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2.2: Four Approaches to Research

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• Page ID 135831

• Dino Bozonelos, Julia Wendt, Charlotte Lee, Jessica Scarffe, Masahiro Omae, Josh Franco, Byran Martin, & Stefan Veldhuis
• Victor Valley College, Berkeley City College, Allan Hancock College, San Diego City College, Cuyamaca College, Houston Community College, and Long Beach City College via ASCCC Open Educational Resources Initiative (OERI)

Learning Objectives

By the end of this section, you will be able to:

• Identify, and distinguish between, the four different approaches to research.
• Consider the advantages and disadvantages of each research approach.
• Compare and contrast the four approaches to research.
• Identify best practices for when and how to use case studies.

Introduction

In empirical research, there are four basic approaches: the experimental method, the statistical method, case study methods, and the comparative method. Each one of these methods involves research questions, use of theories to inform our understanding of the research problem, hypothesis testing and/or hypothesis generation. Each method is an attempt to understand the relationship between two or more variables, whether that relation is correlational or causal, both of which will be discussed below.

The Experimental Method

What is an Experiment? An experiment is defined by McDermott (2002) as “laboratory studies in which investigators retain control over the recruitment, assignment to random conditions, treatment, and measurement of subjects” (pg. 32). Experimental methods are then the aspects of experimental designs. These methodological aspects involve “standardization, randomization, between-subjects versus within-subject design, and experimental bias” (McDermott, 2002, pg, 33). The experimental method assists in reducing bias in research, and for some scholars holds great promise for research in political science (Druckman, et. al. 2011). Experimental methods in political science almost always involve statistical tools to discern causality, which will be discussed in the next paragraph.

An experiment is used whenever the researcher seeks to answer causal questions or is looking for causal inference. A causal question involves discerning cause and effect, also referred to as a causal relationship. This is when a change in one variable verifiably causes an effect or change in another variable. This differs from a correlation, or when only a relationship or association can be established between two or more variables. Correlation does not equal causation! This is an often repeated motto in political science. Just because two variables, measures, constructs, actions, etc. are related, does not mean that one caused the other. Indeed, in some cases, the correlation may be spurious, or a false relationship. This can often occur in analyses, especially if particular variables are omitted or constructed improperly.

A good example involves capitalism and democracy. Political scientists assert that capitalism and democracy are correlated. That when we see capitalism, we see democracy, and vice versa. Notice, that nothing is said about which variable causes the other. It may well be that capitalism causes democracy. Or, it could be that democracy causes capitalism. So X could cause Y or Y could cause X. In addition, X and Y could cause each other, that is capitalism and democracy cause each other. Similarly, there could be an additional variable Z that could cause both X and Y. For example, it may not be that capitalism causes democracy or that democracy causes capitalism, but instead something completely unrelated, such as the absence of war. The stability that comes from an absence of war could be what allows both capitalism and democracy to flourish. Finally, there could be a(n) intervening variable(s), between X and Z. It is not capitalism per se that leads to democracy, or vice-versa, but the accumulation of wealth, often referred to as the middle class hypothesis. In this case, it would be X→A→Y. Using our example, capitalism produces wealth, which then leads to democracy.

Real world examples of the discussion above exist. Most wealthy countries are democratic. Examples include the United States and most of western Europe. However, this is not the case for all. The oil producing countries in the Persian Gulf are considered wealthy, but not democratic. Indeed, the wealth produced in natural resource rich countries may reinforce the lack of democracy as it mostly benefits the ruling classes. Also, there are countries, such as India, which are strong democracies, but are considered developing, or poorer nations. Finally, some authoritarian countries adopted capitalism and eventually became democratic, which would seem to confirm that middle class hypothesis discussed above. Examples include South Korea and Chile. However, we see plenty of other countries, such as Singapore, that are considered quite capitalistic have developed a strong middle class, but have yet to fully adopt democracy.

These potential contradictions are why we are careful in political science with making causal statements. Causality is difficult to establish, especially when the unit of analysis involves countries, which is often the case in comparative politics. Causality is a bit easier to establish when experimentation involves individuals. The inclusion of a treatment variable, or the manipulation of just one variable across a number of cases, can suggest causality. The reiteration of an experiment multiple times can confirm this. A good example includes interviewer effects among respondents in surveys. Experiments consistently show that the race, gender, and/or age of the interviewer can affect how an interviewee responds to a question. This is especially true if the interviewer is a person of color and the interviewee is white and the question that is asked is about race or race relations. In this case, we can make a strong argument that interviewer effects are causal. That X causes some kind of effect in Y.

Given this, are there any causal statements made by comparativists? The answer is a qualified yes. Often, the desire for causality is why comparative political scientists study a small number of cases or countries. One case/country, or small number of cases/countries, analyses lend itself well to searching for a causal mechanism, which will be discussed in further detail in Section 2.4 below. Are there any causal statements in comparative politics that involve lots of cases/countries? The answer is again a qualified yes. Democratic peace theory is explained in Section 4.2 of this textbook:

“Democracies per se do not go to war with each other because they have too much in common - they have too many shared organizational, political and socio-economic values to be willing to fight each other - therefore, the more democratic nations there are the more peaceful the world will become and remain.”

This is as close as it comes to empirical law in comparative politics. Yet even in democratic peace theory there are ‘exceptions’. Some cite the U.S. Civil War as a war between two democracies. However, an argument can be made that the Confederacy was a flawed or unconsolidated democracy and ultimately not a war between two real democracies. Others point to U.S. interventions in various countries during the Cold War. These countries, Iran, Guatemala, Indonesia, British Guyana, Brazil, Chile, and Nicaragua, were all democracies. Yet, even these interventions are not convincing to some scholars as they were covert missions in countries that were not quite democratic (Rosato, 2003).

Statistical Methods

What are Statistical Methods? Statistical methods are the use of mathematical techniques to analyze collected data, usually in numerical form, such as interval or ratio-scale. In political science, statistical analyses of datasets are the preferred method. This mostly developed from the behavioral wave in political science where scholars became more focused on how individuals make political decisions, such as voting in a given election, or how they may express themselves ideologically. This often involves the use of surveys to collect evidence regarding human behavior. Potential respondents are sampled through the use of a questionnaire constructed to elicit information regarding a particular subject. For example, we may develop a survey that asks Americans regarding their intention on taking one of the approved COVID-19 vaccines, if they intend to get a booster in the future, and their thoughts on pandemic-related restrictions. Respondent choices are then coded, usually using a scale of measurement, and the data is then analyzed often with the use of a statistical software program. Researchers may also rely on the existing data from various sources (e.g., government agencies, think tanks, and other researchers) to conduct their statistical analyses. Scholars probe for correlations among the constructed variables for evidence in support of their hypotheses on the topic (Omae & Bozonelos, 2020).

Statistical methods are great for discerning correlations, or relationships between variables. Advanced mathematical techniques have been developed that permit understanding of complex relationships. Given that causation is difficult to prove in political science, many researchers default to the use of statistical analyses to understand how well certain things relate. This is particularly true when it comes to applied research. Applied research is defined as “research that attempts to explain social phenomena with immediate public policy implications'' (Knoke, et. al. 2002, pg. 7). Statistical methods are also the preferred approach when it comes to the analysis of survey data. Survey research involves the examination of a sample derived from a larger population. If the sample is representative of the population, then the findings of the sample will allow for the formation of inferences about some aspect of the population (Babbie, 1998).

At this point, we should review the discussion regarding one of the major partitions in political science, as noted in Chapter One, quantitative methods involve a type of research approach which centers on testing a theory or hypothesis, usually through mathematical and statistical means, using data from a large sample size. Qualitative methods are a type of research approach which centers on exploring ideas and phenomena, potentially with the goal of consolidating information or developing evidence to form a theory or hypothesis to test. Quantitative researchers collect data on known behavior or actions, or close-ended research where we already know what to look for, and then make mathematical statements about them. Qualitative researchers collect data on unknown actions, or open-ended research where we do not already know what to look for, and then make verbal statements about them. This divide has subsided somewhat, with concerted efforts to develop mixed methods research designs, however, researchers often segregate themselves into one of these two camps.

When looking at the three basic approaches, the first two methods - experimental and statistical - fall squarely into the quantitative camp, whereas comparative politics is mostly considered as qualitative. Experimental and statistical methods have their roots in the behavioral revolution of the 1950s, which shifted the focus of the inquiry from institutions to the individual. For example, the fields of behavioral economics and social psychology are well suited for experiments. Both studies focus on the behavior of individual people. For example, behavioral economists are interested in human judgment when it comes to financial and economic decisions. Social psychologists have been traditionally more interested in learning behavior and information processing. As political science has shifted more towards the study of individual political behavior, experimentation and statistical analysis of collected data, through experiments, surveys and other methods.

For more on the history of this divide and how it has affected political science, see Franco and Bozonelos’s (2020) chapter on the History and Development of the Empirical Study of Politics in Introduction to Political Science Research Methods .

The Comparative Method

What is the Comparative Method? The comparative method is often considered one of the oldest approaches in the study of politics. Ancient Greek philosophers, such as Plato, the author of The Republic , Aristotle, the author of Politics , and Thucydides, the author of the History of the Peloponnesian War wrote about politics in their times in a comparative manner. Indeed, as Laswell (1968) said, all science is 'unavoidably comparative'. Most scientific experiments or statistical analyses will have a control or reference group. The reason is so that we can compare the results of our current experiment and/or analysis to some baseline group. This is how knowledge develops; by grafting new insights through comparison.

Likewise, comparison is more than just description. We are not only analyzing the differences and/or similarities, we are conceptualizing. We cannot overstate the importance of concepts in political science. A concept is defined as “an abstract or generic idea generalized from particular instances” (Merriam-Webster). For political scientists, concepts are “generally seen as nonmathematical and deal with substantive issues” (Goertz, 2006). For example, if we want to compare democracies, we must first define what exactly constitutes a democracy.

Even in quantitative analyses, concepts are always understood in verbal terms. Given that there are quite a few ways to formulate quantitative measurements, conceptualization is key. Developing the right scales, indicators, or reliability measures is predicated on having one’s concepts right. A good example is the simple, yet complicated concept of democracy. Again, what exactly constitutes a democracy? We are sure that it must include elections, but not all elections are the same. An election in the U.S. is not the same as an election in North Korea. Clearly, if we want to determine how democratic a country is, and develop good indicators from which to measure, then concepts matter.

Comparative methods occupy an interesting space in methodology. Comparative methods involve “the analysis of a small number of cases, entailing at least two observations”. Yet it also involves “too few [cases] to permit the application of conventional statistical analysis” (Lijphart, 1971; Collier, 1993, pg 106). This means that the comparative method involves more than a case study, or single-N research (discussed in detail below), but less than a statistical analysis, or large-N study. It is for this reason that comparative politics is so closely intertwined with the comparative method. As we tend to compare countries in comparative politics, the numbers end up somewhere in between, anywhere from a few to sometimes over fifty. Cross-case analysis through the comparison of key characteristics, are the preferred methods in comparative politics scholarship.

Case Studies

Why would we want to use a case study? Case studies are one of major techniques used by comparativists to study phenomena. Cases provide for the in-depth traditional research. Many times there is a gap in knowledge, or a research question that necessitates a certain level of detail. Naumes and Naumes (2015) write that the case studies involve storytelling, and that there is power in the story’s message. Clearly, these are stories that are based in fact, rather than in fiction, but nevertheless, are important as they describe situations, characters, and the mechanisms for why things happen. For example, the exact cause of how the SARS-CoV-2 virus, more commonly referred to as COVID-19, will involve telling that story.

A case is defined as a “spatially delimited phenomenon (a unit) observed at a single point in time, or over some period of time” (Gerring, 2007). Others define a case as “factual description of events that happened at some point in the past” (Naumes and Naumes, 2015). Therefore, a case can be broadly defined. A case could be a person, a family household, a group or community, or an institution, such as a hospital. The key question in any research study is to clarify the cases that belong and the cases that do not belong (Flick, 2009). If we are researching COVID-19, at what level should we research? This is referred to as case selection, which we discuss in detail in Section 2.4.

For many comparativists in political science, the unit (case) that is often observed is a country, or a nation-state. A case study then is an intensive look into that single case, often with the intent that this single case may help us better understand a particular variable of interest. For example, we could research a country that experienced lower levels of COVID-19 infections. This case study could consist of a single observation within the country, with each observation having several dimensions. For example, if we want to observe the country’s successful COVID-19 response, that observation could include the country’s level of health readiness, their government’s response, and the buy-in from their citizens. Each of these could be considered a dimension of the single observation - the successful response.

This description listed above is considered the traditional understanding of case study research - the in-depth analysis of one case, in our example of that one country, to find out how a particular phenomenon took place, a successful COVID-19 response. Once we research and discover the internal processes that led to the successful response, we naturally want to compare it to other countries (cases). When this happens, shifting the analysis from just one country (case) to other countries (cases), we refer to this as a comparative case study. A comparative case study is defined as a study that is structured on the comparison of two or more cases. Again, for comparative political scientists, we often compare countries and/or their actions.

Finally, as mentioned in Chapter One, there can also exist subnational case study research . This is when subnational governments, such provincial governments, regional governments, and other local governments often referred to as municipalities, are the cases that are compared. This can happen entirely within a country (case), such as comparing COVID-19 response rates among states in Mexico. Or it can happen between countries, where subnational governments are compared. This often occurs in studies of European and/or European Union policy. There are quite a few subnational governments with significant amounts of political power. Examples include fully autonomous regions, such as Catalonia in Spain, partially autonomous regions, such as Flanders and Walloons in Belgium, and regions where power was devolved, such as Scotland within the UK.

Use of Case Studies in Comparative Politics

As mentioned above, case studies are an important part of comparative politics, but they are not exclusive to political science. Case studies are used extensively in business studies for example. Ellet (2018) notes that case studies are “an analogue of reality”. They help readers understand particular business decision scenarios, or evaluation scenarios where some process, product, service, or policy is being evaluated on their performance. Business case studies also feature problem diagnosis scenarios, where the authors research when a business is not successful, and try to understand the actions, processes, or activities that led to failure. Case studies are also relevant in medical studies as well. Clinical case studies investigate how a diagnosis was made. Solomon (2006) notes that many of the case studies published by physicians are anecdotal reports, where they notate their procedures for diagnosis. These case studies are vitally important for the field of medicine as they allow researchers to form hypotheses on particular medical disorders and diseases.

Case studies are vital to theory development in political science. They are the cornerstones of different discourses in the discipline. Blatter and Haverland (2012) note that a number of case studies have reached ‘classic’ status in political science. These include Robert Dahl’s Who Governs? [1961], Graham T. Allison’s Essence of Decision [1971], Theda Skocpol’s States and Social Revolutions [1979], and Arend Ljiphart’s The Politics of Accommodation [1968]. Each of the classics is a seminal study into an important aspect in political science. Dahl’s work popularized the concept of pluralism, where different actors hold power. Allison studied the decision-making processes during the 1962 Cuban Missile Crisis, whose work was influential for public policy analysis. Skocpol’s book laid out the conditions from which a revolution may take place. Skocpol’s work coincided with the rise of neo-institutionalism in the 1970s, where political scientists began to refocus their attention on the role of institutions in explaining political phenomena. Finally, Ljiphart gave us the concepts of “politics of accommodation” and “consensus democracy”. The terms are central to our understanding of comparative democracy.

As mentioned earlier, cases in comparative politics have historically focused on the nation-state. By this we mean that researchers compare countries. Comparisons often involve regime types, including both democratic and nondemocratic, political economies, political identities, social movements and political violence. All of these comparisons require scholars to look within countries and then compare. As stated in Chapter One, this “looking within” is what separates comparative politics from other fields of political science. Thus, as the nation-state is the most relevant and important political actor, this is where the emphasis tends to be.

Clearly, the nation-state is not the only actor in politics. Nor is the nation-state, the only level of analysis. Other actors exist in politics, from subnational actors, ranging from regional governments to labor unions, and all the way to insurgents and guerillas. There also exist transnational actors, such as nongovernmental organizations, multinational corporations, and also more sinister groups such as criminal and terrorist networks. In addition, we can analyze at different levels, including the international (systemic) level, the subnational level, and at the individual level. However, nation-states remain the primary unit and level of analysis in comparative politics.

CIAO DATE: 8/00

Case Study Methods in International Political Economy

Organizing Your Social Sciences Research Paper: Writing a Case Study

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Multiple Book Review Essay
• Reviewing Collected Essays
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Bibliography

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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Evaluating Research Methods of Comparative Politics

Evaluating the Research Methods of Three Modern Classics of Comparative Politics

The main aim of this essay will be to explore and theoretically evaluate the research designs of three classics of comparative politics: Putnam’s case study method in Making Democracy Work , Linz’s small-N research design in “The Perils of Presidentialism” and Amorim Neto & Cox’s large-N statistical analysis in “Electoral Institutions, Cleavage Structures, and the Number of Parties”. To do this, I will split my essay into two separate sections. In the first section of the essay I intend to illustrate the strengths and weaknesses of the different comparative research designs, focusing exclusively on the types found in the three main texts under evaluation. After a short introduction, some important focuses, amongst others, will be on the objectives of case studies, small-N studies and large-N studies, their evolution throughout the 20 th century, when they are best used, what their purposes are and what they intend to achieve.

The second section of the essay will be a critical evaluation of how successful one particular text – Linz’s “The Perils of Presidentialism” – has been in the execution of the authors’ objectives, looking at other work in the field to assess if its aims have been fully achieved. In short, I will consider how convincing the text is as a comparative study. I will then conclude the essay by considering how alternative research designs may have improved or worsened the selected study, again drawing on important academic works to support my theories and assertions.

Robert Putnam’s Making Democracy Work undoubtedly brought the concept of social capital to the forefront of the socio-political sphere. His celebrated work is a key example of a case study, “an intensive analysis of an individual unit (as a person, event or community) stressing developmental factors in relation to environment or context.” [1] Putnam’s research design has a broad analysis that compares 20 regions across Italy – a MSSD. He exploits a natural experiment to assess the difference institutional reform makes to institutional performance over his 20 year period of study. Upon recognising disparities in regional findings, he then assesses the reasons for cross-sectional and cross-temporal variation in institutional performance, moving to a more in-depth focus on 6 regions.

Although the case study discipline is beginning to decline in the modern academic sphere, the strengths of this kind of research design are still evident. Prominent examples from the field are Tocqueville’s Democracy in America (1888) and Lijphart’s The Politics of Accommodation (1968). Landman highlights some of the main strengths of case studies such as these, writing:

“Single-country studies provide contextual description, develop new classifications, generate hypotheses, confirm and inform theories, and explain the presence of deviant countries identified through cross-national comparison.” [2]

In short, the case study method allows an intensive depth study of a unit with limited resources. As Landman statement suggests, they are extremely flexible and can serve a multitude of purposes. Making Democracy Work is also a prime example of another key strength of case studies – utilising process-tracing to uncover evidence of causal mechanisms or to explain outcomes. [3] For example, Putnam is able to trace the roots of modern civic community and institutional performance back to Italy’s “Golden Age” in the 14 th century through his historical analysis. Other comparative methods, such as large-N, are far less conducive to this type of process tracing and unearthing of causal mechanisms. They are often able to say what happened, but not necessarily how it happened. The detail in which these mechanisms are explained is also far less extensive.

Despite the strengths highlighted above, there are also some considerable limitations to the case study method that have been recorded extensively in the literature of the social sciences. A key limitation, again highlighted by Landman, is that “inferences made from single-country studies are necessarily less secure than those made from the comparison of several or many countries.” [4] Generalizations about other units cannot be made from a case study, unlike with large-N statistical analysis. Furthermore, case studies are usually better at description than establishing causation. The case study method is also considered intensive rather than extensive, as it tends to offer a great deal of in-depth analysis of a single unit at the expense of breadth analysis. With Putnam, for example, the study has a sole focus on Italy and Italian institutions and social capital over 600 years. George & Bennett draw upon the importance of this point, stating that another potential pitfall of the case study method is the “selection bias” that occurs when a unit is chosen on its “intrinsic historical importance”, or “on the accessibility of evidence” [5] This form of “selection bias” could certainly be evident in Putnam’s research design. Finally, where larger-N studies often confirmatory in their nature, seeking to either confirm or reject hypotheses, case studies tend to be more exploratory, attempting to gain new insights into a topic or unit from which new hypotheses might be later developed.

Juan Linz’s “Perils of Presidentialism” differs to Putnam’s work in that it is a small-N research design that compares the effect presidential and parliamentary regime types on democratic stability. Small-N research designs such as Linz’s usually consist of 2-12 intentionally selected cases and are therefore too limited to conduct statistical analysis as with large-N designs. [6] Linz’s research design is a qualitative comparison of a small number of cases selected from Latin America and Western Europe, with a particular focus on the USA also. Many have argued that his case selection is based upon MSSD. The main aim of “Perils of Presidentialism” was to explore political processes over time and within cases as a means of showing that “the superior historical performance of parliamentary democracies is no accident.” [7]

As with the case study method, there are numerous advantages to using small-N research designs – often called the comparative method – in comparative politics. One of the most significant strengths of using the comparative method comes from the intentional selection of cases as previously mentioned. Not only can it be a substitute for the experimental control evident in large-N analysis, the intentional selection of cases that share similar characteristics means that hypothesis testing is made easier. [8] A similarly significant advantage of using this method in comparison to the large-N statistical method is that concepts and ideas used in small-N studies can be operationalized at a lower level of abstraction, meaning that concepts are at a lower risk of being stretched. The result of this is greater confidence that chosen concepts are being accurately measured. Collier’s The Comparative Method also highlights the fact that small-N designs such as Linz’s allow for an intensive analysis of a few cases with limited energy expenditure, financial resources and time. These intensive analyses can be more fruitful than superficial statistical analysis of many cases that can be extremely time-consuming and difficult to execute successfully. The collection of large data sets has also proved extremely difficult. [9]

It could be argued that the weaknesses of the comparative method, or small-N research designs, out-weigh its strengths. Where the selection of cases can work as an experimental control, Landman, amongst others, has highlighted case selection as one of the major pitfalls of small-N designs, stating that “the selection of cases in the absence of any rules of inquiry can lead to insecure inferences, limited findings, and, in some cases, simply incorrect conclusions about a particular topic.” [10] Similarly, the issue of “many variables, small number of cases” is raised extensively in the literature (e.g. Lijphart, 1971; Goggin, 1986). Problems often arise when comparing few countries when there are more factors identified explaining the observed outcome than there are countries observed. Because small-N analysis involves hand-picked specific cases, there are often many variables linking the cases that are not central to the study, hence “too many variables, not enough cases.” [11] A way of potentially solving this problem, as highlighted by Lijphart, is adding more cases to the equation. This eventually becomes problematic when too many cases are added and the research passes from the small-N comparative method in to the large-N statistical analysis method. Using the small-N design that Linz has can therefore be extremely problematic.

Octavio Amorim Neto & Gary Cox’s “Electoral Institutions, Cleavage Structures, and the Number of Parties” is an example of the third type of research design – a quantitative large-N statistical analysis. Amorim Neto & Cox utilise a large-N cross-case statistical analysis which analyses the degree of correlation between the number of effective parties, various measures of electoral system permissiveness and ethnic fragmentation in electoral systems around the world. The design is also cross-sectional, as data is collected from 54 elections around the world (c.1985) and observes both parliamentary and presidential elections. Multivariate regression is used to assess the different impact each of the independent variables has on the dependant variable. Their conclusion is that “the effective number of parties appears to depend on the product of social heterogeneity and electoral permissiveness.” [12]

A particularly key advantage of using the large-N statistical method is the fact that statistical controls can be used to rule out rival explanations for an observed outcome and to control for potentially confounding factors. [13] In small-N analysis, it is clear that a lack of statistical control can lead to the production of incorrect of flawed results. Large-N analyses also allow for an extensive coverage of countries over both space and time. [14] In Democracy and Development (2000), for example, Prezworski et al. use 150 countries over a 40 year period in their analysis. Unlike in case studies and small-N designs, large-N designs are better able to avoid selection bias because units are usually randomly selected. Furthermore, large-N designs are better able to highlight ‘deviant’ or ‘outlier’ countries whose outcomes are not as expected from the study. The nature of such designs also means that generalizations can be made about the wider population from their findings because theories are tested with a far greater number of cases that are more representative of the wider population, whether it be individuals, groups or countries. As Coppedge states, “the principal advantage of the kind of theory that emerges from large-sample work is that it is relatively general, both in its aspirations and in its empirical grounding.” [15]

As made apparent by Collier, one of the main disadvantages of using the statistical method in a study is the difficulty in “collecting adequate information in a sufficient amount of time” when resources and time are limited. [16] Large-N analysis are, without doubt, extremely time consuming and often expensive; they also assume a certain amount of mathematical or computing knowledge in their execution and interpretation. Moreover, only certain types of data can be used using this method of analysis and the reliability of data extracted from developing countries, for example, is often questionable. This means that relevant data can sometimes be omitted or incorrect, meaning inferences can also be misleading. Even when data is available, its higher level of abstraction can sometimes lead to concept stretching, where we cannot be sure if the researcher is still measuring what he/she originally intended to. Another critique of the method is that large-N studies often rely upon assumptions that may not hold true, e.g. there are no omitted variables, cases are fully independent, or unit homogeneity. Finally, many who interpret large-N statistical analysis often mistake correlation for causation. Correlation simply highlights the direction and strength of a relationship, not causation.

Regardless of its wider political impact, Juan Linz’s “The Perils of Presidentialism” has come under a vast amount of criticism in the social sciences for being too implicit and underdeveloped. Horowitz, for example, describes Linz’s claims that parliamentary governments is better able to stabilise democracy than presidential government as being “based on a regionally skewed and highly selective sample of comparative experience, principally from Latin America,” that “rest on a mechanistic, even caricatured, view of presidency.” [17] Criticising Linz choice of Latin America as antagonistic examples of unstable presidential systems, Horowitz highlights the inherited post-colonial Westminster parliamentary systems in Africa and Asia as equally destabilising and not at all conducive to democracy. This is because their winner-takes-all features allow any party with a majority to seize power. [18] Linz makes only a passing comment on these important examples that oppose his central causal arguments. Horowitz’s argument here could also be that a way to avoid the “regionally skewed” design that Linz’s work represents would be to adopt a MDSD – comparing various parliamentary and presidential systems from across the world as cases – instead of his MSSD design. This could also avoid the ‘selection bias’ that is often present through hand-picking cases in a small-N research design.

Similarly, both Horowitz and Shugart & Cary highlight Linz’s omission of the variation in electoral systems, party systems and presidential powers as a reason for “The Perils of Presidentialism” being a somewhat unconvincing comparative study. Shugart & Cary’s Presidents and Assemblies emphasises the variation in presidential systems, introducing semi-presidentialism, premier-presidential and president-parliamentary systems that are only briefly touched upon by Linz in his paper. France and Germany, for example, are mentioned but not elaborated on. When Shugart & Cary include these regime types in their analysis, they find that only twelve ‘full’ presidential systems had broken down in the twentieth century in comparison to twenty one parliamentary systems, [19] contradicting Linz’s argument that parliamentary systems are more conducive to stable democracy. In continuation with this, Horowitz draws on three examples from the USA, Nigeria and Sri Lanka to highlight the variations in electoral systems, processes and party systems that produce presidential executives. [20] Once again these variations are only touched upon in passing by Linz, whose analysis is underdeveloped and openly omitting important cases that contradict what Horowitz called his “caricatured view on presidency.” This caricatured view is also highlighted by Mainwaring & Shugart, who argue that presidentialism is predicated upon a system of checks and balances which usually inhibit winner-takes-all tendencies that Linz describes as a central feature of presidential systems. [21]

Further criticisms of Linz’s design come from Cheibub’s Presidentialism, Parliamentarism, and Democracy . Cheibub opposes Linz’s argument that the historical evidence of democratic breakdown shows presidential regimes to be less democratically stable, instead stating that presidential democracies fail because they arise in countries with a higher probability of democratic breakdown, regardless of regime type. [22] Cheibub highlights the importance of economic development in the survival of democratic regime types. Countries with parliamentary systems happen to be more wealthy, and therefore more likely to survive. [23] Economic development is not the only variable omission present in Linz’s work either, as Cheibub highlights the importance of geographical location and size of the country as well as economic development as important to democratic longevity. Cheibub also questions Linz’s decision to omit coalitions as a variable, an electoral outcome Linz holds in high regard in terms of democratic stability and performance. Again, Linz does not draw on any of these points in his qualitative analysis. A final point highlighted by Cheibub is that impeachment, something Linz asserts is extremely difficult in presidential systems, happened six separate times in 1990s Latin America alone, four of which were passed. This again shows some of Linz’s assertions to be incorrect or at least without empirical support.

It could be argued that Prezworski et al’s 1996 study What Makes Democracy Work? represents a superior research design to Linz’s. This is because Prezworski’s large-N statistical analysis of 135 countries since 1950 controls for affluence, economic performance, international climate and political learning/experience with democracy. [24] Linz’s small-N research design – a design that is never properly justified – has no explicit control variables, even though both studies have a similar aim of determining what/why democratic regimes prevail. The exploratory nature of Linz’s study, who openly admits is only to “recover a debate on the role of alternative democratic institutions in building state democracies,” [25] means that there is no explicit hypothesis testing per se. Prezworski, on the other hand, extensively tests his hypothesis on a large-N and can therefore make generalizations from his inferences. It could be argued that these factors make it much more useful to the social scientist studying the reasons for the longevity of democratic regimes. This is not to say, however, that Prezworski’s findings do not support Linz’s findings. Prezworski does find that “Linz is right about the durability of alternative institutional arrangements,” [26] although factors such as economic development are more important in determining the permanence of specific regime types. This shows that the statistical method has some features that could have improved Linz’s study.

Conclusions

Section 1 of this essay has shown the various strengths and weaknesses of each comparative research design. Case studies allow for an extremely in-depth analysis of a single unit with limited resources. As Landman shows, they can also have a multitude of purposes e.g. developing new classifications, generating hypotheses, confirming and informing theories etc. They are, however, limited. Inferences made from case studies are less secure and generalizations cannot be made from their findings. They tend to be extremely descriptive, being considered intensive rather than extensive because breadth analysis gives way to in-depth analysis. George & Bennett’s theory of potential “selection bias” can also be problematic.

Similarly, small-N research designs have a multitude of strengths and weaknesses. The intentional selection of cases can work as a substitute for experimental control found in large-N statistical analysis. Operationalizing concepts at a lower level of abstraction means that concept stretching is far less likely than in large-N research designs. The comparative method also allows for an intense analysis of a few countries when resources and time are low. On the other hand, though, small-N research designs often suffer from the “many variables, small number of cases,” where there are more observed explanatory factors than there are cases. This is just one of the problems with hand-picking cases, another being the potential of insecure inferences and limited findings.

Finally, the evaluation of large-N research designs shows it to have many strengths and weaknesses also. The fact they allow for statistical controls is one of their greatest strengths. They also allow for an extensive coverage of many cases over both space and time, as with Amorim Neto & Cox. The random selection of cases removes the issue of selection bias in the traditional sense. The ability to highlight ‘deviant’ or ‘outlier’ countries is also a key strength of the large-N design. They are, however, time-consuming, involve a lot of resources and assume a level of mathematical and computing knowledge from the outset. The collection of relevant and correct data, especially from developing countries can also be problematic.

Section 2 of the essay has shown that, upon evaluation, Juan Linz’s “The Perils of Presidentialism” has many flaws in both its research design and its execution. Although Prezworski et al. have shown Linz’s findings to be correct, Horowitz demonstrates that Linz’s case choices are not only unjustified, they are “regionally-skewed.” Evidence from post-colonial Africa and Asia juxtapose Linz’s theory, so a switch from MSSD to MDSD may have therefore improved the design. Moreover, Cheibub’s work shows a blatant omission of important variables in Linz’s research design, as well as an ignorance of the variation in presidential regimes. This, combined with Shugart & Cary’s work that shows a lack of acknowledgement for variation in electoral systems and the party system, demonstrates an undeveloped design with unconvincing causal inferences. Cheibub also counters Linz’s central causal argument by suggesting that regime type is not necessarily the driving force behind democratic longevity, but the individual situations in which the system is born. The importance geographical size, location and economic development as key factors in the survival of a regime type are not highlighted. This once again shows important omissions in the execution of his work. The large-N statistical method has features that could improve Linz’s design, as it allows for what has been described by Kerlinger as “the three criteria of the ideal research design: (1) that the design answer the research question; (2) that it introduce the element of control for extraneous independent variables; and (3) that it permit the investigator to generalize from his or her findings.” [27] An intrinsic weaknesses of case studies and small-N designs, including Linz’s, is that the second and third of these points are often unobtainable.

Bibliography

Amorim Neto, Octavio & Gary Cox, “Electoral Institutions, Cleavage Structures, and the Number of Parties”, American Journal of Political Science 41(1), Indiana: Midwest Political Science Association, Jan., 1997, pp. 149-174.

Boix, Carles & Daniel N. Posner, “Making Social Capital Work: A Review of Robert Putnam’s Making Democracy Work: Civic Traditions in Modern Italy”, The Weatherhead Centre For International Affairs 96(4), Harvard University, June, 1996, pp. 1-22. Accessed on: < http://www.wcfia.harvard.edu/sites/default/files/96-04.pdf > (06/12/2012)

Cheibub, José Antonio, Presidentialism, Parliamentarism, and Democracy , New York: Cambridge University Press, 2007.

Clark, William Roberts, Matt Golder & Sona Nadenichek Golder, Principles of Comparative Politics (2 nd ed.), London: Sage, 2013.

Collier, David, “The Comparative Method”, in Ada W. Finifter (ed.), Political Science: The State of the Discipline II , Washington, D.C.: The American Political Science Association, 1993, pp. 105-119.

Coppedge, Michael, “Theory Building and Hypothesis Testing: Large- vs. Small-N Research on Democratization”, Paper prepared for presentation at the Annual Meeting of the Midwest Political Science Association , Chicago, Illinois, April 25-27, 2002.

Flyvbjerg, Bent, “Case Study”, in Norman K. Denzin & Yvonna S. Linclon (eds.), The Sage Handbook of Qualitative Research (4 th ed.) California: Sage, 2011, pp. 301-316.

George, Alexander L. & Andrew Bennett, Case Studies and Theory Development in the Social Sciences , Massachusetts: MIT Press, 2005.

Gerring, John, “What Is a Case Study and What Is It Good For?”, American Political Science Review 98(2), Cambridge: University of Cambridge Press, May, 2004, pp. 341-354.

Goggin, Malcolm L., “The “Too Few Cases/Too Many Variables” Problem in Implementation Research”, The Western Political Quarterly 39(2), Utah: University of Utah, Jun., 1986, pp. 328-347.

Horowitz, Donald L., “Comparing Democratic Systems”, Journal of Democracy 1(4), Maryland: The Johns Hopkins University Press, Fall 1990, pp. 73-79.

Kerlinger, Fred N., Foundations of Behavioral Research (2 nd ed.), New York: Holt, Rinehart and Winston, 1973.

Landman, Todd, Issues and Methods in Comparative Politics: An Introduction (3 rd ed.), London: Routledge, 2008.

Lijphart, Arend, “Comparative Politics and the Comparative Method,” American Political Science Review 65(3), Cambridge: University of Cambridge Press, Sep., 1971, pp. 682-693.

Linz, Juan J., “The Perils of Presidentialism,” Journal of Democracy 1(1), Maryland: The Johns Hopkins University Press, Win., 1990, pp. 51-56.

Mainwaring, Scott & Matthew S. Shugart, “Juan Linz, Presidentialism, and Democracy: A Critical Appraisal”, Comparative Politics 29(4) Ph.D. Program in Political Science of the City University of New York, Jul., 1997, pp. 449-471.

Morgan-Jones, Edward, “Modern Classics of Comparative Politics,” Lectures at the University of Kent, Canterbury , 27 th Sep.-14 th . Dec, 2012.

Prezworski, A. et al., Democracy and Development: Political Institutions and Well-Being in the World, 1950-1990 , Cambridge: Cambridge University Press, 2000.

Prezworski, A. et al., “What Makes Democracy Endure?”, Journal of Democracy 7(1), Maryland: The Johns Hopkins University Press, Win., 1996, pp. 39-55.

Putnam, Robert D., Making Democracy Work: Civic Traditions in Modern Italy , NJ: Princeton University Press, 1993.

Shugart, Matthew Soberg & John M. Carey, Presidents and Assemblies: Constitutional Design and Electoral Dynamics , Cambridge: Cambridge University Press, 1992.

[1] Bent Flyvbjerg, “Case Study”, in Norman K. Denzin & Yvonna S. Linclon (eds.), The Sage Handbook of Qualitative Research (4 th ed.) (California: Sage, 2011), pp. 301-316

[2] Todd Landman, Issues and Methods in Comparative Politics: An Introduction (3 rd ed.), London: Routledge, 2008)

[3] Alexander L. George & Andrew Bennett , Case Studies and Theory Development in the Social Sciences , (Massachusetts: MIT Press, 2005)

[4] Landman, Issues and Methods in Comparative Politics

[5] George & Bennett, Case Studies and Theory Development in the Social Sciences

[6] Arend Lijphart, “Comparative Politics and the Comparative Method”, American Political Science Review 65(3), (Cambridge: University of Cambridge Press, Sep., 1971), pp. 682-693.

[7] Juan J. Linz, “The Perils of Presidentialism”, Journal of Democracy 1(1), (Maryland: The Johns Hopkins University Press, Win., 1990), pp. 51-56.

[8] In comparison to case study method; see Lijphart (1971)

[9] David Collier, “The Comparative Method”, in Ada W. Finifter (ed.), Political Science: The State of the Discipline II , (Washington, D.C.: The American Political Science Association, 1993), pp. 105-119; see also Lijphart (1971)

[10] Landman, Issues and Methods in Comparative Politics

[11] Ibid .

[12] Octavio Amorim Neto & Gary Cox, “Electoral Institutions, Cleavage Structures, and the Number of Parties”, American Journal of Political Science 41(1), (Indiana: Midwest Political Science Association, Jan., 1997), pp. 149-174.

[13] Landman, Issues and Methods in Comparative Politics ; see Collier (1993) & Lijphart (1971)

[14] Ibid .

[15] Michael Coppedge, “Theory Building and Hypothesis Testing: Large- vs. Small-N Research on Democratization”, Paper prepared for presentation at the Annual Meeting of the Midwest Political Science Association , (Chicago, Illinois, April 25-27, 2002)

[16] Collier, “The Comparative Method”

[17] Donald L. Horowitz, “Comparing Democratic Systems”, Journal of Democracy 1(4), (Maryland: The Johns Hopkins University Press, Fall 1990), pp. 73-79.

[18] Horowitz, “Comparing Democratic Systems”; see Lewis’ lectures on Politics in West Africa .

[19] Matthew Soberg Shugart & John M. Carey, Presidents and Assemblies: Constitutional Design and Electoral Dynamics , (Cambridge: Cambridge University Press, 1992) – includes third world states; 6 other breakdowns only presidential-parliamentary systems.

[20] Horowitz, “Comparing Democratic Systems”

[21] Juan J. Linz, “The Perils of Presidentialism”, Journal of Democracy 1(1), (Maryland: The Johns Hopkins University Press, Win., 1990), pp. 51-56; see also Mainwaring & Matthew Shugart (1997)

[22] José Antonio Cheibub, Presidentialism, Parliamentarism, and Democracy , (New York: Cambridge University Press, 2007)

[23] ^ Cheibub, Presidentialism, Parliamentarism, and Democracy , Table 6.1

[24] A. Prezworski et al., “What Makes Democracy Endure?”, Journal of Democracy 7(1), (Maryland: The Johns Hopkins University Press, Win., 1996), pp. 39-55.

[25] Linz, “The Perils of Presidentialism”

[26] Prezworski et al., “What Makes Democracy Endure?”

[27] Malcolm L. Goggin, “The “Too Few Cases/Too Many Variables” Problem in Implementation Research,” The Western Political Quarterly 39(2), (Utah: University of Utah, Jun., 1986), pp. 328-347, 349; see  Fred N. Kerlinger, Foundations of Behavioral Research (2 nd ed.), (New York: Holt, Rinehart and Winston, 1973), pp. 322-326.

Written by: Luke Johns Written at: University of Kent, Canterbury Written for: Dr. Edward Morgan-Jones Date written: November 2012

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Article Contents

Introduction, section i: individual events and political science methodology, generalizability, research design, evaluating evidence, contingency, falsifiability, section iii, acknowledgments.

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A New Case for the Study of Individual Events in Political Science

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Joseph Torigian, A New Case for the Study of Individual Events in Political Science, Global Studies Quarterly , Volume 1, Issue 4, December 2021, ksab035, https://doi.org/10.1093/isagsq/ksab035

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Despite significant advances in both quantitative and qualitative methods over the last few years, the discipline of political science has yet to explicitly address the special challenges and benefits of studying specific historical events marked by high levels of contingency. The field of security studies, where concrete historical cases have always played a major role in the development of the subfield, should place special focus on the specific challenges and benefits to the study of such events. Taking full advantage of what event-specific research can teach us, however, will require thinking about generalizability, evidence, the role of contingency, and falsifiability in ways that are not yet fully understood in the discipline. More clarity on such questions will benefit our understanding of like nuclear crises in particular.

A pesar de los grandes avances en los métodos cuantitativos y cualitativos de los últimos años, la disciplina de la ciencia política aún no ha abordado de manera explícita los desafíos y los beneficios especiales del estudio de acontecimientos históricos específicos marcados por altos niveles de contingencia. El campo de los estudios de seguridad, en el que los casos históricos concretos siempre han desempeñado una función importante en el desarrollo del subcampo, debería prestar especial atención a los desafíos y los beneficios específicos del estudio de tales acontecimientos. Sin embargo, para aprovechar al máximo lo que la investigación de acontecimientos específicos puede enseñarnos, será necesario pensar en la generalización, la evidencia, la función de la contingencia y la falsabilidad en formas que aún no se comprenden en su totalidad dentro de la disciplina. Una mayor claridad en estas cuestiones nos permitirá comprender mejor las crisis nucleares, en particular.

Malgré les progrès considérables qui sont à la fois intervenus dans les méthodes quantitatives et qualitatives ces dernières quelques années, la discipline des sciences politiques doit pourtant encore aborder explicitement les avantages et défis particuliers inhérents à l’étude d’événements historiques spécifiques marqués par de hauts niveaux de contingence. Le domaine des études de la sécurité, dans lequel des cas historiques concerts ont toujours joué un rôle majeur dans le développement du sous-domaine, devrait accorder une attention particulière aux avantages et défis spécifiques de l’étude de tels événements. Pour tirer pleinement profit de ce que les recherches spécifiques à des événements peuvent nous enseigner, il faudra cependant réfléchir à la généralisabilité, aux preuves, au rôle de la contingence et à la falsifiabilité de manières qui n'ont pas encore été pleinement comprises dans la discipline. Une plus grande clarté sur ces questions sera en particulier bénéfique pour notre compréhension de crises nucléaires similaires.

In the 2000s,  political science underwent a “credibility revolution.” Drawing on innovations first introduced by economists, the field now pays close attention to the exact conditions needed for a causal interpretation of quasi-experiments and natural experiments ( Angrist and Pischke 2010 ). This step forward means we can now much more reliably measure an average treatment effect. Recently, political scientists also have begun to pay more attention to a different set of questions—how can we explain specific cases and what can we learn from them? Yamamoto and Lam have suggested quantitative techniques for determining how many past events can be explained by a particular cause or how to measure individual causal effect ( Yamamoto 2011 ; Lam 2013 ). Goertz and Mahoney argue that Mill's methods, which identify necessary and/or sufficient conditions using cross-case variation, make more epistemological sense for explaining individual cases than an average treatment effect. Political scientists have also made breakthroughs on understanding what can be learned through “process-tracing” within individual cases ( Goertz and Mahoney 2012 , 87).

However, political scientists have not sufficiently moved onto ground that would fully justify looking at specific, concrete historical events or provide complete answers for how they should be studied. Within the subfield of security studies, where qualitative case studies have historically played a foundational role, thinking explicitly about the advantage of event-specific research is a crucial task. Crucially, fully extracting what we can and should learn from individual moments requires analytical priors different from those methods that seek to find an average treatment effect, necessary/sufficient conditions, or links in a chain. In this paper, I both make the case for studying individual events and explain what methodological assumptions are most useful for such research.

Average treatment effects and necessary/sufficient conditions are variables that have a probabilistic or deterministic effect, respectively (“determinative” here refers to the epistemology shaping the method, not the idea that political scientists can or should seek to find perfect determinative relations) ( Goertz and Mahoney 2012 ). These methods by their nature contain a tradeoff—the power we gain by looking at numerous cases together forces our results into an inherently ambiguous relationship with specific events. Causal chains, on the other hand, present challenges for generalizability, easily confuse chronology with causality, make problematic assumptions about how “determined” the links on the chain may be, include causes not interesting from a social-scientific perspective, and risk oversimplifying an iterative, contingent, and rapidly evolving situation.

As this article proposes, finding driving forces that have a gravitational pull on events, such as nuclear crises, is the most scholars can hope for when explaining individual cases. The point of the investigation is not to link cause and effect by coding and operationalizing variables but to conceptualize driving forces that pushed or pulled the outcome in a particular direction and how they worked. While this way of thinking excludes prediction, it does include a form of explanation that helps reduce perplexity in both the case at hand and other similar events as well. This differentiation may sound subtle, but it demands a different way of thinking about a host of methodological issues. Although focusing on individual events in this way does not preclude the use of other methods to gain further insights into a topic under investigation, it does proceed from priors different enough that it cannot be seamlessly included into an integrated multi-method approach.

Section I describes why quantitative methods, Mill's methods, and most forms of process-tracing are only partially useful for understanding specific historical events. Section II explains why we should be interested in individual events but that such a focus demands a special way of thinking about (1) generalizability, (2) research design, (3) the evaluation of evidence, (4) the role of contingency, and (5) falsifiability. Section III applies these ideas to the study of nuclear crises.

Political scientists have made serious breakthroughs in theorizing about the strengths of case study research. Yet the field has still not fully provided a complete case for the inherent benefits of rigorously investigating specific events on their own. As this section demonstrates, despite claims to have moved away from the “quantitative” worldview, the field still usually proceeds from a Hume-ean view that prevents an approach that fully and properly extricates what we can from such research.

Approaches that seek to identify an average treatment effect (described by some as statistics, a term not accepted by most practitioners) adopt a probabilistic, correlational conception of causality and seek to measure the average treatment effect for a theoretical case (or, to be more precise, the average over individual estimates). In 1994, King, Keohane, and Verba famously argued that these same principles of inference applied to both quantitative and qualitative methods ( King, Keohane, and Verba 1994 ).

A few years later, Goertz and Mahoney argued that Mill's methods, also known as set theory or nominal analysis, were based on fundamentally different principles. Set theory uses variables that are mutually exclusive and collectively exhaustive to explain outcomes. The method of agreement eliminates necessary causes by showing that the causal variable is always present when the outcome is present, while the method of difference eliminates sufficient causes by showing that the outcome is always present when the causal variable is present ( Ragin 1987 , 2000 ; Goertz and Mahoney 2012 ).

Standard quantitative causal inference methods’ analysis and set theory both establish general relationships between variables using comparison, not within-case analysis ( Brady 2008 ). The cross-case element in set theory is evident in the fact that “necessary and sufficient” conditions are not intended to explain every single case. For example, although Ertman's cases do not predict every single one of his cases correctly, the theory's usefulness remains ( Ertman 1997 ). As Mahoney himself recognizes, there can be a “probabilistic” understanding of necessary and sufficient conditions ( Mahoney 2003 ). Therefore, both quantitative causal inference methods and Mill's methods are rooted in the theories of causality posited by David Hume, in which causation is “understood in terms of regular patterns of X: Y association, and the actual process whereby X produces Y is black-boxed” ( Beach and Pedersen 2013 , 25). Hume-eans see social forces as regularities or “covering laws”: light switches that lead to automatic outcomes given certain circumstances.

Hume-ean approaches are extremely powerful. When looking at individual cases, they have abundant utility. Moreover, even the most quantitative scholars acknowledge the need to have some knowledge of specific cases. Insights from one methodological approach regularly improve the research design of another approach ( Beck 2010 ). For example, cross sectional analysis can sensitize a qualitative researcher about what types of causes may (or may not) matter. It may also point the researcher in the direction of certain cases ( Laitin 2003 ).

“Case studies and inferential statistics cannot logically mix if the definition of causality is reductionist and regularist . . . How does one know that the mechanism connecting a cause with an effect in a particular case study is the same mechanism connecting causes to effects in all the other cases? What part of the study does the causal work, the case studies or the statistical analysis? If it is the case study then the statistical analysis should not convince us, and if it is the statistical analysis then the case study should not convince us” ( Chatterjee 2009 , 11).

“where one method advances a nomothetic proposition intended to function as a ‘covering law’ while another proceeds from a phenomenological view of the world and offers a context-specific idiographic narrative. Because these approaches are predicated on fundamentally distinct ontologies and conceptions of causality, the findings they generate are ultimately incommensurable and do not serve to strengthen each other” ( Ahmed and Sil 2012 , 936).

Due to these different priors, concepts have fundamentally different meanings in a quantitative context as opposed to a qualitative one, as they are operationalized to achieve different functions ( Ahram 2013 ).

“Does Colombian history show too much of a role for terrain in light of a statistical coefficient that is significant but not substantively moderate? After all, the current civil war began in part because anti-state actors had created refuges for themselves in the mountains; a conceivable counterfactual is that less rugged terrain would have prevented these key actors from organizing in the first place. On the other hand, perhaps the case suggests that the coefficient is too large; armed factors have at times found the jungles and other regions as hospitable a refuge as the mountains, so various forms of difficult terrain may be substitutes in a way that the statistical results fail to demonstrate. Or perhaps these competing considerations are just what the [estimated coefficient] of 0.219 implies? I think it is in fact impossible to decide whether the case study and the logit coefficient agree” ( Seawright 2016 , 6–7).

“as pathways multiply, these techniques get increasingly tenuous. Under such conditions, narrative would need to stand alone, and rules of narrative coherence and completeness would help decide whether the causal structure was as theorized.”

For Laitin, narratives can be used for “residuals” that cannot be explained by variance. Laitin's own characterization of the role of narratives, therefore, points to their problematic role in the multi-method approach ( Laitin 2003 ).

When an average causal effect is identified, scholars who assume that such a finding can be easily integrated into case studies unsurprisingly often engage in qualitative work that necessarily oversimplifies or misrepresents the historic evidence. However, the unproblematic use of average causal effect findings to explain individual events is not only methodologically unsound and a recipe for poor case studies but, in policy-making terms, also occasionally dangerous. As Elster notes, “To apply statistical generalizations to individual cases is a grave error, not only in science but also in everyday life . . . The intellectual fallacy is to assume that a generalization valid for most cases is valid in each case” ( Elster 2007 , 19). Jackson illustrates this point by referring to how policymakers, shaped by democratic peace theory, failed to distinguish general frequencies connecting regime type and violence from case-specific explanations ( Jackson 2017 , 690). Statistical findings are, of course, useful because they point to average causal effects, but they cannot be unproblematically and automatically used to explain specific, individual cases.

“More substantively, following the covering-law model does not in fact enable us to give an explanation of the occurrence of an event – for all that following this model does is to show that the occurrence of Y (‘the explanandum’) was to be expected in the circumstances because ‘it always happens like that’ (or, in a diluted version, ‘it often happens like that’)” ( Suganami 2008 , 331).

Where does process-tracing fit into this discussion? Process-tracing is inherently case-specific, yet the two most common understandings of process-tracing have limited utility for the study of specific events. One form of process-tracing uses “hoop tests” to identify necessary conditions and “smoking gun tests” to identify sufficient conditions with evidence in individual cases. The crucial task is to identify intervening steps that are so proximate to one another that their connection is obvious: “The leverage gained by this kind of test derives from the fact that while X being necessary for Y is in doubt, the status of M [mechanism] being sufficient for Y and of X being necessary for M might be more readily available” ( Mahoney 2012 , 579). In other words, while set theory finds necessary and sufficient conditions by comparing across cases, process-tracing identifies these conditions by linking the original cause to the final outcome by identifying intervening variables close enough to make the causal relationship self-apparent in individual cases ( Waldner 2015 ).

This view of process-tracing has obvious commonalities with a second approach described as a comparative sequential method, comparative narrative analysis, generic narrative, or event-structure analysis. Here, the scholar seeks to formally diagram narratives so that they can be compared across cases to see if they follow the same causal logic. This approach delineates a series of events (conceptually defined) and then shows how their presence in multiple cases leads to the same outcome ( Abbot 1990 ).

Mahoney explicitly states that his conception of process-tracing is different from the Hume-ean worldview: “Scholars who use process tracing . . . reject the view that an event is explained when it can be subsumed under and predicted by a covering law model” ( Mahoney 2012 , 586). However, in crucial ways, Mahoney's understanding of process-tracing remains Hume-ean. Like Sambanis, he understands mechanisms as “variables that operate in sequence” ( Sambanis 2004 , 13; George and Bennett 2005 ). George and Bennett use the term “dominos” ( George and Bennett 2005 , 206). This “billiard-ball” view of explanation, which uses earlier events as causes, originates with David Hume ( Elster 2007 , 3).

The “billiard-ball” understanding of process-tracing suffers from several inherent problems when applied to the study of individual events. As Chatterjee perceptively recognizes, “The difficulty of defending case studies while holding this particular understanding of mechanisms stems from the fact that it implies just another version of the Hume-an definition extended to intervening variables” ( Chatterjee 2009 , 13).

First, a causal chain complicated enough to explain one crisis would almost certainly struggle with explaining another crisis given how contingent, idiographic, and iterative crises are. Second, process-tracing as “bunching” intervening variables is extremely difficult in an environment in which specific moments have an interactive effect. Third, simple sequential accounts, as their supporters admit, still often fail at “abstracting ‘causes’ out of their narrative environments” ( Abbott 1991 , 228). Instead, they accept “temporal flow as the basis of explanation and the narrator's construction of the event as the happening” ( Griffin 1993 , 1105). In other words, narratives can “often miss the distinction between chronology and causality” ( Maxwell 2012 , 45).

Fourth, process-tracing assumes a deterministic relationship. However, in events like a crisis the final outcome is almost beside the point. If counterfactuals are easily imaginable, and uninteresting for social-scientific reasons, then assuming that one outcome was more likely than another would be misleading. We have no reason to assume that if the event was re-started as an experiment, we would get the same result most of the time ( Jervis 1997 ).

Fifth, a causal chain specific enough to explain a given crisis would necessarily include highly contingent events inherent to such dynamic situations. Those events might be crucial for understanding the outcome, but their origins are uninteresting from a social-scientific point of view. Including such elements in a causal chain would complicate the theoretical message of the endeavor. As Jackson argues, “In the open system of the actual world, a causal explanation is not likely to look anything like a linear combination of discrete variables, but will likely feature case-specific sequences and interactions in ways that are difficult to capture generally or formally” ( Jackson 2017 , 691).

Sixth, this approach raises serious questions about how much we gain from the hugely complicated task of explicitly formalizing every part of a narrative and whether such a level of specificity really teaches us anything. While establishing a chronology of the event is of course crucial for the broader research project, and the more detail the better, that chronology should be used as evidence, not theory.

Seventh, process-tracing is predicated on the idea that it should adjudicate among competing hypotheses and that only one hypothesis provides the right answer. However, that viewpoint prescribes an approach to evaluating evidence in individual cases that necessarily include multiple serious pathologies. As Zaks explains in an important article, the assumption that only one hypothesis has any validity “artificially inflates the importance of one explanation at the expense of another.” If hypotheses are mutually exclusive, a researcher can claim that a hypothesis is credible after presenting even the slightest of evidence. Yet, in reality, “mutual exclusivity, however, is a strong modeling assumption; and empirically, it is more often the exception than the rule. Competing explanations may exhibit a variety of relationships to the main hypotheses, each of which has distinct implications for collecting evidence and drawing inferences” ( Zaks 2017 , 344–45).

Ultimately, then, for specific cases, the importance of context makes a covering law approach essentially meaningless. As Cartwright puts it, “At the lower level there are a very great number of laws indeed . . . The conditions are too numerous. They give us too many factors to control. Our experiments would be undoable and the laws they entitle would be narrowed in scope beyond all recognition . . . how a factor operates, at this very concrete level, is far too context-dependent” ( Cartwright 1999 , 91).

These methodological challenges raise questions about the benefits of focusing on individual events, whether as standalone projects or as part of a multi-method approach. Yet political scientists should include the study of individual events in their toolbox for several reasons.

First, people often understand historical moments as totems representing a particular type of politics, and political scientists should provide society with rigorous, serious explanations for them ( Inboden 2014 ). Without professional investigations into the past that explicitly debate what past moments should teach us about how the world works, it is more likely that policymakers and the broader public will use poor analogies to understand the present ( Khong 1992 ).

Second, if power is an iceberg, then specific events reveal more ice than usual ( Pierson 2015 , 124). As Gourevitch put it, “Hard times expose strengths and weaknesses to scrutiny, allowing observers to see relationships that are often blurred in prosperous periods, when good times slake the propensity to contest and challenge” ( Gourevitch 1986 , 9). In other words, despite the view of the “transitologists” who discounted how much we could learn from looking at moments of democratization (or its failure), if we change our methodological priors and research goals we can actually learn quite a bit ( O'Donnell and Schmitter 1986 ).

Third, a very close investigation into specific historical events has already proven deeply beneficial to political science theorizing. Capoccia and Ziblatt argue that “history sits again at center stage of the comparative study of democratization.” Episode analysis, for example, “identifies the key political actors fighting over institutional change, highlights the terms of the debate and the full range of options that they perceived, reconstructs the extent of political and social support behind these options, and analyzes, as much as possible with the eyes of the contemporaries, the political interactions that led to the institutional outcome ( Capoccia and Ziblatt 2010 , 932).” Historical institutionalists, in particular, have emphasized the implications of the political phenomenon as being deeply rooted in particular times and places ( Thelen 2002 ; Pierson 2004 ; Hall 2010 ). Hall, noting the pressures this worldview creates for the demanding assumptions necessary for both the comparative method and the standard regression models, argues that “a substantial gap has opened up between the methodologies popular in comparative politics and the ontologies the field embraces” ( Hall 2003 , 374).

Fourth, historical research has increasingly raised questions about game-theoretic approaches that use formal modeling to create parsimonious and elegant theories that conceptualize actors as utility-maximizers with stable preferences. As a deductive tool, it can play a powerful role in generating hypotheses and giving traction within individual cases ( Schelling 1966 ; Glaser 2010 ). Yet when the pursuit of parsimony is divorced from historical grounding, game theory analysis often drifts toward ahistorically homogenizing assumptions that lack empirical verification about actors like class or sector. As recent scholarship shows, evidence that would have to be identified empirically to prove many famous game-theoretic arguments, especially with regard to international crises and democratization, do not exist in the empirical record ( Kreuzer 2010 ; Morrison 2011 ; Haggard and Kaufman 2012 ; Trachtenberg 2012 ; Gallagher and Hanson 2013 ; Slater, Smith, and Nair 2014 ). Closer attention to history helps scholars avoid assuming unit homogeneity, failing to give due justice to the temporal element, or missing the right direction of causality ( Capoccia and Ziblatt 2010 ).

Fifth, looking at individual events provides a fruitful new direction within the discipline as it increasingly recognizes the constraints of other approaches. The impact of the “credibility revolution” has arrived alongside a recognition that truly persuasive findings are only possible in unique situations. For an analysis to be persuasive, a researcher has to pass many serious hurdles: ensure that all confounding variables have been identified; balance the dilemma of including too many or too few variables; have an accurate and credible idea of how data is generated to avoid unverifiable assumptions about the distribution of independent and dependent variables, error terms, and linearity; and be sure endogeneity is not a problem ( Achen 2005 ; Clarke 2005 ; Freedman 2010 ; Dunning 2012 ; Rodrik 2012 ; Narang 2014 ). Even “big data” cannot make up for problems in research design ( Titiunik 2015 , 75–76). Sekhon similarly concludes that “[w]ithout an experiment, a natural experiment, a discontinuity, or some other strong design, no amount of econometric or statistical modeling can make the move from correlation to causation persuasive” ( Sekhon 2009 , 503). Due to precisely these problems, quantitative scholars have moved in the direction of natural experiments—in which some accident of history has created a situation resembling a real experiment. Natural experiments are able to avoid the problem of confounding variables and are thus much more persuasive than multivariate regression. This comes with a catch, however: if advanced techniques will not be able to solve problems inherent to a problematic research design ( Shalev 2007 ; Freedman 2010 ; Seawright 2010 ; Dunning 2012 ), and natural experiments are by their nature confined to cases given to us by accident, some may feel that we are presented with a very limited field of academic endeavor ( Deaton 2009 ; Rodden 2009 ).

Sixth, including individual events within the field of political science can be seen as yet another approach within the broader toolbox. Political scientists should be cognizant of the analytical priors of different methodological approaches, and individual events cannot be seamlessly integrated into medium- or large- n approaches. Although these approaches cannot ask the same exact question or serve as a “crutch” for one another because they proceed from different ontologies, they can both shed light on the same broader subject in different ways.

For the study of individual events to meet their full potential, we need a very different approach than coding and identifying correlations or bunching up variables in a causal chain. It requires significantly different analytical priors with regard to generalizability, research design, evidence evaluation, contingency, and falsifiability.

The reasons why specific events should be studied, which are listed in the previous section, only tell us the benefits if such an approach is possible. But is generalizability possible with such a narrow aperture of focus? Here, we make the case that, when looking at individual events, we should not try to identify either the precise constellation of variables that led to an outcome or a causal chain. Instead, the goal should be to identify the causal forces that had a gravitational pull on the outcome and the mechanisms through which they manifested.

“are often more important for their value in clarifying previously obscure theoretical relationships than for providing an additional observation to be added to a sample . . . a good case is not necessarily a ‘typical’ case but a ‘telling case’” ( McKeown 1999 , 174).

This viewpoint also has strong intellectual affinities with the definition of a “conditioning” cause provided by Slater and Simmons: “Conditions that vary before a critical juncture and predispose (but do not predestine) cases to diverge as they ultimately do” ( Slater and Simmons 2010 , 891).

With regard to this question of generalizability, critical realists have provided useful theorizing, although not all international relations theorists, such as Chernoff, accept their tenets ( Chernoff 2009 ). Critical realists are not interested in regularities but in understanding “what an object is and the things it can do by virtue of its nature” ( Danermark et al. 2002 , 55). Cases are not “manifestations of one or another theoretically derived instance[s] in a typology” but a combination of different structural elements ( Katznelson 1997 , 99). For critical realists, the first level is what we observe (the empirical or, in other words, the evidence we collect), the second level is what actually happened in the historical record (the actual), and the third level is generative structures (the real) ( Collier 1994 , 42–44; Danermark et al. 2002 , 20). This is fundamentally different from those working in the tradition of Hume, who conflate the three domains (empirical, actual, and real) by assuming that the “real” can be reduced to what happens in the form of a relationship of two observables that can be codified operationally ( Danermark et al. 2002 , 7).

Weber and the critical realists, however, differ on the question of whether the causes they identify are “real” or not. Weber was explicit that his “ideal types” did not exist in the real world. Similarly, Elster, who defines mechanisms as “frequently occurring and easily recognizable causal patterns that are triggered under generally unknown conditions or with indeterminate consequences,” compares them to “proverbs” ( Elster 2007 , 27). Critical realists are not fully satisfied with the “ideal type/proverb” approach, as they argue that a “cause” is real. Yet they do not expect that cause to always work in the same way, as they believe that these forces are always shaped by contingencies and cannot be explained with covering laws. Here, we do not pick a side to this debate, but we do emphasize that both approaches provide a useful but non-Hume-ean approach to generalization.

Can political scientists be happy with using individual events to determine “proverbs” or “transfactual” causes that often manifest differently? One possible charge against this methodology is that it is incapable of predicting outcomes and therefore irrelevant to political science. As Kirshner notes, although political scientists rarely argue that they can accurately predict the future, prediction is still the model upon which standard approaches rely ( Kirshner 2015 , 9–10, fn 19).

Yet social scientists need not predicate useful research on whether it provides an ability to “predict” precisely. Even physics, which many political scientists see as a model, struggles with prediction. Physics theories “are severely limited in their scope. For, to all appearances, not many of the situations that occur naturally in our world fall under the concepts of these theories” ( Cartwright 1999 , 9). A series of new findings demonstrate that prediction in the social world is particularly difficult ( York and Clark 2007b ; Ward, Greenhill, and Bakke 2010 ; Ng and Wright 2013 ; Tikuisis, Carment, and Samy 2013 ; Ahir and Loungani 2014 ; Friedman 2014 ; Hasnain and Kurzman 2014 ; Bowlsby et al. 2019 ). For rare phenomena, “even weak laws of large numbers don't hold” and we have no reason to assume that one particular outcome is typical ( Bendor and Shapiro 2019 , 129). For political scientists who believe that political science should still strive to be “scientific,” of crucial importance is the fact that, even in the natural world, scientists are happy to “explain processes and outcomes but not predict them” ( George and Bennett 2005 , 130–31).

“Or, reading across multiple events and situations, one might start to develop a conceptual vocabulary of mechanisms and processes useful for organizing different cases and showing how in each case there was a unique configuration of mechanism and processes leading to a specific outcome. Instead of the manipulation of inputs, logical elaboration with a myriad of examples establishes the plausibility of each causal claim” ( Jackson 2017 , 705).

Any political scientist who has passed their general exams can explain how to conduct good case selection for research projects in the “regularist” or “frequentist” tradition. Correctly, they would know not to select on the dependent variable under any circumstances. Yet, with the understanding of generalizability described above, a scholar could justify case selection based on a wide variety of other motivations, including: the historical importance of the event, the new availability of crucial sources, policy relevance, the link between that event and a theoretical body of literature, an event whose outcome is puzzling for some theoretical or empirical reason, the ability to leverage language skills or personal experience/contacts, and/or the possibility to find evidence that can answer the theoretical question she is asking. Acknowledging these advantages strengthen the case for encouraging students to pick cases based on their ability and inclination to truly master them ( Kollner, Sil, and Ahram 2018 , 4).

However, what kind of terminology should be used to describe this kind of approach? A useful term here is “retroduction.” The origins of this term (sometimes “abduction”) come from the philosopher Charles Sanders Peirce, who noticed that scientists were able to derive plausible hypotheses without the use of induction or deduction. Peirce believed in “another definite state of things,” which, although no “unequivocal evidence” could prove its existence, would “shed a light of reason upon the state of facts with which we are confronted” ( Piekarinen and Bellucci 2014 , 355). Although the ultimate purpose of retroduction/abduction is sometimes viewed differently, the method is essentially about using individual cases as a fertile ground for identifying useful concepts and is distinct from the Hume-ean understanding of causality ( Friedrichs and Kratochwil 2009 ).

Inspired by Pierce, numerous types of scholars have latched onto abduction as a legitimate form of scientific inference that is not limited to deduction or induction. Interpretivists, for example, engage in abductive reasoning that “begins with a puzzle, a surprise, or a tension, and then seeks to explicate it by identifying the conditions that would make that puzzle less perplexing and more of a ‘normal’ or ‘natural’ event” ( Schwartz-Shea and Yanow 2012 , 27–28). For interpretivists, although abductive reasoning is the “logic,” their ultimate goal is to answer “questions about context and meaning ” ( Agar 2010 , 290). In other words, for interpretivists, “Abductive reasoning on its own does not require that one search for meaning, or that meaning be context-specific, as Agar (2010 , 20) notes. But interpretive research does!” ( Schwartz-Shea and Yanow 2012 , 32). They consciously depart from Pierce, who thought that retroduction should be followed by induction and deduction. For scholars who are looking for causal explanations of events, they will depart from the interpretivist focus on meaning and not causality (although, like interpretivists, they will necessarily look to the intentions and views of the actors under investigation).

In critical realist language, on the other hand, retroduction/abduction is the process by which individual cases are used to understand the domain of the real. It is “about advancing from one thing (empirical observation of events) and arriving at something different (a conceptualization of transfactual conditions)” ( Danermark et al. 2002 , 96). Although the real never appears in its pure observable form, retroduction helps us understand the generative functions of those antecedent conditions. Unlike Pierce, they do not believe that the “next step” is necessarily induction and deduction; instead, they prioritize the discovery of transfactual conditions.

Should a specific-event-centric research project still include more than one case in its research design? Certainly, such a research project would be stronger if some causes are present in some cases but not in others. In fact, that difference would be a good reason to select another case. Such a state of affairs would allow for the researcher to draw interesting insights into the implications of such a factor being present or absent. Yet, this tactic is different from “controlling” certain variables because the number of cases would be too small and idiosyncratic ( Jackson 2016 , 121). The researcher should not go beyond the number of cases that she can master, as the value of this approach is getting the individual cases right, not the number of cases. If the details are wrong, the whole argument is wrong.

“The basic technique is to take some major theoretical claim, bring it down to earth by thinking about what it would mean in specific historical contexts, and then study those historical episodes with those basic conceptual issues in mind . . . Theoretical claims are hard to deal with on a very general level. But those general claims translate, or should translate, into expectations about what you are likely to find if you study a particular historical episode” ( Trachtenberg 2006 , 32, 45; Darnton 2018 ).

The questions themselves are essentially empirical and must be concrete enough to answer with evidence. A question cannot be something like whether income inequality prevented democratization. The researcher can, however, ask questions like: were elites afraid that democratization would leave to redistribution of income? Were elites able to translate economic power into state capacity? Did elites care about issues other than income redistribution and, if so, how much? In other words, the empirical questions are a bit of a “bankshot,” as they do not answer the theoretical questions directly but they do have obvious relevance for theory.

Second, the questions should connect directly to a broader theoretical debate. For example, the idea that democratization is primarily about income distribution comes from Acemoglu and Robinson ( Acemoglu and Robinson 2006 ). Levitsky and Way, on the other hand, emphasize the importance of revolutionary legacies ( Levitsky and Way 2013 ). The task of the researcher would be to formulate questions that strengthen or weaken the purchase of those worldviews for explaining a given case. If, during a political crisis, leaders betrayed no worries about income distribution, but they did betray an obsession with defending the regime because they helped create it, then that would be theoretically meaningful for this discussion (although such a finding would not “disprove” the average causal effect determined by Acemoglu and Robinson).

What kind of evidence addresses questions like these? As Bennett and Checkel write, for case studies historical evidence is not “variables” but “diagnostic evidence,” which is further supplemented with “the ways in which actors privately frame or explain their action” (significantly, however, Bennett and Checkel still seek to identify regularities among variables at the macro-level) ( Bennett and Checkel 2015 , 7).

The key insight provided by some process-tracers is the importance of causal process observations (CPOs): “an insight or piece of data that provides information about context or mechanism and contributes . . . leverage in causal inference.” The CPOs can be contrasted with data-set observations, which are the specific pieces of information used for quantitative analysis ( Brady, Collier, and Seawright 2010 , 184).

When it comes to the nuts and bolts of methods, event-specific research has more in common with the historian, detective, or journalist. As Maxwell puts it, this “resembles the approach of a detective trying to solve a crime, an inspector trying to determine the cause of an airplane crash, or a physician attempting to diagnose a patient's illness.” Because the causal process is not directly observable, they instead search for “clues” ( Scriven 1976 , 47; Maxwell 2012 ). Therefore, creating room within the discipline for this kind of research will require a new dedication to training students on how to collect and interpret qualitative evidence—skills that unfortunately have atrophied among graduate students ( Lebow 2007 , 2).

Some scholars reject any role for contingency in political science, and indeed see the historical, inductive approach as “antitheoretical” ( Kiser and Hechter 1991 ). Yet an understanding of the nature of contingency is absolutely critical to researching specific events, especially since it focuses on those moments when “fortune” is at its most powerful. O'Donnell and Schmitter are correct to identify “the high degree of indeterminacy embedded in situations where unexpected events ( fortuna ), insufficient information, hurried and audacious choices, confusion about motives and interests, plasticity, and even indefinition of political identities, as well as the talents of specific individuals ( virtu ) are frequently decisive in determining the outcomes” ( O'Donnell and Schmitter 1986 , 5). Although Mahoney denies that outcomes are entirely random, he goes so far as to argue that some moments “cannot be explained on the basis of prior historical condition” ( Mahoney 2000 , 508). This presents a dilemma: contingent events without social scientifically interesting origins simply must be included when explaining specific outcomes because otherwise the explanation of an individual case would not make sense ( Beach and Pedersen 2013 , 51). But how do we manage the tension between driving forces and contingency?

A key insight when managing these challenges is the relative nature of contingency ( Pettit 2007 ). As Slater and Simmons point out, “even the most severe crises rarely produce blank slates” ( Slater and Simmons 2010 , 890). A purely contingent event, in the ideal sense, is one with origins whose explanation has no social-scientific value and is essentially unpredictable. However, in the real world, events only very rarely fit these qualifications. Contingency can be the precise, but not perfectly predictable, manifestation of antecedent conditions ( Slater and Simmons 2010 ). In any case, if an actor intended to achieve something, even if they failed, we can still “preserve the proffered motivational account and elaborate on it,” as “explaining means elaborating, justifying, or possibly excusing the action rather than simply ‘refuting’ the hypothesis” ( Kratochwil 1990 , 25). Moreover, if a trigger is almost completely unpredictable, the effect that such contingent events have when they occur is still shaped by antecedent conditions ( Wood 2007 ).

The relative nature of contingency means that political scientists should problematize the extent to which a certain event was likely. An inevitable, possible, or unlikely outcome are all possibilities. To what extent an outcome is determined by structural causes is an empirical question: “documents and other historical evidence can tell whether key actors in a critical juncture acted with a significant degree of freedom or not” ( Capoccia and Kelemen 2007 ). Some outcomes are more open, while others are “not just determined but overdetermined” ( Rueschemeyer 2003 , 315). Bendor and Shapiro even argue that certain types of political phenomena, like military conflicts, are shaped by relatively higher levels of chance and contingency ( Bendor and Shapiro 2019 ).

Terminology like “likelihood” for an outcome might suggest a statistical approach, but the method here is different. Instead of identifying an average causal effect across a population, to address likelihood in an individual case, the researcher can ask: how powerful were countervailing forces that ultimately did not sway the outcome? Could tiny, easily imaginable counterfactuals have fundamentally changed the event ( Lebow 2015 )?

For example, in his essay on World War I, Lebow recognizes that “underlying causes, no matter how numerous or deep-seated, do not make an event inevitable. Their consequences may depend on fortuitous coincidences in timing and on the presence of catalysts that are independent of any of the underlying causes” ( Lebow 2000 , 591–92). He argues that several independent antecedent conditions working in conjunction along with a single triggering mechanism were the cause of the war. The assassination of Archduke Ferdinand was an especially powerful triggering mechanism given the antecedent conditions at the time, but it was not inevitable. If it happened outside of the two-year window when states would choose war over peace when faced with the decision, the war might have been avoided. Margaret MacMillan contributes to this debate by asserting that some crisis like the assassination was bound to happen and would likely have had a similar effect sometime between 1900 and 1914 ( MacMillan 2013 ).

Can we falsify individual analyses of events? Many political scientists would argue that identifying causes in single cases is fundamentally impossible. When coming from a statistical worldview, this viewpoint makes eminent sense. However, outside of this methodological prior, such an idea is rather radical, if not almost postmodern—after all, juries make judgments in a single case without relying on statistics, induction, or deduction. Instead, they are persuaded by which lawyer better uses evidence in a specific case to make a particular claim ( Toulmin 1972 ; McKeown 2004 , 149; Perelman and Olbrechts-Tyteca 2013 ). As Kratochwil clearly explains, we do not determine whether someone is “guilty” of a crime with covering laws. Instead, explanation of an individual case draws upon facts to construct a narrative framework that provides a reason for someone's actions. In other words, “explaining an action means providing a critically vetted, plausible account of the action and its context, which has the structure of a narrative rather than a demonstration” ( Kratochwil 2018 , 414–17).

The answer to whether individual cases are falsifiable depends on whether the questions are formed in a way that can be answered meaningfully with the available evidence. As discussed above, “Does income inequality lead to democratization?” would not be an appropriate question for this type of method, but “Does speech evidence and behavior demonstrate that political leaders in country A were primarily concerned that democratization would lead to redistribution of wealth?” certainly would be.

As opposed to other forms of qualitative analysis, event-specific is probably the least vulnerable to charges of “cherry-picking” or relying on only a few biased historical accounts ( Lustick 1996 ). Event-specific research presumes a much deeper and thicker relationship with the material in order to ask the questions that make it persuasive. Every piece of evidence must be situated, which avoids the problem of overemphasizing some CPOs that look like they confirm a theory but which are taken out of context. Also, event-specific research accepts the presence of multiple powerful forces, which decreases the pressure for over-arguing the case for one particular cause.

However, this kind of research is at its best when the substantive questions are posed in a way that allows the scholar to show how much one theory explains relative to another one. In order to ensure the highest level of rigor, the scholar should investigate at least two potential causes at once, ideally the two most likely to provide a better explanation (as determined by the theoretical literature and previous historiographical accounts of the event in question). Questions can be asked in such a way that “yes” points to one theory while “no” supports another. Or, two sets of questions can be asked: one set that addresses how much one generative structure mattered and a second set centered around another theory.

This research shares a core assumption within the field about the importance of “rigor.” Rigor here stems primarily from (1) the asking of questions that can be answered meaningfully with empirical evidence, (2) the integration of those answers to broader theoretical questions, and (3) evaluation of conclusions as part of a broader academic community. Event-specific research is not a “soft” approach. If science is “a set of shared practices within a professionally trained community,” then event-specific research is scientific ( Lebow 2007 , 7).

Since they have different views of generalizability, Weber, critical realists, and interpretivists naturally differ on the question of falsifiability. For Weber, identifying ideal types through a handful of specific events was a meaningful enough exercise. For critical realists, having discovered the potential existence of a finding, they still want to determine if it is “real.” For example, if the mechanism is psychological, they might then take the finding to a “laboratory” and conduct a psychological assessment. Or, they would investigate a number of cases to see whether the cause or mechanism is present in those cases as well. If that proves to be the case, then the finding is “transfactual,” meaning that the structure in question is commonly present (although with no a priori assumptions about how it would manifest in specific cases) ( Collier 1994 ).

This article is not the place to adjudicate these different views but bracketing this question for now does not hide the basic point they all share: that a great deal of generalizable and useful information can be learned from individual cases and that social science is not constrained to the Hume-ean worldview. Ultimately, specific events matter not because they are outliers, or a crucial case, or a least likely case, or a most likely case.

Elements of the event-specific research described above have already been apparent in the study of nuclear crises. Most famously, George and Smoke, instead of seeking to identify “a frequency distribution of different outcomes,” instead attempted to discriminate “among varieties and patterns of deterrence situations” ( George and Smoke 1974 , 3, 1989 , 171). They contributed to the literature by identifying typologies that would make situations more legible to policymakers. Political scientists have thought carefully about what quantitative methods can teach us about nuclear weapons ( Sechser and Fuhrmann 2017 , 63–71). But what exactly are the particular strengths of an event-focused approach compared with other methodologies for understanding specific nuclear crises?

Based on a statistical analysis, Kroenig argues that “nuclear crises are competitions in risk taking, but that nuclear superiority—defined as an advantage in the size of a state's nuclear arsenal relative to that of its opponent—increases the level of risk that a state is willing to run in a crisis. I show that states that enjoy a nuclear advantage over their opponents possess higher levels of effective resolve” ( Kroenig 2013 , 143). In response, Gavin, using the specific case study of the Berlin 1958–1962 nuclear crisis, charged that Kroenig's argument could not “fully explain the outcomes and causal mechanisms in the most important and most representative case” ( Gavin 2014 , 16).

However, Gavin is not evaluating Kroenig by what Kroenig is actually trying to do—if Kroenig's model is accurate, he may in fact have helpfully identified an average causal effect or at least an interesting correlation. With regard to individual cases, the problem is not so much that Kroenig's finding cannot explain a key case—the issue is that his methodology is not designed to explain individual cases at all.

Drawing on Seawright's analysis of Fearon and Laitin discussed above, we can ask questions that show the meaninglessness of directly applying Kroenig's statistical finding to a single case. Was the average causal effect of nuclear arsenal size not powerful enough to sway the outcome in the Berlin nuclear crisis? In other words, is the statistical coefficient too “high” or “low” for this single case? These are unanswerable questions. Because the core of Kroenig's finding is Hume-ean, his theory for how variables work cannot be tested in a single case because it is possible that the statistical relationship is manifested in fundamentally different ways in different cases. Therefore, judging whether the statistical finding and case study “agree” is impossible.

Not all political scientists are convinced by Kroenig's empirical findings. Sechser and Fuhrmann, for example, believe that nuclear superiority is meaningless in a crisis ( Sechser and Fuhrmann 2017 ). However, if a policymaker found herself in a nuclear crisis and wanted to look at past events for guidance, assuming that either of those empirical findings could be unproblematically applied directly to specific cases would put the world in a dangerous place—regardless of which scholarship is closer to the truth. First, the policymaker would not know whether the present crisis was one of the cases that cut against the grain of the identified average causal effect. Second, that empirical finding would not equip the policymaker to “see” the potential causes or mechanisms or have a sense for how those elements actually interacted with one another in the past.

Holloway's on the Cuban Missile Crisis illustrates the advantages of an approach that (implicitly) uses retroduction ( Holloway 2010 ). Three aspects, in particular, stand out. First, Holloway demonstrates that the world came extraordinarily close to nuclear war in 1962. Although war did not happen, he shows that the forces that had a gravitational pull toward war, like the obvious benefit of going first, were extremely powerful. The fact that contingent events, such as accidents, did not activate them does not mitigate their absolutely crucial importance. The interesting finding here is not that a precise group of variables meant peace but that the structural forces present could just have easily started a world war. These forces would be meaningless or invisible to Hume-eans who seek regularities across cases.

Second, Holloway's deep dive allows him to challenge Schelling's argument that rational behavior during a crisis would be to demonstrate “madness” and cut off the ability to retreat. During the Berlin and Cuban crises, neither side behaved in that way. Instead, Holloway provides a more subtle argument. “Common knowledge” did not encourage threats because it was not only a war of nerves, but also a limiting factor, as cutting off roads meant risking preventing attack. His ability to theorize this concept, while undermining a previously prominent theory in the discipline, draws upon a close reading of the evidence.

Third, this “common understanding” is best understood as a driving force, not a variable. Because the outcome was not determined, the “common understanding” is not best understood as a “sufficient” condition, even in a probabilistic sense. Because Holloway is doing no more (or less) than identifying one previously underestimated dynamic “in the wild,” which may or may not have a similar effect in other cases, he is also not identifying a “necessary” condition. Moreover, since this “common understanding” is not visible anywhere as a specific link in a chain of events, Holloway is not engaged in process-tracing. Instead, powerful speech and behavioral evidence indicates that this dynamic had an important pull throughout the crisis.

Political scientists have reached important and enduring conclusions using standard methods such as game theory, standard quantitative methods, and Mill's methods. In this article, we presented an argument for a more explicit theorizing of what individual events can teach us. Of course, like other approaches, this method has its own built-in limitations. Most immediately, its applicability to specific moments, the inherent limitations of relevant qualitative material, and nonuniversal ambitions are significant drawbacks. This approach cannot provide a number encapsulating the average causal effect of inequality on democracy (although it might show the implications of socioeconomic cleavages in individual cases). Yet, given the importance and complicated nature of the political world, political science can only be strengthened by adding to our tool kit. Many political scientists have reached the conclusion that individual cases tell us nothing except the extent that they provide for cross-case variation. However, by being less ambitious about universal effects and understanding generalizability in a different way, scholars using qualitative source materials to investigate even single cases can shed new light on political processes.

Thank you to Stanford's CISAC and the CFR's Stanton Fellowship for providing time to write this article.

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Case Studies in Political Science Research Paper

View sample Case Studies in Political Science Research Paper. Browse other research paper examples and check the list of political science  research paper topics for more inspiration. If you need a research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Also, chech our custom research proposal writing service for professional assistance. We offer high-quality assignments for reasonable rates.

I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with 24start discount code, ii. the debate within the discipline, iii. examples of the case study approach, a. u.s. politics, b. comparative politics, c. international relations, iv. conclusion.

The case study method has always been an integral tool in the investigation of social science phenomena, being of particular value when the number of observations, or cases studied, is limited in number, restricting the utility of statistical approaches. However, for some time the individual case study approach had been supplanted by large-N, data-intensive quantitative methods as the preferred technique for empirical studies. More recently, the case study has seen a revival of interest by social scientists as part of a multimethod, holistic approach that includes formal, qualitative, and quantitative methods. Indeed, each major methodological approach plays an important role in the research cycle, with the qualitative application of the case study enlightening the inductive aspect of theory development through the identification of alternate causal explanations, new variables, or complex interactions of variables. Fundamentally, case studies allow one to go beyond often simplistic quantitative analysis and develop contextually rich and in-depth pictures of the phenomena being observed.

By itself, a case study is the history of an event, be it of short or long duration—a civil protest movement, for example, or the evolutionary process from colonial rule toward stable democracy. As such, a case study identifies the expected, predictable aspects of an event, while ideally it also captures additional but less quantifiable detail, such as the cultural context, that potentially asserts a causal role as well. Individual or comparative case studies of specific, individual events, actors, or systems allow the researcher to obtain a depth of knowledge and understanding about the object being studied that large-N quantitative studies fail to provide.

A carefully crafted case study serves several purposes within the research cycle. First, while quantitative studies identify outlying or deviant cases, those well beyond the expected normal distribution, quantitative methods are generally not able to explain the specific reasons for a particular case’s extreme variation from its population mean. The case study, however, not only provides the opportunity to identify likely reasons for these individual deviations but may illuminate previously unidentified causal variables and possible alternate explanations as well. This information potentially leads to the extension of existing theory, if not its revision, and may suggest new theoretical explanations altogether.

Additionally, the case study may be the best, or only, way to study certain phenomena because of the relatively small number of identified cases and a resulting scarcity of data, which restricts the use of quantitative methods. And while much of the earliest criticism of case studies (by social scientists) centered on their application as a mainly historical narrative, the substantive purpose of case study is to understand that history but to do so in a way that allows for the identification of critical actions, structures, or other aspects that contribute to the end result. Being able to examine with scientific rigor phenomena that either do not lend themselves well to quantitative study, or for which only a limited set of objective measures is available, makes such an approach valuable. The role case studies can play in identifying and understanding previously unknown variables and in establishing causal paths and the interdependency of variables, as well as being critical tests of existing theory, makes them not just a complement to quantitative methods but potentially of equal value (Geddes, 1990; Gerring, 2004).

Case studies are by definition qualitative, meaning that the focus of the study is not primarily the systematic manipulation of aggregated points of data, an objective exercise, but rather a study that focuses on the quality of the potential data observed, a much more subjective work. This is not to say that case studies are not objective as well: In reality, for a case study to have any influence, it must identify and measure variables to allow for reliable comparison and to build theory that is testable, replicable, and generalizable. Case study is ultimately a method that falls into two forms: the individual, within case study and the comparative across case study, usually limited to a small number of cases. Both types work to identify causal relationships and enlighten theoretical explanations. Good case study work can be either accumulating (building on previous knowledge) or original (establishing entirely new avenues of research).

Political scientists have had an ongoing discussion about the role of the case study approach in their field. This discussion has focused on the relative value of case study compared with other methods for evaluating and advancing theoretical understanding. Of central concern is the perceived methodological limitation of single and small-N case work within a discipline that favors quantitative methodologies. A tension results between the benefits accrued from this method and its limitations. What value can a unique examination contribute? Are hypotheses and theory valid only if they are testable and generalizable? Within these debates over the fundamental usefulness of deliberative case study work are questions that address both the inherent strengths and weaknesses of such an approach. Scholars have generally fallen into two camps, those who argue for its usefulness and those who contend it has limited utility in a discipline with a strong quantitative emphasis and reliance on scientific method.

Addressing this fundamental question over the potentially ambiguous nature of a case study finding, which alone can neither directly inform nor disprove a generalizable finding, Arend Lijphart (1971) states that because of its singular nature, the case study in and of itself does not directly satisfy the standards of scientific research. He does, however, credit the case study with multiple indirect benefits, making it a valuable component in establishing political science theory. He identifies six types of case studies that fall into roughly two categories: those chosen because the case itself is of interest and that are purely descriptive and those chosen to inform and build theory. The first category encompasses single case studies, which are generally detailed histories of a specific event or result and which, he argues, have value for this history alone. The thorough knowledge of a country gained by such an intensive, rich study provides critical information that others can also benefit from. Additionally, these in-depth analyses not only are a source of data for larger comparative studies but may also identify new variables of interest or suggest potentially new theoretical explanations. Lijphart’s other typologies include those case studies that are chosen specifically for theory-building purposes. They include hypothesis-generating cases in areas in which no established theory exists; theory-confirming and -informing cases, both of which test existing theories; and deviant case analysis, for cases known to have varied from the expectations predicted by theory. This third type of case often reveals additional variables previously unidentified. It may suggest a temporal ordering of variables (path dependency) or identify the sometimes critical interactions of variables. The study of deviant cases may merely suggest refinements to the way variables are operationalized within the study, still an important theoretical contribution. These last three case typologies constitute the core of comparative case study, with their usefulness coming from their deliberate selection as a test to existing theory. While Lijphart identifies certain benefits of the case study approach, his praise is still conditional, and he favors the value of large-N quantitative studies whenever possible.

Harry Eckstein (1975) addresses the utility of case studies by first noting the predominant status held by historiographic work in earlier political science research. His main contention is that this early case study work, at both the micro and the macro level, although insightful in its own right, was perceived to be severely limited in its usefulness for producing generalizable theory, because of its singular focus and the statistical consequence of an N of 1. The prevailing assumption was that what theory-building utility case study work had was inductively drawn from the events studied, and those inferences might or might not represent replicable conclusions. Eckstein questions this assumption and lays out a detailed argument supporting the utility of case study work in all stages of the theory development process, not just the nascent ones. He additionally contends that case studies may actually be most valuable at the theory testing stage. Particularly in the field of comparative politics and when studying complex, potentially unique systems, Eckstein suggests that well-designed case study methods may be the best way of testing hypotheses and cumulating generalizable theories. Indeed, he emphasizes the role of case study in its comparative application and perspective.

Before he makes his argument for the value of case study to theory building, Eckstein (1975) provides valuable definitions of case study by emphasizing the concentrated, yet flexible, aspect of an investigation into a single event or individual. This focused yet not narrowly defined approach allows the investigator to be open to unexpected observations and new conclusions. Eckstein additionally makes the important distinction that the study of one event does not necessarily mean only one measure of the results. Rather, he contends that how an event or thing is studied will dictate its number of observations. Thus, one event can be broken down into numerous observations. For example, “Astudy of six general elections in Britain may be, but need not be, an N = 1 study. It might also be an N = 6 study. It can also be an N = 120,000,000 study” (p. 85). This example illustrates his definition of a case as the single measurement of a pertinent variable observed, so that comparative study is then defined as “simply numerous cases along the same lines, with a view to reporting and interpreting numerous measures on the same variables of different ‘individuals’” (p. 85).

After he provides a useful review of the steps toward the development of theory, first the question or puzzle, followed by the formulation of a hypothesis and then a test, with the cycle likely repeating itself as refinements are made, Eckstein proceeds to describe five distinct varieties of the case study and identifies the particular uses each has. The first of these, the configurative idiographic study, is meant to be a comprehensive study of its target but one that allows for intuitive interpretation of the facts. By definition, idiographic is individualizing rather than nomographic or generalizing. Indeed, Eckstein acknowledges that this type of case study was the predominant type he first alluded to in this work. But he makes the point that the strengths of these types of case studies are their very weakness. Their rich description and often persuasive intuitive interpretations may be individually factual, but they aren’t systematic, which makes generalizable conclusions problematic and substantive theories unlikely.

The disciplined configurative study, a term Eckstein (1975) credits to Sidney Verba, turns this relationship around somewhat; rather than building theories on interpretations, interpretations should be driven by theory. This implies that the details of a case should either confirm or disprove a theory that ought to apply to it. The problem with this approach is, as Eckstein points out, its “discipline.” The strict and usually narrow application to a case of a hypothesized theory should either confirm or deny it. In essence, Eckstein suggests that this approach may be too restrictive. It may also lack the flexibility to accommodate more intricate relationships not already identified or suggested by existing theory. He also worries that interpretation of cases on an existing theory presumes that the theory itself is correct and suggests that existing theory, however valid, may “compel particular case interpretations” (p. 104, italics added) with its emphasis on generalizability at the expense of more individualized findings.

Eckstein’s (1975) third type is heuristic case studies, which are deliberate searches for discovery, often a result of trial and error. These are meant to be creative, stimulating the imagination of the researcher toward new ways of looking at a problem, focusing on broader, more generalizable relationships. This discovery is incremental and is often developed in sequential studies as the new theory is further refined. The reason for heuristic case study is given rather succinctly by Eckstein: Theories do not arise from data alone but rather from the imagination of the researcher, after discerning puzzles and then patterns. Case studies, with their intensive analysis, increase the likelihood that these critical relationships will be found, particularly when they are carefully chosen to advance theory building. One caveat Eckstein offers on heuristic case studies is that they often produce too much—multiple explanations, too many variables, and a resulting complexity of interactions that are not only unwieldy but make generalization impossible.

Case studies are also used to probe the likelihood of proposed theories, a form that Eckstein (1975) calls plausibility probes. These are an intervening step before testing, to determine whether the expense of testing is warranted. Although the usefulness of such a study is limited to this end, and alone it cannot confirm a theory, it can, however, improve the prospects of testing, and for this reason it has value.

A more critical example of case study in theory building is the crucial case study. Eckstein (1975) confronts the dilemma of a single observation and the inability to correctly determine a statistical relationship on the basis of such limited information as a source of potential error for any theory based on it. The inductive fallacy is the error made when one derives a theory from only the observed (gathered) data, without further testing. The critical caveat is that one cannot test a theory with the same data used to originate the theory, and therefore another such example must be found. The crucial case is just such a test of a proposed theory. If all those variables deemed critical to a theory exist, then the results should be as predicted by the theory. Conversely, one can study a case similar in most respects, yet lacking in the hypothesized critical components, as a way of demonstrating that similar results did not result because the causal variable was missing. Although these most likely and least likely case study designs cannot absolutely confirm or deny theory, they are important tests of the likelihood of the theory and the correctness of the causal relationships being proposed.

Eckstein’s (1975) thorough typology and analysis of the case study method methodically crafts an argument for the benefits of case study work. These include the insight made possible by the rigorous, thorough inspection in a carefully crafted case study and its across-discipline utility in identifying new variables and new causal mechanisms leading to the generation of new theory. To accomplish this goal, Eckstein emphasizes that case study selection must be driven by theory, and not by interest or convenience.

Charles Ragin (1987), in The Comparative Method, devotes a chapter to the discussion of case-oriented comparative methods and addresses the likelihood that even the most meticulously performed case study is unlikely to produce definitive explanations. However, identifying critical contextual facts may help determine the causal relationships underlying the observed phenomena. It is important to note that Ragin emphasizes the value that an intensive case study accrues to its researcher. Deep understanding of an event or case in its entirety, rather than merely knowing pieces of information, allows for more contextually rich comparison to other events. This richness can only enhance the reliability of the causal inferences drawn. Such depth of knowledge is likely limited to a small number of cases, and indeed this complexity is a constraint on the case study researcher. Case study is, as Ragin shows, a successful strategy for analyzing complex, multicausal events and at the same time still cohesively connecting them theoretically. He concludes with a nice summation of the strengths of the case study method: Case studies make possible the discovery of patterns of relationships and difference, with all deviations requiring an explanation, necessitating a thorough knowledge of the data. Since case study work does not rely on statistical probabilities such as frequency or distribution, a single case can be critical and can potentially prove or disprove a hypothesis. Case study work is holistic and requires a thorough understanding of the entire event, not just targeted aspects of it, and finally, case study encourages creative new ways of examining behavior and events. Particularly in the identification of complex interactions and the importance of context in understanding their role, Ragin makes the point that case studies provide a methodologically distinct approach.

In Designing Social Inquiry, Gary King, Robert O. Koehane, and Verba (1994) argue that the same level of testable, scientific rigor can be applied to qualitative work that quantitative scholars are able to use in their statistically based work; qualitative work includes, of course, case studies. King et al. focus on research design with an emphasis on the logic of inference, to use the facts that are known to learn about facts as yet unknown. This is then used to identify causal relationships and construct theories that can then be tested. King et al.’s emphasis on the latter stages of research design, producing theory that is testable and thus falsifiable, challenges case study researchers to think rigorously about their work, to recognize the similarities of quantitative and qualitative work with respect to empirical rigor, and to approach their work as such. King et al. argue that the primary way to do this is to see qualitative data more quantitatively, and to accomplish this from a practical standpoint, they advise maximizing the number of observations (from which measures are taken) whenever possible. At the same time, when adding an observation is not possible, they recommend summarizing on the outcome of interest instead, in order to avoid issues of micronumerosity (having more variables than observations). Echoing Eckstein (1975), King et al. remind us that the size of a case study, its N, is often determined by the level of analysis chosen: Is it one single event, several incidents within that event, or many more individual acts? In addition to constructing a design that allows for multiple observations, the authors emphasize the requirement of designing theories that can be falsified (i.e., the null hypothesis can be tested). King et al. also address the importance of reducing the potential bias introduced through case selection. They emphasize the care with which cases must be chosen, as there must be “the possibility of at least some variation on the dependent variable” (p. 129). Other potential sources of selection bias they cite are investigator induced: choosing cases because data are available or because one has a particular interest in or understands the language, or the larger bias that often occurs when case selection is correlated with the dependent variable. In this instance, the process being studied has already been selected for over time, leaving as evidence only its most recent iteration and losing any obvious trace of what may have been critically important in the intervening stages.

Not all scholars implicitly agreed with the arguments made by King et al. (1994), and a lively review symposium in response to it appeared in the journal American Political Science Review. In it, Ronald Rogowski (1995) challenges King et al.’s concern with the testability of single-observation studies and relates three examples of just such single-case studies that do succeed under this limitation. He offers additional examples in response to their admonitions against dependent variable selection bias and comments that without deliberate selection based on a case’s anomaly (its status as a statistical outlier), one of the core benefits of case study work would be lost. Rogowski sums up by emphasizing the importance of not losing the benefits of good qualitative work at the expense of increased quantifiability. In the same symposium, David Collier (1995) also takes issue with how King et al. address selection bias. However, although Collier generally concurs with their position, he argues for a bit more nuance when one is faced with some of the realities of the comparative method. Additionally he identifies the importance of valuing the context of research findings as more important perhaps than their generalizability, and he gently suggests that King et al. could be less rigid in their appraisal of qualitative methods.

Since case study is just that, an intensive examination of at least one item, how cases are selected is a fundamental issue. In comparative case studies, this issue is particularly relevant because small-N studies suggest that there exists more than one unique example of what is being examined and therefore a larger population to choose from. As a result, concerns over potential selection bias contribute prominently in discussions of the case study method. In “How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics,” Barbara Geddes (1990) addresses this issue by reexamining three prominent comparative studies. She neatly demonstrates how the potential error of case selection on the dependent variable can particularly impact results in small-N studies. Essentially a primer on selection bias, this article outlines the importance not only of identifying the most likely causal reasons some event occurred, but also of examining the counterfactual as well. Geddes makes the point that by not providing a larger sample, selected randomly (rather than on the dependent variable) for testing the proposed relationship between cause and effect, one is really comparing only “the differences among the selected cases” (p. 132). She then shows how such an error can also occur in a path-dependent argument. In both examples, misleading findings resulted from researchers’ not expanding the population from which the targeted cases were drawn. Had they done so, they would have had a larger and likely more random sample to test. Geddes’s final example involves time-series studies and the determination of the appropriate end point of a case study. In this instance, she shows how changing the dates of a study would affect its results drastically, and she also makes the point that historical case studies are especially vulnerable to selection bias based on the time frames chosen for analysis.

The more recent discussion of case study work has focused increasingly on understanding the role of this method as part of a comprehensive research strategy. John Gerring (2004) emphasizes how, by failing to accommodate the bounded aspect of case work, most commonly used definitions for case study are inadequate. He offers the definition of case study as “an intensive study of a single unit for the purpose of understanding a larger class of (similar) units,” with units being “spatially bounded phenomena” (p. 342). This implies the study of a unique event or thing, at one point in time, with the goal of generalizability, which, he argues, provides a more theoretically useful interpretation. Gerring then provides a comprehensive discussion of the methodological ambiguities that occur in case studies and identifies six areas in which case studies are vulnerable. With these as a guide, he outlines the strengths and weaknesses of case (within-unit) study versus across-unit study. He notes that the case study method is more suited to descriptive inferences than to causal ones. It is a method that has a special affinity with intensive, focused studies rather than those that are extensive and broad. Case study is more likely to have high internal validity and weak external validity. It facilitates the defining of causal mechanisms, and not the testing of causal effects, performing better when causal mechanisms are deterministic instead of probabilistic. Finally, case studies are well suited to exploratory research but are limited in their uses for confirming hypotheses, yet they are preferred when across-case studies cannot provide adequate variance for the relationship being studied. With this enhanced clarity, and by situating case studies not apart from but as a complement to noncase methods, Gerring suggests that case study methods should be accepted as an equally worthy methodological approach by the entire discipline and that rather than favoring one method over another (often exclusively), scholars should use the method most suited to their question, their data, and their theory.

With Alexander George and Andrew Bennett’s (2005) Case Studies and Theory Development in the Social Sciences, the debate within the discipline over case studies is brought up to date. Both authors have been longtime advocates of case study methods, and this latest work is a very thorough argument for the value of case study methods as part of a research strategy that includes both quantitative and qualitative methods as well as formal theory (Bennett, 2004, offers a chapter-length article distilled from this material, as well). George and Bennett disagree with King et al.’s (1994) contention that there can be only one “logic of Inference” (p. 11). George and Bennett discuss the relationship between case studies and the systematic building of theory. They compare the strengths and weaknesses of case studies and first identify four strengths, all areas in which statistical methods tend to be weak. These include concept validity, the potential for discovering new causal variables and deriving new hypotheses, a better understanding of the relationship between causal variables and possible path dependency, and the ability to identify or model the complex interactions of these variables. Weaknesses of case studies include the potential for introducing selection bias from the cases chosen and the inability to accurately measure the relative strength of an effect. Also, because of their single or very small number, case studies are relatively unique and not necessarily representative; cases chosen from a small pool may not necessarily be independent of one another, and they do not have a rich number of observations from which to judge the strength of associations between variables. George and Bennett advocate the use of the structured, focused comparison, which allows for the collection of data that can be systematically compared with other cases as well as accumulated. In this way, scientific rigor is added, and the utility of case methods is likely increased. The authors then outline the method of case study, from designing the research to executing the study and to drawing conclusions from the findings. In all steps, the role of theory is predominant: It drives the design and motivates the findings. In addition to being the definitive authority on case methods, George and Bennett present a compelling argument for using multiple methodological approaches in a research program. Not only do they show how qualitative and quantitative methods complement each other; they integrate formal modeling as well. This approach is gaining momentum in political science today, making a qualitative skill set not merely useful but necessary.

In the field of U.S. politics, the classic example of a grounded, participant observer case study must be Alexis de Tocqueville’s (1835/2004) Democracy in America. Although most modern scholars of U.S. politics solve their N of 1 problem by focusing on the subunits of U.S. government, using states or administrations, court terms or congressional voting records as their unit of analysis, Tocqueville analyzed the United States as a single entity. He drew his conclusion, that it is citizens’ affinity for joining in and participating at all levels of civic life that strengthens democracy and enables it to flourish, from his personal observations as he extensively toured the country in the early 1800s. A more modern work in U.S. politics that is rooted in qualitative case study work is Richard Fenno’s (1978) examination of congressional members, Home Style: House Members in Their Districts, in which he used extensive interviews and considerable time observing congressmen, both in Washington, D.C., and, critically, in their districts. This self-styled “soaking and poking” enabled a comprehensive, in-depth observation that allowed Fenno to identify the paradox of individual representatives’ being very well-liked by their constituents at the same time as the institution of Congress is collectively viewed much more critically, and he explains much of the paradox by the personal relationships developed through district service. David Mayhew’s (1974) Congress: The Electoral Connection also looks at the relationship between members of Congress and their constituents and is another example of a work based on inductive reasoning rooted in extensive in-depth participant observation. Mayhew finds that it is the incentive for reelection that motivates the individual behavior of both congressmen and the Congress. Through committee assignments, leadership positions, and vote trading (among other means), congressmen ensure their reelection chances. Mayhew suggests that with Congress motivated as a whole by mutual self-interest, it is no surprise that the structural arrangements of Congress, its organization of the leadership and committee system, have evolved to facilitate this behavior.

The case study method is used most extensively in the subfield of comparative politics. Using primarily small-N research designs, many significant works have been produced. Included among these is Barrington Moore’s (1966) Social Origins of Dictatorship and Democracy: Lord and Peasant in the Making of the Modern World. Moore examines five societies to compare their experiences with modernization and the economic revolution that ensues. He concludes that there are three likely outcomes, dependent on the country’s social structure, and these in turn predict the likelihood of a successful transition to democracy or descent into dictatorship. The most well-known example of an intensive single-country case study must be Robert Putnam’s (1993) Making Democracy Work: Civic Traditions in Modern Italy, also an excellent example of historiographic work. The subject of lively debate within the discipline, social capital, that is, the extent to which citizens are participatory and invested in their communities as a result of their civic relationships, was found by Putnam to be a necessary component of a successful democratic society. Putnam argues that the associational experience of northern and central Italy developed interpersonal trust and fostered more democratic local governments, but the lack of similar groups in the south left them with less. Another such single-case work is Robert Bates’s (1989) Beyond the Miracle of the Market: The Political Economy of Agrarian Development in Kenya. This work, which focuses on the intersection between land use, government institutions, and public policies, relies on a critical understanding of the economic, political, and cultural forces at work in Kenyan society. The complex interplay of economics and politics that Bates studies is only fully appreciated when the cultural context is included; the influence of tribal affiliations and Kenya’s British colonial legacy are just two examples. These kinds of rich, multilayered observations and intimate knowledge of a society can be accomplished only with case study methods, with which Bates combines quantitative rigor as well.

International relations scholars have also extensively used the case study method to selectively examine the actions of elite actors and organizations during critical events. Case study work is used to evaluate existing theory as well as propose alternate explanations to better understand the often complex motivations of and among nation-states. In Essence of Decision: Explaining the Cuban Missile Crisis, Graham T. Allison (1971) examines the Cuban missile crisis and, primarily through interviews, reconstructs the often conflicted decision-making process of all the major participants. To do this, he approaches the same event from the perspective of three different decisional- behavior models. These competing approaches are collectively used to illustrate the author’s thesis: that despite internal pressures to the contrary, it was the actions and the decisions of the two leaders that successfully resolved the issue. Alexander George and Richard Smoke’s (1974) Deterrence in American Foreign Policy is an example of a focused-comparison case study that examines 11 instances of the failure of U.S. deterrence policy. George and Smoke use process tracing to establish the causal explanation, which would not be possible without the depth of knowledge acquired in these case histories. In doing so, they critique existing theory and are able to offer a new, more dynamic, explanation. In another example of a focused-comparison study, Stephen M. Walt (1987), in The Origins of Alliances, looks at alliance formation and contrasts two distinct types: those made for mutual support to defend against a threat and those that are more opportunistic (or perhaps pragmatic), in which one aligns with the threat itself. Walt then explores the likely causes of these choices, looking specifically at shared ideology and the influences of foreign aid. His concentrated case study of states in the Middle East during a single period allows him to develop the depth of knowledge necessary for such a study, in which data alone would be inadequate.

As the previous examples illustrate, case study work is applicable to a broad range of theoretical questions. Indeed, for many situations, a case study examination is the only way to rigorously examine an event. Case study can be used in either half of the research cycle: to deductively test the hypothesized research question or to inductively explore the results of empirical observations. It is also a valuable method for developing original theoretical insight, which can often form the basis of a research design using more statistically robust methods. Case study in and of itself serves a vital informative purpose as well, allowing in-depth appreciation of often nuanced yet critical conditions of the larger phenomenon being observed. Finally, the case study is increasingly being appreciated as a necessary component of comprehensive political science research today: Together with traditional quantitative methods that provide reliable statistical probabilities for a tightly focused view, and formal theory methods that produce more soft-focused or abstract explanations, case study work provides a necessary contribution by filling in the gaps, compensating for the inevitable shortcomings when formal and quantitative methods are applied to real-life questions and problems. Most critically, a well-crafted case study gives the researcher a level of knowledge and understanding of the matter being examined that no other method allows. This benefit alone justifies the application of case study methods to social science research today and in the future.

Bibliography:

• Allison, G. T. (1971). Essence of decision: Explaining the Cuban missile crisis. Boston: Little, Brown.
• Bates, R. (1989). Beyond the miracle of the market: The political economy of agrarian development in Kenya. Cambridge, UK: Cambridge University Press.
• Bennett, A. (2004). Case study methods: Design, use and comparative advantages. In D. F. Sprinz & Y. Wolinsky Nahmias (Eds.), Models, numbers and cases: Methods for studying international relations (pp. 19-55). Ann Arbor: University of Michigan Press.
• Collier, D. (1995). Review: Translating quantitative methods for qualitative researchers: The case of selection bias. American Political Science Review, 89, 461-466.
• Collier, D., & Mahoney, J. (1996). Insights and pitfalls: Selection bias in qualitative research. World Politics, 49, 56-91.
• Eckstein, H. (1975). Case study and theory in political science. In F. Greenstein & N. Polsby (Eds.), Handbook of political science: Vol. 7. Strategies of inquiry (pp. 79-137). Reading, MA: Addison Wesley.
• Fenno, R. (1978). Home style: House members in their districts. Boston: Little, Brown.
• Geddes, B. (1990). How the cases you choose affect the answers you get: Selection bias in comparative politics. Political Analysis, 2, 131-150.
• George, A. L., & Bennett, A. (2005). Case studies and theory development in the social sciences. Cambridge: MIT Press.
• George, A. L., & Smoke, R. (1974). Deterrence in American foreign policy: Theory and practice. New York: Columbia University Press.
• Gerring, J. (2004). What is a case study and what is it good for? American Political Science Review, 98, 341-354.
• King, G., Koehane, R. O., & Verba, S. (1994). Designing social inquiry: Scientific inference in qualitative research. Princeton, NJ: Princeton University Press.
• Lijphart, A. (1971). Comparative politics and the comparative method. American Political Science Review, 65(3), 682-693.
• Mayhew, D. R. (1974). Congress: The electoral connection. New Haven, CT: Yale University Press.
• Moore, B., Jr. (1966). Social origins of dictatorship and democracy: Lord and peasant in the making of the modern world. Boston: Beacon Press.
• Putnam, R. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.
• Ragin, C. C. (1987). The comparative method: Moving beyond qualitative and quantitative strategies. Berkeley: University of California Press.
• Rogowski, R. (1995). Review: The role of theory and anomaly in social scientific inference. American Political Science Review, 89, 467-470.
• Tocqueville, A. de. (2004). Democracy in America. New York: Library of America. (Original work published 1835)
• Walt, S. M. (1987). The origins of alliances. Ithaca, NY: Cornell University Press.

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Baylor International Studies

Case management, immigration law, and a willingness to learn: an interview with charlie kruljac.

by Tatum Koster (’24)

Kruljac started his time working during undergrad and quickly discovered a passion for teaching. He began a job tutoring student athletes, which as a result connected him more with his peers. Wanting to become more involved with his peers, he became a global engagement ambassador at Baylor University. Kruljac enjoyed welcoming new international students into Baylor and helping them immerse in the culture. After graduation, Kruljac worked with Teach for America from 2019-2021, where he taught at underprivileged schools. As well as work experience throughout his undergraduate years, he gained volunteer experience with the International Institute of St. Louis, where Kruljac is still involved today. Kruljac has taught courses during his time there, including a “bridge to college class”. Kruljac still pursues this passion of teaching today.

After Kruljac’s years of various job and volunteer experiences, he discovered an interest in a career that requires working with families. He is now a case manager at Cofman & Bolourchi, LLC. In Kruljac’s current work, he works closely with attorneys in the field of immigration law. He prepares the filings for the attorneys and contacts the families they are working with to verify any information. When asked what major challenges he deals with on a daily basis, Kruljac said the workload is very busy and there is a rushed feeling. In addition, another challenge Kruljac faces is the “difficulty of working with a complicated and dysfunctional immigration system across cultural and linguistic lines of difference”. While working as a case manager, Kruljac is applying to law schools, including Baylor Law. He hopes to pursue a career in immigration law. Kruljac has years of experience to prepare him for this role.

When asked to give advice to current students, Kruljac expressed the importance of discovering what you’re interested in and passionate about and making a willingness to learn the subject. Kruljac remembers his time at Baylor through courses completed such as Comparative Politics, a semester-long study abroad program in Mendoza, and his time in the Baylor Interdisciplinary core.

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Turan Kayaoglu publishes on Turkey's NHRI and Human Rights

Professor of Political Science and Associate Vice Chancellor for Faculty Affairs, Turan Kayaoglu, recently published an interesting article titled "National Human Rights insitions and the Appropriation of Human Rights: The Case of Turkey's Human Rights and Equality Institution" alongside Devran Gülel. The article examines Turkey's establishment of the National Human Rights Institutions (NHRI) or Human Rights and Equality Institutions of Turkey (HREIT). 

The paper situates the HREIT case within a larger literature on National Human Rights institutions (NHRIs), which have emerged as critical instruments in the global human rights system. While previous studies have mostly focused on measuring the performance of NHRIs, Kayaouglu highlights the necessity to investigate cases where NHRIS is useless or involved in compromising human rights. He claims that, like in Turkey, NHRIs might be used by populist, right-wing administrations to appropriate human rights. 

The full article can be found here . 

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