case study in social science research

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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The Case Study in Social Research

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The Case Study in Social Research proposes and develops an innovative, rigorous, and up to date methodological clarification of the case study approach in the social sciences to consistently and consciously apply it to different fields of social research. It aspires to provide the reader not with a set of prescriptive rules, but rather with a ‘methodological awareness’ of the complexity and peculiarity of applying a case study, so that they may carefully evaluate the limits and potential of conducting this type of investigation.

What is case study research in the sociological field really? How do we carry out a social inquiry of this type? How does it differ from other social research approaches? In answering these questions, this book leads the reader on a historical, epistemological, technical, and applicative path in the methodology of social research, by examining all aspects of the case study approach. The aim is to respond to as-yet still equivocal and misunderstood methodological issues, and provide a systematic illustration and exemplification of the case study approach, beginning from its sociological and methodological roots, its research design, and on through to its preparation and administration. Space is also dedicated to specifically and practically understanding the differences between the case study and the other social research approaches, with which it is often confused in literature, such as ethnographic research, grounded theory, or qualitative research.

This book is suitable for upper level undergraduate and postgraduate students in the social sciences, and as a supplementary textbook to primary methods texts, as well as for social researchers, and other practitioners and academics with a firm grounding in social research methodologies.

TABLE OF CONTENTS

Chapter | 5  pages, introduction, chapter 1 | 16  pages, social research and its methods, chapter 2 | 16  pages, the origins and development of the case study, chapter 3 | 19  pages, first question, chapter 4 | 24  pages, second question, chapter 5 | 20  pages, third question, chapter 6 | 18  pages, fourth question, chapter 7 | 18  pages, examples and fields of application, chapter | 3  pages.

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11 Case research

Case research—also called case study—is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, pre-recorded documents, and secondary data, may be employed and inferences about the phenomenon of interest tend to be rich, detailed, and contextualised. Case research can be employed in a positivist manner for the purpose of theory testing or in an interpretive manner for theory building. This method is more popular in business research than in other social science disciplines.

Case research has several unique strengths over competing research methods such as experiments and survey research. First, case research can be used for either theory building or theory testing, while positivist methods can be used for theory testing only. In interpretive case research, the constructs of interest need not be known in advance, but may emerge from the data as the research progresses. Second, the research questions can be modified during the research process if the original questions are found to be less relevant or salient. This is not possible in any positivist method after the data is collected. Third, case research can help derive richer, more contextualised, and more authentic interpretation of the phenomenon of interest than most other research methods by virtue of its ability to capture a rich array of contextual data. Fourth, the phenomenon of interest can be studied from the perspectives of multiple participants and using multiple levels of analysis (e.g., individual and organisational).

At the same time, case research also has some inherent weaknesses. Because it involves no experimental control, internal validity of inferences remain weak. Of course, this is a common problem for all research methods except experiments. However, as described later, the problem of controls may be addressed in case research using ‘natural controls’. Second, the quality of inferences derived from case research depends heavily on the integrative powers of the researcher. An experienced researcher may see concepts and patterns in case data that a novice researcher may miss. Hence, the findings are sometimes criticised as being subjective. Finally, because the inferences are heavily contextualised, it may be difficult to generalise inferences from case research to other contexts or other organisations.

It is important to recognise that case research is different from case descriptions such as Harvard case studies discussed in business classes. While case descriptions typically describe an organisational problem in rich detail with the goal of stimulating classroom discussion and critical thinking among students, or analysing how well an organisation handled a specific problem, case research is a formal research technique that involves a scientific method to derive explanations of organisational phenomena.

Case research is a difficult research method that requires advanced research skills on the part of the researcher, and is therefore often prone to error. Benbasat, Goldstein and Mead (1987) [1] describe five problems frequently encountered in case research studies. First, many case research studies start without specific research questions, and therefore end up without having any specific answers or insightful inferences. Second, case sites are often chosen based on access and convenience, rather than based on the fit with the research questions, and are therefore cannot adequately address the research questions of interest. Third, researchers often do not validate or triangulate data collected using multiple means, which may lead to biased interpretation based on responses from biased interviewees. Fourth, many studies provide very little details on how data was collected (e.g., what interview questions were used, which documents were examined, the organisational positions of each interviewee, etc.) or analysed, which may raise doubts about the reliability of the inferences. Finally, despite its strength as a longitudinal research method, many case research studies do not follow through a phenomenon in a longitudinal manner, and hence present only a cross-sectional and limited view of organisational processes and phenomena that are temporal in nature.

Key decisions in case research

Several key decisions must be made by a researcher when considering a case research method. First, is this the right method for the research questions being studied? The case research method is particularly appropriate for exploratory studies, for discovering relevant constructs in areas where theory building is in the formative stages, for studies where the experiences of participants and context of actions are critical, and for studies aimed at understanding complex, temporal processes (why and how) rather than factors or causes (what). This method is well-suited for studying complex organisational processes that involve multiple participants and interacting sequences of events, such as organisational change and large-scale technology implementation projects.

Second, what is the appropriate unit of analysis for a case research study? Since case research can simultaneously examine multiple units of analyses, the researcher must decide whether she wishes to study a phenomenon at the individual, group, or organisational level or at multiple levels. For instance, a study of group decision-making or group work may combine individual-level constructs such as individual participation in group activities with group-level constructs, such as group cohesion and group leadership, to derive richer understanding than can be achieved from a single level of analysis.

Third, should the researcher employ a single-case or multiple-case design? The single-case design is more appropriate at the outset of theory generation, if the situation is unique or extreme, if it is revelatory (i.e., the situation was previously inaccessible for scientific investigation), or if it represents a critical or contrary case for testing a well-formulated theory. The multiple-case design is more appropriate for theory testing, for establishing generalisability of inferences, and for developing richer and more nuanced interpretations of a phenomenon. Yin (1984) [2] recommends the use of multiple case sites with replication logic, viewing each case site as similar to one experimental study, and following rules of scientific rigor similar to that used in positivist research.

Fourth, what sites should be chosen for case research? Given the contextualised nature of inferences derived from case research, site selection is a particularly critical issue because selecting the wrong site may lead to the wrong inferences. If the goal of the research is to test theories or examine generalisability of inferences, then dissimilar case sites should be selected to increase variance in observations. For instance, if the goal of the research is to understand the process of technology implementation in firms, a mix of large, mid-sized, and small firms should be selected to examine whether the technology implementation process differs with firm size. Site selection should not be opportunistic or based on convenience, but rather based on the fit with research questions though a process called ‘theoretical sampling’.

Fifth, what techniques of data collection should be used in case research? Although interview (either open-ended/unstructured or focused/structured) is by far the most popular data collection technique for case research, interview data can be supplemented or corroborated with other techniques such as direct observation (e.g., attending executive meetings, briefings, and planning sessions), documentation (e.g., internal reports, presentations, and memoranda, as well as external accounts such as newspaper reports), archival records (e.g., organisational charts, financial records, etc.), and physical artefacts (e.g., devices, outputs, tools). Furthermore, the researcher should triangulate or validate observed data by comparing responses between interviewees.

Conducting case research

Most case research studies tend to be interpretive in nature. Interpretive case research is an inductive technique where evidence collected from one or more case sites is systematically analysed and synthesised to allow concepts and patterns to emerge for the purpose of building new theories or expanding existing ones. Eisenhardt (1989) [3] proposed a ‘roadmap’ for building theories from case research—a slightly modified version of which is described below. For positivist case research, some of the following stages may need to be rearranged or modified, however sampling, data collection, and data analytic techniques should generally remain the same.

Define research questions. Like any other scientific research, case research must also start with defining research questions that are theoretically and practically interesting, and identifying some intuitive expectations about possible answers to those research questions or preliminary constructs to guide initial case design. In positivist case research, the preliminary constructs are based on theory, while no such theories or hypotheses should be considered ex ante in interpretive research. These research questions and constructs may be changed in interpretive case research later on, if needed, but not in positivist case research.

Select case sites. The researcher should use a process of ‘theoretical sampling’—not random sampling—to identify case sites. In this approach, case sites are chosen based on theoretical rather than statistical considerations—for instance, to replicate previous cases, to extend preliminary theories, or to fill theoretical categories or polar types. Care should be taken to ensure that the selected sites fit the nature of research questions, minimise extraneous variance or noise due to firm size, industry effects, and so forth, and maximise variance in the dependent variables of interest. For instance, if the goal of the research is to examine how some firms innovate better than others, the researcher should select firms of similar size within the same industry to reduce industry or size effects, and select some more innovative and some less innovative firms to increase variation in firm innovation. Instead of cold-calling or writing to a potential site, it is better to contact someone at executive level inside each firm who has the authority to approve the project, or someone who can identify a person of authority. During initial conversations, the researcher should describe the nature and purpose of the project, any potential benefits to the case site, how the collected data will be used, the people involved in data collection (other researchers, research assistants, etc.), desired interviewees, and the amount of time, effort, and expense required of the sponsoring organisation. The researcher must also assure confidentiality, privacy, and anonymity of both the firm and the individual respondents.

Create instruments and protocols. Since the primary mode of data collection in case research is interviews, an interview protocol should be designed to guide the interview process. This is essentially a list of questions to be asked. Questions may be open-ended (unstructured) or closed-ended (structured) or a combination of both. The interview protocol must be strictly followed, and the interviewer must not change the order of questions or skip any question during the interview process, although some deviations are allowed to probe further into a respondent’s comments if they are ambiguous or interesting. The interviewer must maintain a neutral tone, and not lead respondents in any specific direction—for example, by agreeing or disagreeing with any response. More detailed interviewing techniques are discussed in the chapter on surveys. In addition, additional sources of data—such as internal documents and memorandums, annual reports, financial statements, newspaper articles, and direct observations—should be sought to supplement and validate interview data.

Select respondents. Select interview respondents at different organisational levels, departments, and positions to obtain divergent perspectives on the phenomenon of interest. A random sampling of interviewees is most preferable, however a snowball sample is acceptable, as long as a diversity of perspectives is represented in the sample. Interviewees must be selected based on their personal involvement with the phenomenon under investigation and their ability and willingness to answer the researcher’s questions accurately and adequately, and not based on convenience or access.

Start data collection . It is usually a good idea to electronically record interviews for future reference. However, such recording must only be done with the interviewee’s consent. Even when interviews are being recorded, the interviewer should take notes to capture important comments or critical observations, behavioural responses (e.g., the respondent’s body language), and the researcher’s personal impressions about the respondent and his/her comments. After each interview is completed, the entire interview should be transcribed verbatim into a text document for analysis.

Conduct within-case data analysis. Data analysis may follow or overlap with data collection. Overlapping data collection and analysis has the advantage of adjusting the data collection process based on themes emerging from data analysis, or to further probe into these themes. Data analysis is done in two stages. In the first stage (within-case analysis), the researcher should examine emergent concepts separately at each case site and patterns between these concepts to generate an initial theory of the problem of interest. The researcher can use interview data subjectively to ‘make sense’ of the research problem in conjunction with using his/her personal observations or experience at the case site. Alternatively, a coding strategy such as Glaser and Strauss’ (1967) [4] grounded theory approach, using techniques such as open coding, axial coding, and selective coding, may be used to derive a chain of evidence and inferences. These techniques are discussed in detail in a later chapter. Homegrown techniques, such as graphical representation of data (e.g., network diagram) or sequence analysis (for longitudinal data) may also be used. Note that there is no predefined way of analysing the various types of case data, and the data analytic techniques can be modified to fit the nature of the research project.

Conduct cross-case analysis. Multi-site case research requires cross-case analysis as the second stage of data analysis. In such analysis, the researcher should look for similar concepts and patterns between different case sites, ignoring contextual differences that may lead to idiosyncratic conclusions. Such patterns may be used for validating the initial theory, or for refining it—by adding or dropping concepts and relationships—to develop a more inclusive and generalisable theory. This analysis may take several forms. For instance, the researcher may select categories (e.g., firm size, industry, etc.) and look for within-group similarities and between-group differences (e.g., high versus low performers, innovators versus laggards). Alternatively, they can compare firms in a pairwise manner listing similarities and differences across pairs of firms.

Build and test hypotheses. Tenative hypotheses are constructed based on emergent concepts and themes that are generalisable across case sites. These hypotheses should be compared iteratively with observed evidence to see if they fit the observed data, and if not, the constructs or relationships should be refined. Also the researcher should compare the emergent constructs and hypotheses with those reported in the prior literature to make a case for their internal validity and generalisability. Conflicting findings must not be rejected, but rather reconciled using creative thinking to generate greater insight into the emergent theory. When further iterations between theory and data yield no new insights or changes in the existing theory, ‘theoretical saturation’ is reached and the theory building process is complete.

Write case research report. In writing the report, the researcher should describe very clearly the detailed process used for sampling, data collection, data analysis, and hypotheses development, so that readers can independently assess the reasonableness, strength, and consistency of the reported inferences. A high level of clarity in research methods is needed to ensure that the findings are not biased by the researcher’s preconceptions.

Interpretive case research exemplar

Perhaps the best way to learn about interpretive case research is to examine an illustrative example. One such example is Eisenhardt’s (1989) [5] study of how executives make decisions in high-velocity environments (HVE). Readers are advised to read the original paper published in Academy of Management Journal before reading the synopsis in this chapter. In this study, Eisenhardt examined how executive teams in some HVE firms make fast decisions, while those in other firms cannot, and whether faster decisions improve or worsen firm performance in such environments. HVE was defined as one where demand, competition, and technology changes so rapidly and discontinuously that the information available is often inaccurate, unavailable or obsolete. The implicit assumptions were thatit is hard to make fast decisions with inadequate information in HVE, and fast decisions may not be efficient and may result in poor firm performance.

Reviewing the prior literature on executive decision-making, Eisenhardt found several patterns, although none of these patterns were specific to high-velocity environments. The literature suggested that in the interest of expediency, firms that make faster decisions obtain input from fewer sources, consider fewer alternatives, make limited analysis, restrict user participation in decision-making, centralise decision-making authority, and have limited internal conflicts. However, Eisenhardt contended that these views may not necessarily explain how decision makers make decisions in high-velocity environments, where decisions must be made quickly and with incomplete information, while maintaining high decision quality.

To examine this phenomenon, Eisenhardt conducted an inductive study of eight firms in the personal computing industry. The personal computing industry was undergoing dramatic changes in technology with the introduction of the UNIX operating system, RISC architecture, and 64KB random access memory in the 1980s, increased competition with the entry of IBM into the personal computing business, and growing customer demand with double-digit demand growth, and therefore fit the profile of the high-velocity environment. This was a multiple case design with replication logic, where each case was expected to confirm or disconfirm inferences from other cases. Case sites were selected based on their access and proximity to the researcher, however, all of these firms operated in the high-velocity personal computing industry in California’s Silicon Valley area. The collocation of firms in the same industry and the same area ruled out any ‘noise’ or variance in dependent variables (decision speed or performance) attributable to industry or geographic differences.

The study employed an embedded design with multiple levels of analysis: decision (comparing multiple strategic decisions within each firm), executive teams (comparing different teams responsible for strategic decisions), and the firm (overall firm performance). Data was collected from five sources:

Initial interviews with Chief Executive Officers . CEOs were asked questions about their firm’s competitive strategy, distinctive competencies, major competitors, performance, and recent/ongoing major strategic decisions. Based on these interviews, several strategic decisions were selected in each firm for further investigation. Four criteria were used to select decisions: the decisions must involve the firm’s strategic positioning, the decisions must have high stakes, the decisions must involve multiple functions, and the decisions must be representative of strategic decision-making process in that firm.

Interviews with divisional heads . Each divisional head was asked sixteen open-ended questions, ranging from their firm’s competitive strategy, functional strategy, top management team members, frequency and nature of interaction with team, typical decision-making processes, how each of the decisions were made, and how long it took them to make those decisions. Interviews lasted between one and a half and two hours, and sometimes extended to four hours. To focus on facts and actual events rather than respondents’ perceptions or interpretations, a ‘courtroom’ style questioning was employed, such as ‘When did this happen?’, ‘What did you do?’, etc. Interviews were conducted by two people, and the data was validated by cross-checking facts and impressions made by the interviewer and notetaker. All interview data was recorded, however notes were also taken during each interview, which ended with the interviewer’s overall impressions. Using a ‘24-hour rule’, detailed field notes were completed within 24 hours of the interview, so that some data or impressions were not lost to recall.

Questionnaires . Executive team members at each firm were asked tocomplete a survey questionnaire that captured quantitative data on the extent of conflict and power distribution in their firm.

Secondary data . Industry reports and internal documents such as demographics of the executive teams responsible for strategic decisions, financial performance of firms, and so forth, were examined.

Personal observation . Lastly, the researcher attended a one-day strategy session and a weekly executive meeting at two firms in her sample.

Data analysis involved a combination of quantitative and qualitative techniques. Quantitative data on conflict and power were analysed for patterns across firms/decisions. Qualitative interview data was combined into decision climate profiles, using profile traits (e.g., impatience) mentioned by more than one executive. For within-case analysis, decision stories were created for each strategic decision by combining executive accounts of the key decision events into a timeline. For cross-case analysis, pairs of firms were compared for similarities and differences, categorised along variables of interest such as decision speed and firm performance. Based on these analyses, tentative constructs and propositions were derived inductively from each decision story within firm categories. Each decision case was revisited to confirm the proposed relationships. The inferred propositions were compared with findings from the existing literature to examine differences, and to generate new insights from the case findings. Finally, the validated propositions were synthesised into an inductive theory of strategic decision-making by firms in high-velocity environments.

Inferences derived from this multiple case research contradicted several decision-making patterns expected from the existing literature. First, fast decision-makers in high-velocity environments used more information, and not less information as suggested by the previous literature. However, these decision-makers used more real-time information—an insight not available from prior research—which helped them identify and respond to problems, opportunities, and changing circumstances faster. Second, fast decision-makers examined more—not fewer—alternatives. However, they considered these multiple alternatives in a simultaneous manner, while slower decision-makers examined fewer alternatives in a sequential manner. Third, fast decision-makers did not centralise decision-making or restrict inputs from others as the literature suggested. Rather, these firms used a two-tiered decision process in which experienced counsellors were asked for inputs in the first stage, followed by a rapid comparison and decision selection in the second stage. Fourth, fast decision-makers did not have less conflict—as expected from the literature—but employed better conflict resolution techniques to reduce conflict and improve decision-making speed. Finally, fast decision-makers exhibited superior firm performance by virtue of their built-in cognitive, emotional, and political processes that led to rapid closure of major decisions.

Positivist case research exemplar

Case research can also be used in a positivist manner to test theories or hypotheses. Such studies are rare, but Markus (1983) [6] provides an exemplary illustration in her study of technology implementation at the pseudonymous Golden Triangle Company (GTC). The goal of this study was to understand why a newly implemented financial information system (FIS)—intended to improve the productivity and performance of accountants at GTC—was supported by accountants at GTC’s corporate headquarters, but resisted by divisional accountants at GTC branches. Given the uniqueness of the phenomenon of interest, this was a single-case research study.

To explore the reasons behind user resistance of FIS, Markus posited three alternative explanations:

System-determined theory : The resistance was caused by factors related to an inadequate system, such as its technical deficiencies, poor ergonomic design, or lack of user friendliness.

People-determined theory : The resistance was caused by factors internal to users, such as the accountants’ cognitive styles or personality traits that were incompatible with using the system.

Interaction theory : The resistance was not caused not by factors intrinsic to the system or the people, but by the interaction between the two set of factors. Specifically, interaction theory suggested that the FIS engendered a redistribution of intra-organisational power, and accountants who lost organisational status, relevance, or power as a result of FIS implementation resisted the system while those gaining power favoured it.

In order to test the three theories, Markus predicted alternative outcomes expected from each theoretical explanation and analysed the extent to which those predictions matched with her observations at GTC. For instance, the system-determined theory suggested that since user resistance was caused by an inadequate system, fixing the technical problems of the system would eliminate resistance. The computer running the FIS system was subsequently upgraded with a more powerful operating system, online processing (from initial batch processing, which delayed immediate processing of accounting information), and a simplified software for new account creation by managers. One year after these changes were made, the resistant users were still resisting the system and felt that it should be replaced. Hence, the system-determined theory was rejected.

The people-determined theory predicted that replacing individual resistors or co-opting them with less resistant users would reduce their resistance toward the FIS. Subsequently, GTC started a job rotation and mobility policy, moving accountants in and out of the resistant divisions, but resistance not only persisted, but in some cases increased. In one instance, an accountant who was one of the system’s designers and advocates when he worked for corporate accounting started resisting the system after he was moved to the divisional controller’s office. Failure to realise the predictions of the people-determined theory led to the rejection of this theory.

Finally, the interaction theory predicted that neither changing the system nor the people (i.e., user education or job rotation policies) would reduce resistance until the power imbalance and redistribution from the pre-implementation phase was addressed. Before FIS implementation, divisional accountants at GTC felt that they owned all accounting data related to their divisional operations. They maintained this data in thick, manual ledger books, controlled others’ access to the data, and could reconcile unusual accounting events before releasing those reports. Corporate accountants relied heavily on divisional accountants for access to the divisional data for corporate reporting and consolidation. Because the FIS system automatically collected all data at the source and consolidated it into a single corporate database, it obviated the need for divisional accountants, loosened their control and autonomy over their division’s accounting data, and making their job somewhat irrelevant. Corporate accountants could now query the database and access divisional data directly without going through the divisional accountants, analyse and compare the performance of individual divisions, and report unusual patterns and activities to the executive committee, resulting in further erosion of the divisions’ power. Though Markus did not empirically test this theory, her observations about the redistribution of organisational power, coupled with the rejection of the two alternative theories, led to the justification of interaction theory.

Comparisons with traditional research

Positivist case research, aimed at hypotheses testing, is often criticised by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalisability of findings—the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing ’natural controls’. This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to the controlling division. In this instance, the change in his behaviour may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook them unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology—for example, waiting for comets to pass close enough to the earth in order to make inferences about comets and their composition.

Third, the problem of replicability refers to the difficulty of observing the same phenomenon considering the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.

Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalised to other settings. Generalisability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalisability of findings if such findings are observed to be consistent across studies.

Lastly, British philosopher Karl Popper described four requirements of scientific theories: theories should be falsifiable, they should be logically consistent, they should have adequate predictive ability, and they should provide better explanation than rival theories. In case research, the first three requirements can be improved by increasing the degrees of freedom of observed findings—for example, by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behaviour of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations. Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.

• Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly , 11(3), 369–386. ↵
• Yin, R. (1984). Case study research: Design and methods . London: Sage Publications. ↵
• Eisenhardt, K. M. (1989). Building theories from case research. Academy of Management Review , 14(4), 532–550 ↵
• Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research . New York: Aldine Pub Co. ↵
• Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy of Management Journal , 32(3), 543–576. ↵
• Markus, M. L. (1983). Power, politics and MIS implementations. Communications of the ACM , 26(6), 430–444. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Comparative Case Studies: Methodological Discussion

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Case Study Research has a long tradition and it has been used in different areas of social sciences to approach research questions that command context sensitiveness and attention to complexity while tapping on multiple sources. Comparative Case Studies have been suggested as providing effective tools to understanding policy and practice along three different axes of social scientific research, namely horizontal (spaces), vertical (scales), and transversal (time). The chapter, first, sketches the methodological basis of case-based research in comparative studies as a point of departure, also highlighting the requirements for comparative research. Second, the chapter focuses on presenting and discussing recent developments in scholarship to provide insights on how comparative researchers, especially those investigating educational policy and practice in the context of globalization and internationalization, have suggested some critical rethinking of case study research to account more effectively for recent conceptual shifts in the social sciences related to culture, context, space and comparison. In a third section, it presents the approach to comparative case studies adopted in the European research project YOUNG_ADULLLT that has set out to research lifelong learning policies in their embeddedness in regional economies, labour markets and individual life projects of young adults. The chapter is rounded out with some summarizing and concluding remarks.

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Introduction to the Book and the Comparative Study

Theoretical and Methodological Considerations

Main findings and discussion.

• Case-based research
• Comparative case studies

1 Introduction

Exploring landscapes of lifelong learning in Europe is a daunting task as it involves a great deal of differences across places and spaces; it entails attending to different levels and dimensions of the phenomena at hand, but not least it commands substantial sensibility to cultural and contextual idiosyncrasies. As such, case-based methodologies come to mind as tested methodological approaches to capturing and examining singular configurations such as the local settings in focus in this volume, in which lifelong learning policies for young people are explored in their multidimensional reality. The ensuing question, then, is how to ensure comparability across cases when departing from the assumption that cases are unique. Recent debates in Comparative and International Education (CIE) research are drawn from that offer important insights into the issues involved and provide a heuristic approach to comparative cases studies. Since the cases focused on in the chapters of this book all stem from a common European research project, the comparative case study methodology allows us to at once dive into the specifics and uniqueness of each case while at the same time pay attention to common treads at the national and international (European) levels.

The chapter, first, sketches the methodological basis of case-based research in comparative studies as a point of departure, also highlighting the requirements in comparative research. In what follows, second, the chapter focuses on presenting and discussing recent developments in scholarship to provide insights on how comparative researchers, especially those investigating educational policy and practice in the context of globalization and internationalization, have suggested some critical rethinking of case study research to account more effectively for recent conceptual shifts in the social sciences related to culture, context, space and comparison. In a third section, it presents the approach to comparative case studies adopted in the European research project YOUNG_ADULLLT that has set out to research lifelong learning policies in their embeddedness in regional economies, labour markets and individual life projects of young adults. The chapter is rounded out with some summarizing and concluding remarks.

2 Case-Based Research in Comparative Studies

In the past, comparativists have oftentimes regarded case study research as an alternative to comparative studies proper. At the risk of oversimplification: methodological choices in comparative and international education (CIE) research, from the 1960s onwards, have fallen primarily on either single country (small n) contextualized comparison, or on cross-national (usually large n, variable) decontextualized comparison (see Steiner-Khamsi, 2006a , 2006b , 2009). These two strands of research—notably characterized by Development and Area Studies on the one side and large-scale performance surveys of the International Association for the Evaluation of Educational Achievement (IEA) type, on the other—demarcated their fields by resorting to how context and culture were accounted for and dealt with in the studies they produced. Since the turn of the century, though, comparativists are more comfortable with case study methodology (see Little, 2000 ; Vavrus and Bartlett 2006 , 2009 ; Bartlett & Vavrus, 2017 ) and diagnoses of an “identity crisis” of the field due to a mass of single-country studies lacking comparison proper (see Schriewer, 1990 ; Wiseman & Anderson, 2013 ) started dying away. Greater acceptance of and reliance on case-based methodology has been related with research on policy and practice in the context of globalization and coupled with the intention to better account for culture and context, generating scholarship that is critical of power structures, sensitive to alterity and of other ways of knowing.

The phenomena that have been coined as constituting “globalization” and “internationalization” have played, as mentioned, a central role in the critical rethinking of case study research. In researching education under conditions of globalization, scholars placed increasing attention on case-based approaches as opportunities for investigating the contemporary complexity of policy and practice. Further, scholarly debates in the social sciences and the humanities surrounding key concepts such as culture, context, space, and place but also comparison have also contributed to a reconceptualization of case study methodology in CIE. In terms of the requirements for such an investigation, scholarship commands an adequate conceptualization that problematizes the objects of study and that does not take them as “unproblematic”, “assum[ing] a constant shared meaning”; in short, objects of study that are “fixed, abstract and absolute” (Fine, quoted in Dale & Robertson, 2009 , p. 1114). Case study research is thus required to overcome methodological “isms” in their research conceptualization (see Dale & Robertson, 2009 ; Robertson & Dale, 2017 ; see also Lange & Parreira do Amaral, 2018 ). In response to these requirements, the approaches to case study discussed in CIE depart from a conceptualization of the social world as always dynamic, emergent, somewhat in motion, and always contested. This view considers the fact that the social world is culturally produced and is never complete or at a standstill, which goes against an understanding of case as something fixed or natural. Indeed, in the past cases have often been understood almost in naturalistic ways, as if they existed out there, waiting for researchers to “discover” them. Usually, definitions of case study also referred to inquiry that aims at elucidating features of a phenomenon to yield an understanding of why, how and with what consequences something happens. One can easily find examples of cases understood simply as sites to observe/measure variables—in a nomothetic cast—or examples, where cases are viewed as specific and unique instances that can be examined in the idiographic paradigm. In contrast, rather than taking cases as pre-existing entities that are defined and selected as cases, recent case-oriented research has argued for a more emergent approach which recognizes that boundaries between phenomenon and context are often difficult to establish or overlap. For this reason, researchers are incited to see this as an exercise of “casing”, that is, of case construction. In this sense, cases here are seen as complex systems (Ragin & Becker, 1992 ) and attention is devoted to the relationships between the parts and the whole, pointing to the relevance of configurations and constellations within as well as across cases in the explanation of complex and contingent phenomena. This is particularly relevant for multi-case, comparative research since the constitution of the phenomena that will be defined, as cases will differ. Setting boundaries will thus also require researchers to account for spatial, scalar (i.e., level or levels with which a case is related) and temporal aspects.

Further, case-based research is also required to account for multiple contexts while not taking them for granted. One of the key theoretical and methodological consequences of globalization for CIE is that it required us to recognize that it alters the nature and significance of what counts as contexts (see Parreira do Amaral, 2014 ). According to Dale ( 2015 ), designating a process, or a type of event, or a particular organization, as a context, entails bestowing a particular significance on them, as processes, events, and so on that are capable of affecting other processes and events. The key point is that rather than being so intrinsically, or naturally, contexts are constructed as “contexts”. In comparative research, contexts have been typically seen as the place (or the variables) that enable us to explain why what happens in one case is different from what happens another case; what counts as context then is seen as having the same effect everywhere, although the forms it takes vary substantially (see Dale, 2015 ). In more general terms, recent case study approaches aim at accounting for the increasing complexity of the contexts in which they are embedded, which, in turn, is related to the increasing impact of globalization as the “context of contexts” (Dale, 2015 , p. 181f; see also Carter & Sealey, 2013 ; Mjoset, 2013 ). It also aims at accounting for overlapping contexts. Here it is important to note that contexts are not only to be seen in spatio-geographical terms (i.e., local, regional, national, international), but contexts may also be provided by different institutional and/or discursive contexts that create varying opportunity structures (Dale & Parreira do Amaral, 2015 ; see also Chap. 2 in this volume). What one can call temporal contexts also plays an important role, for what happens in the case unfolds as embedded not only in historical time, but may be related to different temporalities (see the concept of “timespace” as discussed by Lingard & Thompson, 2016 ) and thus are influenced by path dependence or by specific moments of crisis (Rhinard, 2019 ; see also McLeod, 2016 ). Moreover, in CIE research, the social-cultural production of the world is influenced by developments throughout the globe that take place at various places and on several scales, which in turn influence each other, but in the end, become locally relevant in different facets. As Bartlett and Vavrus write, “context is not a primordial or autonomous place; it is constituted by social interactions, political processes, and economic developments across scales and times.” ( Bartlett & Vavrus, 2017 , p. 14). Indeed, in this sense, “context is not a container for activity, it is the activity” (Bartlett & Vavrus, 2017 , p. 12, emphasis in orig.).

Also, dealing with the complexity of education policy and practice requires us to transcend the dichotomy of idiographic versus nomothetic approaches to causation. Here, it can be argued that case studies allow us to grasp and research the complexity of the world, thus offering conceptual and methodological tools to explore how phenomena viewed as cases “depend on all of the whole, the parts, the interactions among parts and whole, and the interactions of any system with other complex systems among which it is nested and with which it intersects” (Byrne, 2013 , p. 2). The understanding of causation that undergirds recent developments in case-based research aims at generalization, yet it resists ambitions to establishing universal laws in social scientific research. Focus is placed on processes while tracking the relevant factors, actors and features that help explain the “how” and the “why” questions (Bartlett and Vavrus 2017 , p. 38ff), and on “causal mechanisms”, as varying explanations of outcomes within and across cases, always contingent on interaction with other variables and dependent contexts (see Byrne, 2013 ; Ragin, 2000 ). In short, the nature of causation underlying the recent case study approaches in CIE is configurational and not foundational.

This is also in line with how CIE research regards education practice, research, and policy as a socio-cultural practice. And it refers to the production of social and cultural worlds through “social actors, with diverse motives, intentions, and levels of influence, [who] work in tandem with and/or in response to social forces” (Bartlett and Vavrus 2017 , p. 1). From this perspective, educational phenomena, such as in policymaking, are seen as a “deeply political process of cultural production engaged in and shaped by social actors in disparate locations who exert incongruent amounts of influence over the design, implementation, and evaluation of policy” ( Bartlett & Vavrus, 2017 , p. 1f). Culture here is understood in non-static and complex ways that reinforce the “importance of examining processes of sense-making as they develop over time, in distinct settings, in relation to systems of power and inequality, and in increasingly interconnected conversation with actors who do not sit physically within the circle drawn around the traditional case” (Bartlett & Vavrus, 2017 , p. 11, emphasis in orig.).

In sum, the approaches to case study put forward in CIE provide conceptual and methodological tools that allow for an analysis of education in the global context throughout scale, space, and time, which is always regarded as complexly integrated and never as isolated or independent. The following subsection discusses Comparative Case Studies (CCS) as suggested in recent comparative scholarship, which aims at attending to the methodological requirements discussed above by integrating horizontal, vertical, and transversal dimensions of comparison.

2.1 Comparative Case Studies: Horizontal, Vertical and Transversal Dimensions

Building up on their previous work on vertical case studies (Bartlett and Vavrus 2017 ; Vavrus & Bartlett, 2006 , 2009 ), Frances Vavrus and Lesley Bartlett have proposed a comparative approach to case study research that aims at meeting the requirements of culture and context sensitive research as discussed in this special issue.

As a research approach, CCS offers two theoretical-methodological lenses to research education as a socio-cultural practice. These lenses represent different views on the research object and account for the complexity of education practice, policy, and research in globalized contexts. The first lens is “context-sensitive”, which focuses on how social practices and interactions constitute and produce social contexts. As quoted above, from the perspective of a socio-cultural practice, “context is not a container for activity, it is the activity” (Vavrus and Bartlett 2017: 12, emphasis in orig.). The settings that influence and condition educational phenomena are culturally produced in different and sometimes overlapping (spatial, institutional, discursive, temporal) contexts as just mentioned. The second CCS lens is “culture-sensitive” and focuses on how socio-cultural practices produce social structures. As such, culture is a process that is emergent, dynamic, and constitutive of meaning-making as well as social structuration.

The CCS approach aims at studying educational phenomena throughout scale, time, and space by providing three axes for a “studying through” of the phenomena in question. As stated by Lesley Bartlett and Frances Vavrus with reference to comparative analyses of global education policy:

the horizontal axis compares how similar policies unfold in distinct locations that are socially produced […] and ‘complexly connected’ […]. The vertical axis insists on simultaneous attention to and across scales […]. The transversal comparison historically situates the processes or relations under consideration (Bartlett and Vavrus 2017 : 3, emphasis in orig.).

These three axes allow for a methodological conceptualization of “policy formation and appropriation across micro-, meso-, and macro levels” by not theorizing them as distinct or unrelated (Bartlett and Vavrus 2017 , p. 4). In following Latour, they state:

the macro is neither “above” nor “below” the intersections but added to them as another of their connections’ […]. In CCS research, one would pay close attention to how actions at different scales mutually influence one another (Bartlett and Vavrus 2017 , p. 13f, emphasis in orig.)

Thus, these three axes contain

processes across space and time; and [the CCS as a research design] constantly compares what is happening in one locale with what has happened in other places and historical moments. These forms of comparison are what we call horizontal, vertical, and transversal comparisons (Bartlett and Vavrus 2017 , p. 11, emphasis in orig.)

In terms of the three axes along with comparison is organized, the authors state that horizontal comparison commands attention to how historical and contemporary processes have variously influenced the “cases”, which might be constructed by focusing “people, groups of people, sites, institutions, social movements, partnerships, etc.” (Bartlett and Vavrus 2017 , p. 53) Horizontal comparisons eschew pressing categories resultant from one case others, which implies including multiple cases at the same scale in a comparative case study, while at the same time attending to “valuable contextual information” about each of them. Horizontal comparisons use units of analysis that are homologous, that is, equivalent in terms of shape, function, or institutional/organizational nature (for instance, schools, ministries, countries, etc.) ( Bartlett & Vavrus, 2017 , p. 53f). Similarly, comparative case studies may also entail tracing a phenomenon across sites, as in multi-sited ethnography (see Coleman & von Hellermann, 2012 ; Marcus, 1995 ).

Vertical comparison, in turn, does not simply imply the comparison of levels; rather it involves analysing networks and their interrelationships at different scales. For instance, in the study of policymaking in a specific case, vertical comparison would consider how actors at different scales variably respond to a policy issued at another level—be it inter−/supranational or at the subnational level. CCS assumes that their different appropriation of policy as discourse and as practice is often due to different histories of racial, ethnic, or gender politics in their communities that appropriately complicate the notion of a single cultural group (Bartlett and Vavrus 2017 , p. 73f). Establishing what counts as context in such a study would be done “by tracing the formation and appropriation of a policy” at different scales; and “by tracing the processes by which actors and actants come into relationship with one another and form non-permanent assemblages aimed at producing, implementing, resisting, and appropriating policy to achieve particular aims” ( Bartlett & Vavrus, 2017 , p. 76). A further element here is that, in this way, one may counter the common problem that comparison of cases (oftentimes countries) usually overemphasizes boundaries and treats them as separated or as self-sustaining containers, when, in reality, actors and institutions at other levels/scales significantly impact policymaking (Bartlett & Vavrus, 2017 ).

In terms of the transversal axis of comparison, Bartlett and Vavrus argue that the social phenomena of interest in a case study have to be seen in light of their historical development (Bartlett & Vavrus, 2017 , p. 93), since these “historical roots” impacted on them and “continues to reverberate into the present, affecting economic relations and social issues such as migration and educational opportunities.” As such, understanding what goes on in a case requires to “understand how it came to be in the first place.” ( Bartlett & Vavrus, 2017 , p. 93) argue:

history offers an extensive fount of evidence regarding how social institutions function and how social relations are similar and different around the world. Historical analysis provides an essential opportunity to contrast how things have changed over time and to consider what has remained the same in one locale or across much broader scales. Such historical comparison reveals important insights about the flexible cultural, social, political, and economic systems humans have developed and sustained over time (Bartlett & Vavrus, 2017 , p. 94).

Further, time and space are intimately related and studying the historical development of the social phenomena of interest in a case study “allows us to assess evidence and conflicting interpretations of a phenomenon,” but also to interrogate our own assumptions about them in contemporary times (Bartlett and Vavrus 2017 ), thus analytically sharpening our historical analyses.

As argued by the authors, researching the global dimension of education practice, research or policy aims at a “studying through” of phenomena horizontally, vertically, and transversally. That is, comparative case study builds on an emergent research design and on a strong process orientation that aims at tracing not only “what”, but also “why” and “how” phenomena emerge and evolve. This approach entails “an open-ended, inductive approach to discover what […] meanings and influences are and how they are involved in these events and activities—an inherently processual orientation” (Bartlett and Vavrus 2017 , p. 7, emphasis in orig.).

The emergent research design and process orientation of the CCS relativizes a priori, somewhat static notions of case construction in CIE and emphasizes the idea of a processual “casing”. The process of casing put forward by CCS has to be understood as a dynamic and open-ended embedding of “cased” research phenomena within moments of scale, space, and time that produce varying sets of conditions or configurations.

In terms of comparison, the primary logic is well in line with more sophisticated approaches to comparison that not simply establish relationships between observable facts or pre-existing cases; rather, the comparative logic aims at establishing “relations between sets of relationships”, as argued by Jürgen Schriewer:

[the] specific method of science dissociates comparison from its quasi-natural union with resemblances; the interest in identifying similarities shifts from the level of factual contents to the level of generalizable relationships. […] One of the primary ways of extending their scope, or examining their explanatory power, is the controlled introduction of varying sets of conditions. The logic of relating relationships, which distinguishes the scientific method of comparison, comes close to meeting these requirements by systematically exploring and analysing sociocultural differences with respect to scrutinizing the credibility of theories, models or constructs (Schriewer, 1990 , p. 36).

The notion of establishing relations between sets of relationships allows to treat cases not as homogeneous (thus avoiding a universalizing notion of comparison); it establishes comparability not along similarity but based on conceptual, functional and/or theoretical equivalences and focuses on reconstructing ‘varying sets of conditions’ that are seen as relevant in social scientific explanation and theorizing, and to which then comparative case studies may contribute.

The following section aims presents the adaptation and application of a comparative case study approach in the YOUNG_ADULLLT research project.

3 Exploring Landscapes of Lifelong Learning through Case Studies

This section illustrates the usage of comparative case studies by drawing from research conducted in a European research project upon which the chapters in this volume are based. The project departed from the observation that most current European lifelong learning (LLL) policies have been designed to create economic growth and, at the same time, guarantee social inclusion and argued that, while these objectives are complementary, they are, however, not linearly nor causally related and, due to distinct orientations, different objectives, and temporal horizons, conflicts and ambiguities may arise. The project was designed as a mixed-method comparative study and aimed at results at the national, regional, and local levels, focusing in particular on policies targeting young adults in situations of near social exclusion. Using a multi-level approach with qualitative and quantitative methods, the project conducted, amongst others, local/regional 18 case studies of lifelong learning policies through a multi-method and multi-level design (see Parreira do Amaral et al., 2020 for more information). The localisation of the cases in their contexts was carried out by identifying relevant areas in terms of spatial differentiation and organisation of social and economic relations. The so defined “functional regions” allowed focus on territorial units which played a central role within their areas, not necessarily overlapping with geographical and/or administrative borders. Footnote 1

Two main objectives guided the research: first, to analyse policies and programmes at the regional and local level by identifying policymaking networks that included all social actors involved in shaping, formulating, and implementing LLL policies for young adults; second, to recognize strengths and weaknesses (overlapping, fragmented or unfocused policies and projects), thus identifying different patterns of LLL policymaking at regional level, and investigating their integration with the labour market, education and other social policies. The European research project focused predominantly on the differences between the existing lifelong learning policies in terms of their objectives and orientations and questioned their impact on young adults’ life courses, especially those young adults who find themselves in vulnerable positions. What concerned the researchers primarily was the interaction between local institutional settings, education, labour markets, policymaking landscapes, and informal initiatives that together nurture the processes of lifelong learning. They argued that it is by inquiring into the interplay of these components that the regional and local contexts of lifelong learning policymaking can be better assessed and understood. In this regard, the multi-layered approach covered a wide range of actors and levels and aimed at securing compatibility throughout the different phases and parts of the research.

The multi-level approach adopted aimed at incorporating the different levels from transnational to regional/local to individual, that is, the different places, spaces, and levels with which policies are related. The multi-method design was used to bring together the results from the quantitative, qualitative and policy/document analysis (for a discussion: Parreira do Amaral, 2020 ).

Studying the complex relationships between lifelong learning (LLL) policymaking on the one hand, and young adults’ life courses on the other, requires a carefully established research approach. This task becomes even more challenging in the light of the diverse European countries and their still more complex local and regional structures and institutions. One possible way of designing a research framework able to deal with these circumstances clearly and coherently is to adopt a multi-level or multi-layered approach. This approach recognises multiple levels and patterns of analysis and enables researchers to structure the workflow according to various perspectives. It was this multi-layered approach that the research consortium of YOUNG_ADULLLT adopted and applied in its attempts to better understand policies supporting young people in their life course.

3.1 Constructing Case Studies

In constructing case studies, the project did not apply an instrumental approach focused on the assessment of “what worked (or not)?” Rather, consistently with Bartlett and Vavrus’s proposal (Bartlett & Vavrus, 2017 ), the project decided to “understand policy as a deeply political process of cultural production engaged in and shaped by social actors in disparate locations who exert incongruent amounts of influence over the design, implementation, and evaluation of policy” ( Bartlett & Vavrus, 2017 , p. 1f). This was done in order to enhance the interactive and relational dimension among actors and levels, as well as their embeddedness in local infra-structures (education, labour, social/youth policies) according to project’s three theoretical perspectives. The analyses of the information and data integrated by our case study approach aimed at a cross-reading of the relations among the macro socio-economic dimensions, structural arrangements, governance patterns, addressee biographies and mainstream discourses that underlie the process of design and implementation of the LLL policies selected as case study. The subjective dimensions of agency and sense-making animated these analyses, and the multi-level approach contextualized them from the local to the transnational levels. Figure 3.1 below represents the analytical approach to the research material gathered in constructing the case studies. Specifically, it shows the different levels, from the transnational level down to the addressees.

Multi-level and multi-method approach to case studies in YOUNG_ADULLLT. Source: Palumbo et al., 2019

The project partners aimed at a cross-dimensional construction of the case studies, and this implied the possibility of different entry points, for instance by moving the analytical perspective top-down or bottom-up, as well as shifting from left to right of the matrix and vice versa. Considering the “horizontal movement”, the multidimensional approach has enabled taking into consideration the mutual influence and relations among the institutional, individual, and structural dimensions (which in the project corresponded to the theoretical frames of CPE, LCR, and GOV). In addition, the “vertical movement” from the transnational to the individual level and vice versa was meant to carefully carry out a “study of flows of influence, ideas, and actions through these levels” (Bartlett and Vavrus 2017 , p. 11), emphasizing the correspondences/divergences among the perspectives of different actors at different levels. The transversal dimension, that is, the historical process, focused on the period after the financial crisis of 2007/2008 as it has impacted differently on the social and economic situations of young people, often resulting in stern conditions and higher competition in education and labour markets, which also called for a reassessment of existing policies targeting young adults in the countries studied.

Concerning the analyses, a further step included the translation of the conceptual model illustrated in Fig. 3.1 above into a heuristic table used to systematically organize the empirical data collected and guide the analyses cases constructed as multi-level and multidimensional phenomena, allowing for the establishment of interlinkages and relationships. By this approach, the analysis had the possibility of grasping the various levels at which LLL policies are negotiated and displaying the interplay of macro-structures, regional environments and institutions/organizations as well as individual expectations. Table 3.1 illustrates the operationalization of the data matrix that guided the work.

In order to ensure the presentability and intelligibility of the results, Footnote 2 a narrative approach to case studies analysis was chosen whose main task was one of “storytelling” aimed at highlighting what made each case unique and what difference it makes for LLL policymaking and to young people’s life courses. A crucial element of this entails establishing relations “between sets of relationships”, as argued above.

LLL policies were selected as starting points from which the cases themselves could be constructed and of which different stories could be developed. That stories can be told differently does not mean that they are arbitrary, rather this refers to different ways of accounting for the embedding of the specific case to its context, namely the “diverging policy frameworks, patterns of policymaking, networks of implementation, political discourses and macro-structural conditions at local level” (see Palumbo et al., 2020 , p. 220). Moreover, developing different narratives aimed at representing the various voices of the actors involved in the process—from policy-design and appropriation through to implementation—and making the different stakeholders’ and addressees’ opinions visible, creating thus intelligible narratives for the cases (see Palumbo et al., 2020 ). Analysing each case started from an entry point selected, from which a story was told. Mainly, two entry points were used: on the one hand, departing from the transversal dimension of the case and which focused on the evolution of a policy in terms of its main objectives, target groups, governance patterns and so on in order to highlight the intended and unintended effects of the “current version” of the policy within its context and according to the opinions of the actors interviewed. On the other hand, biographies were selected as starting points in an attempt to contextualize the life stories within the biographical constellations in which the young people came across the measure, the access procedures, and how their life trajectories continued in and possibly after their participation in the policy (see Palumbo et al., 2020 for examples of these narrative strategies).

4 Concluding Remarks

This chapter presented and discussed the methodological basis and requirements of conducting case studies in comparative research, such as those presented in the subsequent chapters of this volume. The Comparative Case Study approach suggested in the previous discussion offers productive and innovative ways to account sensitively to culture and contexts; it provides a useful heuristic that deals effectively with issues related to case construction, namely an emergent and dynamic approach to casing, instead of simply assuming “bounded”, pre-defined cases as the object of research; they also offer a helpful procedural, configurational approach to “causality”; and, not least, a resourceful approach to comparison that allows researchers to respect the uniqueness and integrity of each case while at the same time yielding insights and results that transcend the idiosyncrasy of the single case. In sum, CCS offers a sound approach to CIE research that is culture and context sensitive.

For a discussion of the concept of functional region, see Parreira do Amaral et al., 2020 .

This analytical move is in line with recent developments that aim at accounting for a cultural turn (Jameson, 1998 ) or ideational turn (Béland & Cox, 2011 ) in policy analysis methodology, called interpretive policy analysis (see Münch, 2016 ).

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do Amaral, M.P. (2022). Comparative Case Studies: Methodological Discussion. In: Benasso, S., Bouillet, D., Neves, T., Parreira do Amaral, M. (eds) Landscapes of Lifelong Learning Policies across Europe. Palgrave Studies in Adult Education and Lifelong Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96454-2_3

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Case study research in the social sciences

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• 1 Department of Sociology, University of Helsinki, PO Box 18, 00014, University of Helsinki, Finland. Electronic address: [email protected].
• 2 Department of Philosophy, University of Bergen, Postboks 7805, 5020, Bergen, Norway. Electronic address: [email protected].
• PMID: 31818413
• DOI: 10.1016/j.shpsa.2019.10.003

In this paper, we offer an introduction to case study research in the social sciences. We begin with a discussion of the definition of case study research. Next, we point to various purposes that case study research may serve in the social sciences and then turn to outline the main philosophical issues raised by case study research. Finally, we briefly present the papers in this special issue.

Keywords: Case study design; Case study research; Evidence amalgamation; Explanation; Generalization; Social sciences.

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Conducting Case Study Research in Sociology

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A case study is a research method that relies on a single case rather than a population or sample. When researchers focus on a single case, they can make detailed observations over a long period of time, something that cannot be done with large samples without costing a lot of money. Case studies are also useful in the early stages of research when the goal is to explore ideas, test, and perfect measurement instruments, and to prepare for a larger study. The case study research method is popular not just within ​the field of sociology, but also within the fields of anthropology, psychology, education, political science, clinical science, social work, and administrative science.

Overview of the Case Study Research Method

A case study is unique within the social sciences for its focus of study on a single entity, which can be a person, group or organization, event, action, or situation. It is also unique in that, as a focus of research, a case is chosen for specific reasons, rather than randomly , as is usually done when conducting empirical research. Often, when researchers use the case study method, they focus on a case that is exceptional in some way because it is possible to learn a lot about social relationships and social forces when studying those things that deviate from norms. In doing so, a researcher is often able, through their study, to test the validity of the social theory, or to create new theories using the grounded theory method .

The first case studies in the social sciences were likely conducted by Pierre Guillaume Frédéric Le Play, a 19th-century French sociologist and economist who studied family budgets. The method has been used in sociology, psychology, and anthropology since the early 20th century.

Within sociology, case studies are typically conducted with qualitative research methods . They are considered micro rather than macro in nature , and one cannot necessarily generalize the findings of a case study to other situations. However, this is not a limitation of the method, but a strength. Through a case study based on ethnographic observation and interviews, among other methods, sociologists can illuminate otherwise hard to see and understand social relations, structures, and processes. In doing so, the findings of case studies often stimulate further research.

Types and Forms of Case Studies

There are three primary types of case studies: key cases, outlier cases, and local knowledge cases.

• Key cases are those which are chosen because the researcher has ​a particular interest in it or the circumstances surrounding it.
• Outlier cases are those that are chosen because the case stands out from other events, organizations, or situations, for some reason, and social scientists recognize that we can learn a lot from those things that differ from the norm .
• Finally, a researcher may decide to conduct a local knowledge case study when they already have amassed a usable amount of information about a given topic, person, organization, or event, and so is well-poised to conduct a study of it.

Within these types, a case study may take four different forms: illustrative, exploratory, cumulative, and critical.

• Illustrative case studies are descriptive in nature and designed to shed light on a particular situation, set of circumstances, and the social relations and processes that are embedded in them. They are useful in bringing to light something about which most people are not aware of.
• Exploratory case studies are also often known as pilot studies . This type of case study is typically used when a researcher wants to identify research questions and methods of study for a large, complex study. They are useful for clarifying the research process, which can help a researcher make the best use of time and resources in the larger study that will follow it.
• Cumulative case studies are those in which a researcher pulls together already completed case studies on a particular topic. They are useful in helping researchers to make generalizations from studies that have something in common.
• Critical instance case studies are conducted when a researcher wants to understand what happened with a unique event and/or to challenge commonly held assumptions about it that may be faulty due to a lack of critical understanding.

Whatever type and form of case study you decide to conduct, it's important to first identify the purpose, goals, and approach for conducting methodologically sound research.

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Case Study: Research Method for Social Sciences

6 Pages Posted: 4 Nov 2015 Last revised: 7 Nov 2015

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Case study is both method and tool for research. Case study leads to very novel idea and no longer limited to the particular individual. In case study investigator tries to collect the bits in support of proposition. One case study if we take specific than prediction value is less while if the case is the representative sample then it has high prediction value. Case study methodological is not longitudinal study but it depends on the methods of information about the individual as far as possible. Case Study-Method judgment sample and purposive sample is used because the purpose of case study method is to improve the case and not to conclude therefore any “probability sampling is applied.

Keywords: Research method, Social Science, Case study, Types, Application

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The case study analysis is a tried and true method of instruction and evaluation in many academic fields, including social sciences. Researchers frequently use parallel or similar case studies as real-life examples when they begin the questioning process for their research process. For students and scholars, case studies have the potential to impact social growth without employing other expensive, time-consuming methods.

Case-based reasoning allows the social researcher to learn and dissect cultures, moral values, and traditions without concrete intrusions. They can be used to dictate practice in general principle decision-making and following through on potential outcomes and results, or used to develop theory through abstract thought with the comparison of multiple events. 

On the whole, case studies are used as instructional tools, methods of choosing a plan of treatment, or strategizing. In the social sciences, this is essential to make decisions or assumptions about a person, group, or society, without directly evaluating each individual.

Social Research and the Need for Different Methods

Social sciences are different from other fields of humanities because they are based predominantly on epistemic studies, or strategizing theories based on knowledge of a subset of people, beliefs, or attitudes. Case studies are used in social science research to illustrate what is known about a concept or a statement that is made or a postulated theory. They’re also used to make a claim of an outcome after exhaustive research has been made.  

Using case studies in social research aids the researcher in determining hypotheses about situations that have or would possibly happen. Although there is no way to use scientific evidence, as there would be in a mathematical theorem or biological question, the case study itself, when used correctly, is an epistemic strategy that can stand up to scrutiny under staunch measures to demonstrate credibility and relevancy of the hypothesis and predicted outcome.

With no way of predicting potentially widespread results on a population without a problem already occurring, it is obvious that social research requires different methodological practices than other sciences employ

Challenges to Using Case Studies in Social Research

Without concrete formulas and evidence, case studies are bound to have some challenges when they are used. Knowing the obstacles that can move the research results from quantitative to qualitative or diminish their credibility can help the researcher overcome issues before releasing their findings.

Common challenges include:

●      A focus on generalization, but an inability to generalize findings. As a theory, social sciences use case studies and other methods to predict the beliefs, behaviors, or attitudes of a generalized population.  

This often leads to a need for further exploration of other areas in order to determine the causation or effects of the research’s outcome. 

●      Length of time to complete a study. Case studies consist of an action play of interviewing participants, gathering and analyzing data, readjusting focus groups, and repeating as necessary. Deadlines can be delineated but often require adjustments due to other people’s changing circumstances.

There is no way to put a scientific cap on measuring the time necessary to obtain information in a case study, and these tend to take extensive time for data collection and analysis.

●      Difficulty obtaining permission status, since for research of people to be unbiased or skewed, anonymity is often preserved. This way, the participants aren’t concerned about backlash or problems should their honest answers be revealed.

Promises of non-disclosure are usually given, but case studies are detailed reviewings of a situation. It’s difficult to ensure the material is accurate without the permission of the participant, which can’t always be obtained in anonymous works and can be time-consuming to attempt. 

Many challenges make using case studies a difficult practice, but the benefits outweigh these obstacles in social research.

Case Studies Have Advantages

As case studies have grown in the significance of use, they’ve overcome all but the diehard skeptic criticism. In the past, case studies were considered a method of last resort, only to be used if no other alternative existed.

But now, there is a better understanding and acceptance of the potential case studies offer as the opportunity to use knowledge to formulate hypotheses, establish determinations off of findings, and then turn those results into a generalization that can benefit society. 

The use of case studies as a methodology in research allows the researcher to obtain a holistic review of a subject. An entire picture is given, rather than a small idea. With this larger understanding, the researcher can thoroughly build information on the subject with which to continue exploring smaller details in the midst of the situation. 

With case studies, researchers have the opportunity to evaluate and understand a wide range of perspectives, reducing the potential for bias to exist. As long as a diverse study is performed with a wide pool of participants, no one single individual can determine the outcome. 

The need for case studies in social sciences to postulate and prevent societal issues has gradually seen this practice become its own methodology.

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Organizing Your Social Sciences Research Paper: Writing a Case Study

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• Making an Outline
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The term case study refers to both a method of analysis and a specific research design for examining a problem, both of which are used in most circumstances to generalize across populations. This tab focuses on the latter--how to design and organize a research paper in the social sciences that analyzes a specific case.

A case study research paper examines a person, place, event, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or among more than two subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies . Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in this writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a single case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• Does the case represent an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• Does the case provide important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• Does the case challenge and offer a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in practice. A case may offer you an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to the study a case in order to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• Does the case provide an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings in order to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• Does the case offer a new direction in future research? A case study can be used as a tool for exploratory research that points to a need for further examination of the research problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of Uganda. A case study of how women contribute to saving water in a particular village can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community throughout rural regions of east Africa. The case could also point to the need for scholars to apply feminist theories of work and family to the issue of water conservation.

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work. In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What was I studying? Describe the research problem and describe the subject of analysis you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why was this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the research problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would include summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to study the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in the context of explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular subject of analysis to study and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that frames your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; c) what were the consequences of the event.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experience he or she has had that provides an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of his/her experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using him or her as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem.

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, cultural, economic, political, etc.], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, why study Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research reveals Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut? How might knowing the suppliers of these trucks from overseas reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should be linked to the findings from the literature review. Be sure to cite any prior studies that helped you determine that the case you chose was appropriate for investigating the research problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is more common to combine a description of the findings with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings It is important to remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations for the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and needs for further research.

The function of your paper's conclusion is to: 1)  restate the main argument supported by the findings from the analysis of your case; 2) clearly state the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place for you to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in and your professor's preferences, the concluding paragraph may contain your final reflections on the evidence presented applied to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were on social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood differently than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis.

Case Studies . Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent knowledge is more valuable than concrete, practical (context-dependent) knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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• Published: 13 July 2024

A global perspective on social stratification in science

• Aliakbar Akbaritabar   ORCID: orcid.org/0000-0003-3828-1533 1 ,
• Andrés Felipe Castro Torres 1 , 2 &
• Vincent Larivière 3  

Humanities and Social Sciences Communications volume  11 , Article number:  914 ( 2024 ) Cite this article

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• Science, technology and society

To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.

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Introduction.

Science is a social enterprise with inequality among its agents (Chompalov et al. 2002 ; Kozlowski et al. 2022 ; Shrum et al. 2001 , 2007 ). Factors underpinning social stratification include differences within and between countries in institutional capacity and resources available for research (Castro Torres and Alburez-Gutierrez 2022 ), and inequalities among scholars according to gender (Akbaritabar and Squazzoni, 2020 ; Larivière et al. 2013 ), race and ethnicity (Kozlowski et al. 2022 ), migration status (Sanliturk et al. 2023 ; X. Zhao et al. 2023 ), and social class differences in opportunities to access higher education and research (Bourdieu and Passeron, 1979 ; Burris 2004 ; Clauset et al. 2015 ). Such overrepresentation of specific demographics in privileged positions within scientific systems are indicators of stratification (Alper 1993 ; Hofstra et al. 2022 ; Marini and Meschitti 2018 ). Differences in scholars’ strategies in the search for prestige can also influence inequalities in science (Leahey and Cain 2013 ). The durability of stratification depends, among other things, on taken-for-granted ideas about the necessity and benefits of hierarchical order—for example in terms of seniority, impact, or recognition. These taken-for-granted ideas also exist in the broader sphere of social and economic affairs. The belief that a market-oriented organization of the economy without state intervention is optimal legitimizes the existence of socioeconomic inequalities within and between societies (Mazzucato 2018 ; Pikkety 2019 ), which in turn contributes to sustaining social stratification among nations and individuals (Therborn 2013 ). In all likelihood, Science as a subfield of these broader social and economic relations, works analogously. Scientific research also is an inherently competitive endeavor, in which individual-based reputational incentives can undermine the motivation to collaborate (Müller 2012 ; Penman and Goldson 2015 ; van den Besselaar et al. 2012 ).

Inequalities in science are often justified by beliefs regarding the meritocratic nature of science and of academic success and the inherent value of truth. Several indicators, such as the number of publications and citations, help fuel these beliefs. While those are increasingly challenged by scholars from different perspectives (Sugimoto and Larivière 2018 ; Wilsdon et al. 2015 ), bibliometric measures remain used extensively. Moreover, in the context of assesment, those are mostly used in isolation and their interrelations are ignored.

This paper provides an assessment of stratification across fields of science based on a multivariate analysis of large-scale bibliometric information from 1996 to 2021 and highlights the interrelationships between bibliometric indicators. We argue that these interrelations provide a structural measure of inequalities in the scientific community beyond single variables gaps such as authors’ differences in the number of publications or citations. Because measuring inequalities is only a first step in understanding their potential underlying mechanisms, we make a dataset with country-level measures of scientific stratification publicly available for future research (Akbaritabar and Castro Torres 2024 ).

Existing inequalities in science

Data on scholars’ collaboration, geographical mobility, productivity, and citations suggest that academia is growing in absolute numbers and expanding geographically. There are more coauthored papers in recent years compared to earlier decades (Abramo et al. 2009 ; Melkers and Kiopa 2010 ; Wuchty et al. 2007 ), and more scholars experienced geographical mobility today than in the past (Sanliturk et al. 2023 ; Sugimoto et al. 2017 ; X. Zhao et al. 2023 ). Likewise, studies have shown that the number of scholarly publications has increased and that digitization has made searching and citing easier (Kozlowski et al. 2024 ; Lozano et al. 2012 ). Greater productivity and increased citation capacities enhanced academic works’ visibility and potential impact (Liu et al. 2018 ; Sinatra et al. 2016 ). Some of these analyses have pointed out that these rising trends are accompanied by an increased concentration of academic-success indicators among relatively few scholars (Ioannidis et al. 2018 ) or that increased collaboration and rate of productivity per individual has not increased (Fanelli and Larivière 2016 ).

According to the 28+ million publications indexed by Scopus (1996–2021), 33% of scholars have contributed to only one research paper throughout their careers, and the median number of authors per paper is two. This suggests that a few highly productive researchers may drive rising trends in scholars’ productivity reported in the literature (Fox and Nikivincze 2021 ; Ioannidis et al. 2018 ). Likewise, according to Scopus data, approximately 27.2% of the publications have only one author, and more than 75% are authored by scholars from one country, i.e., strictly national publications. Likewise, most authors (87.5%) have been affiliated with a single country throughout their careers, and 73.5% to a single sub-national region, that therefore experienced little geographical mobility (Akbaritabar et al. 2023 ; Sanliturk et al. 2023 ; X. Zhao et al. 2023 ). Similarly, 36.8% of authors have been actively publishing over only one year. These low shares call for a global investigation into whether claims of increased mobility, collaboration, productivity and impact are widespread phenomena, or remain concentrated among a small group of scholars. Bibliometric research has also shown that academic citations display a skewed distribution where only a very small share of publications, journals, and authors receive disproportionately high citations which has increased recently (Nielsen and Andersen 2021 ). These studies suggest that bibliometric indicators for academic-success are concentrated on a few countries, institutions, and authors.

In light of this evidence, the growth of scientific activities and its geographical expansion require a critical examination of their consequences for inequalities and global stratification. In fact, we know less about the interrelatedness of these trends than we know about them in isolation. Therefore, understanding inequalities in science requires a multidimensional approach. There might be positive or negative correlations, feedback effects, and synergistic connections among bibliometric measures of academic success including individual and collaborative productivity, national and international mobility, and research visibility as measured by citations.

For instance, more collaborations could lead to more citations, which in turn may translate into greater productivity and more opportunities for geographical mobility; greater mobility may expand scholars’ networks, enhancing their potential pool of collaborators. Conversely, mobility and changes of affiliation could also reflect negative conditions such as precarious research contracts and lack of opportunities for a life-long or long-term career. Further, multiple instances of mobility can destabilize one’s network of collaborations (Z. Zhao et al. 2020 ). The absence or lack of success in any of these realms may negatively affect performance in the others, as well as positive outcomes in any of these realms may boost success in others i.e., Matthew effect (Merton 1968 ). Social stratification in science will likely emerge from the confluence of successful (and unsuccessful) academic paths in these interrelated realms: productivity, collaboration, geographical mobility, and citations.

Materials and methods

We use 28.5 million articles and review publications indexed in Elsevier’s Scopus between 1996 and 2021. A proper disambiguation of author names is crucial for analysis such as ours that reconstructs publication trajectories over one’s career. Scopus identification numbers (Baas et al. 2020 ) are one of the few reliable options available (Aman 2018 ) and were used here to assign papers to authors and to identify groups of authors who publish together in the global network of co-authorship. We limit these publications to all of those written by the authors having identification numbers in Scopus and declared as “disambiguated” by Elsevier which has a 98.3% precision and a 90.6% recall (Baas et al. 2020 ). In addition to the evaluations by Elsevier (Baas et al. 2020 ), others have previously shown that Scopus author identification numbers are reliable in comparison to other sources (Aman 2018 ). We further disambiguate the academic affiliation of authors in this set of publications using the Research Organization Registry’s (ROR) Application Programming Interface (API) and geocode organizations’ addresses to subnational units (Akbaritabar 2021 ). This reduces our coverage of publications down from 33 to 28.5 million publications by 8.2 million disambiguated authors.

Author level variables and career-long measurement

To categorize scientists into specific groups and identify stratification processes, we reviewed the literature and selected the 12 most-widely used academic performance indicators. The list of indicators is as comprehensive as possible given existing data and it avoids, as much as possible, redundancy across measures. Together, these indicators provide a robust measure of individual-level academic performance. These are the most widely used measures in previous studies which have implemented them mostly in isolation without considering their interrelation.

While our analytical sample includes 8.2 million authors with at least one publication in the Scopus database, we excluded 41,278 authors (0.5%) because their publications have missing metadata. The list below provides each bibliometric indicator’s name and category: productivity, collaboration, mobility, and visibility. These indicators are computed at the author level and comprise all individual publications indexed by Scopus between 1996 and 2021; covering authors’ careers from one up to 25 years.

The number of coauthored papers, Num. coauthored pubs . ( collaboration/internationalization )

The average number of coauthors per paper in career, Avg. collaborations (as a measure for collaboration/internationalization )

The number of internationally coauthored publications, Num. intl. pubs ( collaboration/internationalization )

The number of nationally coauthored publications, Num. national pubs . ( collaboration/internationalization )

The number of international changes in academic affiliation, Num. intl. moves ( mobility )

The number of national changes in academic affiliation, Num nat. moves ( mobility )

The number of affiliated organizations, Num. organizations ( mobility )

The total number of citations, Total citations ( impact/visibility )

The average number of citations per paper in career, Avg. citations ( impact/visibility )

The fractional count of publications, Fractional pubs . ( productivity )

The number of publications, Total publications ( productivity )

The number of first-author publications, First author publications ( productivity )

To favor comparability among scholars, we standardize most indicators by authors’ academic age (age hereafter), measured as the years since their first publication in our database. However, the average number of coauthors per paper and the average number of citations per paper are not normalized by career age but, rather, the number of papers an author publishes throughout their career. Our goal with these two average measures, used in combination with the other 9 variables, is to further identify the effect of outliers in one’s career, such as highly cited papers or highly collaborative ones. To account for differences across disciplines in publication practices, we categorized researchers separately for each of the six macro fields of science according to the OECD classification by using the field where highest share of their publications appeared: Agricultural Sciences, Natural Sciences, Humanities, Medical and Health Sciences, Engineering and Technology, and Social Sciences.

By default, scholars with only one publication display lower variability across these 12 indicators compared to other groups. Because they published only one article, other measures such as national and international mobility, and the number of organizations are bound to zero and one, respectively. The number of citations, co-authors, and fractional count of papers are also limited to the information of the only published paper. Similarly, scholars who have publications in only one year in our data have lower bounds in these indicators. This limited heterogeneity reduces the influence of this group in our analysis despite their relatively high shares, ranging from 31% in the Natural Sciences to 47% in Engineering and Technology. In the Supplementary information (SI), we show separate figures for scholars with only one year of publication activity (Fig. S3 presents the share of one-year old authors). Instead of excluding this group from the analysis, as the usual practice in the literature, we decided for categorizing them under a specific age group to study the specificities of this understudied group.

Bibliometric variables are extremely skewed and the usual practice in the literature is to exclude outliers. As an example, publications with the highest number of authors are sometimes excluded (Nogrady 2023 ; Singh Chawla 2019 ). Here, to better capture non-linear relations across these indicators, and to reduce the influence of outliers, while keeping them in the analysis, all the indicators were categorized into the maximum possible number of categories ensuring relative frequencies of at least 2% in all categories. This categorization method maintains the essential characteristics of the continuous variables while mitigating the impact of outliers on correlation measures. This is achieved by grouping outliers into the lower- and bottom-end categories. This approach to variable coding is beneficial in the context of highly-skewed variables with heavy tails (see Fig. S2 ), as it allows us to: (i) include extreme values in the analysis, (ii) capture potential non-linear relationships among variables, (iii) preserve the distributional characteristics of each indicator, and (iv) avoids potential biases in correlational analyses due to outlier observations. The resulting number of categories across variables ranges from three for the number of international changes in academic affiliation in Agricultural Sciences (i.e., 95% of authors do not experience international mobility) to ten for the total number of citations in the Natural Sciences and Medical and Health Sciences (i.e., the 10th, 20th, …, 100th percentiles).

A multidimensional measure of social stratification within scientific communities

We run a Multiple Correspondence Analysis (Le Roux and Rouanet 2004 ) on the 12 categorized indicators for each macro field of science. Based on the Singular Value Decomposition of the matrix representing the 12 indicators, MCA yields individual-level numerical variables termed factorial axes. These factorial axes summarize the 12 indicators according to their multivariate correlations and relative importance. Due to the high number of categories of the 12 variables, our field-specific MCAs yield more than 50 factorial axes, most of which have very little informational value. We focus on the first three axes because their associated eigenvalues are significantly larger than the others, and therefore capture the most salient differences among scholars’ bibliometric performances (see Fig. S4 ).

Despite our age standardization, the first factorial axis of all MCAs came out as strongly correlated with scholars’ age and indicators of productivity, visibility, and collaboration. This result is partially due to the specificities of the one-year old group (e.g., reduced heterogeneity and very distinct profiles compared to older scholars), but also underscores the cumulative aspect of academic achievements with age. There is a clear age gradient in the first factorial axis for all age groups, not only the one year old, indicating that the incremental improvements in academic productivity, visibility, and collaboration grow as individuals progress in seniority.

Considering the significance of age in our study, and with the aim of improving comparability, we performed cluster analyses independently for six age groups: One-year-old, two to five, six to nine, 10 to 14, 15 to 20, and 21 to 25. Hence, we conducted 36 hierarchical clustering analyses (six macro fields of science multiplied by six age groups) based on the Ward method followed by a cluster consolidation via the K-means algorithms. Neighboring solutions with five, six, seven, and eight clusters were assessed using the ratio of between to total variance. These assessments led us to focus on a six-cluster solution (see SI). We term these clustering bibliometric classes and we use positional words to label them: bottom , low , mid-low , mid-high , high , and top . The marginal distribution of scholars across bibliometric classes measures the social stratification of science in each field. The differences between bibliometric classes in academic performance indicators capture the extent of hierarchies. We visualize these differences using factorial axes where distance implies differences and proximity implies similarity.

Network analysis of intra- and inter-class collaboration

To investigate whether members of identified bibliometric classes collaborate “within” their own class or with members of other classes and age groups, we construct global bipartite networks of co-authorship among the 8.2 million authors, identify its largest connected (giant) component and detect communities of densely collaborating scientists. In other words, we group authors into scientific communities according to their degrees of proximity in collaboration networks. Scholars that coauthor papers are maximally close, whereas authors without any coauthor in common are maximal distal. To identify communities, we use the Constant Potts Model (CPM) (Reichardt and Bornholdt 2004 ) and its extension to bipartite networks (Akbaritabar 2021 ; Akbaritabar and Barbato 2021 ; Traag et al. 2011 ) with a varying range of 18 resolution parameters. For robustness checks, we use three additional community detection algorithms from NetworKit (default algorithm, parallel Louvain, and parallel Label Propagation) and cross-check the identified communities. Additionally, we projected the bipartite network to a one-mode one, despite criticisms on such a projection and information loss it brings (Akbaritabar 2021 ; Akbaritabar and Barbato 2021 ), to use Leiden algorithm and results were robust and our storyline did not change (see SI).

We examine authors’ distribution across bibliometric classes within these identified scientific communities. For this analysis, we pooled all academic-age groups and compared the distribution of authors within each scientific community according to their academic age and bibliometric class. A side-by-side comparison of the bibliometric classes and academic-age distributions within scientific communities and entropy measures for these two distributions allows for assessing the nature and strength of stratification across scientific communities. Figure S1 presents the steps described above.

We represent social stratification in science and bibliometric classes using the first two MCA axes. We interpret these axes according to the variables’ percentage contribution to the variance, as displayed in Fig. 1 . A vertical line is drawn at the mean percentage contribution, i.e., 8.3%. Markers at the right of this vertical line indicate variables with above-average contributions to the axes’ variance. Different markers are used for each macro field of science.

The panels correspond to the first three factorial axes. The X-axis shows the variables' contribution to the axes' inertia. Markers' colors and shapes distinguish the OECD macro field of sciences. The vertical dashed line indicates the average percentage point contribution (100%/12 = 8%).

The variables that contribute the most to the first factorial axis are total publications, number of organizations, number of coauthored publications, average collaborations, and first-authored publications. Field differences are evident in the contribution of these variables to the first axis. For instance, in the Humanities (filled square), “Num. coauthored pubs.” and “Avg. collaborations” have a much lower contribution than “First author publications”, which can be explained by the fact that they are generally a non-collaborative field. The reverse is observed for the Social Sciences (filled diamond), where coauthored papers have a higher contribution to the first axis than first-author publications.

The first factorial axis correlates positively with academic age. This is a somewhat unexpected result given that we use indicators standardized by age. In all macro fields of science, there is an age-gradient in the first axis, and the mean coordinate of first and last age-groups are more than one standard deviation apart. There is no age gradient in any of the other axes. Therefore, when considering total publications, the number of organizations, coauthored publications, average collaborations, and first-author publications per year of age, senior scholars surpass their junior counterparts. In other words, the positive correlation between academic age and the first axis suggests that academic success accumulates with age, leading to progressively greater marginal gains. Thus, we labeled the first MCA axis as “Academic age, number of organizations, and individual productivity” despite the fact that age has not been used as an input in the MCA. A large coordinate in this axis represents older academic age, a relatively high number of organizations, and an above-average number of publications, as first-author in collaborations.

The variables that contribute the most to the second factorial axis are total, fractional (for some fields), and coauthored publications. In addition, the total number of citations and the number of national publications also contribute significantly to the second axis. We labeled the second axis as: “Total productivity, visibility, and collaborations.” Finally, the variables that contribute the most to the third factorial axis are first-authored publications, total publications, fractional publications, number of coauthored publications, and average collaborations. There is a large variety among fields of science in variables’ contributions to the third axis, yet, productivity and collaboration measures excel for their large contributions, particularly for the Humanities.

Hence, the organization of scholars according to their bibliometric indicators revolves around two main dimensions: “academic age, number of organizations, and individual productivity” on the one side, and “total productivity, visibility, and collaborations,” on the other. Scholars’ productivity is distinctly comprised in both dimensions. In the first dimension, productivity goes along with age and first-author publication. In the second dimension, productivity is less dependent on age and is associated with collaborations and citations. Interestingly, none of the mobility measures contribute significantly to the first three MCA axes that could stem from the very small share of mobile authors (about 8% in international and 12% in national moves).

Figure 2 displays authors’ distribution by fields of science according to the above-described main dimensions and the bibliometric classes detected via cluster analysis. Existing differences in academic practices (e.g., publication, collaboration, mobility, and citation) across fields of science require axes’ scales be free and prevent scaled comparisons across them. Authors with identical bibliometric measures are grouped and represented as circles to reduce overplotting. Circles’ size is proportional to the number of authors with identical bibliometric profiles. Although we conduct the analysis for all ages and find similar results across those (gray background circles), Fig. 2 highlights the bibliometric stratification of 15 to 20 year old scholars. The top group comprises the most successful authors based on combining our 12 bibliometric measures. The bottom-left includes those at the bottom of academic achievement indicators’ distributions.

Multiple Correspondence Analysis (MCA) results using the 12 most widely used bibliometric variables allowed identifying six classes of scientists from Bottom, Low, Middle low, Middle high, High, to Top. In all six fields of science and five-year career groups from a minimum of 1 to a maximum of 25 years of publication career indexed in Scopus, we see the same stratified structure appearing. A minority of the top class is identified which consists of less or about 10% (in most fields) of the most successful scientists indicated with dark red colors in the figure. See figures in Supplementary Information (SI) for other academic age groups and disaggregated analysis based on gender of authors to males and females which did not show a change in the reported trends.

The clustering of authors according to their academic achievement is a measure of existing inequalities in these fields of science. Despite disciplinary differences in size and scientific practices, the commonalities in the stratification of authors are notable. In all six fields of science, the top class comprises a minority whose share ranges from a minimum of 6% in Humanities to a maximum of 19% in Natural Sciences. The bottom class ranges from a minimum of 22% in Natural Sciences to a maximum of 32% in Engineering and Technology. On the contrary, the middle- and bottom classes unanimously position towards the bottom left quadrant, meaning they are always worse off in terms of 12 bibliometric measures investigated here.

This structure replicates among other academic-age groups (refer to figures in SI) with the exception of the one-year old. Scholars’ bibliometric stratification is most pronounced within the oldest age group (i.e., 21-to-25 years old) with bibliometric classes comprising more similar shares compared to bibliometric classes among 15-to-20-year-old scholars (refer to Fig. S10 ). This greater uniformity in the size of bibliometric classes indicates a possible cumulative effect of bibliometric performance over time. The 21-to-25 years old group represents scholars who have been actively publishing in Scopus-indexed journals for over 20 years. Thus, they are likely committed to the principles of scientific production, or at least, to the norms governing publication systems, including their penalties and rewards.

In contrast, a strong pyramidal structure (i.e., very small shares at the top classes) appears among scholars with shorter durations in the publishing system, such as those aged one year or two to five years. This strong pyramidal pattern may stem from their limited exposure to publication systems, hindering the establishment of distinct patterns. Consequently, the correlations, feedback mechanisms, and synergistic effects among bibliometric indicators are yet to manifest fully among these younger scholars.

This multivariate approach to academic performance and bibliometric classes challenges the so-called 20/80 rule, showing that it does not apply to all cases. To illustrate this point, Fig. 3 compares the bottom and top classes’ contribution to the total output in 10 metrics among 15 to 20-year-old scholars. The vertical axes represent the outcome share coming from each class, and the numbers at the top indicate class’ sizes. For example, the bottom class in Agricultural Sciences comprises 28% of the authors in our sample. These scholars contribute less than 5% of the total international publications. The scholars who are in the top class, 18%, instead, contribute more than 55%.

A multivariate approach to academic performance shows that the assumption that 80% of outputs are produced by the top 20% contributors (the so-called 20/80 rule) does not hold for bibliometric variables. The top classes in all macro fields of science account for less than 80% of the total outputs across 10 indicators. Bottom classes’ contributions are meager highlighting the extreme heterogeneity across academic careers. Both, top and bottom classes display similar contributions to geographical and institutional mobility.

Figure 3 shows that bottom classes comprise one fourth of authors in all macro fields and contribute less than 5% of the total in seven out of 10 indicators. The three exceptions are the number of organizations, and national and international moves which are measures of mobility. In fact, the share contribution of the bottom classes to these three outcomes is similar to that of the top class, except in the Humanities where bottom class scholars contribute much larger shares. These similarities indicate that mobility, both geographical and institutional, is associated with both success and failure in bibliometric performance. This is coherent with the literature highlighting positive and negative implications for mobility such as higher impact and less stable network of collaborations (Sugimoto et al. 2017 ; Z. Zhao et al. 2020 ).

In contrast, the top classes, between 6% and 19% of authors, lead the contributions to international publications in all macro fields of science. However, even in the Natural Sciences, where their share contribution is the highest, they are far from contributing 80%, meaning that the 20/80 rule does not hold under a multivariate approach to academic performance. The top classes also excel by their contribution to national publications, Coauthored papers, and total citations. Share contributions to other outcomes by the top class are generally lower, particularly for outcomes that imply some mobility or change of institutional affiliation as highlighted above. Figure S5 in the SI displays the shared contribution of all classes for the 10 outcomes.

Another aspect of these bibliometric classes is whether authors from different classes belong to the same research communities identified in the co-authorship network. Figure 4 shows the distribution of authors according to bibliometric classes (Panel A) and academic age groups (Panel B) across 19,970 scientific communities with at least 20 authors (99% of authors and 42.7% of communities). These communities are identified from the collaboration networks measured through co-authorship of publications (see more information in methods section). In panels A and B, scientific communities are represented by horizontal lines sorted from largest (on the top) to smallest and the deciles of the community-size distribution are indicated in the vertical axis. According to these panels, bibliometric-based stratification is similar to stratification based on age, suggesting that collaboration networks comprise authors of all ages and from all bibliometric classes. This similarity of bibliometric-class and academic age compositions is confirmed by Panel C, which displays the empirical density of the community-level entropy of authors’ distribution by bibliometric classes and age groups. We display results for three community detection scenarios out of 18 that were assessed, to maintain the figure’s clarity (see further robustness results including evaluation of authors’ country of affiliation and gender in SI). The fact that all density curves are strongly skewed towards high entropy values (max entropy = 1) confirms our visual assessment of Panels A and B and suggests our results are robust to different community detection scenarios and algorithms.

To investigate the trends shown in Fig. 2 further and control the collaboration structure among the classes, we turned to co-authorship networks of the studied 28 million publications. Networks of collaboration in terms of co-authoring scientific publications among 8.2 million authors worldwide allowed us to identify communities of collaboration. We used the Constant Potts Model (CPM) and its extension for bipartite networks with a varying range of 18 thresholds for the resolution parameter to detect communities. In all these detected communities (only 3 shown in the figure to preserve clarity), we investigated the class ( A ) and age ( B ) composition of members. Independent from the threshold used, all these communities have a heterogeneous composition of classes and age groups and analysis of entropies of this stratification ( C ) indicates an inter-class and inter-age collaboration structure among the most and least prolific, collaborative/internationalized, and mobile scientists. SI includes figures with further robustness analysis using three other community detection algorithms, one-mode projection of the network and results using Leiden (Traag et al. 2019 ) algorithm, and also disaggregated analysis based on gender of authors to males and females which did not show a change in the reported trends.

This paper provided a quantitative assessment of the global inequalities in science using bibliometric data across fields of science and research communities. Our results show that a stratified system in terms of bibliometric performance exists in all macro fields of science, and it is as strong as fields’ stratification by academic age. As scholars age (i.e., progress to more senior academic career stages) and maintain consistent participation in publication systems, their positioning within the bibliometric-based academic hierarchy becomes clearer. This clarity evolves potentially due to increased exposure and experience in publishing, highlighting the role of time and continued scholarly activity in shaping bibliometric classes. In addition, we evaluated collaboration ties among classes and whether specific age groups dominate it. We provide the aggregated data to enable future research on the causes and consequences of this stratification (Akbaritabar and Castro Torres 2024 ).

Our multivariate assessment of bibliometric classes is grounded in the assumption that scholars’ prestige within their respective fields does not rely solely on a single indicator, such as the number of citations or publications. Instead, we assume that scholars’ standing and prestige is based on their performance across multiple indicators. Consequently, the top class includes authors who may not necessarily rank at the highest levels in every individual indicator but possess the most favorable overall academic profiles. Similarly, the middle and lower classes encompass authors with varying degrees of less favorable academic profiles. This conceptualization of academic performance introduces nuances to the conventional 20/80 rule, demonstrating that it does not necessarily apply universally. It emphasizes that individual contributions to a particular output are more intricate than the notion that the top 20% contribute 80% of the outcome. We found that top classes, defined multidimensionally, contribute less than 80% in most of the cases. Bottom classes’ contributions are minimal suggesting the existence of very distinct academic careers. While the causes and implications of these disparities are yet to be examined, we speculate that differential access to resources and additional labor (Zhang et al. 2022 ) that could be higher among the top class and be perpetuated through additional funding and new resources allocated to them in performance-based funding schemes (Akbaritabar et al. 2021 ; Zacharewicz et al. 2019 ) could drive the persisting trends. The positive age pattern of bibliometric stratification suggest that these are no unlikely speculations. Greater exposure to publication systems and continued publishing activities likely serve as reinforcing mechanisms, contributing to the observed patterns of bibliometric stratification advancement over academic age.

Science is transmitted from established scholars to new generations through a mentorship relationship that affects mentees’ future success (Ke et al. 2022 ; Liénard et al. 2018 ; Ma et al. 2020 ). Such supervisor-supervisee relationships inherently have an age component. Hence, we expect that a share of observed scientific collaborations will be among junior and senior scholars. Nevertheless, our results show that the proportion of scholars who exit the system after only one paper amounts to 25% or more of the members of identified communities, which cannot be solely representing the age structure of academia and could be driven by the performance measures described and the hierarchical structure inherent in them that drives a high proportion to exit the system. We emphasize that not all graduate students continue the career paths in research leading to continued publication activity. Nonetheless, the probability of having higher impact and citations in the science system is disproportionately distributed and highly stratified (Nielsen and Andersen 2021 ).

Our study has a descriptive nature, despite the comprehensive inclusion of all most widely used bibliometric variables, their relationships, while considering academic age differences and fields of science. With the current descriptive setup, it is not possible to evaluate if the observed quantitative stratification signals inequality in access to resources such as research assistants and junior collaborators (Zhang et al. 2022 ). We do not know much about the type of contracts or positions these studied researchers hold; we only know their academic age. Similarly, the prestige of these academic institutions is not covered in our analysis, as well as the national policies that might affect the resources one accesses. These differences in resources and environment affect the type of research one can do and could lead to a different position on observational data i.e., bibliometric indicators. While our study sheds light on the stratifications because of its elaborated and comprehensive use of all relevant bibliometric variables, we did not have a causal setup and cannot evaluate the underlying causes leading to the reported stratifications and presented arguments on potential causes are based on our speculations.

Bibliometric indicators are widely used in national research assessment exercises (Akbaritabar et al. 2021 ; Zacharewicz et al. 2019 ) to determine who should be hired and promoted and whose research should be funded (Sugimoto and Larivière 2018 ). Based on our analysis, which was possible by adopting a global, multivariate, and multi-method framework to debunk the widely-spread myths about increased productivity, collaboration, internationalization, mobility, and impact among scientists, we call for a further elaborated investigation of these trends. We propose considering academic age, career cohorts and composition of a multitude of bibliometric variables instead of solely relying on one-indicator explanations which might be appealing to attract policy-makers’ attention, but might be detrimental to our understanding of the science system, its social structure, and its inherent stratification and intersectional inequalities (Kozlowski et al. 2022 ).

Data availability

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Acknowledgements

We thank Cassidy R. Sugimoto for helpful comments on an earlier version of this manuscript. This study has received access to the bibliometric data through the project “Kompetenznetzwerk Bibliometrie” and we acknowledge their funder Bundesministerium für Bildung und Forschung (grant number 16WIK2101A). AFCT received support from the Catalonian Goverment (grant number 2021 BP 00027). Open Access funding enabled and organized by Projekt DEAL.

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Aliakbar Akbaritabar & Andrés Felipe Castro Torres

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AA: Conceptualization, Methodology, Software, Validation, Data Curation, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization. AFCT: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization. VL: Conceptualization, Writing - Original Draft, Writing - Review & Editing.

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Akbaritabar, A., Castro Torres, A.F. & Larivière, V. A global perspective on social stratification in science. Humanit Soc Sci Commun 11 , 914 (2024). https://doi.org/10.1057/s41599-024-03402-w

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Received : 07 October 2023

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Published : 13 July 2024

DOI : https://doi.org/10.1057/s41599-024-03402-w

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The University Research Fellowship (URF) programme aims to support the next generation of research leaders to undertake cutting-edge research. The objectives of the URF programme are to enable outstanding early career scientists with the potential to become leaders in their field to:

• Build an independent research career at a UK university or research institution
• Gain the freedom, time, and long-term flexible support to pursue high-quality and innovative lines of scientific research
• Develop as research leaders by offering tailored high-quality professional development, networking and engagement opportunities.

Fellowships are for eight years, with years six to eight being subject to satisfactory progress demonstrated in a mid-fellowship review at the start of year four.

The Royal Society recognises that diversity is essential for delivering excellence in science, technology, engineering and mathematics (STEM), and wants to encourage applications from the widest range of backgrounds, perspectives and experiences to maximise innovation and creativity in science for the benefit of humanity. We regularly review and revise policies and processes to embed equity, diversity and inclusion (EDI) principles in all aspects of the grant making process and ensure all talented applicants have an equitable chance to succeed as per the assessment criteria.

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Host Organisation Support – More detailed guidance provided to host organisations regarding expectations of their support for the applicant. From this round onwards, the Head of Department statement of support will be visible to applicants. 

We recommend reading the information in full and contacting your host organisation Research Office in the first instance with any questions.

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Applicants can apply for up to a maximum of £1.87 million over eight years. 

Funds can cover:

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Full funding details can be found in the scheme notes and in the Royal Society Funding Guidance .

We provide flexibility to accommodate personal circumstances including part-time working, sabbaticals and secondments. There is provision for maternity, paternity, shared parental, adoptive or extended sick leave , as well as financial support for childcare costs  that arise from attending conferences and research visits.

Royal Society Research Fellows also have the opportunity to access a range of career development and engagement opportunities  including training on leadership, science communication and public engagement, and activities coordinated by our science policy and schools engagement teams. For further detail on these additional benefits, read our opportunities page .

Royal Society awards have made a significant impact on many researchers’ careers. Case studies from grant-holders can be found elsewhere on this page, or read an in-depth report on the careers of our alumni on our Career Pathway Tracker page .

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• You have between three and eight years of research experience, excluding career breaks, since the award of your PhD by the closing date of the round; please refer to the scheme notes for further detail about the review of career breaks
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Applicants can be of any nationality and those requiring a visa are eligible to apply for a Global Talent Visa  under the fast-track process of endorsement.

Read the scheme notes or FAQs (PDF) for further information on eligibility. Those applying from Ireland (ROI) are funded by Science Foundation Ireland and will need to read the eligibility requirements in the SFI-specific scheme notes.

Please ensure that you meet all eligibility requirements before applying.

You will apply through our application and grant management system, Flexi-Grant®. 

See the ‘Application and assessment process’ page  for a general overview of the application and selection steps and below for details specific to this scheme.

Assessment of your application will be overseen by one of our five Research Appointment Panels (Standing Committees) based on your research area:

• Ai: Astronomy, cosmology, physics, earth sciences, environmental physical sciences & geosciences 
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Assessment of your application will be overseen by one of the five Research Appointment Panels. Following eligibility checks, applications are initially assessed by a minimum of two panel members who have the most appropriate scientific expertise. A longlist is drawn up, with longlisted applications subject to independent peer review. Following completion of independent peer review, a shortlist for interview is drawn up with oversight from the Panel Chairs. At the end of the interview stage, the Panels will confirm the recommendations for funding.

Further detail on the application and review process is available in the scheme notes.

The Royal Society welcomes applications from disabled scientists and provides support and adjustments to ensure that they can participate fully in the selection process. If you require support or an adjustment when accessing the application form, attending interview, or for any other part of the application process, please contact the Grants team at [email protected] or call +44 20 7451 2666. All requests for adjustments are made in confidentiality. Any request for an adjustment will not normally be shared with panel members unless it becomes relevant to the selection process itself. If we need to share your request with anyone (for example if panel members are required to implement any adjustments during interviews), we will ask for your permission first.

Adjustments can include, but are not limited to:

• Extension of the deadline
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• Receiving the application form in a different format, such as on a Word document
• Support during interviews as required, including technical support for candidates requiring accessibility software or services
• Additional costs that candidates may incur on account of their particular disability to attend an interview.

For the University Research Fellowship, shortlisted applicants will be invited to an in-person interview at the Royal Society. 

The aim of the interview is for you to demonstrate the importance and scientific validity of your work and for you to also describe how the award will lead to your scientific independence. 

If you have further questions regarding the scheme, please see the FAQs, contact the Grants team on [email protected] or visit our contact us page .

• University Research Fellowship scheme notes
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