4.

Social and Genetic Determinants: Epigenetics and social psychology have highlighted how factors like upbringing, social structures, and even genetic predispositions can shape choices in ways that challenge the notion of free will. Studies in these fields indicate that individuals’ actions are often constrained by both inherited traits and external environments, which may limit genuine autonomy, for example:

Meaney, M. J. (2010). Epigenetics and the biological definition of gene × environment interactions. Child Development, 81(1), 41–79. https://doi.org/10.1111/j.1467-8624.2009.01381.x

While these findings do not entirely refute the assumptions of rationality and free will, they underscore the complexity of human behavior. These assumptions remain useful for modeling certain behaviors but may not capture the full scope of influences shaping decisions, especially under real-world conditions.

It is possible to have social science even if we assume that free will doesn’t exist, like is suggested by Harris (2012),⁴ but it would fundamentally alter how we approach human behavior and societal structures. In this case, social science would likely adopt a deterministic or mechanistic framework, where human actions are viewed as the result of various external factors such as biology, environment, social conditioning, or unconscious drives, rather than individual choices or autonomy.

In this model, social scientists would focus on identifying the underlying causes and patterns that influence behavior. For example, rather than studying individual decision-making as an exercise in free choice, we would look at how societal structures, historical context, genetics, or neuroscience influence people’s actions. Research might focus on understanding the factors that determine group dynamics, social norms, or economic behavior, with an emphasis on predictive models based on these factors.

The research would then look at how institutions, cultural environments, or laws shape people’s actions, possibly with the goal of understanding how to optimize societal outcomes or prevent undesirable behaviors. In this world, policies might be designed not to “empower” individuals to make choices, but to reshape environments, structures, or conditions to guide people’s actions toward desired goals.

While this perspective would still involve empirical observation, data collection, and rigorous analysis, the assumptions and goals of social science would shift. Instead of assuming individual agency, social science might focus on the systemic and contextual determinants of human behavior, with a strong emphasis on structural and scientific explanations rather than notions of individual choice.

There are numerous other assumptions we have to consider when doing social science (Table 4.4):

Table 4.4

Assumptions in the social sciences when we write “ceteris parabus”

Type of assumption

Description

Example

Why it’s problematic

Linearity of relationships

Assumes relationships between variables are linear, making it easier to predict and interpret outcomes

In economics, assuming an increase in income leads to a proportional increase in consumption

Social behaviors are often nonlinear and complex, meaning proportional assumptions can oversimplify or misrepresent how changes in one variable affect another

Independence of variables

Assumes variables do not influence each other, enabling isolation of specific causal effects

Analyzing education’s impact on income while ignoring social networks or family background

In real-world settings, social variables often interact, creating complex dependencies that the assumption of independence fails to capture, leading to inaccurate conclusions

Temporal stability

Assumes that relationships and preferences remain stable over time, allowing findings to be applied consistently

Believing that findings on consumer behavior in one economic period apply to a different economic context

Social contexts evolve, and people’s preferences shift over time, meaning findings based on past data might not hold under new or future circumstances, limiting predictive accuracy

Rational behavior

Assumes individuals make decisions to maximize utility, assuming a predictable and logical pattern of choices

Predicting that consumers will buy based on price advantage alone, disregarding emotional or impulsive factors

Human behavior often defies pure rationality; biases, incomplete information, and emotions frequently influence decisions, challenging the validity of rational-choice models in capturing actual behavior accurately

Uniformity across populations

Generalizes behavior from one group or setting to another, assuming similar responses across varied contexts

Assuming findings from urban populations apply equally to rural populations in areas like health interventions

Different groups have unique cultural, social, or economic influences that can significantly alter responses, making generalized findings potentially irrelevant or misleading for diverse populations

Simplicity in cause-and-effect

Presumes that causal relationships are straightforward, where one variable directly influences another

Studying the effect of education on crime rates without accounting for economic factors as a mediating influence

Social phenomena often involve complex feedback loops and indirect influences, complicating efforts to establish clear-cut causality and oversimplifying conclusions drawn from such models

What conclusion can we draw from this? First, given the inherent biases, assumptions, and complexities of human behavior in social sciences, our confidence in findings should be carefully stated. When there are reasons to believe that certain assumptions may not hold, conclusions must be presented with even greater caution. Social science research can indeed reveal valuable patterns, trends, and relationships, but unlike the natural sciences, its findings are rarely as definitive, as they cannot always be isolated or replicated with the same reliability. This does not imply that the social sciences have failed—rather, they face a greater degree of complexity. The unpredictable nature of human behavior, influenced by individual and cultural variables, adds layers of complexity that make absolute certainty challenging to achieve, to say the least.

This complexity also means that social science findings are often probabilistic, offering insights that are “likely” rather than universally true. Assumptions like rationality or temporal stability, while helpful, can sometimes oversimplify human experiences, leading to findings that may not hold across different contexts.

However, robust methodologies, replicability efforts, and statistical rigor can help improve the reliability of findings, even if perfect accuracy remains elusive. Confidence in social science findings, therefore, comes from a combination of rigorous methodology and awareness of limitations rather than from certainty.

Many natural scientists see this as a weakness with the social sciences, as Nagel pointed out in his book “The Structure of Science” from 1961:

Nagel, Ernest (1961). The structure of Science: Problems in the logic of scientific explanation. Routledge & Kegan Paul Ltd, Oxon.

Note: Ernest Nagel

Ernest Nagel, born in Nové Mesto, then part of the Austro-Hungarian Empire (now in Slovakia), in 1901, immigrated to the United States with his family when he was 10 years old. He was a prominent philosopher of science, known for his work on the logic and methodology of scientific inquiry, particularly in relation to the social sciences.

Nagel was a key figure in the development of what is called logical positivism in the United States.

The reason Nagel is relevant here is that his work is a classic on methodology and emphasized the importance of explanatory models and the need for the social sciences to adopt rigorous, empirically grounded methods, helping bridge the gap between the natural and social sciences in terms of scientific validity and rigor. Here are some extractions as examples:

Much of the “social theory” that has come from such studies, in the past as well as the present, is social and moral philosophy rather than social science” and is made up in large measure of general reflections on the “nature of man” (p. 447).

Most studies are merely descriptive. In no area of social inquiry has a body of general laws been established (p. 447).

Most competent students today do not believe that an empirically warranted theory, able to explain in terms of a single set of integrated assumptions the full variety of social phenomena, is likely to be achieved in the foreseeable future (p. 448).

And there is much to argue this is still the case.⁵ As of 2024, achieving a unified theory that comprehensively explains the diversity of social phenomena remains distant. Social sciences have made strides in using interdisciplinary frameworks, combining insights from fields like psychology, economics, sociology, and anthropology, but these frameworks are often too context-specific or limited to offer universally integrated explanations.

More worryingly

It is also generally acknowledged that in the social sciences, there is nothing quite like the almost unanimity commonly found among competent workers in the natural sciences as to what are matters of established fact, what are the reasonably satisfactory explanation (if any) for the assumed facts, and what are some of the valid procedures in sound enquiry (p. 448)

So, in short, the social sciences have not even been able to agree upon what the facts are, why certain events happen, and what the procedure is to find out. This still holds true today.

And as if that was not enough, there are “serious disagreements on methodological as well as substantive questions” (p. 449). However, Nagel thinks there is hope for the social sciences, if we can figure out a way to be more rigorous, to gather and agree upon what we find and to be specific about what methods are deemed “scientific” across all the social sciences.

So, how do we get there?

4.2 Developing a Scientific Perspective

It begins with a reflection on what we are truly examining and a clear articulation of our intellectual stance—identifying what we see as the most viable path forward, the assumptions we consider realistic, and our commitment to maintaining coherence across the social sciences. Once these guiding principles are established, the approach demands rigorous adherence, ensuring that our research remains consistent and grounded in these assumptions. However, this rigorous commitment can sometimes lead to social science disciplines and communities feeling exclusionary toward those they perceive as not adhering to the same high standards.

Choosing a scientific perspective is not like picking up a hobby or adopting a habit; rather, it should be a logical decision that aligns with your research practices. Ideally, this choice is grounded in a clear understanding of the methods you use most frequently. In other words, there should be a direct line of reasoning that connects the specific methods in your research to the broader perspective you adopt. For instance, if you regularly use interpretive methods like ethnography, your perspective might naturally align with interpretivism. Alternatively, if you rely heavily on quantitative methods and statistical analysis, an empiricist or positivist perspective may suit you best. This alignment ensures coherence between your practical approach and the theoretical framework guiding your work, ultimately strengthening both.

Once this foundation is established, we must encourage other researchers to adopt the same approach while at the same time being open to abandoning them if someone has a better idea.

It’s essential to require that new PhD students invest time in understanding these philosophical and methodological issues, seeing themselves as ambassadors tasked with advancing and sharing a rigorous scientific perspective.

Although the social sciences are divided into distinct fields, each with its own traditions and intellectual cultures, fostering a shared understanding is challenging but not unattainable. By fostering a shared language and a commitment to rigor across disciplines, we can start to bridge these divides, building a more cohesive and reflective academic community. Addressing the critiques raised by thinkers like Nagel and others won’t happen overnight, but we can make meaningful progress in that direction. Above all, we must ensure that the social sciences remain steadfastly committed to inquiry and understanding, free from the influence of political agendas. If we fail in this, the social science endeavor in its current form risks being abandoned, with much of the progress achieved so far fading into obscurity—much like the water systems of the Pecile, the grand garden surrounded by a swimming pool and arcade in Hadrian’s Villa, a marvel of engineering largely forgotten for over a millennium.

4.2.1 Approaches to Inquiry

Approaches to inquiry are about identifying the appropriate philosophical perspective for a given problem, as this will determine how we approach the problem, how we analyze it, and it is likely to impact our findings. As such, it could be considered a type of assumption.

However, this does not necessarily mean that there are no objective truths in the social sciences. Rather, it suggests that the understanding of truth can be viewed from different perspectives, and each perspective may reveal a different aspect of reality. Social science methodologies are often flexible and interpretative, meaning that different theoretical frameworks can offer valuable insights depending on the context and nature of the research.

I hope the reader will forgive me for once again using examples from economics and business studies, but the underlying principles and philosophy of science are broadly applicable across all social sciences.

Example: Bridging Empiricism and Idealism: A Researcher’s Reflection on Kantian Influence in Social Science Methodology

As an economist, my work often oscillates between literature reviews, sometimes incorporating larger bibliometric analyses, more argumentative contributions, and positivism with elements of logical empiricism. In these moments, I attempt to capture objective, measurable data to understand behavior and trends, believing that empirical evidence can provide valuable insights. However, when I step back to critically assess my own modest contributions to research and reflect on how I balance findings with self-criticism, I find myself gravitating toward a broader, more introspective lens—one that can best be described as transcendental idealism, the scientific perspective associated with Immanuel Kant.

This philosophical approach allows me to bridge empiricism and rationalism, integrating both sensory experience and the active role of the mind in shaping our understanding of reality. When attempting to describe what I see in the world of social science, I must acknowledge that there is always an interaction between data and interpretation, between the external world and the internal processes that filter and organize our experiences. While I value empirical data, I have come to recognize that the mental frameworks I use to understand this data are just as important.

Immanuel Kant spent his entire academic career in Königsberg, Prussia (now Kaliningrad, Russia), where he taught at the University of Königsberg. Known for his strict routines, he reportedly structured his day meticulously, including his daily walks around the town, which were so regular that locals used them to set their clocks. His walks reflected his disciplined approach to life, which he applied rigorously to his academic work. Kant argued that our understanding of the world is shaped both by sensory experience and by the mental frameworks we impose on that experience. He believed that while we cannot know things as they are in themselves (“noumena”), we can understand them as they appear to us (“phenomena”), structured by our mind’s inherent categories like space and time.

In my own research, this Kantian influence shapes how I view the duality between objective, data-driven research and the subjective, interpretive nature of human behavior. While empirical methods and positivism provide valuable insights, they often fail to fully capture the complexities of human experience. Instead, a transcendental idealist approach reminds me that our mental frameworks—whether shaped by social context, historical conditions, or individual cognition—play a vital role in interpreting and shaping research findings.

This recognition, however, does not negate the importance of empirical research. On the contrary, it serves as a reminder that scientific inquiry in the social sciences requires both rigorous data collection and thoughtful interpretation. Bridging these perspectives allows us to create a richer, more nuanced understanding of the phenomena we study.

Thus, much like Kant’s belief that the world is shaped by both sensory input and the mind’s inherent categories, the research process involves a continuous interplay between data and the theoretical frameworks we use to understand it. The challenge, as I see it, is to recognize these influences and remain open to the ways in which both empirical data and philosophical inquiry shape our understanding of social phenomena. This is, in my opinion, where true scientific rigor and intellectual humility converge.

I didn’t begin by studying all the philosophical perspectives and choosing one; instead, it was a gradual process, as I observed my own conclusions naturally leaning in a particular direction.

I believe there are many ways to develop a scientific perspective: Some researchers have no defined philosophy, others do, and of those who do, some arrive at it before they start writing, while others find it along the way.

We can model these choices using a 2x2 game theoretical matrix of approaches (Table 4.5):

Table 4.5

Alternatives for applying a scientific perspective in your research

Scientific perspective

Defined before writing

Developed during writing

Has a perspective

Researcher begins with a clear philosophical direction and applies it consistently throughout

Researcher discovers their perspective organically through the writing process, allowing insights to shape their approach

No perspective

Researcher does not adopt a specific philosophy, focusing instead on empirical data or practical outcomes

Researcher begins writing without a defined perspective and does not develop one during the process

Honestly, I can’t say which approach is more common or why. Maybe that’s a research gap for a study in pedagogics.

The philosophical lens, whichever direction we choose, shapes not only how we conduct research but also how we perceive and interpret the world around us—what happens, what doesn’t, and what should (normative). Philosophy is an invaluable tool for us, providing personal insight, and our professional growth as researchers.

For me, philosophy serves two primary purposes. First, it provides a pathway to contentment in life. I consciously avoid the term “happiness,” as I see happiness as fleeting, tied to specific events or achievements, like graduating or buying a new car. This perspective isn’t unique but aligns with eudaimonism, a concept rooted in classical philosophy, especially in Aristotle’s thought, and also found in Schopenhauer. Second—and more important here—philosophy offers a framework for understanding and analyzing the world.

If you’re a dentist, nurse, or high school teacher, you may not feel compelled to define your philosophical stance—but as a researcher, this question inevitably follows you, even if you try to set it aside. For researchers, philosophy is essential not only in defining our scientific perspective but also in situating ourselves within a broader intellectual tradition, recognizing that all knowledge is deeply interconnected.

To move from social science methods, like phenomenology, ethnography, and discourse analysis, to broader scientific perspectives, we start by recognizing that each method reflects underlying assumptions about knowledge and reality.

For example, phenomenology in psychology, which seeks to understand subjective experiences, aligns with the philosophical perspective of phenomenology, founded by Edmund Husserl, emphasizing first-person experience and consciousness as the primary means of understanding reality:

Husserl, E. (1907). Die Idee der Phänomenologie. Niemeyer.

Or, in its translation

Husserl, E. (1970). The idea of phenomenology (W. P. Alston & G. Nakhnikian, Trans.). Nijhoff. (Original work published 1907).

Similarly, ethnographic methods in sociology, which focus on cultural practices and values within communities, connect with interpretivism as developed by Max Weber, emphasizing the importance of context and subjective meaning in understanding social phenomena:

Weber, M. (1922). Wirtschaft und Gesellschaft: Grundriss der verstehenden Soziologie. Mohr Siebeck.

And in translation

Weber, M. (1978). Economy and society: An outline of interpretive sociology (G. Roth & C. Wittich, Eds.). University of California Press.

Or, discourse analysis in political science, which examines language to understand ideologies, draws upon postmodern and critical theory perspectives, especially those developed by theorists like Michel Foucault:

Foucault, M. (1969). L'archéologie du savoir. Gallimard.

Foucault, M. (1972). The archaeology of knowledge (A. M. Sheridan Smith, Trans.). Pantheon Books. (Original work published 1969)

and Jürgen Habermas:

Habermas, J. (1981). Theorie des kommunikativen Handelns (Vols. 1–2). Suhrkamp.

Habermas, J. (1984-1987). The theory of communicative action (T. McCarthy, Trans., Vols. 1–2). Beacon Press. (Original work published 1981)

The Theory of Communicative Action was fundamental in shaping my understanding of the social sciences, though perhaps even more influential were Niklas Luhmann’s ideas on social systems:

Luhmann, N. (1984). Soziale Systeme: Grundriss einer allgemeinen Theorie. Suhrkamp.

Luhmann, N. (1995). Social systems (J. Bednarz Jr. & D. Baecker, Trans.). Stanford University Press. (Original work published 1984)

Niklas Luhmann was a German sociologist and philosopher of social science who became a leading figure in systems theory, particularly known for exploring the self-referential nature of social systems. His work has significantly influenced social science theory and methodology, especially in Europe. Unlike Talcott Parsons, an American sociologist, who saw social systems as oriented toward societal integration and cohesion through norms (implying that human agency could influence them), Luhmann argued that systems are largely autonomous, operating independently based on their own internal logic and rules.

Numerous theorists have expanded systems theory from diverse perspectives. Ludwig von Bertalanffy, an Austrian biologist, is widely regarded as the founder of general systems theory, which aimed to identify universal principles across scientific fields. Born in Vienna, Bertalanffy studied biology and philosophy at the University of Vienna. His seminal work, General System Theory, was originally written in English to reach an international audience:

Bertalanffy, L. von. (1968). General System Theory: Foundations, Development, Applications. George Braziller.

The book is not particularly accessible, in my opinion, and I have set it aside more than once already, much out of sheer frustration.

In the USA, Norbert Wiener introduced cybernetics, emphasizing control and communication within systems, shaping both engineering and social sciences. Talcott Parsons applied systems theory to social structures, suggesting that all social systems work to maintain stability through shared values.

British theorist Stafford Beer pioneered management cybernetics, focusing on self-regulation in organizations. In ecological systems, American Howard T. Odum applied systems theory to energy flows, impacting environmental science. Jay Forrester from the USA brought systems dynamics to economic and organizational modeling, highlighting feedback loops in complex systems. Gregory Bateson, originally from the UK, applied systems theory in anthropology and psychology to explore feedback and communication within human behavior. Russian scientist Vladimir Vernadsky introduced the concept of the biosphere as a self-regulating system, influencing earth sciences. Finally, Chilean biologists Humberto Maturana and Francisco Varela proposed the theory of autopoiesis, foundational for understanding self-organizing systems across fields. Each of these theorists has enriched systems theory, contributing diverse insights that have extended its applications across multiple disciplines.

In another example, for economists using historical analysis, such as when tracing labor laws, the approach often connects to historicism and Marxist perspectives—especially as Marx and Engels saw history and class struggle as fundamental to understanding economic systems and policy evolution. It’s not only Marx himself but also Weber’s historical analysis focused on the relationship between economic systems and cultural values:

Weber, M. (1905). Die protestantische Ethik und der Geist des Kapitalismus. Verlag von Duncker & Humblot.

Weber, M. (2002). The Protestant ethic and the spirit of capitalism (P. Baehr & G. C. Wells, Trans.). Penguin Books.

Werner Sombart, a German economist and sociologist, is known for his work on capitalism and economic history:

Sombart, W. (1902). Der moderne Kapitalismus: Historisch-systematische Darstellung des gesamteuropäischen Wirtschaftslebens von seinen Anfängen bis zur Gegenwart. Duncker & Humblot.

Sombart, W. (1967). Modern capitalism: An historical and systematic study of the capitalist economic system (W. F. Stolper, Trans.). Norton. (Original work published 1902)

Karl Polanyi, born in 1886 in Vienna, to Hungarian parents, has also made important contributions to historical analysis:

Polanyi, K. (1944). The great transformation: The political and economic origins of our time. Farrar & Rinehart.

Polanyi here explored the historical roots of modern economic systems, focusing on the transition to market-based economies and the social upheavals that accompanied this shift. His work highlights the interconnection between economic policy and historical context.

But, it’s also the great Joseph Alois Schumpeter:

Schumpeter, J. A. (1942). Kapitalismus, Sozialismus und Demokratie. Francke.

Schumpeter, J. A. (1976). Capitalism, socialism, and democracy. Harper & Row. (Original work published 1942)

What I hold dearest among Schumpeter’s works is his History of Economic Analysis, a book he did not complete in his lifetime, but which was compiled and published posthumously with the help of his wife, Elizabeth Boody Schumpeter.

In this work, he outlines (but does not complete) the reasons why economics should be approached through an evolutionary lens, viewing economic development as a dynamic, ever-changing process.

Schumpeter was larger than life—born into a well-established family in Austria, he rose to prominence not only as a brilliant economist but also as Austria’s Minister of Finance for a brief period in 1919. He then spent the last two decades of his career as a professor at Harvard University, where he had a profound influence on economics, though he also occasionally lectured at other institutions. Schumpeter’s intellectual legacy continues to shape economic theory today, particularly through his ideas on “creative destruction” and the role of innovation in economic growth. But Schumpeter was so much more than just these simple ideas, which anyone who reads his History of Economic Analysis will see.

Schumpeter, J. A. (1954). History of economic analysis. Oxford University Press.

We also find historicism and Marxist perspectives in education, and the use of grounded theory to develop new theories from observed data aligns with constructivism, e.g., by Jean Piaget:

Piaget, J. (1937). La construction du réel chez l'enfant. Delachaux et Niestlé.

Piaget, J. (1954). The construction of reality in the child (M. Cook, Trans.). Basic Books.

And in pragmatism, e.g., by John Dewey:

Dewey, J. (1916). Democracy and education: An introduction to the philosophy of education. Macmillan.

Dewey, J. (1925). Experience and nature. Open Court.

John Dewey was born in 1859, in Burlington, Vermont. His upbringing in this relatively small community clearly influenced his values and initial intellectual pursuits, grounding him in a modest and practical environment that later shaped his pragmatic philosophical outlook.

He emphasizes learning as an active, iterative process based on experiences rather than static truths. Grounded theory’s emphasis on building knowledge from data reflects pragmatism’s focus on practical applications and the idea that truth is shaped by interaction and inquiry rather than discovered objectively.

Dewey was not the founder of grounded theory, which was developed by Barney Glaser and Anselm Strauss in the 1960s:

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Strauss, A. L., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

Grounded theory emphasizes an iterative process of data collection and analysis, where patterns, themes, and categories emerge organically rather than being imposed by prior hypotheses. Researchers using grounded theory continually compare new data with previous findings, refining and expanding categories as they go, which allows theories to be closely aligned with participants’ experiences. Because this method may seem open-ended, it requires considerable discipline to ensure that findings are coherent and reflective of the data rather than personal biases.

While systems theory and Marxism are schools of philosophy, grounded theory is a method grounded in the philosophy of pragmatism.

In all these examples and by understanding these connections, we see how different methods in the social sciences are grounded (pun intended) in distinct scientific perspectives. Each perspective provides a theoretical foundation that shapes how researchers choose methods, interpret findings, and draw conclusions. These philosophical frameworks—phenomenology, interpretivism, critical theory, Marxism, constructivism, and pragmatism, among others—offer unique lenses through which knowledge is approached and guide both methodological choices and broader epistemological commitments.

Below are some of the most common scientific perspectives used in the social sciences (Table 4.6):

Table 4.6

Scientific perspectives

Scientific perspective

Main author(s)

School

Key book

Publication year

Transcendental idealism

Immanuel Kant

Kantian philosophy

Critique of pure reason

1781

Positivism

Auguste Comte

Positivist

The course in positive philosophy

1830–1842

Scientific Marxism

Karl Marx, Friedrich Engels

Marxist

Das Kapital

1867

Interpretivism

Max weber

Sociology

Economy and society

1922 (posthumous)

Constructivism

Jean Piaget

Constructivism (psychology)

The child’s conception of the world

1926

Logical empiricism

Rudolf Carnap

Vienna circle

The logical structure of the world

1928

Analytic philosophy

Ludwig Wittgenstein

Analytic philosophy

Tractatus Logico-Philosophicus

1921

Pragmatism

Charles Sanders Peirce, John Dewey

Pragmatism

How we think (Dewey)

1910

Phenomenology

Edmund Husserl

Phenomenology

Logical investigations

1900–1901

Critical rationalism

Karl popper

Vienna circle (philosophy of science)

The logic of scientific discovery

1934

Existentialism

Jean-Paul Sartre, Martin Heidegger

Existentialism

Being and nothingness (Sartre)

1943

Critical theory

Max Horkheimer, Theodor Adorno, Jürgen Habermas

Frankfurt school

Dialectic of enlightenment (Horkheimer & Adorno)

1947

Structuralism

Claude Lévi-Strauss

Structuralism (anthropology)

The elementary structures of kinship

1949

Hermeneutics

Hans-Georg Gadamer

Hermeneutics

Truth and method

1960

Relativism

Thomas Kuhn

Philosophy of science

The structure of scientific revolutions

1962

Communicative action theory

Jürgen Habermas

Frankfurt school

The theory of communicative action

1981

Postmodernism

Michel Foucault, Jean-François Lyotard

Postmodernism

The postmodern condition (Lyotard)

1979

Next comes the question of how each of these scientific perspectives approaches the process of discovering truth or solving the problems they are confronted with. Each perspective often employs a distinct methodology, whether it’s through empirical observation, logical deduction, or interpretive analysis, depending on their underlying philosophy about how knowledge is generated.

Moreover, it’s important to understand what types of problems these perspectives are more suited to address—some are more effective in dealing with quantitative data and objective measurements, while others excel in analyzing social constructs, cultural phenomena, or subjective experiences. Additionally, knowing which disciplines tend to adopt these perspectives helps us grasp their relevance: For instance, positivism is prevalent in economics, while interpretivism is commonly used in anthropology.

Identifying the appropriate perspective for a given problem is key to both selecting the right methods and achieving more meaningful findings, as shown in the table below (Table 4.7):

Table 4.7

Which disciplines tend to use which scientific perspective

Scientific perspective

Approach to truth

Types of problems best suited for

Commonly used in

Transcendental idealism

Truth is constructed through the human mind’s interaction with experiences. Knowledge depends on our perception

Understanding how human cognition shapes reality

Philosophy, epistemology, psychology

Positivism

Truth is discovered through empirical observation and scientific methods. Knowledge is objective and measurable

Problems requiring quantifiable data, such as cause-and-effect relationships.

Economics, sociology, political science

Scientific Marxism

Truth is understood through material conditions and class struggles. Knowledge reveals the dialectical relationship between societal structures

Power dynamics, class relations, economic inequalities

Sociology, political science, economics

Interpretivism

Truth is subjective and constructed through individual or cultural interpretations. Knowledge is based on understanding meaning in social contexts

Understanding cultural and social meanings, behaviors, and beliefs

Sociology, anthropology, cultural studies

Constructivism

Truth is co-constructed by individuals and groups through social interactions. Knowledge is based on how societies build their realities

Social construction of norms, identity, and knowledge itself

Sociology, education, communication studies

Logical empiricism

Truth is derived through logical analysis of empirical observations. Scientific knowledge must be verifiable

Formal analysis of logical arguments and empirical data

Economics, philosophy of science, formal sciences

Analytic philosophy

Truth is discovered through precise analysis of language, concepts, and arguments. Logical clarity is paramount

Conceptual analysis, ethical issues, formal logic problems

Philosophy, logic, linguistics

Pragmatism

Truth is based on what works practically. Knowledge is validated through its practical consequences and applicability

Solving real-world problems where theory is applied to practice

Political science, education, social sciences

Phenomenology

Truth is discovered through examining subjective experiences and consciousness. Knowledge is based on how phenomena appear to individuals

Understanding individual and group experiences, perceptions

Sociology, psychology, anthropology

Critical rationalism

Truth is provisional and must be falsifiable. Knowledge progresses by challenging and disproving existing theories

Problems where testing and falsifying hypotheses are important

Philosophy of science, economics, political science

Existentialism

Truth is subjective and grounded in individual freedom, choice, and existence. Knowledge is rooted in personal experience and authenticity

Exploring human freedom, personal responsibility, and alienation

Philosophy, psychology, literature

Critical theory

Truth is uncovered through critiquing power structures and ideologies. Knowledge reveals how societal structures influence oppression and liberation

Power, domination, ideology, social justice issues

Sociology, political science, cultural studies

Structuralism

Truth is found by uncovering underlying structures in culture and language. Knowledge is based on analyzing patterns and systems in society

Social, linguistic, and cultural patterns that underlie behaviors

Anthropology, linguistics, sociology

Hermeneutics

Truth is interpreted through the understanding of texts, symbols, and meanings within context. Knowledge arises from interpreting historical and social texts

Problems related to textual analysis, historical understanding, and meaning-making

Literature, philosophy, sociology, theology

Relativism

Truth is dependent on perspectives and is not absolute. Knowledge varies based on cultural, historical, and individual contexts

Cultural relativism, subjective perspectives on morality and knowledge

Anthropology, sociology, cultural studies

Communicative action theory

Truth is achieved through rational discourse and mutual understanding. Knowledge is co-created in communicative processes

Social cooperation, democracy, discourse ethics, decision-making

Political science, sociology, communication studies

Postmodernism

Truth is fragmented, subjective, and often socially constructed. Knowledge challenges grand narratives and emphasizes diversity of perspectives

Deconstruction of norms, identities, and societal structures

Cultural studies, sociology, literature

Sociology stands out as one of the most versatile disciplines in terms of the range of scientific perspectives it employs. The discipline often uses everything from positivist approaches, relying on empirical data and quantitative methods, to interpretive and constructivist perspectives that delve into subjective experiences, social meanings, and cultural phenomena. This flexibility allows sociology to explore both macro-level social structures and micro-level interactions, making it adaptable to a variety of research problems.

Next in versatility would likely be anthropology, which also employs a broad spectrum of perspectives, including interpretivism, phenomenology, and postmodernism. Anthropology often focuses on human cultures, making use of both qualitative and quantitative methods, depending on the research question.

Political science follows, utilizing perspectives such as positivism, critical theory, and communicative action theory, particularly when studying power dynamics, governance, and political behavior. However, it is less inclined to use more interpretive or phenomenological approaches compared to sociology.

Economics tends to be more empirically focused, relying heavily on positivism and logical empiricism, with occasional ventures into Marxist or critical theory perspectives when exploring issues like class and inequality.

Psychology, while versatile, is often anchored in positivist and empirical methods but has branched into interpretivism and constructivism when studying subjective experiences or human development.

Other disciplines like philosophy and history are more specialized, often focusing on hermeneutics, critical theory, or phenomenology, but are less likely to use empirical methods. In contrast, they excel at dealing with theoretical and existential questions rather than large-scale data analysis or prediction models.

Most of these philosophical perspectives also embody a particular theory of knowledge, or epistemology (Erkenntnistheorie), providing a distinct framework for understanding the world and producing knowledge. Each epistemological approach offers a unique way to interpret reality, formulating how we perceive, validate, and generalize findings about human behavior and social structures. These perspectives guide researchers not only in what to observe but also in how to interpret findings, shaping both the questions we ask and the methodologies we employ. In essence, each epistemology sets the parameters for what is considered “knowable” and “valid,” creating a foundation for research practices across various disciplines in the social sciences (Table 4.8).

Table 4.8

Different forms of epistemologies used in the social sciences

Epistemology

Description

Key proponents

Positivism

Posits that knowledge should be derived from observable phenomena and empirical evidence

Auguste Comte, Émile Durkheim

Interpretivism

Focuses on understanding the subjective meanings and experiences of individuals within social contexts

Max weber, Wilhelm Dilthey

Constructivism

Suggests that knowledge is constructed through social interactions and shared understandings

Jean Piaget, lev Vygotsky

Pragmatism

Emphasizes the practical application of knowledge and the utility of ideas based on their consequences

Charles Sanders Peirce, William James, John Dewey

Critical theory

Seeks to uncover and critique power structures within society, aiming for emancipation and social justice

Karl Marx, max Horkheimer, Jürgen Habermas

Phenomenology

Studies the lived experiences of individuals to understand the essence of phenomena

Edmund Husserl, Alfred Schutz

Post-structuralism

Challenges fixed meanings and structures, emphasizing fluidity and the role of discourse in shaping reality

Michel Foucault, Jacques Derrida

Grounded theory

Aims to develop theory grounded in data collected from real-world observations without preconceived hypotheses

Barney Glaser, Anselm Strauss

Realism (critical realism)

Asserts that reality exists independently of human perceptions but acknowledges that knowledge is socially influenced

Roy Bhaskar

We won’t have the space to cover each philosophical perspective in detail, but we will focus on one of the more prominent ones in social sciences: positivism and, more specifically, logical positivism as it evolved in the twentieth century.

4.2.2 Logical Positivism

The social sciences are connected to logical positivism through their shared emphasis on empirical observation, the scientific method, and the pursuit of objectivity in studying human behavior and society.

Logical positivism, developed in the early twentieth century, promoted the idea that meaningful knowledge is derived from observable phenomena and verifiable statements. Social scientists, influenced by this approach, sought to apply similar methods to the study of society, believing that social phenomena could be systematically observed, measured, and analyzed in ways akin to the natural sciences.

Logical positivism impacted the methodology of social sciences by encouraging the use of quantitative data, statistical analysis, and hypothesis testing to achieve reliable knowledge. This led to the establishment of empirical, data-driven methods as standard in fields like sociology, psychology, and economics. Concepts such as “value-free” social research, or the idea that researchers should minimize personal bias to obtain objective findings, also emerged under this influence, reflecting the logical positivist aspiration for neutrality in science.

However, while logical positivism contributed to the rigor of social science research, it also sparked debates about whether human behavior can be studied as objectively as natural phenomena. Critics argue that social contexts, subjective meanings, and complex human emotions resist the reductionism that logical positivism promotes. Thus, many social scientists have integrated interpretive and critical approaches, recognizing the need for both empirical methods and qualitative insights to fully capture human experiences and societal structures.

The essence of Rudolf Carnap’s Der logische Aufbau der Welt (The Logical Structure of the World), originally published in 1928, centers on his ambitious attempt to reconstruct all scientific concepts using a logical framework based on a “phenomenalist” approach:

Carnap, R. (1928). Der logische aufbau der welt (Vol. 514). Felix Meiner Verlag.

Carnap, R. (1998). The logical structure of the world (A. George, Trans.). Felix Meiner Verlag.

Carnap aimed to build an ideal scientific language that precisely describes the structure of knowledge by reducing all empirical knowledge to a formal and logical structure.

Phenomenalism (Phänomenalismus), as seen in the work of Carnap and other logical positivists, focuses on understanding reality strictly through observable phenomena and experiences. In this approach, statements about the world are only considered meaningful if they can be directly verified by sensory experience (Sinneserfahrung) or logical verification (Logische Überprüfung). The approach involves reducing all knowledge of the world to statements about sensory experiences, avoiding assumptions about underlying, unobservable structures or entities. The approach emphasizes empirical verification, aiming to keep science and philosophy rooted in what can be directly observed or logically validated.

Carnap was part of the “Berlin Circle” which originated in seminars by Hans Reichenbach from 1926–1928 and included figures like Kurt Grelling and Walter Dubislav. Separately, Joseph Petzoldt and others founded the Berlin branch of the International Society for Empirical Philosophy, which later merged with Reichenbach’s group. After Petzoldt’s death in 1929, Reichenbach’s group took over, renaming it the Berlin Society for Scientific Philosophy in 1931. Key members included Carl Gustav Hempel, David Hilbert, and Richard von Mises. Together with the Vienna Circle, they published Erkenntnis and organized congresses on the philosophy of science, starting with Prague in 1929.

It was Carl Gustav Hempel (1905–1997), a major figure in logical empiricism, who introduced the deductive-nomological model, considered the “standard model” of scientific explanation in the mid-twentieth century. He is also known for “Hempel’s paradox” and “Hempel’s dilemma.”

The deductive-nomological (D-N) model is a framework used to explain events by logically deducing them from general laws and specific conditions. It consists of two main parts: the explanans (the set of laws and initial conditions) and the explanandum (the event to be explained). For an explanation to fit the D-N model, the event (explanandum) must necessarily follow from the laws and conditions provided in the explanans. This model works well in the natural sciences, where laws provide predictable outcomes, but is limited in fields without universal laws. In essence, the D-N model shows how specific events can be understood as logical outcomes of known laws and conditions (Table 4.9).

Table 4.9

The deductive-nomological (D-N) model

Step

Description

Example

1. Identify the explanandum

Determine the event or phenomenon that needs explanation

A metal rod expands when heated

2. Find general laws

Specify universal or causal laws related to the event

Metals expand when heated (thermal law)

3. Define initial conditions

Describe the specific circumstances under which the event occurs

The metal rod was heated

4. Deductive reasoning

Use deductive logic to show the event necessarily follows from the laws and conditions

Given heating and the thermal law, the rod expands

5. Conclude

State that the explanandum (expansion) logically results from the explanans (laws and conditions)

The rod expands due to the heat applied

Carnap continues this tradition in the USA, but his ideas are also different. In the book, he proposes a rigorous method for defining scientific concepts purely through logical constructions, drawing on a hierarchical system that starts from elementary sensory experiences and systematically builds up to complex concepts like physical objects and societal structures.

His goal was to show that all scientific knowledge could, in theory, be derived from a minimal set of basic experiences and logical rules.

The deductive-nomological (D-N) model explains specific phenomena by showing how they logically follow from general laws and specific conditions. In contrast, Carnap’s model aims to build scientific knowledge from basic sensory experiences through a structured, logical construction, not focusing on specific explanations but on defining concepts. The D-N model relies on existing laws and deductive logic to explain why events happen, while Carnap’s approach constructs scientific knowledge from the ground up, emphasizing a foundational structure.

We can say that the D-N model is explanation-driven, whereas Carnap’s model is definition-driven. Essentially, the D-N model addresses the “why” of individual events, while Carnap’s approach tackles the “how” of systematically building scientific concepts (Table 4.10).

Table 4.10

Difference between Hempel’s and Carnap’s and on logical positivism

Aspect

Deductive-nomological model

Carnap’s model

Purpose

Explains specific phenomena using laws and conditions

Defines scientific concepts from sensory experiences

Approach

Top-down: Assumes existing laws and applies them deductively

Bottom-up: Builds knowledge from basic experiences systematically

Focus

Provides explanations for particular events

Establishes a logical, hierarchical system for all scientific concepts

Role of laws

Relies on general laws as a foundation for explanations

Does not rely on laws but on logical constructions

Primary goal

Explanation of “why” events occur

Definition and structure of “how” scientific knowledge is built

Carnap’s work is foundational in analytic philosophy, especially logical positivism, and greatly influenced figures like A.J. Ayer and the Vienna Circle. Der logische Aufbau der Welt emphasizes the role of logic in understanding empirical reality, framing logical analysis as central to scientific knowledge. Logical positivism, an empiricist approach, insists that knowledge comes only from observable and measurable phenomena, distinguishing it from broader empiricism by requiring direct verification or logical validation for meaningful statements. This philosophy aligns closely with scientific empiricism, promoting verification and falsification to establish reliable knowledge and impacting empirical research in fields like psychology and sociology.

Different forms of empiricism, particularly logical empiricism, present challenges for researchers, especially as standards of empirical rigor evolve. Logical positivism’s strict criteria can conflict with traditional empiricism’s allowance for broader sensory data, making methodological choices complex for researchers who must navigate these shifting standards across disciplines.

The main difference between Ernest Nagel and Rudolf Carnap on empiricism lies in their approaches to scientific explanation and the role of logic in science. Carnap, as a logical positivist, emphasized the importance of logical syntax and formal languages in structuring scientific theories, arguing that meaning in science is derived from verifiable statements within a logical framework. Nagel, however, took a more pragmatic and pluralistic approach, emphasizing that scientific explanations could vary across disciplines and that a rigid formal structure wasn’t always necessary to capture scientific insights. While Carnap was focused on creating unified scientific languages that adhere to strict logical rules, Nagel believed that the methodology and structure of scientific explanations could adapt to the unique demands of different sciences, particularly in the social sciences. Thus, Carnap aimed for a universal logical foundation for all science, whereas Nagel allowed for methodological diversity, especially when addressing complex phenomena.

The reason I highlight these differences is not merely to show that being an empiricist is enough, but to emphasize that the type of empiricism you align with matters greatly. For instance, Carnap’s framework leaves little room for the social sciences, whereas Nagel’s approach is far more inclusive. Nagel’s emphasis on methodological diversity accommodates the unique complexities of social phenomena, legitimizing the social sciences as distinct fields. In contrast, Carnap’s strict focus on logical structures and universal verification tends to exclude these fields, as their methods and subjects often fall outside his logical positivist framework.

In the next section, let’s delve deeper into the various forms of empiricism.

4.2.3 Empirical Approaches

For simplicity, see the table that presents different schools of empiricism, their main supporters, and the core differences between them. These forms emphasize distinct interpretations of empirical evidence and how knowledge should be derived from sensory experience (Table 4.11).

Table 4.11

Schools of empiricism

School of empiricism

Main supporters

Core principles and differences

Classical empiricism

John Locke, George Berkeley, David Hume

Emphasizes that all knowledge originates from sensory experience, rejecting innate ideas (e.g., Locke’s “tabula rasa” concept). Hume further argued for skepticism, noting the difficulty of establishing causation and limits of inductive reasoning

Logical positivism

A.J. Ayer, Rudolph Carnap, Moritz Schlick

Focuses on verifiability as the criterion for meaning, asserting that meaningful statements must be either empirically verifiable or logically deducible. Different from classical empiricism as it integrates formal logic and rejects metaphysics as meaningless

Constructive empiricism

Bas van Fraassen

Argues that science aims only to generate empirically adequate models rather than to uncover “truth,” thus accepting only what can be empirically verified, while remaining agnostic on the truth of theoretical entities beyond observable phenomena

Scientific realism

Hilary Putnam, Richard Boyd, W.V.O. Quine

Supports the idea that scientific theories aim to provide a true representation of the world. Unlike logical positivism, scientific realism argues that theoretical entities (like atoms) exist and that science can provide true explanations of unobservable phenomena

Phenomenalism

Ernst Mach, J.S. Mill

Suggests that physical objects cannot be said to exist independently of sensory experiences; rather, they are logical constructions of sense data. Differing from classical empiricism, phenomenalism emphasizes sensory phenomena as the only “real” basis of knowledge

Pragmatic empiricism

William James, Charles Sanders Peirce, Ernest Nagel

Focuses on practical consequences as the basis of meaning and truth. Knowledge is valuable based on its practical effects and usefulness rather than strictly verifiable truths. Pragmatic empiricism emphasizes adaptability in knowledge, which differs from strict verificationism

Critical rationalism

Karl popper

Proposes that scientific theories cannot be proven true, only falsified. Emphasizes that scientific progress comes through hypothesis testing and falsification, unlike classical and logical empiricism which focus more on verification through observation

Each form of empiricism contributed uniquely to scientific methodology, from prioritizing verification (logical positivism) to stressing utility (pragmatic empiricism) or falsifiability (critical rationalism). While these schools share a commitment to observational evidence, they vary in how they address unobservable entities, theoretical truth, and the ultimate aims of scientific inquiry. It’s actually more complicated than this, too; for example, Ernest Nagel is generally best aligned with both Pragmatic Empiricism and Scientific Realism.

Defining one’s epistemological stance amid two or more philosophical perspectives is a necessary consideration in any discussion or explanation of methodology in scientific discourse or writing. Again, here is an example of my own position:

Understanding Knowledge: Influences by Kant and Heidegger

Immanuel Kant does not fit neatly into any standard form of empiricism listed above either, as his philosophy represents a synthesis of empiricism and rationalism. He argued that while knowledge begins with sensory experience (empirical input), the mind actively organizes this information through innate categories, meaning that pure empirical observation alone is insufficient for true understanding.

Kant is sometimes described as an empiricist, though his position is complex and distinct from the empiricism of figures like Hume or Locke. Kant critiqued both rationalism and empiricism in his “Critique of Pure Reason,” which aimed to bridge these philosophies by suggesting that while all knowledge begins with experience, not all of it arises solely from experience.

Instead, Kant’s philosophy posits that our knowledge of the world is structured by innate categories (like causality, space, and time) that shape how we perceive sensory data. This means he held that while empirical observations provide the content of knowledge, the mind itself provides an a priori (prior to experience) structure that organizes this content into meaningful experiences.

This approach led to Kant’s critical philosophy or “transcendental idealism,” setting him apart from traditional empiricists, who argued that experience alone could explain knowledge, and rationalists, who posited innate ideas independent of sensory experience.

I mention Kant not only because his perspective on knowledge aligns with my convictions in certain respects, and that makes him a more approachable example, but also because his idea that we can only know phenomena (the world as it appears to us) rather than noumena (the world in itself) closely mirrors insights from modern research in the neurosciences about perception.

A neuroscientific example that mirrors Kant’s distinction between phenomena and noumena is the concept of predictive coding in perception. This theory suggests that our brains don’t passively receive information from the environment; instead, they actively generate hypotheses or predictions about sensory input and then adjust these predictions based on the incoming data. Thus, what we perceive is not the world “in itself” but an interpretation, constantly refined through our sensory experiences.

Another example is color perception. Neuroscientists understand that color is not a property of objects but a construct of our brain based on how it processes different wavelengths of light. This aligns with Kant’s idea that we only perceive phenomena (our brain’s interpretation of color) and not noumena (any inherent “color” of the object itself, which doesn’t exist independently of perception). These examples illustrate how some research in neuroscience supports Kant’s view that our understanding of the world is shaped by cognitive processes, meaning we experience an interpreted reality rather than an objective one.

My point is that as we continue to read, we are likely to find that our own convictions align more closely with some philosophers than with others, and we may ultimately develop a mix of ideas from several sources. In some instances, we may find ourselves developing perspectives that feel uniquely our own, diverging slightly from what we’ve encountered in existing literature. This can happen when our readings have not been broad enough, or, occasionally, it may stem from a truly novel standpoint. However, it’s important to note that striving for complete originality shouldn’t be the goal. In fact, such instances may signal that further reading and exploration of existing work is needed, rather than indicating an entirely new direction.

My epistemological perspective is a variation of Kant’s approach, maybe also combined with some parts from Merleau-Ponty discussing embodiment and perception, which I refer to as “Bewegung und Erkenntnis” (Movement and Knowledge). In essence, this perspective suggests that what we perceive and understand is shaped primarily by our physical movements and how we interpret new experiences as we shift positions in space, over time. Our choices, especially regarding where and when to move, are pivotal but often influenced by habits, irritations, or unconscious reactions rather than pure free will. These ideas draw on theories from evolutionary science, which explore the factors that drive animals to behave in certain ways.

Merleau-Ponty, M. (1945). Phénoménologie de la perception. Gallimard.

Unlike the expansive domains studied in the natural sciences, our field of observation in the social sciences is, for all practical purposes, limited to about 1–3% of the Earth’s surface, significantly constraining our epistemological scope. Knowledge arises from the imperfect recording of these experiences, on this limited surface, which we document more or less consciously, and from the logical conclusions we attempt to draw. However, this process is fraught with logical inconsistencies and biases that we often struggle to recognize or mitigate.

As Kant proposed, this knowledge doesn’t necessarily reflect an objective reality but is largely constructed within the mind, shaped by how we process and interpret sensory information. This scientific perspective, which I can also sometimes refer to as the “Marco Polo tradition”—keeping it more approachable—when used in economics or political science, emphasizes learning about the world through firsthand exploration and asking essential questions about survival and competition in each new setting, rather than relying solely on abstract reflection and past experience. To survive and prosper means to be curious, to want to go out and explore, and to adapt. This approach is foundational to much of my work in intelligence studies, and especially what I tried to do in geoeconomics.

My view is also influenced by Heidegger’s ideas in “Sein und Zeit” (Being and Time), where he examines Dasein, or human existence as “being-in-the-world.” Heidegger presents existence as a holistic, pre-reflective experience in which our understanding, moods, and intentions shape how we perceive and interact with the world:

Heidegger, M. (1927). Sein und Zeit. Niemeyer.

And the translation:

Heidegger, M. (1962), Being and Time, J. Macquarrie & E. Robinson, Trans., Harper & Row).

Unlike a framework centered on spatial movement, Heidegger’s philosophy places existential and temporal dimensions at the core, underscoring that human life is intricately bound to time, purpose, and an ever-evolving sense of “being.” This is not simply interpreting sensory input but perceiving the world through a subjective, meaning-laden experience. While Heidegger emphasizes the subjective and existential, my approach leans more toward a structured spatial interpretation, focusing on the choices that allow us to guess about cause and effect within our environment as we replace ourselves.

Thus, epistemologically, I wouldn’t classify myself as strictly Kantian or Heideggerian but as a variation of the two, leaning more toward the former, mixed with a grain of Merleau-Ponty.

I also acknowledge that my outlook aligns with different philosophical traditions depending on the context. For instance, when addressing societal challenges, I often find myself drawn to Stoicism for its emphasis on resilience and rationality. In more personal social settings, I resonate with Epicureanism, which celebrates pleasure and human connection. In other words, my epistemological convictions do not strictly govern these specific situations, and I see no issue in borrowing concepts from entirely different philosophical traditions and schools when they provide practical guidance throughout different aspects of life.

Philosophy, much like science, is nuanced and multifaceted, allowing us to apply its principles flexibly according to different aspects of life.

So far, this discussion addresses only the epistemological frameworks, without touching on the specific methods and theories used.

In the table below is a list of key philosophical schools, their primary authors, seminal works, and the year of publication, all arranged chronologically. There is an overlap here with previous tables in this section, but this one is larger and provides a broad overview of the foundational ideas and thinkers that continue to influence both philosophical and scientific thought (Table 4.12).

Table 4.12

Schools of philosophy

Philosophical school

Main author(s)

Key book

Year of publication

Pre-Socratic philosophy

Heraclitus, Parmenides, Anaximander

Fragmentary texts

Sixth century BCE

Classical philosophy

Socrates (via Plato)

Apology (by Plato)

399 BCE (approx.)

Platonism

Plato

The republic

375 BCE

Aristotelianism

Aristotle

Nicomachean ethics, metaphysics

Fourth century BCE

Epicureanism

Epicurus

Letter to Menoeceus

Third century BCE

Stoicism

Zeno of Citium, Epictetus, Marcus Aurelius

Meditations

Second century CE

Skepticism

Pyrrho of Elis, Sextus Empiricus

Outlines of Pyrrhonism

Second century CE

Neoplatonism

Plotinus

The enneads

Third century CE

Scholasticism

Thomas Aquinas

Summa Theologica

1274 CE (posthumous)

Renaissance humanism

Erasmus, Pico della Mirandola

Oration on the dignity of man

1486 CE

Rationalism

René Descartes

Meditations on first philosophy

1641 CE

Empiricism

John Locke

An essay concerning human understanding

1689 CE

Transcendental idealism

Immanuel Kant

Critique of pure reason

1781 CE

Idealism

George Berkeley, Immanuel Kant

Critique of pure reason (Kant)

1781 CE

Utilitarianism

Jeremy Bentham, John Stuart mill

Utilitarianism

1863 CE

Positivism

Auguste Comte

The course in positive philosophy

1830–1842 CE

Dialectical materialism

Karl Marx, Friedrich Engels

Das Kapital

1867 CE

Pragmatism

Charles Sanders Peirce, John Dewey

How we think (Dewey)

1910 CE

Phenomenology

Edmund Husserl

Logical investigations

1900–1901 CE

Analytic philosophy

Ludwig Wittgenstein

Tractatus Logico-Philosophicus

1921 CE

Existentialism

Jean-Paul Sartre, Martin Heidegger

Being and nothingness (Sartre)

1943 CE

Logical positivism

Rudolf Carnap, Vienna circle

The logical structure of the world

1928 CE

Critical theory

Max Horkheimer, Theodor Adorno, Jürgen Habermas

Dialectic of enlightenment

1947 CE

Structuralism

Claude Lévi-Strauss

The elementary structures of kinship

1949 CE

Hermeneutics

Hans-Georg Gadamer

Truth and method

1960 CE

Relativism

Thomas Kuhn

The structure of scientific revolutions

1962 CE

Communicative action theory

Jürgen Habermas

The theory of communicative action

1981 CE

Postmodernism

Michel Foucault, Jean-François Lyotard

The postmodern condition (Lyotard)

1979 CE

There is a notable overlap between schools of philosophy and scientific perspectives, as many philosophical traditions have influenced how science is understood, practiced, and justified. For example, empiricism and rationalism offer distinct scientific perspectives—empiricism, championed by thinkers like John Locke, emphasizes observation and experimentation as the basis for knowledge, while rationalism, led by Descartes, prioritizes logic and reasoning. Positivism, introduced by Auguste Comte, directly shaped the scientific method by advocating for knowledge derived from observable phenomena and structured experimentation.

Not all philosophical schools propose direct scientific perspectives. For instance, existentialism, with figures like Jean-Paul Sartre, focuses on human existence, freedom, and meaning, rather than on scientific inquiry. Similarly, postmodernism, particularly through thinkers like Michel Foucault, critiques grand scientific narratives and is skeptical of universal truths, questioning the authority of science itself. Some might see this as a contradiction—a school of thought that produces answers about how it’s impossible to produce definitive answers—but it follows a coherent line of reasoning.

Many efforts that initially seem unproductive in science can yield groundbreaking results, often guided by philosophical perspectives. For instance, the positivist approach emphasizes empirical data collection, which may not immediately lead to major discoveries but can pave the way for future breakthroughs, as seen in early quantum mechanics experiments. Kant’s transcendental idealism suggests that our conceptual frameworks shape how we perceive reality, and this abstract idea influenced revolutionary scientific advances like Einstein’s theory of relativity, which had far-reaching practical applications. Similarly, the Frankfurt School’s critical theory initially focused on theoretical critiques of society and media, but later these ideas were key to understanding the power of propaganda. Pragmatism, which values practical outcomes, also reflects how research, like the development of lasers, seemed pointless at first but later transformed industries like medicine and telecommunications.

In phenomenology, early studies of human consciousness seemed detached from hard science, yet they later influenced fields like cognitive psychology and neuroscience, showing how subjective experiences impact brain function. These examples illustrate that seemingly abstract or unproductive research can contribute significantly to future advancements. Often, these efforts are guided by philosophical schools that encourage exploration beyond immediate practical results. This underscores the importance of long-term thinking and open-mindedness in scientific inquiry, where initial perceptions of “unproductiveness” can be misleading. Philosophical perspectives often provide the groundwork for the persistence and creativity necessary to make scientific discoveries that might only be appreciated long after their initial conception.

The reason some schools align with scientific perspectives while others do not may lie in their foundational focus. Epistemology-focused schools like rationalism, empiricism, and positivism naturally lead to frameworks for scientific inquiry. In contrast, schools focused on ethics, human nature, or metaphysical questions (e.g., existentialism, hermeneutics) are less concerned with how science operates, making them less likely to develop their own scientific perspectives. However, every school of thought, in its own way, contributes to discussions about the role of knowledge and truth in human understanding.

The problem, as I see it, is not that scholars pursue varied directions but that today’s PhD students are often unprepared to engage deeply with these discussions, largely because the philosophy of science is not a prominent part of their training when preparing for their theses. However, this is an issue that can be addressed. Although this book cannot delve into the intricacies of each philosophy of science, it aims to provide an overview. I strongly recommend that students explore this line of thinking, whether through university courses or self-study (Table 4.13).

Table 4.13

Suggested reading for self-studies in the philosophy of science

Book title (English translation)

Author

Year of publication

Description

Der logische Aufbau der welt (the logical structure of the world)

Rudolf Carnap

1928

Carnap’s foundational work on logical positivism, constructing scientific concepts from basic experiences

Logik der Forschung (the logic of scientific discovery)

Karl popper

1934

Introduces popper’s ideas on falsification as a criterion for scientific theory, challenging verificationism

Introduction à la métaphysique (introduction to metaphysics)

Henri Bergson

1903

Examines intuition versus scientific knowledge, bridging metaphysics and empirical science understanding

Le Normal et le pathologique (the Normal and the pathological)

Georges Canguilhem

1943

A critical analysis of scientific norms, especially in biology, questioning the definitions of health and pathology

The structure of scientific revolutions

Thomas Kuhn

1962

Kuhn’s influential theory on paradigm shifts in science, arguing that scientific progress is nonlinear

An enquiry concerning human understanding

David Hume

1748

A classic exploration of empirical knowledge, questioning causation and the limits of human understanding

Philosophical investigations

Ludwig Wittgenstein

1953

Discusses the nature of language and meaning, shaping modern analytic philosophy and theories of science

These are some examples of classics; there are also compilations that can be a good place to start for those interested in exploring a broad range of topics in the philosophy of science. Below are a few well-regarded compilations (Table 4.14):

Table 4.14

Examples of compilations on the philosophy of science

Book title

Editor(s)

Year of publication

Description

Readings in the philosophy of science

Theodore Schick

2000

A comprehensive anthology covering major themes, from scientific methods to the nature of explanation

Philosophy of science: The central issues

Martin Curd & J.A. Cover

1998

Contains classic and contemporary essays on key debates, from scientific realism to the role of models in science

The philosophy of science: A very short introduction

Samir Okasha

2002

An accessible overview of fundamental questions in science and philosophy, ideal for beginners

Logical empiricism at its peak: Schlick, Carnap, and Neurath

Moritz Schlick et al., edited by Sahotra Sarkar

1996

A selection of primary texts by key figures of logical empiricism, focusing on their contributions to scientific philosophy

The philosophy of science

Richard Boyd

, Philip gasper, & J.D. Trout

1991

Offers a mix of historical and contemporary readings, organized around major philosophical questions in science

The most effective approach to reading is often to alternate between a well-chosen compilation and the original texts. This approach allows you to gain a broad perspective from the compilation while also exploring the specific details and nuances of the original works, particularly as you begin to find the school of thought that resonates most with your understanding.

The philosophy of science and how to define yourself in it is probably going to be the heaviest intellectual work you will ever take on in your career. Schools of thought, on the other hand, can be seen as more of a shortcut and serve as broad philosophical frameworks, providing general principles that guide our understanding across various fields. They offer foundational perspectives, like empiricism or rationalism, that help us understand knowledge and reality at a high level, making it relatively easy for individuals to engage with.

In contrast, a specific philosophy of science, such as logical positivism or falsificationism, is a more detailed approach within a school of thought, focusing on precise methods and criteria for scientific inquiry and explanation. Adopting a specific philosophy of science often requires a commitment to particular methodologies and a deeper understanding of scientific processes, making it more restrictive and specialized. Therefore, defining oneself by a school of thought is generally easier, while selecting a philosophy of science aligns one with more specific, nuanced views on scientific practice.

It’s clear from this section that we’re engaging with intellectually demanding works, extending far beyond the scientific articles typically expected of PhD students. This raises important questions about the intellectual demands placed on researchers and how these requirements distinguish the profession from other forms of knowledge work.

4.2.4 Why Researchers Engage with the Philosophy of Science

There are many types of professions working with information. Researchers differ from other information-focused professions in that their primary goal is to generate new knowledge and insights, not simply to manage or disseminate existing information. Unlike bureaucrats, who often process information within structured frameworks to support organizational objectives, it is the job of researchers to actively question, analyze, and seek to expand understanding in their fields, or stated otherwise:

A social science researcher, by definition, is an intellectual—at least if they are truly skilled in their craft—though many intellectuals operate outside the realm of research.

The role of a researcher also differs from that of an intellectual, who may reflect on and critique society, culture, or knowledge but may not necessarily engage in systematic investigation or empirical study. Researchers, particularly in the sciences and social sciences, have to follow rigorous methodologies and contribute to building a cumulative body of knowledge, often with an emphasis on reproducibility and peer review. This dedication to inquiry, critical analysis, and evidence-based conclusions sets researchers apart from other professions handling information.

On the other hand, the consequences of disseminating false information are often more immediate and severe in other professions. For instance, journalists, lawyers, or corporate employees who provide inaccurate or misleading information may face dismissal, legal repercussions, or reputational damage. In contrast, researchers, despite being expected to adhere to the highest standards of truth and rigor, rarely face such swift accountability unless ethical violations or blatant misconduct are proven. This discrepancy raises an interesting paradox: While researchers are tasked with upholding rigorous standards of truth, the practical consequences of failing to meet those standards are often less direct (Table 4.15).

Table 4.15

Examples of professions working intensively with information

Profession

Definition

Primary focus

Methods and approach

Key differences

Researchers

Professionals dedicated to systematic investigation and knowledge creation, often in academia or R&D

Generating new knowledge

Use scientific methods, experiments, data analysis, and peer review

Aim to produce original, validated insights that contribute to a cumulative body of knowledge

Journalists

Individuals who gather, verify, and report news and information to the public through various media

Reporting and informing the public

Conduct interviews, verify facts, investigate sources

Focus on timely and accurate reporting of current events, not on original research or theoretical development

Intellectuals

Thinkers who analyze and critique societal, cultural, or philosophical issues, often through writing

Reflecting and critiquing society

Engage in critical thinking, essays, public speaking

Prioritize broader cultural or philosophical discourse over systematic empirical study; may not produce primary research

Fiction authors

Writers who create narratives and stories primarily for entertainment and cultural expression

Storytelling and cultural expression

Use imagination, narrative structure, character development

Focus on creativity and artistic expression rather than fact-based analysis or informational accuracy

Financial analysts

Professionals who analyze financial data to make investment recommendations or economic forecasts

Providing investment insights

Analyze economic trends, financial statements, market data

Primarily aim to interpret and predict financial trends; focus is on data-driven analysis rather than new knowledge creation

Librarians/archivists

Information specialists who manage, organize, and preserve information resources

Information management and preservation

Cataloging, indexing, archiving, and digital preservation

Emphasis is on organizing and maintaining access to information rather than producing new insights

Bureaucrats

Public sector employees who process, manage, and enforce policies and regulations within organizations

Implementing policies and regulations

Document processing, policy interpretation, adherence to formal procedures

Focus on executing existing policies and procedures rather than generating or analyzing new information

Data analysts

Professionals who interpret large datasets to derive actionable insights, often in business contexts

Extracting insights from data

Use statistical tools, data mining, machine learning

Aim to interpret data patterns for practical applications, not necessarily to create theoretical or exploratory knowledge

Educators/teachers

Individuals who disseminate knowledge, educate, and inspire students across various fields

Teaching and knowledge dissemination

Lectures, curriculum development, mentorship

Focus on transmitting existing knowledge and skills, with less emphasis on producing original information or insights

Today, many professions involve working with information, a shift from a generation ago when fewer roles required such focus. We live in the Information Age, where access to and management of data are integral to almost every field. However, some professions—such as researchers, analysts, and journalists—engage with information far more intensively, requiring deeper analysis and synthesis than others.

Typically, only researchers engage deeply with the philosophy of science, as their work often involves understanding the foundational principles behind knowledge creation, methodology, and validity within their field. Researchers are also expected to define their approach in terms of established schools of thought, such as positivism, interpretivism, or critical theory, as these influence their methods, interpretations, and contributions to their disciplines.

Other groups, like intellectuals or educators, may occasionally engage with philosophy, especially if they operate within academic or highly reflective environments, but they are not generally required to adopt a formal stance on the philosophy of science. Intellectuals, especially those discussing societal, cultural, or philosophical topics, might explore philosophical frameworks informally but are not bound by the systematic rigor expected of researchers. This, however, does not necessarily imply that what intellectuals do is less relevant to society. In fact, some might argue that having a class of intellectuals who reflect, critique, and challenge societal norms is even more vital than the specific contributions made by social scientists.

Case: The Roles of Public Intellectuals and Social Scientists in Society

Many countries around the world have a strong intellectual culture, both historically and in contemporary times. For instance, Germany has long been recognized for its intellectual rigor, producing influential thinkers that we have already visited in this book, such as Immanuel Kant, Georg Wilhelm Friedrich Hegel, and Max Weber, with universities serving as global centers of learning. Similarly, China’s intellectual tradition, grounded in Confucian scholarship, emphasizes ethics and governance, while India’s ancient contributions to philosophy and science include figures like Aryabhata and Shankara. Ancient Greece remains foundational to Western intellectual traditions with luminaries like Socrates, Plato, and Aristotle, and Italy’s Renaissance era saw a fusion of art, science, and philosophy through figures like Galileo and Leonardo da Vinci. The Islamic Golden Age advanced science and philosophy through scholars like Avicenna and Averroes, while Japan and Russia have each cultivated unique intellectual cultures blending tradition with modernity.

Yet, when one thinks of intellectual culture, France is often the first country that comes to my mind. France has a deep tradition of intellectual engagement, with a culture of reading, writing, and debating ideas about society. This legacy is embodied by public intellectuals such as Michel Foucault, Pierre Bourdieu, Jacques Derrida, and Alain Finkielkraut. Literary and philosophical discussions are further popularized by figures like Bernard Pivot, whose television programs Apostrophes and Bouillon de Culture brought intellectual discourse into French homes. Most French intellectuals hold academic positions, many as philosophers or sociologists, but the country also boasts a vibrant tradition of nonacademic intellectuals.

These nonacademic figures include Jacques Attali, a prolific writer, economist, and advisor to French presidents, and Bernard-Henri Lévy, a philosopher and activist known for his presence in public debates. Emmanuel Carrère blends literature with philosophical reflection in works like The Kingdom, while Frédéric Beigbeder offers sharp cultural commentary as a novelist and critic. Michel Onfray, a philosopher who founded his own Université Populaire, operates independently of academia, and Pascal Bruckner critiques culture and politics through his essays and novels. Controversial voices like Éric Zemmour and Alain Soral bring debates on French identity and society to the forefront, albeit with polarizing perspectives.

Other influential figures include Raphaël Enthoven, known for his public lectures and media appearances, and Élisabeth Lévy, a journalist prominent in cultural debates. Diplomat and activist Stéphane Hessel inspired global movements with his pamphlet Time for Outrage!, while Jean-Christophe Rufin, a member of the Académie Française, blends diplomacy and literary accomplishment. Figures like Philippe Sollers and Catherine Millet have shaped intellectual and artistic discourse through literature and criticism, and individuals like Pierre Bergé and Alain Minc extend intellectual influence into business and politics.

French intellectuals stand out, in my opinion, because of their deep engagement with philosophy, history, and culture, which is central to their intellectual identity. Unlike many other cultures, French intellectuals are not only typically well read in the classics of Western thought, allowing them to situate contemporary ideas in broader historical and philosophical contexts, but they are also willing to engage in these discussions and take pleasure in them. The intellectual culture in France is deeply ingrained in its society, where the act of reading, writing, and debating is seen as a way of life, contributing to the shaping of national discourse. As a result, these intellectuals are not only scholars but also influential figures in public life, guiding societal thought and cultural discussions.

While other countries certainly have their own intellectual traditions, the role of nonacademic intellectuals is often quite different. In many cases, these figures may become closely tied to the interests of political or economic elites, effectively serving as mouthpieces for those in power rather than offering independent critiques or innovative ideas. This dynamic can limit the influence and integrity of intellectual discourse, reducing it to mere echo chambers for established power structures. In contrast, in countries like France, intellectuals—both academic and nonacademic—often maintain a more independent stance, using their platforms to challenge prevailing norms and engage with broader societal issues. The distinction lies in the degree of autonomy and critical engagement that intellectuals in these other nations possess, with many falling short of the robust, self-critical tradition seen in places like France.

Here’s a set of follow-up questions for the reader:

Role Comparison:

1.

Is it important for society to have social scientists, or could a group of public intellectuals suffice? (It certainly would be less expensive).

2.

What unique contributions do social scientists provide that public intellectuals might not?

Value to Society:

3.

Are social scientists more valuable to society than public intellectuals? Why or why not?

4.

In what ways do the roles of social scientists and public intellectuals complement or conflict with each other?

5.

Should society invest more in training public intellectuals or in developing more specialized social scientists? How would that work?

Building an Intellectual Culture:

6.

What conditions are necessary to foster a strong public intellectual class?

7.

Is it solely a matter of having enough universities and a large population, or are other factors like cultural attitudes, media presence, and freedom of expression equally important?

8.

Can a country with limited resources still cultivate a thriving intellectual culture? If so, how?

Social Impact:

9.

How do public intellectuals influence societal change compared to social scientists?

10.

In which areas (e.g., policy, public understanding, culture) are public intellectuals likely to have more impact than social scientists, and vice versa?

11.

Could a balance between the two groups better serve societal needs, and if so, how should such a balance be achieved?

These questions are designed to encourage you to critically examine the roles of social scientists and public intellectuals, considering their respective impacts and significance in society. While I have my own opinions on these matters, I am eager to hear your perspectives. Additionally, I am particularly interested in understanding how you approach these questions—how you evaluate the arguments, the weight you assign to each perspective, and the reasoning behind your conclusions.

Most information workers typically do not engage with the philosophy of science, as their roles are more practice-oriented, focusing on applying information rather than questioning the nature of knowledge or situating their work within theoretical frameworks. For researchers, however, the philosophy of science remains foundational, as it shapes the principles and methodologies that underpin their work.

That said, it is increasingly possible for researchers to build careers by operating within established frameworks rather than pursuing original thought, and some choose this path. These academics often take on more administrative roles, producing papers that resemble reports, attending meetings, and serving on committees, without necessarily pushing the boundaries of knowledge. In smaller colleges, this group—comprising teachers, administrators, and non-researching researchers—often constitutes the large majority of staff. They are neither active researchers nor public intellectuals but are more akin to bureaucrats operating within the academic system.⁶

It could be argued that society would benefit more from systems that encourage originality and set higher standards, ensuring that research remains both productive and impactful. However, achieving this is not primarily about increasing the number of academics; evidence suggests the opposite—that reducing the number of social scientists and making the profession more exclusive could lead to higher quality and better outcomes.⁷ This exclusivity would not serve as an end in itself but as a means to raise standards, ensuring that students and colleagues are equipped to meet the rigorous demands of advancing knowledge.

While the increased accessibility of higher education has brought significant benefits, it has also diluted the depth and value once associated with degrees, which no longer consistently signify profound expertise.⁸

Looking forward, universities may evolve to focus more on guiding students toward independent study, offering resources, and assessing knowledge in more controlled environments, as online platforms increasingly provide access to information. In such a model, universities might shift their role toward certifying competencies and offering structured support rather than traditional teaching. Yet, even in this scenario, guidance will remain essential—providing inspiration, mentorship, and critical personal interactions that steer students in the right direction.

Amid these potential changes, I like to think there will always be a distinct researcher type—someone naturally drawn to and well suited for the social sciences. This researcher is not only intellectually curious but also methodological, disciplined, and, above all, open-minded. Such individuals are willing to admit mistakes and revise their direction when hypotheses prove incorrect. This openness to revision is a defining trait of researchers and a crucial consideration when identifying and recruiting future PhD students.

We do not expect PhD students to emerge as fully formed “super-intellectuals.” Philosophical discussions, particularly within the social sciences, are relatively uncommon. However, it is quite typical and should be expected that researchers can engage in conversations about the schools of thought to which they align. This can be seen as a streamlined or practical version of a deeper philosophical dialogue—a bare minimum expectation that students should consider and articulate, especially when defining the framework for their thesis.

4.3 Academic Schools of Thought

All social science disciplines have distinct schools of thought that shape their approaches to research and theory. In psychology, for instance, behaviorism and cognitivism represent different frameworks, with behaviorism focusing on observable actions and cognitivism exploring mental processes. Sociology includes theories like structural functionalism, which views society as a system of interdependent parts, and conflict theory, which examines power dynamics and inequality. Anthropology offers cultural relativism, emphasizing that cultures should be understood within their own context, as well as structuralism, which seeks underlying patterns in cultural practices. In political science, realism and liberalism are prominent schools in international relations, focusing respectively on state power and cooperation. Finally, communication studies include semiotics, analyzing how signs and symbols convey meaning, and media effects theory, examining how media influences society. Each discipline brings unique perspectives, contributing to a broad spectrum of social science knowledge. I shall use the only area I know well here, which is from economics and business studies (Table 4.16):

Table 4.16

Schools of thought in the example of economics

Umbrella term

School of thought

Main authors

Belief about the best way to find truth

Mainstream economics

Neoclassical economics

Adam smith, Alfred Marshall, Paul Samuelson

Focus on mathematical modeling, marginalism, and rational choice theory to predict and understand economic behavior

New Keynesian economics

John Maynard Keynes, Paul Krugman

Emphasizes market imperfections and how government intervention can stabilize the economy through fiscal and monetary policy

Monetarism

Milton Friedman

Advocates for controlling the money supply as the primary method of regulating economic activity and preventing inflation

New classical economics

Robert Lucas, Thomas Sargent

Focuses on rational expectations, market efficiency, and the idea that markets are clear without the need for government policy

Heterodox economics

Marxist economics

Karl Marx, Friedrich Engels, Rosa Luxemburg

Seeks truth through historical materialism, understanding class struggle and economic relationships as key to social change

Post-Keynesian economics

Joan Robinson, Hyman Minsky

Emphasizes uncertainty and demand-driven markets and rejects the idea that economies naturally reach equilibrium

Evolutionary economics

Thorstein Veblen, Joseph Schumpeter

Believes economic systems evolve over time through innovation, competition, and institutional change, rather than equilibrium

Austrian economics

Ludwig von Mises, Friedrich Hayek, Carl Menger

Advocates subjective value theory and methodological individualism, seeking truth through deductive reasoning from first principles

Feminist economics

Nancy Folbre, Amartya Sen

Seeks to uncover gender biases in traditional economics, emphasizing social equity and human Well-being as economic measures

Ecological economics

Herman Daly, Nicholas Georgescu-Roegen

Emphasizes the economy as a subsystem of the environment, focusing on sustainability and long-term ecological balance

Keynesian economics

Keynesian economics

John Maynard Keynes, Alvin Hansen

Focuses on aggregate demand as the driving force of the economy, using government policy to manage demand and stabilize growth

New Keynesian economics

Greg Mankiw, Olivier Blanchard

Combines Keynesian economics with microeconomic foundations like price stickiness and market imperfections

Classical economics

Classical economics

Adam smith, David Ricardo, John Stuart mill

Truth is found through free markets, supply and demand, and minimal government intervention in the economy

Behavioral economics

Behavioral economics

Daniel Kahneman, Richard Thaler

Seeks to understand how cognitive biases and heuristics affect economic decisions, challenging the notion of full rationality

Institutional economics

Old institutional economics

Thorstein Veblen, John R. Commons

Focuses on how social institutions shape economic behavior, seeing institutions as key to understanding economic outcomes

New institutional economics

Ronald Coase, Douglass north

Emphasizes transaction costs, property rights, and institutions as central to economic efficiency and long-term growth

Development economics

Structuralist economics

Raúl Prebisch, Celso Furtado

Argues that developing economies require structural change and government intervention to achieve sustainable development

Dependency theory

André Gunder frank, Samir Amin

Believes that global economic systems perpetuate inequality between developed and developing nations, emphasizing exploitation

Pragmatism

Pragmatism in economics

John Dewey, Richard Rorty

Truth is found through practical experience and experimentation, using what works in real-world conditions to guide policies

We often hear these terms in the news when economic questions are discussed or presented: They may say that someone is “a monetarist” or “a Keynesian.” For most listeners, this doesn’t mean much, as it is rarely well explained to the audience. Fundamentally, however, we can say that these are different ways of understanding economic phenomena based on differences in the philosophy of science.

The same table with authors can be expanded in much greater detail. For a comprehensive overview, the New School’s webpage on schools of economic thought, available at https://​www.​hetwebsite.​net/​het/​thought.​htm, is a go-to place.

Note: The HET Website

The History of Economic Thought (HET) website was created by Gonçalo L. Fonseca, who grew up in Zambia. At 18, he moved to New York to attend the New School, initially focusing on African studies with an emphasis on politics and history. In 1998, driven by a passion for the history of economic ideas, he began the HET website, which was initially hosted on the New School’s Department of Economics server.

I recall it as one of the first truly valuable resources on the early Internet, a time when options were still limited mainly to shopping sites and IMDb’s movie database, a long time before music and movies became popular online. Over the years, the site has been revised, expanded, and eventually moved to its current address, providing one of the most detailed resources available on the subject (Fig. 4.5).

Fig. 4.5

Schools of thought

Under schools, we find five groups of schools on this webpage: schools of political economy, neoclassical schools, alternative schools, and thematic schools. Under each group of schools, we find a number of other schools (Fig. 4.6).

Fig. 4.6

Schools of political economy

The term “school” can function as both a specific designation and an umbrella term. Therefore, both “Schools of Political Economy” and “School of Political Economy” are appropriate, depending on context, with “schools” often referring to multiple approaches within the broader field.

Approaches that do not fall under political economy or neoclassical economics are generally considered “alternative schools” when they aren’t organized around a specific theme. For example, what was once a dominant approach, such as the German Historical School 150 years ago, is now categorized as an “alternative” school. This shift reflects how certain theories evolve in relation to the mainstream, rather than diminishing their value or impact in the broader field.

While I don’t agree with all of the categorizations presented here, having a structured framework is certainly preferable to having none at all. For instance, some might also question the use of the term “classics” for a school that wasn’t the first dominant school; that title belongs to the Physiocrats, one could argue. Similarly, “neo-classics” is a somewhat ambiguous term, especially given the significant differences between the economists represented within each of these groups. However, this discussion extends beyond the scope of this book.⁹ The main point is that researchers with similar perspectives on certain issues often form schools—or, as they’re sometimes called when they become more protective than open, “tribes”—each with its own associations, conferences, and journals. These schools of thought are helpful for quickly distinguishing between varied methodological approaches, providing a structure that allows researchers to navigate the field more effectively and recognize where their work may align or diverge from established traditions.

Creating a similar table for all of the social sciences would indeed be extensive. We must remember that the research world is vast, with an estimated 1.5 to two million social scientists worldwide across fields such as economics, sociology, political science, and psychology. These researchers contribute to thousands of academic journals, with current estimates suggesting over 20,000 active social science journals globally. Each year, hundreds of international and regional conferences provide platforms for research presentation, networking, and discussion, and it’s estimated that social scientists publish around 1 to 1.5 million papers annually.

Given the scope of this output and the diversity within each discipline, it’s likely there are hundreds of distinct schools of thought in the social sciences. Major disciplines typically contain several dominant schools (e.g., behavioral economics, institutional economics, or symbolic interactionism in sociology), and subfields often generate additional, specialized schools. Broadly speaking, each major discipline could easily encompass 10–20 identifiable schools of thought, leading to an estimated 200–300 schools across the social sciences globally. However, considering economics alone has around 70 distinct schools of thought, as documented by the History of Economic Thought website, this estimate is probably conservative.

The table below presents a comparison of selected schools of thought across the social sciences, not limited to economics, as discussed earlier (Table 4.17).

Table 4.17

A selection of schools of thinking in the social sciences

Discipline

Major school of thought

Key authors

How it attempts to find truth

Economics

Classical economics

Adam smith, David Ricardo

Focuses on natural laws of supply and demand. Truth is derived through rationality, self-interest, and competition, often using deductive reasoning and models to explain how markets regulate themselves

Keynesian economics

John Maynard Keynes

Seeks truth through understanding aggregate demand and its role in influencing output and inflation. Emphasizes government intervention and policy analysis, using empirical data and case studies

Neoclassical economics

Alfred Marshall, Milton Friedman

Truth is derived from marginal analysis and equilibrium concepts. Uses mathematical models and statistical methods to predict economic behavior based on individual preferences and utility maximization

Behavioral economics

Daniel Kahneman, Richard Thaler

Challenges the rational actor model, focusing on psychological insights and human biases. Truth is sought through experimental data, surveys, and behavioral studies that explore decision-making

Marxist economics

Karl Marx, Friedrich Engels

Truth is derived from analyzing historical materialism and class struggles. Economic change is explained through dialectical methods, focusing on the relations of production and exploitation

Evolutionary economics

Thorstein Veblen, Joseph Schumpeter

Truth is found by studying the dynamic processes of change in economic systems, emphasizing innovation, entrepreneurship, and technological evolution. Uses historical analysis and case studies

Sociology

Structural functionalism

Émile Durkheim, Talcott parsons

Seeks truth by analyzing the function of social institutions and their role in maintaining societal stability. Focuses on empirical observation and statistical analysis to identify social norms and values

Conflict theory

Karl Marx, C. Wright Mills

Truth is derived from studying power dynamics, inequalities, and class struggles. Uses historical materialism, qualitative data, and critical analysis to uncover how power relations shape society

Symbolic interactionism

George Herbert Mead, Erving Goffman

Seeks truth through the study of everyday interactions and the meanings individuals assign to symbols. Relies on qualitative methods like ethnography, participant observation, and interviews

Critical feminist theory

Judith Butler, bell hooks/Gloria Jean Watkins

Truth is sought by analyzing gender relations, patriarchy, and systemic inequalities. Uses critical theory, discourse analysis, and intersectional approaches to challenge traditional structures

Postmodernism

Michel Foucault, Jean Baudrillard

Rejects the notion of absolute truth, focusing on power, discourse, and social construction of knowledge. Uses deconstruction and genealogical analysis to challenge dominant narratives

Political science

Realism

Hans Morgenthau, Kenneth waltz

Seeks truth through understanding state behavior in an anarchic international system. Uses historical case studies and power analysis to explain conflict, alliances, and diplomacy

Liberalism

Immanuel Kant, John Locke

Truth is found through the study of cooperation, international institutions, and the role of democracy in promoting peace. Relies on qualitative case studies, theoretical models, and comparative politics

Constructivism

Alexander Wendt, Peter Katzenstein

Seeks truth by examining how ideas, identities, and norms shape state behavior. Uses qualitative research, discourse analysis, and historical case studies to understand the role of social constructs in politics

Institutionalism

Douglass north, Elinor Ostrom

Truth is sought through studying how institutions shape political and economic behavior. Uses comparative case studies, historical institutional analysis, and empirical data to understand institutional development

Psychology

Behaviorism

B.F. Skinner, John Watson

Seeks truth through the study of observable behavior and reinforcement. Relies on experimental data and controlled studies to establish cause and effect in human and animal behavior

Cognitive psychology

Jean Piaget, Aaron Beck

Seeks truth by studying mental processes like memory, perception, and problem-solving. Uses controlled experiments, cognitive tests, and brain imaging to understand cognition

Psychoanalysis

Sigmund Freud, Carl Jung

Truth is derived from uncovering unconscious motives and early childhood experiences. Relies on case studies, introspection, and therapeutic dialogue to interpret dreams, symbols, and behaviors

Humanistic psychology

Carl Rogers, Abraham Maslow

Seeks truth by focusing on individual potential and self-actualization. Uses qualitative methods, client-centered therapy, and case studies to explore personal growth and fulfillment

Evolutionary psychology

Leda Cosmides, John Tooby

Seeks truth by studying how evolutionary forces shape human behavior and cognitive processes. Uses comparative data, experiments, and cross-cultural studies to explain adaptive behaviors

Anthropology

Structuralism

Claude Lévi-Strauss, Edmund leach

Seeks truth by analyzing deep structures in human culture, language, and mythology. Relies on cross-cultural comparisons and ethnographic studies to uncover universal patterns

Cultural materialism

Marvin Harris, Leslie white

Truth is derived from examining material conditions and economic factors that shape cultural practices. Uses ethnography, historical analysis, and cross-cultural studies to understand cultural evolution

Postcolonialism

Edward said, Homi K. Bhabha

Seeks truth by analyzing the impact of colonialism on culture and identity. Uses critical theory, literary analysis, and ethnographic research to understand power relations between the colonizer and colonized

Historya

Annales school

Fernand Braudel, Marc Bloch

Seeks truth by examining long-term historical structures, geography, and social trends. Uses interdisciplinary methods, combining history with geography, economics, and sociology to uncover patterns in history

Marxist historiography

Eric Hobsbawm, E.P. Thompson

Truth is found by analyzing historical materialism, class struggles, and the economic base shaping historical events. Relies on archival research and critical theory to understand history from the perspective of the working class

Linguistics

Generative grammar

Noam Chomsky

Seeks truth by studying the innate structures of the human mind that govern language acquisition and production. Uses formal analysis and theoretical modeling to explain language universals

Sociolinguistics

William Labov, Dell Hymes

Truth is derived from studying how social factors such as class, ethnicity, and gender influence language use. Uses surveys, fieldwork, and sociological data to examine language variation

Structuralism

Ferdinand de Saussure

Seeks truth by analyzing the structure of language, focusing on the relationships between signs and meanings. Relies on linguistic analysis, structural models, and comparative studies

aIf we were to include all the classics in history that I believe a social scientist should read, we would need to add many more pages—so let’s save that for another time. Suffice it to say that in this book, we are primarily interested in the history of ideas as they pertain to science

The table could be made more specific, but it presents a good initial collection of schools of thought in the social sciences.

Exercise: Finding Your School of Thought

Imagine you are beginning a new research project in your field, but before diving in, you’re asked to reflect on your own philosophical orientation. Are you someone who sees truth in universal principles and objective structures, like a structuralist in sociology or linguistics? Or do you lean toward understanding knowledge as shaped by social contexts and power dynamics, as with constructivism or postcolonial theory?

When starting a research project, it’s often wise to avoid committing too firmly to a specific school of thought early on. This approach can allow you to remain open to new ideas and adjust your perspective as you gather more experience and data. Over time, you may find that certain methods or theories resonate more with your findings, while others may prove less useful. A hallmark of a good social scientist—or any scientist—is the ability to change their views when new evidence emerges. While it may be shocking for established professors to declare, “I was wrong,” acknowledging growth and adaptation in response to new facts is ultimately a positive trait, encouraging intellectual humility and progress.

Consider the following questions to help you pinpoint your perspective:

1.

Which of these schools of thought resonates most with how you approach research? Think about whether you prefer objective data and empirical models, like a positivist, or if you are drawn to subjective meanings and interpretations, like a symbolic interactionist.

2.

Do you believe knowledge is primarily objective and universal, or do you view it as context-dependent and socially constructed? For instance, how might a Marxist economist approach research differently from a neoclassical economist?

3.

What is the role of theory in your research? Do you use it to explain behavior across various contexts, or do you focus on specific case studies that reveal unique insights about a single context?

4.

Which authors or researchers in your discipline have influenced you the most and why? Reflect on whether they align with particular schools of thought in your field.

5.

Consider a research problem you’re interested in. How might different schools of thought approach this problem? For example, how might an evolutionary psychologist differ from a behaviorist in studying human motivation?

Discuss these questions and identify whether there’s a school of thought that aligns with your own approach—or if, like many researchers, you draw from multiple perspectives. Reflect on whether these perspectives might evolve as you progress in your research journey.

Reflect also on whether you genuinely engaged with the strengths and weaknesses of different perspectives (rational-evaluative approach) or simply chose a method that looked good, aligned with your existing beliefs, or felt familiar (intuitive-selective approach). Did you take the time to systematically compare and weigh the arguments for each approach, or did convenience and intuition play a bigger role than you might admit?

4.3.1 Mapping Knowledge Frameworks: From Disciplines to Philosophy

In the previous sections, we outlined a logical process that begins with our interests and the books we read, eventually leading us to choose a discipline to study and reflect on the philosophy of science. If we read up, we may arrive at an understanding of which philosophy of science resonates with us and choose to adopt it for ourselves.

In practice, however, researchers more often begin within a specific discipline, where the institution and advisor they work with may already adhere to a particular school of thought. The researcher is then encouraged to continue in that direction, producing research aligned with that tradition. It is often only later that they come to realize which philosophy of science underpins their work.

This introduction describes two ways researchers might come to define their own philosophy of science: the independent discovery method and the institutional path method. The independent discovery method is idealized because it allows researchers to explore their own interests, read widely, and reflect deeply before choosing a philosophy of science that resonates with their views. This path emphasizes autonomy, where researchers exercise free will in selecting a framework that feels most logical and meaningful to them. It involves a process of personal discovery and intellectual freedom, where the researcher consciously decides what they believe about the nature of knowledge, scientific methods, and truth.

In contrast, the institutional path method is more common in practice and involves less initial freedom of choice. Researchers following this path often begin their work within a particular discipline or at an institution that already aligns with a specific school of thought. Their supervisors or advisors, who guide their research process, may have an established philosophy that subtly (or explicitly) sets the direction for the researcher. As a result, the researcher’s work begins to reflect the values, methodologies, and assumptions of that existing school of thought. In this context, the researcher may not consciously choose their philosophy but rather absorb it as part of the training process.

Over time, researchers following the institutional path often develop a clearer understanding of the philosophical framework they have been working within, recognizing the assumptions and beliefs that underpin their work. Like a child raised within a specific religion, they may later choose to embrace, refine, or even break away from this inherited philosophy. Similarly, those not raised within a particular framework may spend more time exploring various perspectives—a process that can be enriching but also frustrating and time-consuming. While the institutional path may initially limit autonomy, it offers a structured and supportive environment that can be invaluable for early-career researchers. Ultimately, both approaches can lead to meaningful insights and professional growth, though they provide different levels of freedom in shaping a researcher’s philosophical trajectory.

Consider a topic in sociology, such as social inequality. Different approaches to this issue may lead to three distinct schools of thought: structural functionalism, conflict theory, and symbolic interactionism. Each school not only interprets social inequality differently but also employs unique methods that reflect its underlying philosophical perspective. Below, we’ll explore these schools of thought, provide examples of two methods within each, and describe both the common philosophical perspective and an alternative philosophy of science unique to each school.

1.

Structural Functionalism views social inequality as a functional component of society, contributing to stability by motivating individuals to fulfill essential roles. Two common methods within this school are social role analysis and quantitative surveys. Social role analysis examines how various social roles contribute to the stability and functioning of society, while quantitative surveys are used to assess the distribution of resources and opportunities among different social classes. Both methods share a positivist perspective: they assume that social phenomena can be measured objectively, much like natural sciences, and that social roles are observable elements contributing to the stability of society. This school, however, could also be studied through a systems theory perspective in the philosophy of science, which views society as an interconnected system where each part influences the others and must be understood within the context of the whole.

2.

Conflict Theory interprets social inequality as a result of competition over resources and power struggles between different social groups. Two methods frequently used here are historical analysis and case studies of specific marginalized communities. Historical analysis helps identify how inequalities developed and persisted over time, while case studies provide in-depth insights into specific instances of class or racial conflict. Both methods are rooted in a critical realist perspective, which acknowledges underlying structures and power dynamics that shape social reality beyond immediate appearances. However, an alternative approach within conflict theory could adopt a critical theory perspective in philosophy, emphasizing the importance of challenging dominant ideologies and empowering marginalized groups to overcome social oppression.

3.

Symbolic Interactionism focuses on how individuals interpret and negotiate social inequality through everyday interactions and symbols. Common methods in this school include ethnography and in-depth interviews, both of which aim to understand individuals’ lived experiences and perceptions. These methods share a phenomenological perspective, which centers on the subjective meanings people attach to their social world, emphasizing the role of individual agency in constructing reality. In contrast, symbolic interactionism could also incorporate a social constructionist perspective, viewing social inequality not as an objective fact but as a product of shared meanings and social interactions that shape individuals’ experiences and understandings over time.

Each of these schools—structural functionalism, conflict theory, and symbolic interactionism—offers distinct philosophical and methodological approaches to studying social inequality, reflecting the diversity of thought within sociology. Structural functionalism and its systems theory approach view society holistically, conflict theory with critical realism aims to unveil hidden structures, and symbolic interactionism with social constructionism highlights the subjective nature of social reality. Together, they illustrate how varied philosophies of science shape sociological inquiry into a single topic from vastly different angles (Fig. 4.7).

Fig. 4.7

Disciplines, schools of thought, scientific perspectives, and philosophical schools

The figures illustrate a process in which each discipline steers researchers toward specific schools of thought, heavily influenced by the institution and the supervisors they work with. Each school of thought encompasses a variety of perspectives, tied to particular scientific methods that align with specific scientific and philosophical frameworks. In this system, a researcher’s school of thought is rarely chosen independently; instead, it is effectively determined when they select their discipline and PhD program. The dominant framework often reflects the preferences of the program head or supervisor, including their favored scientific methods and philosophical perspectives. As a result, PhD students are typically guided to adopt these established approaches rather than charting their own path, making this more of an inherited journey than a personal one.

In many PhD programs, especially in structured fields like economics, students are expected to align with a predetermined research framework from the outset. Those unwilling to adopt this path may struggle to remain in the program. For students with limited prior exposure to these frameworks, this can create a dilemma: They may simply accept the prescribed approach to stay enrolled, leaving little room to challenge or deviate from the institution’s established methodologies. While this structure might seem restrictive, it offers certain advantages. A clearly defined framework provides a solid foundation, streamlines research training, and ensures alignment with the program’s strengths and resources. It also helps students build expertise in proven methodologies, equipping them for careers in academia or industry with a focused and marketable skill set.

However, this model raises significant concerns for critical thinking, scientific progress, and academic growth. First, it can limit critical thinking by discouraging engagement with alternative perspectives or questioning foundational assumptions, potentially stifling independent thought. Second, it may hinder scientific advancement, as students are more likely to replicate established ideas than to innovate, leading to a lack of diverse insights and discoveries. Third, it risks creating intellectual echo chambers within disciplines, where only certain views are encouraged while others are marginalized, restricting the breadth of perspectives represented in the literature.

Despite these drawbacks, the structured model has its merits, particularly in providing mentorship and a coherent research path for young scholars. It fosters continuity within research programs, allowing experienced scholars to effectively guide students within a shared approach. However, this reliance on hierarchical decision-making can stifle creativity, as students may feel discouraged from pursuing novel questions or alternative methods. There are also ethical implications, as students might be pressured to adopt philosophies or methods they don’t fully align with, potentially affecting their motivation and academic integrity. Scientifically, this approach risks prioritizing established theories and methods over newer, potentially groundbreaking approaches.

In contrast, an independent discovery model encourages intellectual diversity, empowering students to challenge norms and introduce fresh ideas—critical components for scientific progress. Yet this model requires a strong foundation of knowledge, which students often lack early in their careers. To address this gap, science education should be emphasized early in doctoral programs or even in the second year of a research master’s program. A robust knowledge base enables young researchers to develop counterarguments, explore alternative methods, and engage deeply with diverse schools of thought. Extensive reading and exposure to foundational ideas are crucial, ideally starting as early as possible, though it is never too late for a curious mind to begin.

Encouraging students to build this foundation has been one of my primary motivations in writing this book. By fostering a broader intellectual base, we can equip future researchers to navigate, question, and ultimately contribute to the advancement of science in more meaningful ways.

4.3.2 Defining Disciplines

Awareness of your school of thought begins with defining your discipline or disciplines. In the social sciences, however, this is often more complex than what it sounds. For instance, if you are interested in economics or business, the question arises: which field do you truly belong to—economics, business, or perhaps both? This question is further complicated by cultural variations, as different countries and languages often have unique terms and divisions for disciplines and sub-disciplines.

In German, for example, terms like Nationalökonomie, Sozialökonomie, and Wirtschaftswissenschaft are commonly used, but the precise distinctions between them can be subtle and context-dependent. Such differences highlight the importance of clarifying disciplinary boundaries to understand the school of thought with which you align (Table 4.18).

Table 4.18

Main disciplines that all study economic factors

German term

Meaning

English equivalent

Scandinavian equivalent

French equivalent

Degree of similarity

Nationalökonomie (Volkswirtschaftslehre)

Focuses on economics at the national or macroeconomic level, often encompassing policy and economic theory

Economics (macroeconomics)

Nationaløkonomi (Danish, Norwegian), Nationalekonomi (Swedish)

Économie nationale

High similarity; concept generally equivalent across languages, emphasizing macroeconomics and national policy

Sozialökonomie

Deals with the social aspects of economics, integrating sociology and economic relations, and often explores how economic activities impact society

Seldom used, social economics or socioeconomics

Samfundsøkonomi (Danish), Samfunnsøkonomi (Norwegian), Samhällsekonomi (Swedish)

Économie sociale

Moderate similarity; generally aligns but with some cultural variations in scope, especially regarding social vs. economic balance

Wirtschaftswissenschaft

Broad term for the science of economic activity, covering both macro- and microeconomics and often business studies

Economics (general) or economic sciences

Økonomi (Danish, Norwegian), Ekonomi (Swedish)

Sciences économiques

High similarity; encompasses a broad understanding of economics, though it may also include aspects of business studies depending on the context

The translations of economic terms across languages can be confusing and inconsistent. For instance, the German term Volkswirtschaftslehre lacks a direct English translation on platforms like Wikipedia and is translated as Samhällsekonomi in Swedish—a term also used for Sozialökonomie, which deals with the social dimensions of economics. Even within the economics field, differentiating between terms like economics (general), economic sciences, and economics (macroeconomics) can be challenging, to say the least.

Moreover, several other disciplines study economic behavior beyond those listed above (Table 4.19):

Table 4.19

Other disciplines that all study economic factors

Discipline

Description

English translation

Scandinavian equivalent

French equivalent

Difference from previous disciplines

Politische Ökonomie

Analyzes the relationship between economics, law, and politics, focusing on how political forces impact economic systems

Political economy

Politisk økonomi

Économie politique

Broader focus on political institutions and policies, integrating legal and governance structures

Entwicklungsökonomie

Studies economic development, particularly in low-income countries, focusing on poverty and growth

Development economics

Udviklingsøkonomi (Danish), Utviklingsøkonomi (Norwegian), Utvecklingsekonomi (Swedish)

Économie du développement

Focuses on growth and poverty in specific regions, distinct from general economic theories

Verhaltensökonomie

Combines psychology with economic theory to study decision-making processes and behavior

Behavioral economics

Adfærdsøkonomi (Danish), Atferdsøkonomi (Norwegian), Beteendekonomi (Swedish)

Économie comportementale

Emphasizes psychological influences on economics, diverging from traditional rational-choice models

Wirtschaftssoziologie

Examines the social networks, norms, and institutions that shape economic behaviors and outcomes

Economic sociology

Økonomisk sociologi

Sociologie économique

Focuses on social structures’ impact on economics, differing from purely economic analysis

Umweltökonomie

Studies the economic impact of environmental policies and natural resource use

Environmental economics

Miljøøkonomi (Danish, Norwegian), Miljöekonomi (Swedish)

Économie de l’environnement

Integrates environmental concerns with economics, a distinct focus from classic economic structures

Finanzwissenschaft

Studies financial systems, investments, markets, and corporate finance, focusing on risk and assets

Finance

Finans

Finance

Specializes in financial markets and asset management, less emphasis on broader economic systems

Wirtschaftsgeographie

Explores spatial aspects of economic activity, including resource distribution and regional growth

Economic geography

Økonomisk geografi

Géographie économique

Examines economic activity in a spatial context, differing from general economic systems

The term political economy is used less frequently outside of England, though it retains significance in certain academic contexts globally. In England and historically in Europe, political economy has been a foundational concept, emphasizing the interplay between economics, law, and politics. This term originated in the eighteenth century with thinkers like Adam Smith and David Ricardo, who analyzed how economic systems function within specific political and legal frameworks.

As seen on Wikipedia, Development Economics is often described as a “branch” of economics. The term “branch” is frequently used to describe fields within broader disciplines, even when these fields have developed into independent disciplines with their own specialized methods, theories, and research goals. For instance, development economics is called a “branch” of economics, yet it operates independently with distinct objectives, literature, and applications focused on low- and middle-income countries. This terminology can be confusing, as referring to it as a “branch” implies integration within traditional economics, despite its unique interdisciplinary elements and career pathways.

This same confusion exists between behavioral economics and psychology. Both fields study human decision-making and behavior, but behavioral economics specifically examines how psychological factors influence economic decisions, often challenging traditional assumptions of rational behaviour. Psychology, however, covers a broader range of human behaviors and cognitive processes beyond economic contexts, including areas like cognitive development and emotional health, without necessarily linking findings to economic theories or markets.

Similarly, economic sociology and sociology can be challenging to differentiate, as both fields address social behavior and structures, though economic sociology focuses specifically on the social influences on economic outcomes.

Environmental economics faces similar challenges as it intersects closely with ecology and environmental science, which can make boundaries unclear. Environmental economics specifically examines the economic impacts of environmental policies and resource usage, yet it often overlaps with broader ecological goals that focus on conservation and natural systems, rather than economic perspectives.

Interestingly, such boundary issues appear to be less pronounced with sub-disciplines, as illustrated in the table below (Table 4.20):

Table 4.20

More disciplines and their subsequent sub-disciplines

Discipline

Description

Sub-disciplines

Marketing

Focuses on understanding consumer behavior, creating value, and strategically positioning products and services in the market

– Consumer behavior: Studies how individuals make purchase decisions

– Brand management: Develops and maintains brand identity

– Digital marketing: Utilizes online platforms and social media for marketing

– Market research: Analyzes market conditions, trends, and consumer preferences

– Advertising: Focuses on promoting products through various media

– Sales management: Manages sales teams and strategies to reach sales targets

– Public relations: Manages the public image and communications of the brand

Management

Encompasses the organization, administration, and coordination of resources to achieve strategic objectives efficiently

– Human resource management: Focuses on recruitment, training, and employee relations

– Operations management: Oversees production, logistics, and quality control

– Strategic management: Formulates long-term goals and strategies

– Project management: Plans and executes projects to meet specific goals

– Organizational behavior: Studies employee behavior, motivation, and culture

– Supply chain management: Manages the flow of goods and services from production to delivery

– Leadership studies: Explores theories and practices of leadership within organizations

Accounting

Dedicated to tracking, analyzing, and reporting financial transactions, focusing on accuracy and regulatory compliance

– Financial accounting: Prepares financial statements for external stakeholders

– Managerial accounting: Provides financial data for internal decision-making

– Tax accounting: Focuses on tax return preparation and compliance

– Auditing: Reviews and verifies financial records for accuracy and compliance

– Forensic accounting: Investigates financial discrepancies and fraud

– Cost accounting: Analyzes costs of production to improve profitability

– Government accounting: Focuses on financial management and reporting within the public sector

As the table shows, the challenges in defining and separating fields often increase with larger disciplines. For instance, the overlap between business studies and management is significant, as both aim to improve organizational success and strategic outcomes. However, more specific areas, such as advertising and digital marketing within marketing, are less contentious because their boundaries and relationships to their broader field are clearer.

The more detailed and defined a discipline becomes, particularly through its sub-disciplines, the closer it aligns with practical application. As we approach practical application, the terminology tends to become clearer and more precise. As we shall see a bit further on in this section, in these observations also lies the solution.

The confusion surrounding discipline boundaries extends even to business schools, which often struggle to define their identity and may rebrand according to current trends. For example, right now “Management” appears to be making a comeback as a label for schools, while “Business Studies” seems to be declining in popularity, but these terms change. The shifts often occur in cycles, reflecting trends within academia and industry.

Management is generally considered a subset of business studies, focusing specifically on leadership, decision-making, and resource organization to achieve goals. Business studies, by contrast, have a broader scope, encompassing not only management but also finance, marketing, accounting, and entrepreneurship. Both fields develop similar skills in students, such as analytical thinking, strategic planning, and financial analysis, though business studies often cover additional areas like market analysis and financial modeling.

In practical terms, business studies graduates may pursue diverse roles across business functions, while management graduates are typically steered toward leadership roles. The overlap between these two fields is especially evident in areas like strategic planning and organizational behavior, where both management and general business principles apply. Overall, while management is crucial to business studies, business studies encompasses a broader range of disciplines beyond management alone.

The boundaries between fields are frequently blurred. Economists, in particular, are known for protecting the integrity of their field, often restricting the title of “economist” to those who adhere to specific schools or use specific methods, for example, adhering to the neoclassic school. This protectionism can sometimes resemble an environment where fields become more like exclusive clubs rather than open scientific communities. This contradicts the very fundamental principles of science—collaboration, knowledge-sharing, and rigorous scrutiny. As fields subdivide into smaller, more specialized areas, disagreements over terminology and scope will often hinder interdisciplinary work and make it harder for new researchers to navigate academic landscapes effectively. This entire situation is a prime example of a “sui generis” problem that has expanded over time.

4.3.3 The “Sui Generis” Problem

The concept of sui generis, meaning “of its own kind,” explains how new disciplines emerge in the social sciences to address unique, complex phenomena that don’t fit neatly into existing frameworks. This process often occurs when established fields, like economics, expand into specialized areas such as business and management to address issues like organizational behavior, consumer psychology, and strategic decision-making.

Unlike the natural sciences, which typically create new fields through discovery and categorization (e.g., physics branching into quantum mechanics to address different scales of investigation), social sciences often develop new fields in response to societal needs that require tailored approaches, and often without thoroughly checking what other fields are already doing.

For example, business studies evolved from economics to focus on market dynamics and organizational contexts, resulting in subfields like management, marketing, and entrepreneurship. Each of these subfields represents a sui generis category, exploring social phenomena that don’t fully align with the principles of traditional economics. This tendency reflects the diversity and complexity of human behaviour, which resists standardization, making it challenging to establish universal principles across contexts. Thus, as new disciplines emerge, they often overlap and intersect with one another—such as organizational psychology intersecting with management studies—creating fragmented disciplines that develop unique, context-driven methodologies.

The overlap among disciplines frequently results in methodological inconsistencies, as each subfield may use different theoretical frameworks or approaches to examine similar phenomena, making standardization difficult. While these divisions address specialized needs, they also highlight the challenge of achieving coherence across social science research, a complexity less pronounced in fields governed by universal laws. Excessive specialization risks creating small sub-disciplines with few active scholars, which can limit collaboration and hinder the development of each field. Ideally, each discipline should have a critical mass of scholars to sustain essential activities like conferences, journals, and PhD programs, which support the field’s growth.

In some cases, the proliferation of disciplines blurs boundaries and creates overlapping fields with ambiguous definitions. For example, disciplines like knowledge management, market intelligence, competitive intelligence, futures studies, and geoeconomics often overlap, occasionally sharing conferences and research teams. However, these disciplines may face challenges when interacting with more established fields like geopolitics, information systems, or business intelligence. Switching between disciplines is also challenging, as each field may rely on distinct methodologies, philosophical foundations, and academic training requirements that act as barriers. For instance, business intelligence now heavily incorporates software engineering, while collective intelligence is an established area within information systems, often dominated by engineering perspectives.

This expansion of sui generis classifications has led to a fragmented ecosystem where each subfield may seem distinct yet remains interdependent, creating confusion about which field offers the most relevant or accurate approach to issues in business and social science. Institutions, cultures, and languages further complicate this landscape, as each may define and structure these fields differently. One may wonder whether it’s necessary to define oneself strictly within a specific discipline. Can’t researchers simply identify as social scientists and move freely across disciplines to address real-world problems? Excessive division of fields might limit the ability to address complex, multifaceted problems effectively.

Some of the most significant advancements occur at the intersections of disciplines, where diverse perspectives and methodologies can combine to tackle complex issues. For example, Herbert Simon, a Nobel laureate in economics, made foundational contributions across psychology, computer science, and organizational theory, advancing knowledge in areas like decision-making, bounded rationality, and artificial intelligence. Similarly, Peter Drucker, often considered the “father of modern management,” achieved profound impact by focusing on practical insights for managers rather than academic conventions, connecting ideas from economics, sociology, and psychology. These examples demonstrate that interdisciplinary work can yield holistic insights that single-discipline approaches may overlook.

However, grounding oneself in a specific discipline also has value, as each field offers essential theories, research methods, and approaches necessary for rigorous analysis. While a strong foundation in one discipline doesn’t preclude interdisciplinary work, it provides the expertise needed to contribute meaningfully across fields. Nonetheless, increasing specialization in academia can lead to narrowly focused research that may lack relevance to broader societal needs. This narrow focus can create barriers to collaboration on shared issues, such as public health, environmental sustainability, or social inequality, and limit awareness of the wider implications of research.

Identifying primarily as a social scientist can help researchers navigate boundaries across disciplines. However, this flexibility comes with the risk of lacking practical experience within the area of study. In business studies, for instance, many researchers lack direct industry experience, leading to limited understanding of the field they aim to study. This lack of practical grounding extends to fields like political science, where real-world familiarity is essential. Broadly adopting the title of “social scientist” without specialized expertise risks diluting practical relevance. For example, teaching accounting or auditing ideally requires a professional background and certification to lend credibility to the instruction. Practical experience is crucial for producing research and education that is both meaningful and credible.

Overspecialization in certain fields has led to bizarre situations within academia, where researchers can become experts in a subfield without having a grasp of the fundamental principles of the broader discipline. Take accounting, for example:

Example: “Do your accounting professor know any accounting?”

The issue of overspecialization in academia becomes particularly apparent in fields like accounting, where many researchers are experts “about” accounting rather than “in” accounting. In other words, many faculty members function more as accounting sociologists or theorists yet hold professorships in accounting rather than in sociology or business history. This creates a significant gap when students need to learn real accounting practices, such as preparing year-end financial statements, income statements, and balance sheets—not just conceptually but also in practical application, including the detailed interpretation of accompanying financial notes.

Since many universities lack faculty with hands-on accounting expertise, they are often forced to hire entry-level accountants—typically without PhDs—to teach students the essential technical skills required in professional settings. The contradiction here is striking: Institutions award professorships in accounting to those who study the social, historical, or political aspects of the field while relegating the actual practice of accounting to instructors who lack academic standing.

For businesses and professionals who rely on graduates with a solid grasp of financial reporting and analysis, this disconnect is both frustrating and costly. Yet, within academia, this misalignment rarely seems to cause concern, as researchers continue prioritizing highly specialized theoretical work over the practical needs of students and employers.

This phenomenon can be described as “Ivory Tower Specialization,” academic functional blindness, or theoretical overreach, where researchers become highly specialized in theoretical or sociological aspects of a field while lacking expertise in its fundamental, practical applications. As a result, universities produce scholars who excel in abstract analysis but struggle to equip students with the real-world skills their disciplines demand.

From time to time, universities will produce knowledge and specialties that have little relevance outside academia. When this happens, the institution must be reined in to prevent research from drifting into self-referential, insular territories. Otherwise, the risk is that academia begins to resemble a political movement, convinced that society should adapt to its theories rather than the other way around.

A more effective approach could be to organize social research around professional areas, aligning each academic discipline with established professions in the public or private sectors. This alignment would ensure that academic research remains relevant to real-world applications and professional standards. Larger, high-impact sectors would naturally attract more resources and attention to their corresponding disciplines, fostering a dynamic relationship between academia and industry. Such a model would encourage disciplines to prioritize practical knowledge and industry-specific insights, enhancing both research relevance and funding opportunities.¹⁰

Aligning social science programs with established professions could present challenges for some fields. For instance, sociology, anthropology, and political science provide valuable insights but lack straightforward career paths. While sociology graduates can pursue roles in research, policy, and community development, “sociologist” isn’t a common professional title outside academia. Similarly, anthropology encompasses cultural, biological, and linguistic studies, but “anthropologist” roles are rare outside academia or niche industries. Political science also lacks exclusive career paths, as graduates often work in policy, government, and public administration without the title “political scientist.” For these fields to align more closely with career paths, they might need to adopt an interdisciplinary focus on public policy, consulting, or community services to broaden their professional relevance.

To bridge these gaps, interdisciplinary and transdisciplinary approaches are increasingly encouraged, promoting flexibility and collaboration across fields. Defining oneself within a discipline need not restrict a researcher’s work; rather, it can provide a foundation for engaging with other areas. Balancing depth in a core discipline with breadth across others allows social scientists to apply rigorous methods while addressing complex, real-world challenges. This balance enables researchers to contribute both specialized knowledge and broader insights to pressing issues.

AI will also be able to help address the “sui generis” problem by analyzing and categorizing vast amounts of data across disciplines to identify overlaps, redundancies, and unique contributions. Natural language processing (NLP) can review academic texts, research articles, and curricula to detect similarities and differences in terminology, methodologies, and research objectives. By clustering related fields and highlighting redundancies, AI can recommend areas for streamlining or potential new developments. Additionally, AI can track the evolution of research topics, pinpointing obsolete fields or identifying emerging interdisciplinary areas that warrant dedicated focus. This AI-driven approach provides a systematic, data-backed method for refining academic structures, offering clarity in areas where human intuition may struggle with complexity and scale. Interesting times lie ahead.

4.3.4 Problem-Centered Research in Social Sciences

Problem-centered research in the social sciences addresses two key challenges: the sui generis issue, where disciplines and sub-disciplines become increasingly intertwined and difficult to separate, leading to organizational and methodological confusion, and the problem of over-theorization, which distances the content of these disciplines from their practical applications and relevance to actual professions.

Building on this, until we establish clearer definitions and boundaries within social science disciplines, it may be more productive to focus on addressing specific societal problems directly, rather than getting entangled in debates over disciplinary distinctions. This approach resonates with Deng Xiaoping’s pragmatic philosophy during China’s economic reforms, where he famously stated, “It doesn’t matter whether a cat is black or white, as long as it catches mice.” The emphasis here is on achieving practical outcomes rather than adhering rigidly to ideological or theoretical frameworks. By adopting this mindset, the social sciences can foster more impactful interdisciplinary research, prioritizing solutions over academic compartmentalization.

With the help of AI, we can begin to define and classify an extensive range of human problems systematically. Each problem could be assigned a unique identifier or code, enabling clear organization and retrieval across various fields and studies. AI could then analyze and categorize the methods that have been applied to address each problem, mapping out the range of techniques used in different disciplines or contexts. This approach would allow for a table or database that not only lists problems and their solutions but also specifies which methods have been most effective in particular contexts. Such a resource could include both quantitative and qualitative results for each problem, offering a comprehensive view of the outcomes across various applications. By aggregating this information, AI could identify patterns in successful methods and even recommend optimized approaches based on past findings. Ultimately, this classification system would facilitate knowledge-sharing across disciplines and improve problem-solving strategies by drawing on a diverse set of proven methods and results.¹¹

Such a database focused on classifying studies according to problems instead of disciplines or areas of study could look something like in the table below (Table 4.21):

Table 4.21

A division of the social sciences into problems to be solved

Problem code

Problem description

Methods used

Context

Result summary

P-001

High school student dropout rates

– Survey analysis

Urban public schools in the USA

Survey analysis indicated key risk factors; intervention programs reduced dropout rates by 20% over 3 years; parental involvement showed mixed results depending on socio-economic factors

– Educational intervention programs

– Parental involvement initiatives

P-002

Workplace stress and productivity loss

– Cognitive behavioral therapy (CBT)

Corporate offices in Japan

CBT and mindfulness training reduced stress levels by 30%; flexible working hours improved productivity and job satisfaction in 60% of cases

– Flexible working hours

– Mindfulness training

P-003

Low voter turnout in local elections

– Social media campaigns

Rural and urban communities in Canada

Social media campaigns increased turnout by 10% in younger voters; door-to-door outreach was most effective in rural areas; incentives led to short-term boosts but not sustained engagement

– Door-to-door outreach

– Incentivized voting programs

P-004

Homelessness and housing instability

– Affordable housing programs

Major cities in the USA and Europe

Affordable housing significantly decreased homelessness by 25%; job training improved long-term stability; emergency shelters addressed immediate needs but lacked long-term impact

– Job training and placement

– Emergency shelters

P-005

Obesity and lifestyle-related diseases

– Nutritional education

Low-income communities in the UK

Nutritional education raised awareness but had limited effect without structural support; community fitness programs increased participation by 40%; subsidized healthy food improved dietary choices

– Community fitness programs

– Subsidized healthy food

By classifying problems, hypotheses, and research questions into well-organized research libraries, we could bypass the need for rigid disciplinary structures and instead build knowledge directly around key societal challenges.

To guide this approach, we can postulate the following framework for effective scientific practice in the social sciences drawing also from some of the main issues from Chap. 2 on how to do science:

Note: Principles of Problem-Centered Research in Social Sciences

Consider the following program: Social science research that adheres to principles of real-world relevance, clarity, logical progression, methodological transparency, replication, and data completeness will produce more reliable, replicable, and practically applicable knowledge, enhancing the field’s impact on society and fostering cumulative advancement. By broadening the focus beyond rigid disciplinary boundaries, this approach fosters collaboration across fields and encourages the development of solutions directly aligned with societal challenges.

Established Principles:

1.

Real-World Relevance: Research should address genuine, observable issues that surpass common-sense interpretations, ensuring applicability to societal needs. The importance of this principle cannot be overstated—by engaging with pressing societal challenges, such as poverty, inequality, climate change, or public health, social science research not only advances theoretical knowledge but also plays a direct role in shaping policy and practical interventions. Interdisciplinary collaboration is crucial in this area, as combining perspectives from economics, psychology, sociology, political science, and other fields can yield more comprehensive insights into complex issues.

2.

Clarity and Conciseness: Problems must be articulated with precision, using straightforward language and avoiding ambiguity, to facilitate understanding and replication. Clarity in communication extends beyond academic circles—it is vital for translating complex findings into actionable recommendations for policymakers, industry leaders, and the public. For interdisciplinary research to thrive, clear communication becomes even more critical as it allows scholars from different fields to engage with and contribute to a unified body of work without being bogged down by jargon or disciplinary silos.

3.

Logical Progression: Each problem should logically lead to testable hypotheses or research questions, establishing a structured and coherent investigative path. This principle ensures that research is not only methodologically sound but also allows for the creation of frameworks that can be universally applied. In an interdisciplinary research program, logical progression enables teams from diverse academic backgrounds to contribute toward a shared goal while maintaining consistency in how problems are approached and solutions are designed. This approach enhances the collective ability to build upon findings and to integrate different types of knowledge into a cohesive narrative.

4.

Methodological Transparency: Studies should provide a complete, replicable account of methods to enable accurate reproduction by other researchers. In interdisciplinary research, methodological transparency is essential for fostering trust and collaboration between researchers from different fields. A commitment to transparency ensures that methods can be understood and evaluated by others, promoting confidence in the results and allowing researchers from various disciplines to adapt the approach to their own context or research problem. Furthermore, transparent methodologies allow for cross-pollination of ideas, strengthening the integrity of the research process.

5.

Value of Replication: Emphasis on replicating existing studies over creating new problems strengthens reliability and builds a more stable theoretical foundation. Replication across disciplines not only validates existing findings but also reveals how research methods and results hold up when applied to different contexts or populations. Replication ensures that conclusions are not just artifacts of a specific researcher’s perspective but part of a broader, more robust body of knowledge. In an interdisciplinary context, replication is even more crucial, as it helps assess whether insights gained from one discipline can be generalized and applied across other fields, thus enriching the interdisciplinary dialogue.

6.

Data Completeness: Full datasets must accompany research findings, as incomplete data limit the validity and replicability of the study. In an interdisciplinary research program, the sharing of data among disciplines facilitates the pooling of diverse insights and strengthens the reliability of findings. Complete datasets allow for a deeper, more nuanced understanding of the research problem, enabling other scholars to test the findings, build upon them, or apply them in different settings. Furthermore, transparency in data sharing fosters trust between disciplines, encouraging more collaborative work and reducing the likelihood of errors or biases in data interpretation.

Implications and Conclusion:

By centering on these principles, a program on problem-focused research in the social sciences can enhance the applicability of findings to societal needs and build a more reliable and cumulative body of knowledge through transparency, replicability, and completeness. I would argue that interdisciplinary research programs that embrace these principles are better equipped to address the complex, multifaceted challenges facing society today.

This framework not only emphasizes the importance of rigorous and reproducible research but also promotes a collaborative environment in which researchers from diverse fields can contribute their expertise and engage with one another in a productive and innovative manner. The practical considerations for fostering interdisciplinary research include establishing common goals, creating shared methodologies, and ensuring effective communication across disciplines. By focusing on these principles, social science research can bridge the gap between theory and practice, producing actionable insights that make a meaningful impact on society.

Ultimately, adhering to these principles within an interdisciplinary context will create research outcomes that are not only scientifically robust but also socially impactful, driving the advancement of knowledge and fostering positive change across sectors.

We could call it the problem-focused social science initiative (PFSSI).

AI can be a powerful tool to advance problem-centered research in the social sciences by facilitating interdisciplinary collaboration and providing innovative solutions to societal challenges. By systematically organizing problems and methods, AI can enable researchers to bypass traditional disciplinary boundaries, focusing instead on practical outcomes. This approach aligns with real-world relevance and methodological transparency principles, ensuring that findings are scientifically robust and socially impactful.

4.3.5 Leveraging AI and Advanced Methodologies in Research

Modern techniques, particularly AI and advanced methodologies, are revolutionizing scientific research by enhancing efficiency and uncovering insights that were previously difficult to achieve. AI-driven bibliometric analysis, for example, allows researchers to identify emerging trends, influential authors, and key journals, streamlining literature reviews and highlighting research gaps. In the social sciences, this technology can analyze large datasets, such as social media, to track public sentiment on policies using natural language processing (NLP), providing invaluable insights into public discourse.

Predictive analytics further enables modeling the potential societal impacts of proposed policies, helping decision-makers craft strategies grounded in evidence. AI tools also bring transformative speed and scale to qualitative research by coding and analyzing interview data to identify patterns and themes. Similarly, machine learning algorithms detect hidden correlations in datasets, such as connections between socioeconomic factors and educational outcomes, deepening our understanding of complex phenomena.

Practical tools like Zotero and EndNote automate literature management, saving time and reducing errors in citation processes. Sentiment analysis tools process textual data from news outlets or forums to gauge public opinion, while AI-powered visualization tools like Tableau present findings in dynamic, interactive formats that enhance comprehension. Additionally, systematic reviews can be expedited using platforms like Covidence, which quickly identify and screen studies for inclusion. Or you can simply upload any text and ask for a critical review directly in the chat board.

The integration of these AI tools into research processes not only saves time but also enhances the quality and reproducibility of results. For instance, randomized controlled trials (RCTs) in the social sciences can be optimized with AI, improving their design and execution. Alongside these advancements, addressing ethical considerations such as mitigating biases in training data and ensuring transparency in AI-driven analyses is crucial. Embracing these modern methodologies equips researchers with the tools to tackle complex questions responsibly and effectively, paving the way for more impactful and innovative scholarship.

In other words, by incorporating AI, social scientists can focus on what truly matters: driving meaningful societal change. AI allows researchers to streamline tedious tasks, such as data collection, analysis, and visualization, freeing up time and resources to address pressing issues more effectively.

By leveraging AI tools, they can uncover patterns and insights that might otherwise remain hidden, enabling them to propose actionable solutions grounded in robust evidence. This shift not only enhances the practical impact of social science research but also ensures that efforts are directed toward solving real-world problems, fostering innovation, and improving the quality of life for communities worldwide. In this way, AI serves as a bridge between theoretical research and tangible societal progress.

This shift to incorporating AI into social science research will undoubtedly feel strange at first, as it requires a fundamental change in the researcher’s role. Instead of being deeply involved in every detail of data collection and manual analysis, researchers will need to pivot toward information quality control and process management. Their focus will move to ensuring the accuracy of the data inputs, verifying the reliability of AI-generated outputs, and steering the research process to maintain high standards of methodological rigor. This transformation demands new skills, such as overseeing automated systems and interpreting AI-driven insights, but it also allows researchers to dedicate more time to critical thinking, hypothesis refinement, and the broader implications of their findings (Table 4.22).¹²

Table 4.22

AI functions supporting the scientific process

Stage of the scientific process

AI function/product

Example in social sciences

Literature review

AI-driven bibliometric analysis

Using dimensions or Scopus to map research trends

Data collection

Web scraping and NLP

Scraping social media to analyze public sentiment on a policy

Data cleaning and organization

Data preprocessing tools

Automating the cleaning of survey datasets using python libraries

Data analysis

Machine learning and predictive modeling

Analyzing socioeconomic factors affecting educational outcomes using scikit-learn

Hypothesis testing

AI for statistical analysis

Using IBM SPSS or R for multivariate regression analysis

Visualization of results

AI-based visualization tools

Creating interactive dashboards in tableau

Writing and editing

AI writing assistants

Using Grammarly or ChatGPT to improve clarity and grammar

Citation and references management

Reference management tools

Automating citations with Zotero or EndNote

Dissemination and outreach

Content recommendation engines

Optimizing article sharing on platforms like ResearchGate

Ethical considerations

AI bias detection tools

Ensuring unbiased sentiment analysis for social research

The table gives some examples of how various AI functions and tools can support different stages of the scientific research process, from literature review to dissemination. For instance, AI-driven bibliometric analysis aids in mapping research trends and identifying gaps, enabling more efficient literature reviews. Tools for data preprocessing streamline cleaning and organizing datasets, while machine learning models enhance the depth of data analysis, uncovering patterns in complex datasets. Visualization tools like Tableau help researchers present findings interactively, making results accessible and engaging. Additionally, AI tools for citation management and writing assistance improve the efficiency of drafting and referencing, while ethical AI applications ensure responsible and unbiased research practices.

AI has the potential to go beyond simply analyzing content; it can identify the methodologies used in each article, determine the closest alignment with different philosophies of science, and classify works by their school of thought, though not always correctly, at least not yet. In addition, AI will gladly suggest potential improvements to research articles. Looking ahead, AI agents may be capable of designing and deploying surveys to the appropriate sample groups over the Internet, collecting responses, and conducting data analysis—all with a level of efficiency and reliability that surpasses human capabilities. As these technologies evolve, it’s possible that we may even begin to question the future roles and contributions of human researchers. Instead, we could envision autonomous research systems where researchers take on roles more akin to designers and administrators.

Speculating about the future of the social sciences is not just thought-provoking but also crucial for understanding the purpose and evolution of science itself. The aim of social sciences is not to produce an endless stream of academic articles, but to actively contribute to societal change—not as political advocates, but as objective providers of well-founded studies. These studies should serve as reliable foundations for informed decision-making, offering unbiased insights that guide practical and impactful actions.

4.3.6 Navigating the Challenges of Interdisciplinary Research

Interdisciplinarity is increasingly essential for addressing complex research questions that surpass the boundaries of a single discipline, but conducting such studies presents unique challenges.

Researchers must navigate the integration of diverse methods and perspectives, particularly reconciling differences between qualitative and quantitative paradigms. Mixed-method designs, which combine the depth of qualitative insights with the generalizability of quantitative data, provide a powerful approach. For instance, combining these methods can uncover new perspectives in areas like public health or education reform, offering a more comprehensive understanding.

Case studies that demonstrate interdisciplinary research in practice can guide researchers on how to align diverse methodologies effectively. Examples include merging sociological theories with economic modeling to analyze inequality or using machine learning to study historical texts in humanities research. These examples illustrate how interdisciplinary approaches can achieve shared goals and overcome methodological challenges.

To address common issues such as disciplinary biases, misaligned terminology, and conflicting research goals, researchers can use shared conceptual frameworks or bridging theories. Building cohesive teams, setting clear objectives, and ensuring methodological coherence are practical strategies for overcoming these obstacles. Understanding the epistemological differences between disciplines also helps identify the roots of potential conflicts and develop strategies for resolution.

By employing these tools and frameworks, researchers can approach interdisciplinary projects with confidence, enabling us to address societal challenges more impactfully, hopefully free from common-sense jargon, unexplained references, and tiresome buzzwords (Table 4.23).

Table 4.23

Problems with interdisciplinary research

Problem

Definition

Example

Lack of deep expertise

Researchers may lack in-depth knowledge in any single discipline due to spreading focus across multiple areas

A researcher studying climate change using economics, sociology, and biology struggles to publish impactful work because their expertise in each field is limited

Terminology misalignment

Different disciplines often use the same terms with varying meanings, leading to misunderstandings

The term “resilience” might mean ecological stability in environmental science but psychological adaptability in social science, confusing collaborative projects

Conflicting methodologies

Disciplines rely on different methods that may be difficult to reconcile

Economists use quantitative models, while anthropologists rely on qualitative fieldwork, creating challenges when merging findings in a study on rural economies

Disciplinary bias

Researchers may prioritize their own discipline’s approach, undervaluing others

A physicist dismisses qualitative insights from social scientists while collaborating on a project on renewable energy adoption in urban areas

Institutional barriers

Academic systems reward specialization and discipline-focused work, discouraging interdisciplinary efforts

A researcher’s interdisciplinary paper faces difficulty finding a journal willing to publish it due to its lack of alignment with a single discipline’s standards

Divergent goals

Team members from different fields may have conflicting objectives or research priorities

A psychologist and an AI engineer collaborating on a mental health app disagree on whether the focus should be on user experience or predictive accuracy

Limited funding opportunities

Funding agencies may prioritize projects within traditional disciplinary frameworks

A proposal for a project on climate-resilient agriculture, blending engineering and sociology, is rejected for not fitting neatly into funding categories

Data integration challenges

Combining datasets from different disciplines can be complex due to varying formats and standards

Merging geospatial climate data with survey responses from farmers becomes problematic due to incompatible data collection methodologies

Lack of communication skills

Researchers may struggle to explain concepts from their field to collaborators in other disciplines

A computer scientist fails to clearly explain machine learning algorithms to environmental scientists in a project on wildlife conservation

Time-intensive coordination

Interdisciplinary work requires more time to align methods, goals, and deliverables

A project on public health and urban planning takes twice as long as expected due to frequent team meetings to resolve methodological differences

As seen, interdisciplinary research faces several challenges, such as a lack of deep expertise when researchers spread their focus across multiple fields, leading to gaps in knowledge. Misalignment of terminology and conflicting methodologies between disciplines often cause misunderstandings and hinder collaboration. Institutional barriers, including funding limitations and traditional academic structures, discourage interdisciplinary work, while issues like data integration challenges and time-intensive coordination further complicate efforts. These problems highlight the need for clear communication, shared goals, and innovative frameworks to align diverse perspectives and foster successful interdisciplinary collaboration.

Despite the challenges, interdisciplinarity offers a compelling solution for revitalizing the social sciences. Since the mid-twentieth century, individual disciplines within the social sciences have struggled to achieve groundbreaking progress independently. Drawing on the thoughts of Niklas Luhmann and Kenneth Boulding, it becomes evident, as I have argued in this book, that the artificial separation of social sciences does not reflect the integrated nature of societal challenges. These disciplines interact constantly in practice, whether addressing issues of economic inequality, educational reform, or public health.

From a methodological perspective, all scientific inquiry ultimately adheres to a unified set of principles derived from the scientific method. The fragmentation of disciplines obscures this shared foundation, creating unnecessary divisions that hinder collaboration and innovation. By embracing interdisciplinarity, the social sciences can consolidate their strengths, fostering research that is both methodologically rigorous and practically relevant.

Moreover, complex societal problems—climate change, migration, or global inequality—demand multifaceted approaches that no single discipline can address effectively. Interdisciplinary research allows for the integration of diverse perspectives, creating holistic solutions that are greater than the sum of their parts. This approach aligns with the idea that there is only one science, guided by universal principles and methods, and its full potential can only be realized when disciplinary boundaries are bridged. By working together, the social sciences can not only enhance their scientific impact but also regain their relevance in shaping the future of society.

What is needed, and which I hope this book has illustrated through practical examples, is a much stricter adherence to the scientific method. We must critically evaluate and significantly narrow the scope of what we consider science, focusing on rigor and evidence-based practices. As the saying goes, it’s time to “kill your darlings”—letting go of cherished but unsubstantiated theories or approaches that fail to meet the highest scientific standards.

Concluding Remarks

Interdisciplinary research is crucial for tackling complex societal issues that no single discipline can fully address. Despite challenges such as differing methodologies, misaligned terminologies, and institutional barriers, interdisciplinarity offers a more holistic understanding of pressing issues like climate change, inequality, and public health. By integrating diverse perspectives, researchers can develop solutions that are more comprehensive and impactful than those offered by isolated fields. The fragmentation of social sciences stifles progress, but a unified approach rooted in the scientific method can drive innovation and collaboration. Embracing interdisciplinary approaches not only strengthens the scientific rigor of social science research but also enhances its relevance in addressing real-world challenges.

However, for interdisciplinary research to truly thrive, practical strategies must be devised to overcome its inherent challenges—whether bridging knowledge gaps or aligning varying methodologies. Effective collaboration is key, and this involves finding like-minded peers who share complementary goals and values. This brings us to a critical aspect of academic life: discovering your academic “tribe.” In the next section, which is the very last; we will delve into how to identify the individuals and networks that will support and inspire you throughout your research journey.

4.3.7 Finding your Academic “Tribe”

Navigating the landscape of academic ideas and choosing the right intellectual “tribe” can, as we have seen, be challenging for young scholars. However, at the end of this journey of ideas, the researcher ultimately still needs to find their own tribe.

There are organizations dedicated to helping young scholars enhance their networks. For instance, the Global Young Academy (GYA) serves as an international society of young scientists, aiming to give a voice to young scientists across the globe.¹³ Similarly, the Young Academy of Europe (YAE) is a pan-European nongovernmental academy of top young scientists and scholars, providing input to scientific exchange and science policy across all European Union member states or an associated country.¹⁴ These organizations exemplify how young scholars can find their academic “tribe” by connecting with peers who share similar research interests and goals. Then, of course, there is social media.

Based on a structured approach, the first step is to deeply understand your core interests, aligning them with areas that have active communities, as these communities offer essential support, networking, and feedback. Instead of rushing to commit to a discipline, a young scholar should consider each field’s compatibility with their preferred research methods, theoretical perspectives, and potential for interdisciplinary work. This also means exploring conferences, seminars, or workshops across related disciplines to gain a firsthand sense of the research culture, typical questions, and the types of collaborations available.

While engaging with these communities, it’s essential to seek mentorship or guidance from experienced scholars who can provide insights on navigating specific academic challenges within each “tribe.” As you consider various subfields, ask whether the practical applications align with your career aspirations and values, especially if you’re interested in applying research to real-world contexts.

Ultimately, navigating this academic landscape involves a combination of exploration, self-reflection, and strategic alignment, so you can build a research career that is both fulfilling and impactful (Table 4.24).

Table 4.24

Locating your academic tribe

Step

Description

Guiding questions

1. Reflect on core interests

Identify subjects that genuinely interest you and resonate on a personal level, beyond trends or expectations

– What questions or problems do I find most compelling?

– Which areas do I naturally gravitate toward when I read or discuss academic topics?

2. Identify active research communities

Look for fields with thriving research communities for collaboration, support, and forums

– Are there conferences, journals, or associations dedicated to this field?

– How many researchers actively publish and participate in this area?

3. Evaluate methods and philosophical foundations

Ensure alignment with the methods and philosophies dominant in the field to enhance engagement and contribution

– Do I prefer quantitative or qualitative methods?

– How comfortable am I with the field’s methods and theoretical perspectives?

4. Assess overlap and compatibility with other fields

Look for compatibility with related fields for flexibility, interdisciplinary collaboration, and adaptability

– Are there related fields where I can apply my insights?

– Does this area share conferences, journals, or teams with other fields?

5. Investigate mentorship and collaboration opportunities

Seek potential mentors and collaborators to guide you and help build a support network

– Are there experienced researchers who can provide guidance?

– Do I see potential for building a network of collaborators, in or outside my institution?

6. Consider practical applications and career opportunities

Reflect on the broader applications and career prospects that the field may provide

– Are there real-world applications for this research?

– Do industry or government institutions value expertise in this area?

This approach presents an ideal and rational process for finding one’s place in academia. In reality, however, many of us end up where we are due to personal interests we developed along the way, landing in a particular “tribe” or combination of tribes that reflect our unique journeys much by chance.

As an example, I found my own “tribe” first through reading, then by identifying conferences that hosted topics close to my interests. I went to these events, talked to people, learned where they publish, and discovered which organizations supported that field. Bit by bit, an ecosystem of people, institutions, and associations emerged.

You can also start to see which companies take an interest in your work and how they tackle similar issues. Academics and practitioners often intermingle, and eventually, it will dawn on you: You’ve found your tribe.

To illustrate this process, let me tell you the story in some greater detail.

Example: The Broader Scholarly Community and the Tribe

We all continue our research journeys as part of a tribe, whether we know it or not. For some, this tribe consists of many members, spanning international networks; for others, it is a smaller, more localized group. Some tribes are tightly knit, offering daily collaboration and support, while others are looser, connected only occasionally through shared interests. A tribe, in itself, is neither inherently good nor bad—it is a neutral construct that, when composed of serious and engaged individuals, can help you grow and thrive as a researcher.

My Journey into the Tribe

For me, the broader scholarly community includes an expansive network of social science researchers, and a narrower circle focused on economics and business, often on an international scale. My interactions with the larger group are relatively limited, occurring mainly through shared publications, like this book, which serves as a platform to engage a wider audience. Occasionally, I connect with colleagues at Swedish business and economics events, such as the annual FEKIS conference or as a judge at the Swedish Championship in Economics (SM i Ekonomi). Additionally, I maintain informal ties with an international network of marketing professionals. However, these interactions represent only a fraction of my professional identity and do not define what I consider my “tribe.”

To me, a tribe is the group where the actual work of research happens. It is the smaller, focused community where ideas are developed, challenged, and refined. My primary tribe is centered on intelligence studies, encompassing subfields such as market intelligence, competitive intelligence, business intelligence, future studies, collective intelligence, anticipatory systems, and insights. This community functions like an expansive Venn diagram, with overlapping areas of specialization reflected in the conferences I attend. These gatherings attract both academics and practitioners, creating a unique convergence of theory and practice.

Over the years, I’ve participated in key conferences such as ICI, ECIS, SIIE, ICTMOD, VSST, SCIP, ITICTI, EBRF, ECKM, and AIM. Each conference has its rhythm—some are annual, others biennial, and their scopes vary from regional to international. While some flourish, others fade as interests shift or leadership changes. Modern academic tools make it easier to find upcoming conferences, but even with this convenience, attending conferences requires careful consideration. Early-career researchers often attend several events each year, exploring potential tribes and networks. Later in one’s career, with greater responsibilities, selectivity becomes crucial. Identifying your tribe early can profoundly shape your academic path.

I would love to belong to more than one “tribe,” but unfortunately, time doesn’t allow for that, I have discovered. My current tribe consumes all the time I have left. I could switch tribes, or even move back and forth, but committing to a tribe takes time—it’s a long process of integration and finding your place. If you step out for a while, re-entering can take just as long, resulting in a lot of lost time due to what economists would call “transaction costs.” An alternative is to maintain one core tribe but build multiple networks—groups of people, communities, or venues where you can exchange ideas and find inspiration, without needing to spend a significant amount of time actually producing research.

A Tribe of Two Worlds: Academia and Practice

Many tribes bridge two worlds: academics and practitioners. These groups meet at conferences, collaborate on research, and engage in consulting projects. While practitioners might be viewed as “the other team,” their involvement is essential for testing academic theories and applying them in real-world contexts. This dynamic ensures that research remains relevant and impactful. If ideas resonate with practitioners—whether companies or organizations—it validates the work; if not, it signals the need for reassessment. Relevance lies at the heart of this interaction, making the partnership between these two groups indispensable.

Finding Your Place: The Evolution of Belonging in a Scholarly Tribe

I came to realize my place in this scholarly tribe about 25 years ago. This awareness wasn’t immediate or formalized but emerged gradually through professional relationships and shared experiences.

Like a family, you don’t choose every member of your tribe. New colleagues join through unexpected connections, and while not everyone gets along perfectly, you learn to value each person’s strengths and work around differences. Once established, a tribe often becomes a lifelong professional community unless you decide to pivot into an entirely new field.

Roles Within the Tribe

As careers progress, so do roles within the tribe. Scholars often take on responsibilities like chairing conferences, editing journals, or organizing academic gatherings. After publishing articles, some move on to write books, culminating in a work that distills their insights and contributions to the field. This process becomes part of a scholar’s legacy, reflecting the collective effort of their tribe and their place within it.

Most of us may not achieve groundbreaking discoveries or revolutionary insights. However, there is immense value in contributing meaningfully, even modestly, to a community that nurtures and supports your career over decades. This shared effort, collaboration, and gradual progression are what define a scholarly tribe, in my mind.

While my story offers insights into the dynamics of scholarly tribes, it may not necessarily serve as the ideal model for those aiming to excel in their academic careers. A tribe’s ultimate purpose is to connect with good people to collaborate with, and while my journey has been fulfilling, it reflects a nonstrategic path that might not fully leverage the collaborative potential of a tribe.

This organic process of finding one’s place within a scholarly tribe isn’t just a practical necessity; it often leads to deeper engagement and a more authentic connection to the work. By aligning ourselves with sub-disciplines or communities that inspire and support us, we learn to cultivate the curiosity needed to make meaningful contributions. A strong intellectual community enriches our research and advances the collective understanding within the field.

Ultimately, the goal is to align our interests with areas that allow us to contribute something of value to society. While the paths we take as scholars may differ, the essence lies in finding a niche where our skills and passion intersect, enabling us to make a lasting impact. When this is achieved, the scholar can be said to have reached what may be described as the ultimate goal of their professional journey as a researcher.

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Albert H (1991) Traktat über kritische Vernunft. Mohr Siebeck

Bachelard G (1938/2002) La formation de l’esprit scientifique: contribution à une psychanalyse de la connaissance objective. Presses Universitaires de France

Berthelot J-M (1996) Épistémologie des sciences sociales. Presses Universitaires de France

Bortz J, Döring N (2016) Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler (5. Auflage). Springer

Creswell JW, Creswell JD (2018) Research design: qualitative, quantitative, and mixed methods approaches, 5th edn. SAGE Publications

Desrosières A (1993/2008) La politique des grands nombres: histoire de la raison statistique. La Découverte

Esser H (1999) Soziologie: Allgemeine Grundlagen. Campus Verlag

Feyerabend P (1975/1993) Against method, 3rd edn. Verso

Kuhn TS (1962/2012) The structure of scientific revolutions (50th anniversary ed.). University of Chicago Press

Lakatos I (1970) Criticism and the growth of knowledge. Cambridge University PressCrossref

Maxwell JA (2013) Qualitative research design: An interactive approach, 3rd edn. SAGE Publications

Menger C (1883) Untersuchungen über die Methode der Sozialwissenschaften und der politischen Ökonomie insbesondere [Investigations into the method of the social sciences with special reference to economics, translated in 1985], vol 42. Duncker & Humblot, Leipzig, p 353

Merleau-Ponty M (1945) Phénoménologie de la perception. Gallimard

Opp K-D (2020) Methoden der empirischen Sozialforschung (3. Auflage). Springer VS

Passeron J-C (2006) Le raisonnement sociologique: Un espace non poppérien de l’argumentation. Albin Michel

Popper KR (1959/2005) The logic of scientific discovery. Routledge

Quivy R, Van Campenhoudt L (2017) Manuel de recherche en sciences sociales (5e édition). Dunod

Saldaña J (2021) The coding manual for qualitative researchers, 4th edn. SAGE Publications

Schäfer B, Kuhl R (2019) Einführung in die empirische Sozialforschung (9. Auflage). Beltz Verlag

Schmoller G (1900) Grundriß der allgemeinen Volkswirtschaftslehre [Outline of General Economics]. Duncker & Humblot, Leipzig

Sismondo S (2010) An introduction to science and technology studies, 2nd edn. Wiley-Blackwell

Saunders M, Lewis P, Thornhill A (2019) Research methods for business students, 8th edn. Pearson

Toulmin S (2003) The uses of argument (Updated ed.). Cambridge University PressCrossref

Weber M (1949) The methodology of the social sciences. Free Press

Footnotes

1

Originally published in 1954, translated by Aksel Bull Njå in 1978.

2

The Norwegian university system deserves credit for maintaining their “Examen Philosophicum,” (ex.phil.) an educational program in logic and scientific methods that serves as the foundation for all university studies, often going over 2/3 to a full semester. Many in my generation were offered to take this course while in the military.

3

See, for example, Foundation for Individual Rights and Expression (2024).

4

Harris, S. (2012).

5

See Stinchcombe (1991) and Frodeman et al. (2017).

6

See also Kyvik (2013) and Kwiek and Roszka (2024).

7

See Larivière and Costas (2016).

8

See Noui (2020).

9

See also Backhouse (1998) and Britannica. (n.d.). Physiocrat.

10

The idea is not unique but can be found with serval authors, e.g., Scandura and Iammarino (2022) and Breznitz and Feldman (2012).

11

Similar ideas are suggested by Altshuller (1999) and Organisation for Economic Co-operation and Development (OECD). (2023).

12

Söilen (2024).

13

Global Young Academy. (n.d.). Global Young Academy.

14

Young Academy of Europe. (n.d.). Young Academy of Europe.

Appendix A: Statistical Techniques

This appendix does not cover the step-by-step calculations for each statistical method, as such details are typically the focus of statistics books. Instead, the aim here is to guide you on when to apply each technique and, more importantly, to explain the reasoning behind selecting one method over another. In this book, we emphasize that performing the calculations is often the simplest part—modern tools like calculators or AI handle the math effortlessly once you know the appropriate method. The real challenge lies in understanding what technique to use and why.

To support this process, we’ve created heuristic tools to simplify your decision-making: For instance, Table 4.20 outlines how many rows of data are required for various operations, while Fig. 4.7 illustrates how statistical techniques often build on one another. Additionally, Table 4.21 provides a comprehensive overview of key statistical techniques, serving as a “menu” for methods you can explore further in supplementary coursework or advanced texts.

An exception to our approach is the section on sample size calculations. Understanding the basic principles behind sample sizing is essential for any study. While sample size calculators are commonly used, we believe that seeing the underlying math offers valuable insights for you to understand the concepts.

The Statistical Techniques

To start with the basics, what statistics we use will depend on what kind of data we have. For example, if we gather the number of white cars parked on a street, it makes sense to do some simple descriptive statistics, such as calculating the percentage of cars that are white on that street. Similarly, if we collected data over several days, we might compute the average number of white cars per day to identify any patterns. If the daily counts vary significantly, using measures like standard deviation could help us understand the level of variability.

If we want to visualize the data, a bar chart or pie chart could be useful to show the proportion of white cars compared to other colors. On the other hand, if we were comparing two streets, calculating the median number of white cars might help if the data includes extreme values that skew the average. If we had data about car colors across multiple streets, we might look at frequency distributions to identify the most common car colors.

When working with two variables, such as the number of cars and the time of day, creating a scatterplot could reveal any trends, like whether more cars tend to park during certain hours. For larger datasets with more variables, techniques like correlation analysis could determine whether a relationship exists between car colors and other factors, such as weather conditions or parking costs. For highly detailed data, factor analysis might even uncover hidden patterns, like why certain streets attract more white cars.

In short, the choice of statistical methods depends on the data’s complexity and the questions we want to answer, moving from simple descriptive techniques to more advanced tools as needed (Table A.1).

Table A.1

Deciding on what statistics to do based on what available data

Number of rows (variables)

Statistical technique

Why it makes sense

1

Descriptive statistics (mean, median, mode, standard deviation)

Summarizes the central tendency and variability of a single variable

1

Histogram or boxplot

Visualizes the distribution of the data, revealing patterns such as skewness or outliers

1

One-sample t-test

Tests whether the sample mean differs significantly from a known value or population mean

2

Correlation analysis

Measures the strength and direction of the relationship between two variables

2

Simple linear regression

Predicts one variable (dependent) based on the other (independent) and evaluates the linear relationship

2

Paired t-test

Compares means of two related datasets (e.g., before-and-after measurements)

2

Scatterplot

Provides a visual representation of the relationship between two variables

2+

Multiple linear regression

Explores how multiple independent variables predict a dependent variable

2+

Factor analysis or PCA

Reduces data dimensionality and identifies latent structures or components in the variables

2+

ANOVA (analysis of variance)

Compares means across more than two groups to detect significant differences

2+

MANOVA (multivariate ANOVA)

Extends ANOVA to test for differences across multiple dependent variables

2+

Clustering (e.g., K-means)

Groups observations based on similarities across multiple variables

2+

Time series analysis (e.g., ARIMA)

Analyzes and models patterns over time when data is collected at regular intervals

These statistical techniques will probably be all you need to learn when starting out as a social science researcher. Descriptive statistics, for instance, help summarize data and give you a quick understanding of trends, central tendencies, and variability in your dataset. However, as you delve deeper into your research, more advanced techniques become essential, particularly when you need to investigate relationships, causation, or complex patterns in the data.

For example, analysis of variance (ANOVA) and MANOVA are invaluable when comparing means across groups to determine whether differences are statistically significant, which is often useful in experimental studies. When dealing with observational data, regression analysis or logistic regression can help identify relationships between variables and predict outcomes. If your study involves time-based observations, time series analysis and the Granger causality test can explore dynamic relationships and causal links between variables over time.

In situations where experimental designs are not possible, techniques like propensity score matching (PSM) or instrumental variables (IVs) can control for confounding factors and improve the validity of causal inferences. For hierarchical data, where measurements are nested (e.g., students within schools), hierarchical linear modeling (HLM) or multilevel modeling can provide more accurate analyses by accounting for data structure. When investigating latent constructs, such as attitudes or satisfaction, structural equation modeling (SEM) or confirmatory factor analysis (CFA) offers robust frameworks for testing complex theoretical models.

Moreover, when traditional parametric methods are not applicable due to data constraints, resampling techniques like bootstrapping provide flexibility for estimating confidence intervals or conducting hypothesis tests. Advanced methods such as cluster analysis are useful for grouping data into meaningful categories, and survival analysis is indispensable for studying time-to-event data, such as customer churn or disease progression.

In short, as your research questions grow in complexity, these advanced techniques allow you to extract more nuanced insights, test sophisticated hypotheses, and ensure your findings are both rigorous and impactful (Table A.2).

Table A.2

Statistical techniques used in the book

#

Statistical technique (Alph. order)

When to use

Key formula/expression

1

Analysis of variance (ANOVA)

Comparing means across multiple groups

F=MSbetweenMSwithin

2

Bootstrapping

Estimating sampling distribution by resampling

Repeated resampling of data to estimate distribution without a specific formula

3

Chi-square test

Testing for association between categorical variables

x2=∑O−E2E

4

Cluster analysis

Grouping objects based on similarities

Distance measures, e.g., Euclidean

dxy=∑i=1nxi−yi2

5

Confirmatory factor analysis (CFA)

Testing hypothesis about the factor structure of a set of variables

Σ = ΛΦΛ′ + Θ

6

Correlation analysis

Measuring relationship strength between two variables

r=∑Xi−X¯Yi−Y¯∑Xi−X¯2.∑Yi−Y¯2

7

Descriptive statistics

Summarizing data characteristics (mean, median, mode)

X¯=∑Xn

8

Factor analysis

Reducing data dimensions and identifying factor structure

Principal component method

Eigenvalues and eigenvectors of covariance matrix

9

Granger causality test

Testing causality in time series data

yt=c1+∑i=1pα1,i.Yt−1+∑i=1pβ1,i.Xt−1+εt

10

Harman’s single-factor test

Testing for common method bias

Performed in factor analysis with a single-factor constraint

11

Hierarchical linear modeling (HLM)

Analyzing data with nested structures

Multilevel regression models in several layers

12

Instrumental variables (IVs)

Dealing with endogeneity in regression

First stage: X̂=α+βZ+ε

Second stage: Y=γ+δX̂+

13

Logistic regression

Modeling binary outcome variables

Pi=PY=111+e−α+βxi

14

Longitudinal analysis

Analyzing data over time to observe changes for the same unit of observation

Time-dependent covariates in regression models

15

MANOVA (multivariate ANOVA)

Comparing group means on multiple dependent variables

The general F-test is

F=VariancebetweengroupsVariancewithingroups

16

Mediation analysis

Testing if a variable mediates the relationship between two others

Direct effect + indirect effect (via mediator)

17

Multigroup analysis

Testing if structural model parameters differ across groups

Multiple-group CFA or SEM

18

Multilevel modeling

Modeling data that is clustered or hierarchical

Yij = β0j + β1jXij + eij

19

P-value and confidence intervals

Estimating statistical significance and precision

P=P(T ≥ ∣t∣) or P(Z ≥ ∣z∣)

CI0x¯±zsn

20

Partial least squares structural equation modeling (PLS-SEM)

Analyzing complex models with latent variables

Iterative weight estimation, no single formula

21

Propensity score matching (PSM)

Reducing selection bias in observational studies

e(x) = P(T = 1∣X = x)

22

Randomized controlled trials (RCTs)

Testing interventions in controlled experiments

Random assignment to treatment/control

23

Regression analysis

Modeling the relationship between independent and dependent variables

Yi = β₀ + β₁Xi + εi

24

Structural equation modeling (SEM)

Testing relationships among latent variables or simultaneous determined variables

Does not have a single formula since it involves a set of mathematical equations representing multiple relationships between observed and latent variables. However, the general structure combines path analysis and factor analysis and can be summarized using matrices

25

Survival analysis

Analyzing time-to-event data non-parametrically

t=∏i=1tni−hini

26

T-test

Comparing means between two groups

t=X¯1−X¯2s12n1+s22n2

27

Time series analysis

Analyzing data collected at regular intervals over time

Yt = c + ϕ₁Yt − 1 + θ₁ϵt − 1 + ϵt

28

Variance inflation factor (VIF)

Detecting multicollinearity in regression analysis

VIFj=11−Rj2

29

Z-test

Testing population mean against a standard

z=X¯−μσn

Statistical techniques can be divided into three main branches, each with a distinct purpose: Methods starting with the chi-square test focus on analyzing categorical data to test for independence or goodness of fit. Techniques beginning with the T-test assess differences between groups or conditions, helping determine whether observed variations are statistically significant. Finally, methods building on correlation analysis explore relationships between variables, often leading to regression and predictive modeling to uncover patterns and causal effects (Fig. A.1).

Fig. A.1

The process for distinct purposes of statistical techniques

The workflow of statistical techniques begins with descriptive statistics, which provide a foundational understanding of the dataset by summarizing key characteristics like mean, median, and mode. From here, one might move to correlation analysis to explore relationships between variables and assess whether they move together, laying the groundwork for more sophisticated analyses. If relationships are suspected, regression analysis becomes the next logical step, enabling predictions and the examination of cause-and-effect relationships between the variables. For datasets requiring categorical comparisons, we use chi-square tests and T-tests which allow us to determine significant differences across groups or conditions.

Once relationships are identified, techniques like factor analysis and confirmatory factor analysis (CFA) can be employed to uncover underlying structures within the data, which can then inform structural equation modeling (SEM) to examine complex relationships involving latent variables. For nested data structures, such as students within schools, hierarchical linear modeling (HLM) and multilevel modeling are natural extensions, capturing variation at multiple levels. When data distribution assumptions are violated, techniques like bootstrapping and PLS-SEM can help address these challenges by estimating results through resampling or focusing on predictive accuracy.

As we continue, specialized methods like time series analysis come into play when analyzing trends over time, often supplemented by the Granger causality test to explore predictive relationships. For experiments or intervention studies, randomized controlled trials (RCTs) and propensity score matching are critical in minimizing bias and isolating treatment effects. Finally, tools like variance inflation factor (VIF) can help ensure model robustness by identifying multicollinearity, while Harman’s single-factor test checks for potential bias in single-source data. Each step in this workflow is logically connected, progressing from foundational data exploration to advanced modeling and validation, ensuring both rigor and relevance in our analysis.

Below is a breakdown of the statistical techniques, one at a time with an equation and a brief explanation for each. This will provide a foundation, so you can see how each technique is calculated and when it might be used in research.

Equation 5: Analysis of Variance (ANOVA)

Equation:

F=MeanSumof Squares Between GroupsMeanSumof Squares Within Groups

Explanation: ANOVA is used to compare means across multiple groups to see if at least one differs significantly. You calculate the F-statistic by dividing the mean sum of squares between groups by the mean sum of squares within groups. If F is large, it suggests at least one group mean is different from others.

Equation 6: Bootstrapping

Procedure: Bootstrapping doesn’t use a specific formula but involves repeatedly resampling your data with replacement to create many simulated samples. From these samples, you calculate estimates (like the mean or median) and build confidence intervals around these estimates.

Explanation: Bootstrapping is often used when assumptions about data distribution are uncertain. By creating thousands of resampled datasets, you can approximate the sampling distribution and create more reliable estimates.

Equation 7: Chi-Square Test

Equation:

χ2=∑Oi−Ei2Ei

Explanation: The chi-square test compares observed (O) and expected (E) frequencies in categorical data. If the computed χ²-statistic is high, it suggests a significant difference between observed and expected frequencies, indicating that the variables may not be independent. Alternatively, it indicates that the variable does not follow an expected distribution.

Equation 8: Cluster Analysis

Equation:

dxy=∑i=1nxi−yi2

Or more explicitly

dxy=x1−y12+x2−y22+,..+xn−yn2

Procedure: Cluster analysis groups data points into clusters based on similarity. The commonly used algorithm, K-means clustering, iterates to minimize within-cluster variance by assigning points to the nearest centroid.

Explanation: Cluster analysis is useful for segmenting data into natural groupings. It’s commonly used in marketing to identify customer segments.

Equation 9: Confirmatory Factor Analysis (CFA)

Equation:

Σ=ΛΦΛ′+Θ

Explanation: CFA tests whether data fits a hypothesized measurement model. Here, Σ (matrix) is the covariance matrix of observed variables, Λ (Lambda) is the factor loading matrix, Φ (Phi) is the covariance of the latent variables, and Θ (Theta) represents error variances. CFA is often used to validate survey structures or constructs in psychological research.

∑ is the matrix for predicted covariances for observed variables.

Λ is the item factor loading matrix, and Λ´ is the inverse of the factor loading matrix.

Φ (Phi) is the variances and covariances of the latent variables, and Θ (Theta) represents error variances. CFA is often used to validate survey structures or constructs in psychological research.

Equation 10: Correlation Analysis

Equation:

r=∑Xi−X¯Yi−Y¯∑Xi−X¯2•∑Yi−Y¯2

Explanation: Correlation analysis measures the strength and direction of a linear relationship between two variables. The r-value ranges from −1 to 1, where values closer to ±1 indicate strong linear correlations.

Equation 11: Descriptive Statistics

Basic Measures:

Mean:

X¯=∑Xin

Median: There is no formula, as it’s typically the middle value of a sorted list.

Mode: Also does not have a mathematical formula but represents the most frequently occurring value.

Variance:

For a sample

s2=∑Xi−X¯2n

For a population

σ2=∑X−X¯2n

Standard deviation:

s=∑Xi−X¯2n−1

Explanation: Descriptive statistics summarize data. The mean provides an average, the median shows the middle value, and the standard deviation indicates the spread of the data.

Equation 12: Factor Analysis

Equation:

X=ΛF+ϵ

Explanation: In exploratory factor analysis, the observed variables X are explained by a few underlying unobserved factors F, with Λ as the loading matrix and ϵ representing error. Factor analysis reduces data dimensionality by identifying clusters of related variables.

Equation 13: Granger Causality Test

Equations (simplified):

1.

Yt=a0+∑i=1paiYt−i+∑j=1qbjXt−j+ϵt

2.

Null hypothesis: bj = 0 ∀ j

The second equation provides an alternative representation of the Granger causality test, using different notations to highlight similar relationships.

yt=c1+∑i=1pα1,i.Yt−1+∑i=1pβ1,i.Xt−1+εt

Explanation: The Granger causality test checks if one time series can predict another. If adding past values of X significantly improves the prediction of Y, X is said to “Granger-cause” Y.

Equation 14: Harman’s Single-Factor Test

Procedure: This test does not have a specific formula. Instead, it involves running a factor analysis to check if a single factor accounts for most of the variance in the data. It is often used with principal component analysis and exploratory factor analysis.

Explanation: Harman’s test is used to detect common method bias, a concern in self-report data. If one factor explains most of the variance, it may suggest bias from a single source.

Equation 15: Hierarchical Linear Modeling (HLM)

Equation (simplified form):

Yij=β0j+β1jXij+u0j+eij

where

Yij is the outcome for individual i in group j.

Β0j is the intercept for group j.

Β1j is the slope for X.

u0j is the random effect at the group level.

eij is the residual error.

Explanation: HLM, or multilevel modeling, accounts for nested data (e.g., students within schools). This method estimates effects at multiple levels (e.g., individual and group) to capture variation within and between groups. It is sometimes called a random parameters model depending on whether the coefficients are fixed or random.

Equation 16: Instrumental Variables (IVs)

Equation: Two-stage least squares (2SLS) method:

First stage: X̂=α+βZ+ε

Second stage: Y=γ+δX̂+

Explanation: IV is used when an independent variable is correlated with the error term, causing endogeneity. An instrument Z (uncorrelated with the error) predicts X, allowing estimation of Y’s relationship with X without bias.

Equation 17: Logistic Regression

Equation:

Pi=PY=111+e−α+βxi logistic and it can be written as

logitp=logp1−p=β0+β1X1+β2X2+…+βkXk

In this equation

logit(p) represents the log-odds of the probability p (log is natural log).

β₀ is the intercept.

Β₁,β₂,…,βk are the coefficients for the independent variables X1,X2,…,Xk.

Explanation: Logistic regression predicts binary outcomes (e.g., success/failure) by estimating the probability p of an event. It’s widely used when the response variable is categorical, and it outputs odds ratios for each predictor.

Equation 18: Longitudinal Analysis

There isn’t a single, universally applicable equation that represents longitudinal analysis. Instead, longitudinal analysis encompasses a variety of models and techniques, such as

Repeated-measures ANOVA, which analyzes within-subject changes over time

Mixed-effects models, which include both fixed effects (predictors that are the same across all individuals) and random effects (individual-specific variations)

While these methods are mathematical, the equations used depend on the specific model. For example, a mixed-effects model might be expressed as

yij=β0+β1xij+ui+εij

where

yij (“y sub i j”) = response for individual i at time j

xij (“x sub i j”) = predictor variable(s)

β₀ (“beta naught”) = fixed-effect intercept

β₁ (“beta one”) = fixed-effect coefficient for the predictor

ui (“u sub i”) = random intercept for individual iii

εij (“epsilon sub i j”) = residual error term

General approach: Longitudinal analysis often involves repeated-measures ANOVA or mixed-effects models.

Explanation: This approach studies data collected over time, allowing for analysis of changes within individuals. It’s beneficial for understanding the development, progression, or behavior changes in subjects over time.

Equation 19: MANOVA (Multivariate Analysis of Variance)

Equation (basic):

The general F-test is

F=Variance between groupsVariance within groups

The specific test statistic used in MANOVA to test the overall null hypothesis that group means are equal across all dependent variables is

Wilks'Lambda=detWdetT

where W is the within-group matrix and T is the total matrix.

Explanation: MANOVA extends ANOVA by assessing multiple dependent variables simultaneously. It evaluates if group differences exist across multiple outcomes, useful in complex experiments with correlated measures.

Equation 20: Mediation Analysis

Equations:

Y=i1+cX+ε1

Y=i2+c′X+bM+ε2

M=i3+aX+ε3

Explanation: Mediation analysis determines if an intermediate variable (mediator M) explains the relationship between X and Y. If c′ is significantly reduced compared to c, mediation is suggested, indicating an indirect effect of X on Y through M.

Equation 21: Multigroup Analysis

Procedure: In structural equation modeling (SEM), multigroup analysis tests model invariance across groups by comparing model fit.

Explanation: This method is often used to test if relationships hold across different groups, such as testing whether an educational intervention has the same impact across age or gender groups.

Equation 22: Multilevel Modeling

Equation

General Structure of a Two-Level Model

Level 1 (Within-Group Model)

Yij=β0j+β1jXij+eij

Xij: dependent variable for individual i in group j

Βoj: intercept for group j

Β1j: slope of predictor Xij for group j

Xij: predictor for individual i in group j

eij: residual/error term at the individual level

Level 2 (Between-Group Model)

βoj=γ00+uoj

β1j=γ10+u1j

βoj (“beta sub zero j”) = the intercept for group j

γ00 (“gamma zero zero”) = the grand mean of the dependent variable across all groups

uoj (“u sub zero j”) = the random effect for group j, representing a deviation from the grand mean

γ10 (“gamma one zero”) = average slope across groups

u1j (“u sub zero one j”) = group-specific random effect for slope

Combined Model

Yij=γ00+γ10Xij+uoj+u1jXij+eij

Similar to HLM: Multilevel modeling allows data with nested structures to be analyzed.

Explanation: Like HLM, multilevel modeling can account for dependencies within groups (e.g., students in classrooms). It’s useful for examining effects at multiple levels and is frequently used in education and organizational research.

Equation 23: P-Value and Confidence Intervals

Equations:

P-value: the probability of observing data at least as extreme as the current data under the null hypothesis.

Confidence interval (CI) for a mean: X¯±Zσn.

In this equation

X¯ represents the sample mean.

Z is the Z-score corresponding to the desired confidence level.

σ is the population standard deviation.

n is the sample size.

Explanation: The p-value indicates the likelihood that the observed results occurred by chance. Confidence intervals provide a range within which the true population parameter is expected to fall. Both are crucial in hypothesis testing and understanding uncertainty in estimates.

Equation 24: Partial Least Squares Structural Equation Modeling (PLS-SEM)

Partial least squares structural equation modeling (PLS-SEM) does not have a specific equation to show, as it involves an iterative algorithm rather than a single, closed-form mathematical formula. The process relies on maximizing explained variance for latent variables and observed indicators through partial least squares estimation.

Instead of an equation, we can include an explanation of the steps or an illustrative diagram of the PLS-SEM process. Alternatively, if an equation is requested, we could present the basic regression formula used iteratively in PLS-SEM:

Y=βX+ϵ

This regression step is central to estimating paths between constructs and indicators during the iterative estimation process.

Procedure: PLS-SEM maximizes explained variance for the dependent variables in complex models, particularly for exploratory research with small sample sizes.

Explanation: PLS-SEM is ideal for predictive models and when data distribution assumptions are violated. It is widely used in social sciences for analyzing complex variable relationships. While SEM in general may involve simultaneous equation modeling, PLS-SEM focuses on partial least squares estimation to handle complex models and smaller sample sizes.

Equation 25: Propensity Score Matching (PSM)

The formula for propensity score matching (PSM) is not a single mathematical formula but rather a procedure for matching treated and untreated observations based on their propensity scores, which are typically estimated using logistic regression or probit regression. The propensity score e(x) is the conditional probability of receiving treatment given covariates x:

ex=PT=1|X=x=expβ0+β1X1+β2X2+…+βkXk1+expβ0+β1X1+β2X2+…+βkXk

where

e(x): the propensity score, representing the probability of receiving the treatment (T = 1) given the covariates (X)

T: the treatment indicator (T = 1 for treated, T = 0 for untreated)

X = x: the set of observed covariates

β₀ + β₁ X₁ + β₂ X₂ +...+ βk Xk: coefficients estimated from logistic regression (or probit regression)

Key steps in PSM are as follows:

1.

Estimate propensity scores: Use logistic regression to estimate e(x) for each observation.

2.

Match observations: Pair treated and untreated observations with similar propensity scores using methods like nearest neighbor matching, radius matching, or kernel matching.

3.

Estimate the treatment effect: Compute the average treatment effect on the treated (ATT):

ATT=1NT∑iϵTYiT−YiC

NT = number of treated observations

YiT= outcome for treated units

YiC= outcome for matched untreated units

Procedure: PSM creates comparable groups based on propensity scores (predicted probability of treatment) to reduce selection bias in observational studies.

Explanation: By matching individuals with similar characteristics, PSM approximates randomized controlled trials in nonexperimental data, increasing causal inference validity.

Equation 26: Randomized Controlled Trials (RCTs)

For randomized controlled trials (RCTs), there isn’t a specific mathematical formula to represent the design itself because RCTs are primarily a methodological framework. However, RCTs rely on statistical techniques to analyze the data collected, such as comparing means or proportions between treatment and control groups using tests like t-tests, ANOVA, or logistic regression.

Design: RCTs randomly assign participants to treatment or control groups to isolate the treatment’s effect.

Explanation: RCTs are the gold standard for testing causation, as randomization minimizes biases. Used in clinical and social science research, they provide strong evidence for causality.

Equation 27: Regression Analysis

Equation (Simple Linear Regression):

Yi=β0+β1Xi+εi

where

Y is the dependent variable.

β₀ is the intercept.

β₁ is the slope coefficient for X, the independent variable.

ϵ represents the error term.

Explanation: Regression predicts the dependent variable Y based on one or more independent variables X. It’s foundational for understanding relationships between variables and making predictions.

Equation 28: Structural Equation Modeling (SEM)

Equation (Basic Structure):

Y=Λη+ϵY

where Λ (Lambda) is the loading matrix, η (eta) is the latent variable, and ϵ (epsilon) is the error term.

Explanation: SEM tests complex relationships between observed and latent variables, useful in psychology, sociology, and other social sciences.

The structural equation comes from theory and has a causal interpretation. Basically, it is estimated with structural equation modeling (SEM)

y1=α1y2+x1β1+ε1

y2=α2y2+x2β2+ε2

where y₁ and y₂ are endogenous variables.

Equation 29: Survival Analysis

Equation (Kaplan-Meier Estimator):

St=∏i=1tni−hini

where S(t) is the survival probability at time t.

di is the number of events at time ti and ni is the number at risk.

ni = the number of individuals for whom the event has not occurred from the start-up to time point i (those at risk of experiencing the event).

hi = the number of individuals for whom the event occurs at time point t.

Explanation: Survival analysis examines the time until an event occurs (e.g., failure, death). It’s crucial in fields like medicine and engineering to understand longevity and risk.

Equation 30: T-Test

Equation (Independent Samples T-Test):

t=X¯1−X¯2s12n1+s22n2

where

X¯₁ and X¯₂: sample means of the two groups

s21and s22: sample variances of the two groups

n₁ and n2: sample sizes of the two groups

Explanation: The t-test compares the means between two groups to see if they differ significantly. Commonly used in experimental research, it tests hypotheses about group differences.

Equation 31: Time Series Analysis

Equation (ARIMA Model):

Autoregressiv process

Yt=c+ϕ1Yt−1+θ1ϵt−1+ϵt

Moving average

Yt=c+ϕ1Yt−1+εt+θεt−1+vt

where

Yt is the value of the time series at time t.

c is the constant term.

ϕ₁is the autoregressive coefficient.

Yt-1 is the previous value of the time series.

Θ₁ is the moving average coefficient.

ϵt-1 is the error term from the previous time period.

ϵt is the current error term.

Explanation: Time series analysis examines data points collected sequentially over time, forecasting future values based on past trends. It’s vital for economics, finance, and other fields requiring temporal prediction.

Note that ARIMA models concern nonstationary time series which are made stationary by integration (this is where the letter I comes from).

ARMA is for stationary time series:

Yt=c+ϕ1Yt−1+εt+θεt−1+vt

Equation 32: Variance Inflation Factor (VIF)

Equation:

VIFj=11−Rj2

where

VIFj is the variance inflation factor for predictor j.

Rj2 value when predictor j is regressed on the other predictors.

Explanation: VIF measures multicollinearity in regression models, with high values suggesting redundancy among predictors. Lower VIF values are desirable for reliable regression coefficients.

Equation 33: Z-Test

Equation (One-Sample Z-Test):

z=X¯−μσn

where

Z is the test statistic in a z-test.

X¯ is the sample mean.

μ is the population mean.

σ is the population standard deviation.

n is the sample size.

Explanation: The Z-test compares the sample mean to a population mean when the population variance is known, helping to test hypotheses about population parameters. It’s common in large-sample research.

Each of these methods serves a specific purpose in social science research, providing the tools you need for data analysis and hypothesis testing.

Another, more practical approach to selecting the right statistical techniques is to start by identifying the type of research article you aim to write. From there, you can determine the statistical methods necessary to achieve your objectives and support your findings effectively (Table A.3).

Table A.3

More common statistical techniques used in social science research

Type of research article

Purpose

Common statistical methods

Examples

Descriptive studies

To summarize data and describe characteristics of a population or phenomenon

Descriptive statistics (mean, median, mode), frequency distributions, cross-tabulations

Demographic studies, surveys

Comparative studies

To compare differences between groups or variables

t-tests, ANOVA (analysis of variance), chi-square tests

Gender differences in education outcomes

Correlational studies

To examine relationships between two or more variables without implying causation

Pearson’s/spearman’s correlation, regression analysis

Relationship between income and health

Experimental studies

To test causal hypotheses by manipulating one or more variables and observing the effects

ANOVA, MANOVA, mixed-design ANOVA, post hoc tests

Effects of a training program on productivity

Quasi-experimental studies

To test causal relationships when random assignment isn’t feasible

ANCOVA (analysis of covariance), propensity score matching, regression

Policy impact studies

Longitudinal studies

To observe changes over time within a population or group

Time series analysis, repeated-measures ANOVA, growth curve modeling

Cohort studies on aging

Meta-analysis

To synthesize results across multiple studies and estimate an average effect size

Effect size calculations (Cohen’s d, hedges’ g), mixed-effects models

Meta-analyses of interventions

Structural equation modeling (SEM) studies

To test complex models with multiple dependent and independent variables, often latent

SEM, path analysis, confirmatory factor analysis (CFA)

Social behavior models, attitudinal studies

Survey research

To collect standardized information from a large population sample

Factor analysis, reliability analysis (Cronbach’s alpha), multivariate regression

Public opinion surveys

Case studies

To provide an in-depth understanding of a specific case, often without generalizing findings

Descriptive statistics, content analysis

Organizational case studies

Network analysis studies

To examine relationships and structures within a network of entities

Social network analysis (SNA), centrality measures, community detection

Analysis of social media interactions

Some statistical techniques are less common in social sciences due to the complexity of data requirements, the specificity of the problems they address, or their suitability for niche research designs.

For example, Granger causality tests are infrequently used, as they require time series data, which is less common in social sciences outside of fields like economics. Similarly, hierarchical linear modeling (HLM) and multilevel modeling are complex and best suited for data with nested structures, such as student performance within schools; hence, they are typically applied only in specialized fields like education research.

Structural equation modeling (SEM) and confirmatory factor analysis (CFA) are also relatively advanced techniques that require large sample sizes and complex models. They are often used in psychology and behavioral studies but are less practical for general social science research due to their steep learning curve and need for specialized software.

Additionally, bootstrapping and instrumental variable (IV) analysis are less common because they address specific methodological issues. Bootstrapping is often reserved for cases where data distribution assumptions are questionable, while IV analysis is mainly used in econometrics to address endogeneity—a concept that isn’t always relevant in broader social science studies.

Cluster analysis and mediation analysis are also less frequently employed outside of niche applications, as they involve complex interpretation and specific data needs. Cluster analysis, for example, is mainly valuable in fields like market segmentation but is less relevant for standard social science studies. Finally, survival analysis and longitudinal analysis require data collected over extended periods, making them suitable only for studies focusing on time-dependent processes, such as health outcomes.

These techniques, while powerful, are reserved for particular cases and are often replaced by more commonly used methods like t-tests, ANOVA, regression analysis, and descriptive statistics, which are more straightforward and broadly applicable across a range of social science research designs. So most of the statistics we use in the social sciences are pretty straightforward—nothing that a few hours (and maybe a strong cup of coffee) can’t cover!

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Index

A

Abductive reasoning

Abstract

Academic departments

Academic ecosystem

Academic freedom

Academic functional blindness

Academic honesty

Academic integrity

Academic Journal Guide (ABS index)

Academic ladder

"Academic leadership rotation" system

Academic types

Accept (A)

Acceptance

Acknowledge study limitations

Active communities

Actual expertise

Addressing the ‘So What?’ Question

Adjunct lecturer

Administrative staff

Admissions office

Adorno, T.W.

Agnostic

AI

Alexander von Humboldt Foundation

Algebra

Alternative Hypothesis (H1)

Altmetrics

Alvesson, M.

Ambiguities in the methodology

American Sociological Association

Amin, S.

Analysis of Variance (ANOVA)

Analysis

Analytic philosophy

Anaximander

Anchoring bias

Annales School

Antithesis

Apollonius of Perga

Applied research

Aquinas, T.

Archimedes

Areas

Aristotelianism

Aristotle

Arithmetic

Arrow’s impossibility theorem

Asch, S.E.

Asimov, I.

Assistant professor

Associate professor

Associative patterns

Assumptions

Attribute-based comparison

Australian Business Deans Council (ABDC)

Austrian economics

Author contributions

Author guidelines

Author responsibilities

Authorship inflation

Availability heuristic

Avoid overgeneralizing

Awareness of observation (Hawthorne effect)

Axelrod, R.

Ayer, A.J.

B

Bachelor’s degree

Bacon, F.

Basic research

Bateson, G.

Baudrillard, J.

Bayesian decision theory

Bayes’ theorem

Beccaria, C.

Beck, A.T.

Becker, G.S.

Behavioral economics and psychology

Behavioral economics

Behavioral experiments

Behavioral psychology

Behaviorism

Benchmark comparison

Bentham, J.

Bergson, H.

Berkeley, G.

"Berlin Circle"

Berlin Society for Scientific Philosophy

Berman, R.A.

Bernal, J.D.

Bhabha, H.K.

Bhaskar, R.

Bibliometric review

Bibliometric

Big case

Bildung

Bill & Melinda Gates Foundation

Binary/dichotomous scale

Binary/dichotomous variable

Biological evolution

Blanchard, O.

Blend of disciplines

“Blinded”

Bloch, M.

Boas, F.

Body of theories

Bohlin, N.

Book

Book chapter

Bootstrapping

Boulding, K.E.

Boyd, R.

Boyle’s Law

Brahe, T.

Brain drains

Branches

Braudel, F.

British Academy

Bruno, G.

Bücher, K.

Budget justification

Buffon, G.L.L.

Bureaucrats

Burkhardt, F.

Burnout

Business studies

Butler, J.

C

Calculus

Campus

Canguilhem, G.

Careful observation

Carnap, R.

Carroll diagrams

Cartesian system

Case

Guided by Chance: Reflections on a Researcher’s Journey

Case: Barriers to Research Access and the Emergence of Sci-Hub

Case: Book Cultures vs. Article Cultures in the Social Sciences

Case: Climbing the Greasy Pole in Academia

Case: Comparing Global Dissertation Processes: Germany, France, the UK, and the US as compared to Sweden

Case: Cooperating with your spouse

Case: Finding your quiet moments and places to work

Case: How to set you your working routine

Case: My Doktorvater: A Tale of Tradition, Respect, and Mentorship

Case studies

Case study framework

Case: The Realities of Collaborative Case Study Research: Time, Patience, and Persistence

Case: The Roles of Public Intellectuals and Social Scientists in Society

Case: The Wallenberg Foundations and Research Excellence in Sweden

Castle of Urbino

Categorical variable

Causal

Causal analyses

Causal explanation

Causality vs. correlation

Causation

Cause-and-effect relationships

Challenging issues

Chancellor/president

Chi-square tests

Chomsky, N.

Churchill, W.

Cialdini, R.B.

Citation cartels

Citation circles

Citation padding

CiteScore

CiteScore - Scopus

Civil rights

Clarity and specificity

Clarity in findings

Clark, K.

Classical economics

Classical Period

Classical philosophy

Classifications

Class structure

Clear, direct language

Clinical trials

Closed-ended questions

Closure

Cloud-based platforms

Cluster analysis

Cluster sampling

sampling

Coase, R.H.

Coding and data entry

Co-funding

Cognitive biases

Cognitive dissonance bias

Cognitive predispositions

Cognitive psychology

Cognitivism

"Collaborative grant hedging"

Collaborative grants

Collaborative research models

Collection of facts

Collective knowledge

College

Colloquiums

Comments

Common sense

Common-sense issues

Commons, J.R.

Communicative action theory

Community dynamics

Comparative analysis

Comparative case

Comparative explanation

Comparative methods

Complexity of subjectivity

Complexity of variables

Comte, A.

Conant, J.B.

Conceptual model

Conclusion

Conclusion and implication

Conference paper

Confidence intervals

Confidence level

Confirmation bias

Confirmatory factor analysis (CFA)

Conflict of interest (COI)

Conflict of interest (COI) form

Conflict theory

Confounding variable

Connect the dots

Consider the variables

Constructing ideas

Constructivism

Constructivist pedagogy

Construct validity

Consultancy work

Consultant types

Consumer-based studies

Contact information

Content analysis

Content validity

Context

Context and understanding

Contextual focus

Continuous variable

Contrived (artificial)

Controlled environment

Controlled experiments

Controlled laboratory experiment

Control group comparison

Control variable

Convergent validity

Convenience sampling

sampling

Copernicus, N.

Copyright and Open Access

Copyright transfer agreement

Corporate/industry grants

Correlation analysis

Correlations

Cost and resource demands

"Cother goose" scenario

Counterarguments

Course registration office

Covariation (Correlation)

Cover letter

Cover, J.A.

Crane, N.

Creative destruction

Credits

Criteria

Criterion validity

Critical rationalism

Critical review

Critical self-assessment

Critical theory

Critical thinking

Cross-disciplinary

Crossref

Cross-sectional comparison

Cross-sectional study

Cultural behaviors

Cultural context

Cultural factors

Cultural materialism

Cultural practices

Cultural relativism

Cultural universals

Cultures

Curd, M.

Cuvier, G.

Cybernetics

D

Daly, H.E.

Darwin, C.

Das Kapital

Data

Data analysis

Data analysts

Data capture

Data collection

Data entry

Data sharing policies

Data transformation

Dawkins, R.

Deans

de Beauvoir, S.

Decision-based case

Decision theory

Deductive

Deductive explanation

Deductive method

Deductive reasoning

Degree of certainty

Demographic context

de Montaigne, M.

Department chairs

Dependency theory

Dependent variables

Derrida, J.

de Saussure, F.

Descartes, R.

Description

Descriptive

Descriptive analysis

Descriptive case

Descriptive research

Descriptive statistics

Desk rejection

de Tocqueville, A.

Development economics

Dewey, J.

Dialectical materialism

Dichotomous method

Difference-in-differences (DiD)

Difficulties in randomization

Dilthey, W.

Dimensions

Diploma

Directionality

Discipline

Discipline-driven approach

Discourse analysis

Discriminant validity

Discussion

Disputation

Dissertation committee

Distilling for the abstract

Diverse family structures

DNA research

Domain

Double-blind or peer review process

Drucker, P.F.

Dubislav, W.

Duhem, P.

Durkheim, E.

Dynamic capabilities theory

Dynamic social contexts

E

Ecological economics

Ecological systems

Econometrics

Economic context

Economic policy

Economics

Economic systems

Edited books

Editing Data (actual cleaning)

Editors

Educated guesses

Educational and health outcomes

Education policy

Education research

Educators

Educators/teachers

Eibl-Eibesfeldt, I.

EIC Accelerator (European Innovation Council)

Eigenfactor

Einstein, A.

Electives

Elimination of Confounders (Regression)

Elizabeth Boody Schumpeter

Emperor Charles IV

Empirical evidence

Empirical-positivist

Empiricism

Empiricist scientific perspective

Endowment effect

Energy policy

Engels, F.

Enlightenment

Ensuring objectivity and rigor

Environmental and sustainability studies

Environmental policy

Environmental science

Epictetus

Epicureanism

Epicurus

Epistemological frameworks

Epistemological understanding

Epistemology

Erasmus

Erasmus of Rotterdam

ERC Starting Grants

Error analysis

Ethical and access constraints

Ethical and legal considerations

Ethical concerns

Ethical considerations

Ethical standards

Ethics

Ethics and impact

Ethics approval letter

Ethnographic methods

Ethnography

Euclid

Eudaimonism

European Science Foundation (ESF)

European Strategy Forum on Research Infrastructures (ESFRI)

Evidence-based knowledge

Evolutionary approach

Evolutionary biology

Evolutionary economics

Evolutionary perspective

Evolutionary psychology

Evolutionary school

Evolution of research

Example: Case Study Meets Laboratory Experiment: Simulating Information Asymmetry in Negotiations

Example: “Conflict of Interest Declaration

Example: Do your accounting teacher know any accounting?

Example: Institutional Review Board (IRB) Ethics Approval Letter

Example: “Leadership Style, Organizational Design, and Knowledge Hiding in the Workplace”

Example: Legacies

Example: Mapping Drinking Trends and Behaviors

Example in social sciences

Example: Scurvy and car accidents

Example: The Evolutionary Approach in Social Sciences: A Historical and Contemporary Perspective

Examples of Studies

Exams

Exams and Records Office

Excessive interference

Exercise A: Replicate a study

Exercise: A Metacognitive Reflection Task

Exercise: Finding Your School of Thought

Exercise: How to Conduct a Real-World Causal Investigation

Exercise: The ‘Money Acceptance Theory’

Exhaustive division

Existentialism

Experience

Experiential learning

Experimental methods

Experimental simulations

Experimenter effect

Experiments

Expert knowledge

Explanatory

Explanatory models

Explicandum

Explicandum of seat belt efficacy

Exploratory

Exploratory case

Exploratory nature of qualitative research

Exploratory research

External academic appointments

External collaborators

External evaluations

External pressures

External stakeholders

Extraneous Variable

F

Face validity

Fact

Factor analysis

Faculty

Faculty support services

Fairness criteria

False consensus effect

Falsehood

Falsifiability

Fäntzel-Clairmont

Favoritism

Fayol, H.

Feedback

Fellowships

Feminist economics

Ferguson, N.

Fiction

Fiction authors

Field experiments

Field researchers

Fields

Figures and tables

Figures/images

Final thought-provoking statement

Finance

Financial Aid & Tuition Office

Financial analysts

Financial Times 50 (FT50)

Find an outlet for the paper

Findings

Finn-Junge Jensen

Fisher, R.A. Sir

Focus area

Folbre, N.

Fonseca, G.L.

Formal logic

Formas

Forrester, J.

Foucault, M.

Foundational understanding

Fraassen, B.C.

Frameworks/approaches

Framing effect

Frank, A.G.

Frankfurt School

Freedom of expression

Free riders

Free will

Frequency distributions

Freud, S.

Friedan, B.

Friedman, M.

Full-length case with teaching notes

Full-length proposal

Full paper

Full professor

Functional/teleological

Fundamental attribution error

Funding bias toward non-experimental studies

Funding information

Funding opportunities

Funneling technique

Furtado, C.

Future research

Future studies

G

Gadamer, H.-G.

Galen

Galilei, G.

Galton, F.

Game-theoretical model

Game theory

Gasper, P.

GDPR

Geertz, C.

Gender equality

Generalizability

Generalizations

Generalizing

General systems theory

Generative grammar

Genetic explanation

Geoeconomics

Geographic context

Georgescu-Roegen, N.

Gephi

Gibbon

Glaser, B.G.

Global bibliometric review

Goffman, E.

Good science

Google Scholar

Google Scholar Metrics

Google Trends

Gosset, W.S.

Gould, S. J.

Governing board

Governmental Research Grants

Granger Causality Test

Grelling, K.

Gribbin, J.

Grounded theory

Group dynamics and conformity

Group norms

Group pressure

Groups

Groupthink

Guttman Scale

H

Habermas, J.

Halo effect

Hamilton, W.D.

Hansen, A.

Harman’s single-factor test

Harris, M.

Harzing’s Publish or Perish

Hawking, S.

Hawthorne effect

Hayek, F.A.

Health policy

Hegel, G.F.

Hegels dialectics

Heidegger, M.

Hempel, C.G.

Heraclitus

Hermeneutics

Herodotus

Hesiod

Heterodox school

Hidden biases

Hierarchical layers

Hierarchical linear modeling (HLM)

Hilbert, D.

H-index

Hindsight bias

Hipparchus

Historical analysis

Historical and cultural context dependence

Historical context

Historical events

Historicism

History of philosophy

Hobbes, T.

Hobsbawm, E.J.

Horizon Europe collaborative projects

Horkheimer, M.

HR department

Humanism

Humanistic psychology

Humanities

Human origins

Human resource management

Human resources department

Human right

Hume, D.

Husserl, E.

Hütte

Huygens, C.

Hymes, D.

Hypothesis formation

Hypothesized model

I

Idealism

Identifying biases

Identity-driven approach

Illusory correlation

Illustrative case

Impact

Impact factor

Impartial judgments

Implications

Incremental advances

Independence of irrelevant alternatives

Independence of variables

Independent discovery

Independent variable

Individual vs. collective behavior

Individual research grants

Individuals

Inductive

Inductive method

Inductive reasoning

Industrial Relations and Labor Studies

Industry-academia partnerships

Inferential statistics

Influence of language and interpretation

Influence of social context

Information age

Information quality control

Infrastructure grants

In-lab research

Innovation

Institute for Social Research

Institutional affiliation

Institutional context

Institutional guidelines

Institutionalism

Institutional path

Institutional theory

Instrumental variables (IV)

Instrumental variables (IV) analysis

Integrative review

Intellectual comfort zones

Intellectual cross-pollination

Intellectual journey

Intellectuals

Intellectual stance

Intelligence studies

Inter pares

Interdependence of Social Factors

Interdisciplinary

Interests

Inter-institutional collaborations

Internal consistency (reliability)

Internal political interference

Internal stakeholders

International collaborations

International economics

International Joseph A. Schumpeter Society

Interpret

Interpretation

Interpretation of results

Interpretative explanation

Interpretivism

Inter-rater reliability

Interval scale

Intra-institutional collaborations

Introduction

Intuitive-selective approach

Ippocrates

Iterative process

IT support

Ivory Tower Specialization

J

Jaeger, W.

James, W.

JASP

Jefferson, T.

Jenster, P.

Journal articles

Journal Citation Reports

Journal Impact Factor (JIF) - Web of Science

Journal of Evolutionary Economics

Journalists

Journal quality

Journal ranking systems

Judgmental/purposive sampling

sampling

Jung, C.G.

K

Kahneman, D.

Kant, I.

Kappa

Katzenstein, P.J.

Kepler, J.

Keynesian economics

Keynes, J.M.

Keynote speaker

Keywords

King Casimir III the Great

King, C.G.

Knowledge work

Knut and Alice Wallenberg Foundation (KAW)

Kreyszig, E.O.

Krugman, P.

Kuhn, T.

L

Laboratory experiments

Labor economics

Labov, W.

Lamarck, J. B.

Lange, K.

Language

Laplace, P.-S.

Latent variable

LaTeX

Launch ethical investigations

Law of diminishing returns

Laws

Laws or generalized principles

Leach, E.

Lead researcher

Le Bihan, J.

Leda Cosmides

Leech, G.N.

Legacy

Legal action

“Legitimate” research

Leibniz

Lenses

Lessing, G.E.

Level of inquiry

Level of variability

Lévi-Strauss, C.

Lewis, C.

Liberalism

Libet, B.

Librarians/archivists

Library services

Lijphart, A.

Likert, R.

Likert scale

Limitations

Limited methodological training

Lind, J.

Scurvy

Linearity of relationships

Literature list

Literature review

Literature review specialist

Locke, J.

Logical deduction

Logical empiricism

Logical positivism

Logical reasoning

Logical verification

Login credentials

Logistic regression

Lombroso’s Theory of Atavism

Longitudinal analysis

Longitudinal case

Longitudinal comparison

Longitudinal studies

Loss aversion

Lucas, R.E.

Lucretius

Luhmann, N.

Luxemburg, R.

Lyotard, J.-F.

M

Mach, E.

Machiavelli, N.

Major revisions

Making informed inferences

Management

Mandatory courses

Mankiw, G.

Manuscript

Manuscript submission guidelines

Marcus and Amalia Wallenberg Foundation (MAW)

Marcus Aurelius

Marcuse, H.

Margin of Error

Marginal analysis

Margins of error

Marianne and Marcus Wallenberg Foundation (MMW)

Marie Skłodowska-Curie Actions (European Commission)

Marketing

Market research

Marmot, M.

Marshall, A.

Marxist economics

Marxist historiography

Marxist perspectives

Marx, K.

Maslow’s Hierarchy of Needs

Master’s degree

Maturana, H.

Mead, G.H.

Meaney, M.J.

Meaningful problem

Measuring and quantifying relationships

Media/communication context

Mediating variables

Mediation analysis with control variables

Mendeley

Mendel, G.

Menger, C.

Mental frameworks

Mentorship

Meritocracy

Meritocrat

Meta-analysis

Meta-discipline

Meta-field

Meta-review

Method description

Methodenstreit

Methodology sections

Methodology

Methods of investigation

Metrics obsession

Microsoft Academic

Mill, J.S.

Mill’s Methods

Mini-case

Minimal interference

Minor revisions

Minsky, H.P.

Misinformation

Mittseminarium

Mixed-methods approach

Models

Moderate interference

Moderating variables

Modern neuroscience

Modern psychology

Modern social theories

Modular approach to social science epistemology

Mommsen

Monetarism

Montefeltro family

Moral frameworks

Morgenstern, O.

Morgenthau, H.J.

Moritz Schlick

Maslow, A.H.

Motivation

Multidisciplinary

Multidisciplinary collaborations

Multidisciplinary teams

Multigroup analysis

Multilevel modeling

Multiple cross-sectional studies

Multistage sampling

sampling

Multitude of variables

Multivariate ANOVA (MANOVA)

Multivariate regression

Murdock, G.P.

Musk, E.

N

Nagel, E.

Narrative explanation

Narrative review

National Science Foundation (NSF)

Natural cycles in scientific exploration

Natural environment

Natural phenomena

Natural sciences

Nature of problem

Nature of science

Neoclassical economics

Neoplatonism

Network

Network analysis

Network theory

New classical economics

New institutional economics

New Keynesian Economics

New Public Management (NPM)

Newton, I.

Newton’s Law of Universal Gravitation

Neyman, J.

Niche subfield

Nobel Prize in Physics

Nominal scale

Non-academic intellectuals

Non-contrived (natural)

Non-dictatorship

Non-governmental organization (NGO) grants

Nonlinearity of social systems

Non-systematic review

Normative explanation

Normative influence

Normative interpretations

North, D.C.

Norwegian Humanist Association

Norwegian List

Note: Any science paper is basically just two things

Note: Background for the Car Example in this Book

Note: Ernest Nagel

Note: How we write statistics

Note: Principles of Problem-Centered Research in Social Sciences

Note: Rensis Likert

Note: the HET website

Note: The University Campus

Note: What is ORCID ID?

Noumena

Novelty

Nozick, R.

"Nudge" theory

Null and alternative hypotheses

Null hypothesis (H0)

O

Objective approach

Objective facts

facts

Objectivity and neutrality

Observation

Observational methods

Observer bias

Off-campus

Official mentors

Ohm’s law

Okasha, S.

Old institutional economics

Ongoing research

Online browsing data

Online sample size calculators

Open access

Open-access journals

Open-access movement

Open-ended

Open-ended interviewing

Operationalization

Operations

Opponent

ORCID ID

Ordinal scale

Organizational behavior

Organizational level

Organizations

Ostrom, E.

Quota sampling

sampling

Outliers

Overclaiming

Overconfidence bias

Overfunding inefficiency

Oversimplify

P

Paired comparison

Panel discussions

Paradigms

Pareto efficiency

Pareto, V.

Parmenides

Parsons, T.

Partial least squares structural equation modeling (PLS-SEM)

Participant recruitment issues

Patents

Path analysis

Patterns

Pearson, K.

Peer review

Peer-reviewed articles

Peer review feedback providers

Peer review process

Peirce, C.S.

Perceptions/beliefs

Petzoldt, J.

PhD (Doctorate)

PhD positions

PhD programs

Phenomena

Phenomenalism

Phenomenological approaches

Phenomenology

Philosophical and methodological approaches

Philosophical perspectives

Philosophical schools

Philosophy

Philosophy of science

Phrenology

Physique sociale

Piaget, J.

Pico della Mirandola, G.

Plato

Platonism

Plotinus

PLS-SEM

Plutarch

Poincaré, H.

Polanyi, K.

Policy impacts

Political context

Political correctness

Political economy

Polo, M.

Popper, K.

Population proportion

Population

Porter, M.E.

Positivism

Postcolonialism

Post-conference communication

Post-doctoral position

Postdoctoral Researcher

Poster presentation

Posters

Poster sessions

Post-Keynesian economics

Postmodernism

Post-structuralism

Potential for contribution to knowledge

Practical plan

Pragmatism

Pragmatism in economics

Pre- and post-intervention comparison

Prebisch, R.

Predatory journals

Predefined list

Predict

Predictability

Premises

President/Chancellor

Pre-Socratic Philosophy

Pressing societal issues

Principal Investigator (PI)

Probabilistic causation

Probabilistic explanation

Probabilistic statement

Probability theory

Problem-driven approach

Production phase

Professors’ association

Progress

Project-based grants

Project management software

Project manager

Proof

Propensity score matching (PSM)

Prospect theory

Proust, M.

Psychoanalysis

Psychological context

Psychology

Psychometrics

Ptolemy

Publication bias

Publication strategy

Public defense

Public discourse

Public health interventions

Public Policy and Administration

PubMed

Purpose of the research

Purpose of the study

Putnam, H.

P-value and confidence intervals

Pyrrho of Elis

Pythagoras’ Theorem

Python

Q

QGIS

Qualitative methods

Qualitative research

Quantifying relationships and effects

Quantitative methods

Quesnay, F.

Quine, W.V.O.

R

R

Randomized controlled trials (RCTs)

Ranke

Rapid review

Ratio scale

Rational behavior

Rational choice theory

Rational-evaluative approach

Rationalism

Rationality

Rawls, J.

Realism (critical realism)

Realism

Recency bias

"Reciprocal funding allocation"

Recommendations for future research

References

Refining

Registrar’s office

Regression analysis

Reichenbach, H.

Reject (R)

Rejections

Relativism

Relevance and value

Relevance for society

Reliability

Renaissance humanism

Replication

Replication challenges

Reporting and documentation

Reproducibility

Required knowledge/skills

Research centers and institutes

Research direction

Research ecosystem

Researcher interference

Researcher type

Researchers

Research funding

Research gaps

Research identity

Research model

Research paper

Research partners

Research questions (RQs)

Research strategy

Research teams

ResearchGate

Research-worthy

Resource-based view (RBV)

Respondent

Response bias

Results

Retracting

Review and submission manager

Reviewer

Revisions and resubmissions

Ricardo, D.

Rigorosum

Robinson, J.

Robust estimation

Rogers, C.R.

Rorty, R.

Roscher, W.

Rosen, R.

Rule of thumb

Russell, B.

S

Sabbatical year

Sagan, C.

Said, E.W.

Sample

Sample characteristics

Sampling bias

Samuelson, P.A.

Sargent, T.

Sartre, J.-P.

Savage axioms

Savage, L.J.

Scales

Scepticism

Schick, T.

Scholarly positioning framework

Scholar quality

Scholasticism

Schools

Schools of thought

Schopenhauer

Schumpeter

Schumpeter, J.A.

Schutz, A.

Schwanitz, D.

Science

Science-by-slogan

Sciences humaines

Sciences sociales

Scientific article

Scientific inquiry

Scientific Marxism

Scientific methods

Scientific papers

Scientific perspective

Scientific revolution

Sci-Hub

SCIMago Journal Rank (SJR)

SCImago Journal Rank (SJR) - Scopus

Scope and focus

Scope of generalization

Scope of impact

Scope of the journal

Scoping review

Scopus

Scurvy

Seat belts

Selection bias

Self-citations

Self-directed learning

Self-serving bias

Semantic differential scale

Semantic keyword search

Semantics

Seminar

Semiotics

Semmelweis

Sen, A.

Sensitivity

Sensitivity analysis

Sensory experience

Set theory

Sextus Empiricus

Sforza Family

Shannon, C.E.

Short proposals

Siemens research Grants

Simmel, G.

Simplicity in cause-and-effect

Simulation experiments

Single observation

Singular moment in time

68 Rebellion

Skepticism

Skinner, B.F.

Slutseminarium

Smith, A.

Snowball sampling

sampling

Snow, C.P.

Social contract theory

Social Darwinism

Social desirability bias

Social exchange theory

Social facts

Social inequality

Socialization

Social justice

Social norms and values

Social policy

Social science complexity

Social sciences

Social stability

Social stratification

Social trends

Socioeconomic backgrounds

Socioeconomic status (SES)

Sociolinguistics

Sociology of Organizations

Sociology

Socrates

Sombart, W.

Sorbonne

Soutenance

Speaker

Specific culture

Spontaneous research groups

Stafford Beer

Stakeholder involvement

Stakeholder theory

Startseminarium

Statens Ansvarsnämnd

State Responsibility Board

Statistical evidence

Statistically significant sample

Statistical methods

Statistical techniques

Statisticians

Statistics

Stoicism

Strategic management

Stratified sampling

sampling

Strauss, A.L.

Strength of empirical support

Structural equation modeling (SEM)

Structural functionalism

Structuralism

Structuralist economics

Structural model

Student government

Students

Student services

Study guide

Sub-discipline

Subfields

Subject to change

Subjective facts

facts

Subjective probability

Submission guidelines

Sudden leaps forward

Sui generis

Suitability for university research

Supervisor

Supplementary data

Survey

Survey research

Survival analysis

Survivorship bias

Swedish Research Council (Vetenskapsrådet)

Syllogisms

Symbolic interactionism

Symbolic logic

Synonym expansion

Synthesis

Synthesis of key findings

Systematic review

Systemic bias

Systemic explanation

Systemic inequalities

Systematic sampling

sampling

Systems theory

Szent-Györgyi, A.

T

Tables

Teaching notes

Team Leader

Technical report

Technological context

Technology grants

Template

Temporal context

Temporal precedence

Temporal stability

Tenure position

Testability

Testable hypotheses

Tested

Testing

Testing hypotheses

Test-retest reliability

Thaler, R.H.

The academic community

The Academic Journal Guide (AJG)

The advancement of knowledge

The Blank Slate Theory

The broader impact

The Controlled Experiment

The deductive-nomological (D-N) model

The editorial system

The Este family

The formula funding model

The Gonzaga family

The Harzing Journal Quality List

The Hawthorne effect

The heliocentric theory

The Homogeneous Family Model

The impact of socioeconomic status on health

The influence of time

The internet

The law of three stages

The Medici family

The meritocratic system

The Norwegian List

The Norwegian Register for Scientific Journals, Series, and Publishers (Norwegian List)

Theorems

Theoretical framework

Theoretical or conceptual studies

Theoretical overreach

Theoretical perspectives

Theoretical research

Theoretical review

Theories of social norms

Theories

Theory Evaluation

Theory of natural selection

The philosophy of science

The physical world

The pipeline

The scientific method

Thesis proposal

The 'so what?' question

The third mission

Third-party funding sources

Thompson, E.P.

Thought-provoking statement

Three Remedies for Academic Integrity: Transparency, Competition, and External Evaluations

Thucydides

Time constraints

Time horizons

A timeline

Time management

Time series analysis

Tinbergen, J.

Tinbergen, N.

Tooby, J.

Transcendental idealism

Transcendental idealist approach

Transdisciplinary

Transparency

Transparent consent protocols

Transportation policy

Tree of porphyry

Trend analysis

Tribe

Triple Helix model

Trout, J.D.

Truths

T-tests

Tversky

"Two Cultures" debate

Two-stage evaluation process

Tylor, E.B.

Type of measurement scale

Type of questions

U

Umbrella review

UNESCO

Unidentified biases

Unified theory

Uniformity across populations

Union

Unit of analysis

Universal laws

Universal truths

Universal

University in Paris

University

Unmeasured factors

Unofficial mentors

Unrestricted domain

Urban planning

Utilitarianism

V

Validating

Validity

Valid research gap

research gap

Varela, F.

Variables

Variance inflation factor (VIF)

Veblen, T.

Verificationism

Vernadsky, V.

Vested interests

Vice Chancellor/Dean

Vice Chancellors/Pro-Vice Chancellors

Vienna Circle

Virtual meetings & workshops

Visual analog scale (VAS)

Vitamin C

Lind, J.

Vitruvius

Voltaire

von Bertalanffy, L.

von Helmholtz, H.

von Humboldt, A.

von Mises, L.

von Mises, R.

von Neumann, J.

Vygotsky, L.S.

W

Wagner, R.

Waltz, K.

Watkins, G.J.

Watson, J.

Watson, J.D.

Web of Science

Weber, M.

Wendt, A.

White, L.

White paper

Widely applicable

Wiener, N.

Wilkinson, R.G.

Williams, G.C.

Williams, S.

Wilson, E.O.

Wilson, W.

Wittgenstein, L.

Working Paper

Workshops

World War II

Wright Mills, C.

Writers

Wundt, W.

X

Xenophon

Y

Young, M.

Z

Zeno of Citium

Zotero

Z-Test