case study method in political science

• A-Z Publications

Annual Review of Political Science

Volume 9, 2006, review article, qualitative research: recent developments in case study methods.

• Andrew Bennett 1 , and Colin Elman 2
• View Affiliations Hide Affiliations Affiliations: 1 Department of Government, Georgetown University, Washington, DC 20057; email: [email protected] 2 Department of Political Science, Arizona State University, Tempe, Arizona 85287; email: [email protected]
• Vol. 9:455-476 (Volume publication date June 2006) https://doi.org/10.1146/annurev.polisci.8.082103.104918
• First published as a Review in Advance on February 17, 2006
• © Annual Reviews

This article surveys the extensive new literature that has brought about a renaissance of qualitative methods in political science over the past decade. It reviews this literature's focus on causal mechanisms and its emphasis on process tracing, a key form of within-case analysis, and it discusses the ways in which case-selection criteria in qualitative research differ from those in statistical research. Next, the article assesses how process tracing and typological theorizing help address forms of complexity, such as path dependence and interaction effects. The article then addresses the method of fuzzy-set analysis. The article concludes with a call for greater attention to means of combining alternative methodological approaches in research projects.

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Most cited most cited rss feed, framing theory, discursive institutionalism: the explanatory power of ideas and discourse, historical institutionalism in comparative politics, the origins and consequences of affective polarization in the united states, political trust and trustworthiness, public attitudes toward immigration, what have we learned about the causes of corruption from ten years of cross-national empirical research, what do we know about democratization after twenty years, economic determinants of electoral outcomes, public deliberation, discursive participation, and citizen engagement: a review of the empirical literature.

Publication Date: 15 Jun 2006

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51 The Case Study: What it is and What it Does

John Gerring is Professor of Political Science, Boston University.

• Published: 05 September 2013
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This article presents a reconstructed definition of the case study approach to research. This definition emphasizes comparative politics, which has been closely linked to this method since its creation. The article uses this definition as a basis to explore a series of contrasts between cross-case study and case study research. This article attempts to provide better understanding of this persisting methodological debate as a matter of tradeoffs, which may also contribute to destroying the boundaries that have separated these rival genres within the subfield of comparative politics.

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7.5: Case Studies

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• Page ID 76223

• Josue Franco
• Cuyamaca College

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Learning Objectives

By the end of this section, you will be able to: ● Define a case study as a qualitative research method

● Understand the process of case selection

What is a case study?

In the words of political scientist John Gerring, a case study is “an intensive study of a single unit for the purpose of understanding a larger class of (similar) units.”25 A case study is above all else an in-depth description and exploration of an event, person, group, and/or place. In addition to deep analytical description, case studies may be critical and present evidence to build counter- narratives to the dominant narrative of an event. The “intensive study” of a case study may stem from utilizing all of the methods described above, from interviewing subjects to engaging in ethnographic fieldwork, in order to build a comprehensive understanding of the case. Quantitative data may also be marshaled to deepen the case study. The goal of crafting a case study is to draw inferences from that case to test theory.

The first issue the researcher must address is case selection. First, given the definition above, a case study should be relevant to the theory or hypothesis that a researcher wishes to test. For example, if a researcher wanted to investigate how mineral wealth might contribute to poor governance outcomes in a country, it would not make much sense to select a country without mineral wealth. (To be concrete, the Democratic Republic of Congo might be a good country case to explore, but Haiti less so. However, to make the inferences from that case study more valid, a researcher might want to consider crafting a second case study on a country similar to the DRC, but without sources of mineral wealth, to explore whether governance outcomes differ across the two cases.)

Second, the selected case should be representative of a larger group. This is to head off criticism that the chosen case is too much of an outlier to provide leverage on understanding the general phenomena of interest. To take up the previous example, if a researcher wishes to study the DRC as a case of the so-called “resource curse,” in what ways is the DRC like other mineral-rich countries? In which ways does it differ? And are those differences so significant that the DRC is not representative of the “class” of mineral-rich countries that the researcher would be exploring with this case study? Of course, every place and person is sui generis , but an important consideration is whether there are such enormous differences that a case is an outlier rather than representative.

Third, case selection hinges on practical considerations. Is this a case for which there exists a robust body of secondary literature to build a baseline of preliminary knowledge? Does understanding the case require language skills? Does the researcher know which organizations or individuals to contact to collect information? Do they have access to those organizations and individuals? Will building the case study require conducting fieldwork? If so, for how long, and how much might this require in research funds?

Case studies are a powerful tool in the qualitative methods toolbox. They are a means to investigate the causal processes which are often lost in traditional quantitative approaches such as regression analysis. They are also empirical and hence testing theory against what is transpiring in the “real” world. They demand a researcher to think creatively and holistically about a subject, then dive fully into learning as much about it as possible.

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Process tracing methods in the social sciences.

• Derek Beach Derek Beach Department of Political Science, Aarhus University
• https://doi.org/10.1093/acrefore/9780190228637.013.176
• Published online: 25 January 2017

Process tracing (PT) is a research method for studying how causal processes work using case study methods. PT can be used for both case studies that aim to gain a greater understanding of the causal dynamics that produced the outcome of a particular historical case and to shed light on generalizable causal mechanisms linking causes and outcomes within a population of cases. PT as a method has three core components: theorization about causal mechanisms linking causes and outcomes, the analysis of the observable empirical manifestations of theorized mechanisms, and questions of case selection and generalization. There are at least three distinct variants of PT that stem from differences in how scholars understand the ontological nature of the theories being traced. This means there is not one “correct” way to use PT.

• process tracing
• case studies
• causal inference
• causal mechanisms
• causal process

Updated in this version

The title, summary, and keywords have all been updated. Additionally, new figures, tables, and text have been added to reflect the latest developments.

Introduction

Process tracing (PT) is a research method for tracing causal processes using case studies. PT can be used to investigate questions such as how low intelligence capacity of security forces can contribute to produce mass violence against civilians ( Winward, 2021 ) and how an epistemic community can gain influence over policy ( Löblová, 2018 ). The analytical added value of PT is that it enables causal inferences to be made about how causal processes/causal mechanisms work using in-depth analysis of one or a small number of cases. 1 Furthermore, when more disaggregated process theories are traced empirically, light is shed on the contextual conditions under which particular processes operate. The trade-off is that not many cases can be traced because it is difficult and time-consuming to study how things work in particular cases using PT.

PT methods can be used for either theory-building or theory-testing purposes. In theory-building mode, the researcher engages both in a thorough “soaking and probing” of the empirics of the case and in a far-reaching search in the theoretical literature to gain clues about potential mechanisms that could link a cause and outcome together, whereas in theory-testing mode, hypotheses about the observable manifestations that a theorized mechanism might leave are tested empirically in a case. In reality, most users of PT engage in a more iterated design that moves back-and-forth between theories and empirics (aka abductive design), with the end product an evidenced process theory that explains how a cause (or set of causes) is linked to an outcome in a case or set of cases.

PT as a method has two very obvious constituent elements: what is being traced at the theoretical level and how processes can be traced empirically. But in contrast to some methods, there is not one “correct” way to do PT because there are considerable differences between how scholars understand the two elements. These differences at the ontological and epistemological levels result in three different variants of PT methods, depicted in Table 1 .

At the level of theory of causal processes/causal mechanisms, the core distinctions are ontological. First, there is a more superficial divide in the literature regarding the level of abstraction that process theories should have, ranging from simple, one-liner-type theoretical explanations to more complex, and even case-specific, process theories. Other things equal, the lower the level of abstraction of the process theory, the more knowledge is gained about how things worked at the theoretical level, and stronger causal inferences about process are possible at the empirical level. However, this does not mean that more detail is always better. After an initial, in-depth case study that traces a detailed process theory in one case, it can be helpful to lift the level of theoretical abstraction to ease the task of assessing whether things worked in a similar fashion in a number of other cases.

More fundamental divides exist with regard to the ontological nature of the causal claims that are made when discussing causal processes and mechanisms. Here, there are (at least) three distinct positions: (a) mechanisms are in essence counterfactual causal claims, (b) mechanisms are causal because of the productive relationships of actors engaging in activities that link causes and outcomes together in cases, and (c) a more interpretivist position that contends that process theories are causal when they are able to capture how social actors construct and reconstruct the social reality they are a part of.

At the empirical level, the distinctions between counterfactual, productive, and social constructivists accounts of the nature of causation have epistemological implications for how causal processes should be traced. When tracing counterfactual claims, some form of controlled comparison of the actual with the potential counterfactual is required to be able to infer that the mechanism made a difference. In contrast, the productive account is sometimes termed the actualist position because causal inferences are made based on assessing whether the expected observable traces left by the activities of actors are present in an actual case. In the social constructivist account, more interpretivist methods are also utilized (sometimes as supplements, sometimes exclusively) to understand how social actors make sense of the practices of actors and social context in which they are embedded (i.e., meaning-making).

Whereas some scholars take a “my-way-or-the-highway,” methodological monist approach to questions related to causation and/or epistemology (e.g., Bevir & Blakely, 2018 ; King et al., 1994 ), a methodological pluralist position is recognizing and understanding that different positions are possible and that different variants have relative strengths and weaknesses in different research situations ( Beach, 2021 ; Runhardt, 2021 ). 2 For instance, if one believes that it is important to capture how social actors understand the diplomatic practices in which they are engaging, a more interpretive variant of PT would have comparative strengths. In contrast, when studying relatively “simple” processes that are repeated frequently (e.g., problem-solving processes in a team within an organization), a counterfactual-based variant that makes inferences through controlled comparisons has relative strengths ( Runhardt, 2021 , pp. 13–14). Methodological pluralism does not mean that anything goes. Instead, what is important is that there is alignment between the underlying ontological positions and the epistemology used to assess them in the PT research design ( Beach & Kaas, 2020 ; Hall, 2003 ).

Unfortunately, there are also examples of published studies in the social sciences in which scholars claim to be engaging in PT but instead merely pay lip service to the method by providing a citation or two in the Methods section, followed by a descriptive narrative of events in a case without linking to an explicit mechanistic theory. An atheoretical description of a sequence of events in a case is a form of narrative analysis that tells who did what and when, but it does not tell why they did it and, most important, why the events were linked in a causal sense. Although a descriptive narrative of what happened can be an important first step in any PT analysis, an atheoretical tracing of events does not shed light on the underlying causal linkage between a cause and an outcome, which is why a narrative description of a process is not the same thing as PT (e.g., Oppermann & Spencer, 2016 ). Fortunately, there are also numerous examples of scholars who implement a PT research design that lives up to the best practices of one of the variants of PT. This article discusses only these studies.

This article proceeds in three steps. First, it introduces the ontological distinctions about what is being traced in PT. Second, it discusses the different positions with regard to how processes and mechanisms can be traced empirically. Third, it discusses issues related to case selection and generalization of process theories in the different variants of PT.

What Is Being Traced? Causal Processes and Mechanisms

PT research probes the theoretical causal mechanisms (i.e., processes) linking causes and outcomes together. This section first discusses differing levels of aggregation of processual theories, followed by a review of the more fundamental differences in how scholars understand the nature of mechanistic causal claims.

Levels of Abstraction of Process Theories

Process tracers work with theories at varying levels of abstraction, ranging from minimalist, one-liner-type theories to detailed, case-specific theories that attempt to capture the particularities of how causal processes played out in a historical case. 3 In minimalist theories, the causal arrow between a cause and outcome is not theorized in any detail ( Elster, 1998 ; Goertz, 2017 ). Minimalist process theories typically take the form of one-liner-type theories of the pathway linking a cause and outcome together. An example is seen in Tannenwald’s (1999 ) article on the nuclear taboo. Tannenwald theorizes three possible pathways that can link norms and the non-use of nuclear weapons; these are depicted in Figure 1 .

Figure 1. Simple, abstract causal process theories and the nuclear taboo.

The process theorization in Tannenwald’s (1999 ) article does not go beyond very abstract one-liners, meaning that what is actually going on theoretically within the arrow(s) remains in a black box. Very abstract terms are used to theorize causal processes, such as “constraint on self-interested decision maker” ( Tannenwald, 1999 , p. 462), but readers are not told more about how these constraints actually work. Do decision makers have to discuss potential use of nuclear weapons among themselves, thereby providing opponents of usage with arguments they can deploy through normative speech acts to shame other actors? Or do the norms mean that decision makers never even discuss nuclear use because they believe it is “wrong”?

The goal of disaggregating causal processes into their constituent parts is to better understand how they work. This requires lowering the level of theoretical abstraction by providing a more-or-less detailed theorization of the actors involved and the activities that provide the causal linkages in the process ( Beach & Pedersen, 2019 ). In the social sciences, actors can be micro-level (i.e., individuals) or macro-level (i.e., collective social actors), with the requirement being that the latter have properties and orientations that enable them to do things that can impact other actors in a process. Activities are what social actors actually do; activities can take the form of speech acts, voting, paying bribes, etc.

It is important to note that a more detailed process theory will still be an analytical abstraction for all but the simplest of processes. Instead of detailing each and every individual and what they are doing in their interactions with each other during a period of time (days, weeks, or months), a detailed process theory attempts to capture the central actors and simplify their activities by only focusing on the most critical interactions. Using a metaphor, not all parts of a movie are equally “interesting.” In an action movie, screen time and special effects money will be concentrated on the final dramatic showdown between the good and bad persons. Similarly, not all parts of a process theory are equally “interesting,” and it can be warranted to focus on the most interesting parts ( Steel, 2008 ). These are the critical causal linkages in the process, involving interactions between actors.

Table 2 illustrates a disaggregated process theory used by Winward (2021 ) to explain the process whereby low intelligence capacities of security forces in a conflict area can lead to mass categorical violence against particular groups. Although the process theory is unpacked into several steps, it is still an analytical abstraction that includes only the most critical steps and linkages without theorizing everything that actors are doing in their interactions with each other. In the theorized process, the cause (low intelligence capacity in a conflict situation) spurs the security forces to approach local civilian elites for assistance in gathering information on threats (Part 1). The local civilian elites then exploit this dependence to settle scores in relation to pre-existing local conflicts with a particular group by providing false information targeted against individuals from the group, and also by encouraging other locals to take matters into their own hands by perpetrating violence against members of the targeted group (Part 2). The security forces use the (false) information provided to detain and interrogate individuals from the targeted group, resulting in an escalating cycle of torture and violence in which the many false confessionals from torture lead to even more detained and tortured individuals (Part 3). Furthermore, an increase in the number of detainees strains the capacities of the security forces, leading them to take extreme steps such as extrajudicial killings to clear out prisons (Part 3). Taken together, the process produces a marked increase in mass categorical violence by state security forces targeted against a particular group.

In contrast to minimalist one-liners, what is going on in-between is more explicitly theorized. This does not mean that disaggregated process theories are necessarily better than minimalist theories. Maintaining a high level of abstraction is a methodological choice that can be warranted in several research situations. First, early in research of a topic, there might be considerable uncertainty about which pathway links a cause and outcome together. Here, a PT study that takes the form of a plausibility probe exploring whether there is any evidence of a particular linkage can be a useful first step before more detailed process theories are traced empirically. Second, when research is more focused on investigating associations between causes and outcomes across many cases, a study that provides confirming evidence of a pathway linking them together makes it more plausible that the found association is actually causal. In the research situation faced by Tannenwald (1999 ), staying at a very high level of abstraction was warranted because there was a low prior confidence in the existence of any form of causal process linking norms and non-use (p. 438). In addition, minimalist studies can be useful after a series of more disaggregated studies to explore the scope of potential process generalizations. In contrast, when the goal of research is focused on understanding how the process worked in a historical case, the theorized process typically has numerous steps and can even include case-specific elements (e.g., particular named actors and specific activities that they perform).

Different Understandings of the Causal Nature of Mechanistic Claims

Causal mechanisms are one of the most widely used but also least understood types of causal claim in the social sciences (e.g. Beach, 2021 ; Brady, 2008 ; Gerring, 2010 ; Hedström & Ylikoski, 2010 ). The essence of making a mechanism-based claim is that the analytical focus shifts from causes and outcomes to the process in-between that links them. That is, mechanisms are not causes but, rather, are causal processes that are triggered by causes in particular contexts and that provide the causal linkage with outcomes. However, beyond this core point, there is disagreement among process tracers about the ontological nature of mechanisms as causal claims. There are (at least) three distinct understandings, all of which imply different epistemological strategies for PT research.

A counterfactual-based understanding of mechanisms defines causation as a situation in which a cause (or causal process/mechanism) is related to an outcome because its absence results in the absence of the outcome, all other things held equal ( Morgan & Winship, 2007 ). The term potential outcomes framework is often used to denote counterfactual claims because one can only assess whether a factor is causal by comparing what actually took place with what potentially could have taken place when it was absent, either in comparable cases or in logical hypotheticals ( Aviles & Reed, 2017 , p. 722; Mahoney & Barrenechea, 2019 ; Runhardt, 2015 ). Conceptually, counterfactual-based mechanism claims are often theorized in minimalist terms as X → M → Y (e.g., Mahoney, 2015 ).

In contrast, in the productive account, a mechanism is causal when there is a sequence of actors engaging in activities that transmit causal forces from the cause to the outcome ( Beach & Pedersen, 2019 ; Clarke et al., 2014 ; Machamer et al., 2000 ). At its core, a mechanistic explanation attempts to explain theoretically how things work within a case or set of cases within a particular context ( Cartwright, 2011 ) by unpacking the activities and the causal linkages they provide to explain why the cause is linked to the outcome. Mechanistic theories are typically viewed as systems in which the spatiotemporal organization of actors and the activities that they perform matter for how the systems works, as does the context within which the systems operate. Taken as a whole, a causal process is viewed in the productive account as more than the sum of its parts, and how it operates is very sensitive to context ( Cartwright, 2011 ; Falleti & Lynch, 2009 ; Sawyer, 2004 ). This means that one cannot just “remove” a part of a process and replace it with some other actor doing something else without changing the rest of the process. Nor can one claim that just because it has been found to work in one case, it should work everywhere.

In the productive account, causal claims are made about actual causal linkages as they operate within single cases. However, there is disagreement within the productive understanding on the question of whether a causal process can occur only once and still be termed causal (i.e., singular causation) ( Beach & Pedersen, 2019 ; Cartwright, 2021 ) or whether there has to be some form of regularity in its operation across cases before the term causal can be applied (e.g., Andersen, 2012 ). Both positions are logically defensible, but they point research in different directions. Thinking in more singular causal terms leads to research aimed at understanding the complexities of how processes worked in a single case, in which process theories are case-specific. In contrast, accepting some form of regularity as a prerequisite for making mechanistic causal claims implies research that has the ambition of making contingent generalizations about mechanisms across a set of cases.

A third understanding of mechanisms takes as its ontological point of departure the claim that the social world is fundamentally different from the natural world. This view is shared among social constructivist ( Guzzini, 2017 ; Norman, 2016 ) and critical realist scholars ( Danermark et al., 2019 ; Sayer, 2000 ). At the ontological level, how actors understand their actions and those of others, and how they understand the social context in which they are embedded, matters for how causal processes play out. This means that processual theorization needs to take seriously the intersubjective understandings and meaning-making of social actors in a particular social context. Pouliot (2014 ) develops an interpretive variant of PT that he terms “practice-tracing,” in which practices are defined as habitual patterns of actions performed by actors. Norman (2016 , 2021 ) puts forward an interpretivist variant in which causal processes are nested in what he terms “constitutive” explanations. The constitutive element is the structural conditions, latent dispositions, and causal capacities that come from a given social context, whereas the causal element is the moving parts. He defines causality as the relations between events, focusing on “how specific actions and events can alter the context in which they appear” ( Norman, 2016 , p. 87).

How Can Process Theories Be Traced Empirically?

There are (at least) three different positions taken on the question of epistemology in the literature that stem from differences in how scholars understand what is being traced: (a) controlled comparisons across cases at the level of process (or parts thereof) that use either real-world cases or logical hypotheticals, (b) evidencing based on the observable traces they leave within cases, and (c) a more interpretive variant of PT that studies processes using interpretive methods such as ethnographies and interpretive interviewing to examine discourses and practices.

Controlled Cross-Case Comparisons

In the controlled comparison variant, evidence takes the form of the difference that the presence/absence of a pathway (or parts thereof) has for the outcome in two or more cases. In effect, this means that the pathway is treated as a counterfactual claim evidenced empirically by measuring the difference that presence/absence makes across the cases ( Mahoney & Barrenechea, 2019 ; Runhardt, 2015 , 2021 , p. 9). Two or more “most similar” cases are compared that are similar in all respects except whether the pathway is present or absent. The cases selected for comparison can be either both real-world cases or involve a comparison with a nonexistent, logical hypothetical case (a “what if” case) ( Levy, 2015 ; Mahoney & Barrenechea, 2019 ).

If the most similar comparison finds that the outcome was present in both cases despite the process being present in one case and absent in the other, this would disconfirm the theorized process as the causal linkage, and vice versa. Note that when the PT variant of controlled comparisons operates with real-world cases, only a small number of cases are used in order to strategically select cases that are as most similar as possible. This marks a methodological difference with large- n mediation analysis, in which one or more observables of a process (i.e., causal mechanisms) are used as indicators that are tested in a controlled comparison across a large number of cases to assess the difference that presence/absence makes ( Imai et al., 2011 ).

The controlled comparison method is illustrated in Figure 2 , based on Runhardt’s (2015 ) description of what a most similar comparison design could look like. Using Bakke’s (2013 ) study of the impact of transnational insurgents in the Second Chechen War, Runhardt suggests that to be able to assess whether the process of watching videos of suicide bombings actually made a difference, the Second Chechen War case should have been compared with another case in which the only difference is that the process is not present. If the difference in presence/absence of the pathway is found to have made a difference, then the conclusion can be made that the pathway produced the difference. The strength of this conclusion depends on the degree to which other factors are similar between the cases.

Figure 2. A controlled comparison of pathway “watching videos.”

In principle, the same form of most similar controlled comparison could be used for assessing disaggregated process theories, although the comparison would have to be repeated for each step of the process theory. Irrespective of whether process theories are disaggregated or not, the core challenge is finding real-world cases in which everything else is so similar that it can be claimed that the only meaningful difference is the presence/absence of the process itself. Therefore, many case study scholars argue for the utility of logical hypotheticals where there are the fewest changes to the actual world as possible ( Mahoney & Barrenechea, 2019 , p. 316). Levy (2008 ) writes, ‘Counterfactual analysis ideally posits an alternative world that is identical to the real world in all theoretically relevant aspects but one, in order to explore the consequences of that difference” (p. 634).

Tracing the Within-Case Observable Manifestations of Processes

An alternative approach to evidencing processes is to trace them empirically through the observable, within-case manifestations of either pathways taken as a whole (minimalist one-liners) or the traces left by the activities performed by actors in each part of a disaggregated process. In the literature, terms such as “causal process observations” ( Brady & Collier, 2011 ), “diagnostic evidence” ( Bennett & Checkel, 2014 , p. 7), or “mechanistic evidence” are also used to denote the observable manifestations left by processes playing out within cases ( Beach & Pedersen, 2019 ; Clarke et al., 2014 ). Observable manifestations can be thought of as the empirical fingerprints left by the operation of a causal process in a case, of if disaggregated, the fingerprints left by the activities of actors that are linkages in the process. Common to all of these terms is that the evidence used does not involve a controlled comparison across two or more cases but instead is any type of empirical material that has probative value in relation to determining whether the process actually took place within a case. These traces can take many different forms. For example, they can involve patterns within cases, such as social networks that emerge between actors during a process. They can also involve temporal sequences—for example, whether a search for alternative solutions in past cases happened before a solution was suggested by an actor. Alternatively, they can be the content of empirical material, such as the speech acts of a particular actor during a meeting.

Following Van Evera (1997 ), many process tracers have tried to systematize how to evaluate the probative value of this type of evidence, often drawing explicitly on Bayesian logic. The following discussion does not introduce Bayesian logic in any detail (for introductions to this, see Beach & Pedersen, 2019 ; Bennet & Checkel, 2014 ; Fairfield & Charman, 2017 ), noting only that there is a discussion in the literature on whether PT should use more formalized, explicit Bayesian analysis (e.g., Bennett et al., 2022 ; Fairfield & Charman, 2017 ) or whether more informal applications of Bayesian reasoning are more useful for PT (e.g., Beach & Pedersen, 2019 ; Zaks, 2021a , 2021b ). Instead, the following discussion presents the underlying Bayesian intuition that not all evidence has the same probative value, and the amount of “updating” is determined by whether one had to find it and whether there are plausible alternative explanations for finding it.

The probative value of evidence is determined by the answers to several questions about what evidence in theory can tell researchers and whether researchers can trust sources (Figure 3 ). When operationalizing a process theory, the researchers is asking what empirically observable fingerprints they might expect to observe in a case if a given actor and activity took place (or overall process if working with a minimalist process theory). For instance, one part of Winward’s (2021 ) process theory was that security forces would approach local civilian elites for information (see Table 2 ). He operationalizes a series of potential observables that could act as evidence, such as “We would also expect to find former elites, or those close to them, who would divulge such soliciting of information when interviewed” ( Winward, 2021 , p. 562).

Figure 3. Moving from process theory to actual empirical sources.

The probative value of a given expected observable is a function of two factors: whether it had to be found (certainty in Bayesian terms) and, if found, whether there are alternative explanations for finding it (uniqueness in Bayesian terms). Whether evidence has to be found (i.e., certainty) relates to the disconfirmatory power of evidence, whereas whether alternative explanations are plausible or not relates to the confirmatory power of evidence.

Some activities can be expected to leave empirical fingerprints in a case, whereas others might leave few traces. Returning to the Tannenwald (1999 ) example discussed previously, if the pathway related to personal moral convictions works through actors not even considering what they consider “wrong” actions, this pathway would leave few empirical traces—indeed, the absence of evidence of actors discussing the nuclear option could potentially act as evidence of deep normative convictions keeping options off the table for discussion.

The confirmatory power of hypothesized observables relates to whether, if found, there are alternative explanations for finding the evidence. If the hypothesized observable is found and there are few alternative explanations, this would enable a strong confirming inference to be made, and vice versa. For instance, if one had theorized that a part of a diplomatic negotiation process is “actor A persuades actor B in meeting using arguments,” a hypothesized observable could be that actor B changes how they talk about a policy after the meeting. However, the change might be purely strategic, in which they changed their position due to other reasons than normative arguments (e.g., material factors such as the threat of military action). Another alternative explanation of finding the observable could be that actor B does not change position but instead is only paying lip service to the views of actor A in public. When there are plausible alternative explanations, finding an observable only provides weak or no confirmation.

Hypothesized observables are still not evidence upon which inferences about the presence/absence of the part of the process can be made. In the within-case tracing PT variant, inferences are only able to be made when the actual sources of the observations (or lack thereof) of the propositions are also evaluated. At the empirical level, researchers have to ask whether a particular observation matches what they expected to find and whether they had access (if not found) and, if found, whether they can trust the source. Researchers might only have very untrustworthy sources for observing the hypothesized observable, even though it would have been very confirming if actually found. Often process tracers use interviews with participants who have a stake in the events being studied, and therefore have incentives to provide biased accounts. In this instance, actually found evidence in an interview would not enable strong confirmation because the source could not be trusted. The questions that should be asked here are common source critical questions. It is only through corroboration across multiple sources and types of evidence that evidence can be trusted. As an example of best practices, Winward (2021 ) includes two appendices that discuss individual sources of evidence and the degree to which they can be trusted.

Interpretive Variants of Process Tracing

Several attempts have been made to develop more interpretivist versions of PT ( Guzzini, 2017 ; Norman, 2016 , 2021 ; Pouliot, 2014 ). What they share is the contention that to understand how social causal processes work, interpretive hermeneutic methods must also be used that try to tap into intersubjective understandings of social actors and how they make sense of their interactions with other social actors—what is termed “experience-near” evidence in the literature ( Danermark et al., 2019 , pp. 30, 33; Geertz, 1974 ).

As examples, Pouliot (2014 ) argues for adding an extra layer of “meaningfulness” to PT analyses by studying the stock of unspoken assumptions and tacit know-how of practitioners to understand their intentions and beliefs with regard to sets of practices that they perform, through the use of interpretivist discourse, interviewing, or ethnographic observation techniques.

Norman (2016 , 2021 ) suggests that interpretive PT uses what he terms “contrastive” explanations that build on counterfactual controlled comparisons of discourses. These can include before/after comparisons or “normal” events compared with an “abnormal” event that changes the trajectory of developments. He puts forward a three-step analytical procedure for interpretive PT that ( Norman, 2021 , p. 19)

map change in the broader institutional setting—for example, by asking actors of “normal” actions, and then what constituted the “abnormal” and thus conflict between actors at a later time;

explore how change reconstituted actor self-understandings; and

assess how the changed self-understandings gave rise to particular actions.

Case Selection and Generalizing About Mechanisms

This section reviews the state-of-the-art regarding case selection, followed by a discussion of whether and how mechanistic generalizations can be made.

Selecting Appropriate Cases for Process Tracing

The core case selection principles of PT are generally agreed upon. First, PT is a case-based method, which requires that concepts (causes, context, and outcomes) are theorized in set-theoretical terms ( Beach & Pedersen, 2016 ; Goertz & Mahoney, 2012 ). This means that concepts are defined by the theoretical attributes that determine whether a given case is in or out of the “set” of the concept—that is, it is a member of the concept or not. For example, if one is interested in tracing a process linking development and democratization, clear definitions of both concepts would be required that enable the scholar to determine whether a given case is a member or not of the concepts.

All PT variants therefore share an interest in positive, “typical” cases in which cause, outcome, and relevant contextual conditions are all present (Table 3 ) ( Beach & Pedersen, 2018 ; Schneider & Rohlfing, 2013 ). In negative cases, where the cause and outcome are not present, there is no ‘process’ to trace. When controlled-comparison variants of PT are used, a typical case is compared with a most similar case in which the mechanism (or part) was not present. If the mechanism makes a difference, the most similar case would be a deviant case consistency in which the outcome as a result does not occur. Cases in which both a cause and an outcome are not present are termed “irrelevant” cases (Table 3 , quadrant III), in which there is obviously no process to trace because the cause and outcome are not present ( Mahoney & Goertz, 2004 ).

Two types of deviant cases are relevant for PT, but only after one has a good understanding of how processes work in typical cases. These are deviant coverage and deviant consistency cases ( Schneider & Rohlfing, 2013 ). A deviant coverage case is one in which a cause or contextual condition is not present, but the outcome has occurred. In this type of case, one is interested in determining what other cause(s) might have produced the outcome. Here, all three variants of PT are relevant for probing what other cause(s) might be present. Given the focus on detecting new causes, processes are typically kept at a very abstract level.

Alternatively, when a cause is present but one or more contextual conditions that allow the operation of a given process are not present, a theory-building PT case study can shed light on alternative causal pathways between a cause and an outcome.

Deviant consistency case are instances in which a process should have linked to the outcome but the process breaks down. Here, tracing a relatively disaggregated process until it breaks down can shed light on omitted contextual conditions that have to be present for a cause to work and/or other causes that also have to be present for the outcome to be produced ( Beach & Pedersen, 2018 ).

Can Mechanistic Generalizations Be Made, and If So, How?

Irrespective of which variant is used, PT case studies end up saying a lot about a little—or in many applications of PT, only one case. In most interpretivist PT applications, there is little or no ambition to generalize process theories because they are understood to be contextually specific to a specific social setting. In contrast, many other PT scholars have ambitions of build more “general” process theories, understood as causal processes that can be expected to work in a similar manner in other cases.

In the literature, there are several different approaches to how mechanistic generalizations can be made. First, there are approaches that claim that when a typical case is “representative” of the population to which generalizations are intended to be made, generalizations can be made after studying a single case. When there is

causal homogeneity among cases that are of the same type and belong to the same term and causal heterogeneity across different types . . . findings from the study of one, say, typical case, travel to all other typical cases of the same term, but not beyond this term. ( Schneider & Rohlfing, 2016 , p. 556)

A similar approach is to claim that the selected typical case was “least likely” for the causal mechanism, and therefore when it works in the least-likely case, it should work everywhere ( George & Bennett, 2005 , pp. 109–125; Levy, 2008 , p. 12). This is even a stronger form of one-to-many generalization because of the amount of knowledge about the underlying population in terms of how the cause and context interact with other causes and contextual conditions in other cases that is required if we are to claim a given case is ‘least-likely’ in relation to other more ‘typical’ cases in the population.

There are other PT scholars who contend that one-to-many strategies risk making generalizations based on hope instead of actual evidence from other cases ( Khosrowi, 2019 ). The literature on causal processes frequently notes that the ways they unfold in a specific case are sensitive to the context that surrounds them ( Falleti & Lynch, 2009 ; Lindquist & Wellstead, 2019 , pp. 31–33). Contextual conditions are sometimes termed “scope” conditions in the literature, but the terms mean the same thing. Contextual conditions can be defined as all “relevant aspects of a setting (analytical, temporal, spatial, or institutional)” in which the analysis is embedded in and which might have an impact on the constitutive parts of a process ( Falleti & Lynch, 2009 , p. 1152). Even when the same causes and outcome are present, different contextual conditions can create differences in the processes linking them together ( Falleti & Lynch, 2009 ; Steel, 2008 ). This means that two cases that look causally homogeneous based on sharing similar causal conditions and an outcome might be heterogeneous at the process level because of contextual differences. For different processes, the whole process may be completely different, or only one or several parts may diverge between two cases.

The solution to the problem of causal complexity and mechanisms is not just to lift the level of theoretical abstraction of the process theory. Of course, logically the more abstract a theorized mechanism (lower intension), the more cases can be found in which it is present (higher extension), and vice versa. This means there is an inherent trade-off between disaggregating causal process theories and how far they can potentially travel, illustrated in Figure 4 . Very abstract one-liner process theories can in principle be present in many different cases because they tell very little about what people are actually doing in the case. However, even simple process theories operate only within particular contexts. For instance, Haggard and Kaufman (2016 ) theorize that mass mobilization is a mechanism linking economic inequality and a democratic transition. However, as they detail in their book, mass mobilization should not be expected to be operative in every case in which economic inequality and democratic transitions took place because there are other pathways in other contexts.

Figure 4. Levels of theoretical abstraction and the external validity of mechanistic claims.

A stronger strategy for generalizing mechanistic claims is to adopt a multiple cases approach ( Beach et al., 2019 , pp. 133–144). Here, an initial PT case study is undertaken to build a theorized process theory at a medium level of abstraction (relatively simple, midrange, or quite detailed). Other PT case studies are then undertaken on other cases that are strategically selected within a population of potential cases in which the process might work based on their scores on conditions that might impact how processes work. These follow-up, generalizing PT case studies can utilize a strategy in which the subsequent PT studies operate with a simpler, more abstract process theory in order to make the analysis more feasible (requires fewer resources). Depending on how many cases are traced, and how contextually sensitive the given process is, process-level generalizations can be made which claim that the process works in at least a roughly analogous manner across a set of cases.

Challenges When Using Process Tracing

Engaging in “good” process tracing (PT) is difficult, but the same can be said of any rigorous social science method. However, potential users of PT face a range of challenges that are relatively unique to PT. First, in contrast to methods such as experimental designs, in PT there is a large variety of different understandings of the method itself and what best practices should be followed. This article has argued that as long as there is alignment between the underlying ontological assumptions and epistemological approach, these differences in PT methods should be viewed as a strength because it allows scholars to choose the variant that best fits the research question and context.

Second, irrespective of which variant is used, PT case studies say a lot about a little—or in many applications of PT, only one case. This can make it difficult to get past peer reviewers, who often want to hear about the “general.”

Finally, scholars engaging in PT face challenges when trying to communicate their findings with scholars from other traditions ( Beach & Kaas, 2020 ). For instance, when working with a disaggregated tracing variant of PT, claims deal with causal linkages between causes and outcomes within a single case ( Beach & Kaas, 2020 ; Clarke et al., 2014 ). This makes it difficult to communicate with scholars who have been trained to assess causal effects (especially those within the so-called potential outcomes framework) because these individuals might have difficulty accepting that fundamentally different types of claims are being made. For instance, a common review comment is that there is no “variation” in the design, even though “variation” is not relevant to evidencing mechanisms when they are understood as productive linkages. Similar challenges face scholars doing interpretivist PT, with reviewers who think in neo-positivist terms finding it very difficult to understand how an interpretivist PT case study taps into the lifeworld of social actors. Furthermore, the process theories used in interpretivist PT studies often are more fluid and sensitive to specific social contexts, making it difficult to communicate with PT scholars who have the ambition of producing more generalizable process theories that can be used across many cases.

There is not an easy answer to these questions, beyond reiterating a plea for methodological pluralism in which scholars appreciate different approaches to learning about the social world that they all care so much about.

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1. Note the terms causal process and causal mechanism are used as synonyms throughout this article.

2. A milder version of monism is to claim that although different understandings of causation are valid, there is one variant that has “most value for the social sciences” ( Rohlfing & Zuber, 2021 , p. 1626).

3. The level of theoretical abstraction should not be confused with actual empirical evidence. When one lowers the level of theoretical abstraction, one is unpacking how the process works in terms of theorized interactions between social actors. The actual evidence of the operation of a mechanism, or parts thereof, will always be case-specific.

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The Advantages and Limitations of Single Case Study Analysis

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).

Limitations

Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.

References 

Bennett, A. and Checkel, J. T. (2012) ‘Process Tracing: From Philosophical Roots to Best Practice’, Simons Papers in Security and Development, No. 21/2012, School for International Studies, Simon Fraser University: Vancouver.

Bennett, A. and Elman, C. (2006a) ‘Qualitative Research: Recent Developments in Case Study Methods’, Annual Review of Political Science , 9, 455-476.

Bennett, A. and Elman, C. (2006b) ‘Complex Causal Relations and Case Study Methods: The Example of Path Dependence’, Political Analysis , 14, 3, 250-267.

Bennett, A. and Elman, C. (2007) ‘Case Study Methods in the International Relations Subfield’, Comparative Political Studies , 40, 2, 170-195.

Bennett, A. and Elman, C. (2010) Case Study Methods. In C. Reus-Smit and D. Snidal (eds) The Oxford Handbook of International Relations . Oxford University Press: Oxford. Ch. 29.

Berg, B. and Lune, H. (2012) Qualitative Research Methods for the Social Sciences . Pearson: London.

Bryman, A. (2012) Social Research Methods . Oxford University Press: Oxford.

David, M. and Sutton, C. D. (2011) Social Research: An Introduction . SAGE Publications Ltd: London.

Diamond, J. (1992) ‘Economic development and democracy reconsidered’, American Behavioral Scientist , 35, 4/5, 450-499.

Eckstein, H. (1975) Case Study and Theory in Political Science. In R. Gomm, M. Hammersley, and P. Foster (eds) Case Study Method . SAGE Publications Ltd: London.

Flyvbjerg, B. (2006) ‘Five Misunderstandings About Case-Study Research’, Qualitative Inquiry , 12, 2, 219-245.

Geertz, C. (1973) The Interpretation of Cultures: Selected Essays by Clifford Geertz . Basic Books Inc: New York.

Gerring, J. (2004) ‘What is a Case Study and What Is It Good for?’, American Political Science Review , 98, 2, 341-354.

Gerring, J. (2006a) Case Study Research: Principles and Practices . Cambridge University Press: Cambridge.

Gerring, J. (2006b) ‘Single-Outcome Studies: A Methodological Primer’, International Sociology , 21, 5, 707-734.

Gerring, J. (2007) ‘Is There a (Viable) Crucial-Case Method?’, Comparative Political Studies , 40, 3, 231-253.

King, G., Keohane, R. O. and Verba, S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton University Press: Chichester.

Layne, C. (1994) ‘Kant or Cant: The Myth of the Democratic Peace’, International Security , 19, 2, 5-49.

Levy, J. S. (2008) ‘Case Studies: Types, Designs, and Logics of Inference’, Conflict Management and Peace Science , 25, 1-18.

Lipset, S. M. (1959) ‘Some Social Requisites of Democracy: Economic Development and Political Legitimacy’, The American Political Science Review , 53, 1, 69-105.

Lyotard, J-F. (1984) The Postmodern Condition: A Report on Knowledge . University of Minnesota Press: Minneapolis.

MacMillan, A. (2008) ‘Deviant Democratization in India’, Democratization , 15, 4, 733-749.

Maoz, Z. (2002) Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey and M. Brecher (eds) Evaluating Methodology in International Studies . University of Michigan Press: Ann Arbor.

May, T. (2011) Social Research: Issues, Methods and Process . Open University Press: Maidenhead.

Owen, J. M. (1994) ‘How Liberalism Produces Democratic Peace’, International Security , 19, 2, 87-125.

Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

Van Evera, S. (1997) Guide to Methods for Students of Political Science . Cornell University Press: Ithaca.

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

Further Reading on E-International Relations

• Identity in International Conflicts: A Case Study of the Cuban Missile Crisis
• Imperialism’s Legacy in the Study of Contemporary Politics: The Case of Hegemonic Stability Theory
• Recreating a Nation’s Identity Through Symbolism: A Chinese Case Study
• Ontological Insecurity: A Case Study on Israeli-Palestinian Conflict in Jerusalem
• Terrorists or Freedom Fighters: A Case Study of ETA
• A Critical Assessment of Eco-Marxism: A Ghanaian Case Study

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• > Journals
• > PS: Political Science & Politics
• > Volume 47 Issue 2
• > Paired Comparison and Theory Development: Considerations...

Article contents

Case analysis and theory development, the logics of paired comparison, cautions and guidelines, case selection in low information settings, when the dv is unknown, paired comparison and theory development: considerations for case selection.

Published online by Cambridge University Press:  14 April 2014

Despite the widespread use of paired comparisons, we lack clear guidance about how to use this research strategy in practice, particularly in case selection. The literature tends to assume that cases are systematically selected from a known population, a major assumption for many topics of interest to political scientists. This article speaks to this gap. It describes three distinct logics of paired comparison relevant to theory development, presents a simple way of considering and comparing them, and explores how this approach can inform more intentional research design, with particular attention to low information settings where substantial research is needed to ascertain the values of independent or dependent variables. The discussion underscores inter alia the need to be aware and explicit about the implications of case selection for the ability to test and build theory, and the need to reconsider the well-cited “rule” of not selecting on the dependent variable.

Paired comparisons have long been a staple of research in political science. Comparative politics, in particular, is commonly defined by the comparative method, one approach to paired comparison (see Lijphart Reference Lijphart 1971 , 682; 1975, 163; Slater and Ziblatt Reference Slater and Ziblatt 2013 ). Despite their widespread use, however, we lack a “theory of practice” (Tarrow Reference Tarrow 2010 ). The methodological literature has not paid much attention to paired comparisons, focusing instead on single case studies and large-n analysis. King, Keohane, and Verba’s classic Designing Social Inquiry, for instance, offers no specific strategies distinct from those of other forms of multicase qualitative analysis (King, Keohane, and Verba 1994, 43–46). This lacuna in the literature poses particular challenges for scholars using paired comparative methods, especially in their initial selection of cases. To the extent that the literature has addressed case selection for paired comparisons, it has assumed that researchers know ex-ante about the universe of cases and can select cases systematically from a known population (see George Reference George and Gorden Lauren. 1979 ; Plümper, Troeger, and Neumayer n.d.; Seawright and Gerring Reference Seawright and Gerring 2008 , 296). This is a big assumption for many topics of interest to political scientists, including ethnic conflict, new political parties, protests, and social movements, all of which are “almost impossible to observe and to clearly delimit” (Hug Reference Hug 2003 , 257). More broadly, a common complaint among political scientists is that the case selection guidelines that the literature does suggest are, in practice, impossibly difficult to follow. Indeed, it is quite common for scholars to admit that their cases were selected for largely practical reasons and paired comparisons were constructed post hoc, with finesse.

Building on the literature, and especially on Tarrow ( Reference Tarrow 2010 )’s effort to move us toward a theory of practice, this article speaks to this gap. It argues that there are three distinct logics of paired comparison for theory development, presents a straightforward way of considering and comparing them using simple tables, and explores how this approach can be used to inform more intentional research design using paired comparisons. Low information settings, in which substantial research is needed to ascertain the values of independent variables (IV) and dependent variables (DV), present special challenges for case selection. The article focuses on how the approach outlined here may be useful in developing research designs in such settings. In reviewing several different scenarios, the discussion suggests that there are no easy answers. It underscores inter alia the need for scholars to be aware and explicit about the implications of their case selection for their ability to test and build theory, and the need to add major qualifications to the well-cited “rule” of not selecting on the DV: a researcher interested in testing hypotheses about necessary conditions often should select on the DV, if information allows, and use the logic of the method of agreement to test hypotheses.

First, the article explores the use of case studies in theory development. Then, it turns to the logics of paired comparison and their implications for theory building. It concludes by exploring how this framework can inform more intentional strategies of case selection in situations of incomplete information about IVs and DVs.

Paired comparisons as reviewed here involve the study of two cases selected for theory development, that is, of generating and/or testing theoretical propositions that offer generalizable causal explanations. A “case” is understood broadly in Lijphart (1975, 160)’s terms as “an entity on which only one basic observation is made and in which the independent and dependent variables do not change during the period of observation which may cover a long term, even several years.” Footnote 1 This wording is somewhat confusing when the DV itself implies change, as in much comparative research (e.g., the emergence of ethnic political parties, a rise in social protest, the reconstruction of postconflict states). In such cases, the dependent variable can be understood as “change” or “lack of change” for a period of study, or in a finer-grained way with reference to characteristics of change, such as its degree or speed.

Paired comparisons differ from single case studies primarily in their ability to control for variables (see Lijphart Reference Lijphart 1971 , 684; Tarrow Reference Tarrow 2010 , 244). Like single-case studies, they also allow for causal-process analysis, which is generally not possible in large-n studies (Brady and Collier Reference Brady and Collier 2004 ). They can further provide a useful stepping stone from single-case to multi-case comparison (Becker Reference Becker 1968 ). Comparative analysis of more than two cases theoretically can allow for even more analytical control, but paired comparisons may have benefits over comparative analysis of more than two cases because they allow, in practice, for greater descriptive depth and more control of relevant variables (Slater and Ziblatt Reference Slater and Ziblatt 2013 ).

Because single-case studies and paired comparisons are so closely related, much of the literature on case studies and theory development is relevant to paired comparisons (Eckstein Reference Eckstein, Greenstein and Polsby 1975 ; George Reference George and Gorden Lauren. 1979 ; Gerring Reference Gerring 2004 ; Lijphart Reference Lijphart 1971 ). Eckstein ( Reference Eckstein, Greenstein and Polsby 1975 ) and Lijphart ( Reference Lijphart 1971 )’s classic frameworks highlight four types of case studies relevant to theory-building and, sometimes, theory-testing: (1) “heuristic” or “hypothesis generating” case studies; (2) “plausibility probes” to assess the potential plausibility of hypotheses; (3) “theory infirming” or “theory confirming” cases, which, sometimes may be “crucial” tests of hypotheses; and (4) “deviant” cases that are not well-explained by existing theories and strategically selected for study to help refine theories. In Lijphart (1971, 692)’s view, theories should not necessarily be accepted or rejected on the basis of a single theory-confirming or theory-infirming case, but rather the cases should be seen as strengthening or weakening the hypothesis in question: if “the proposition is solidly based on a large number of cases,” another theory-confirming case obviously “does not strengthen it a great deal.” (Eckstein and Lijphart argue that “crucial” theory-infirming or theory-confirming cases can provide definitive tests for hypotheses, a point on which there is some debate that is beyond the scope of this article (see Gerring Reference Gerring 2007 ).) Heuristic or hypothesis-generating cases are most relevant to areas of study where theory is not well-developed, while theory-infirming, theory-confirming, and deviant cases can only be conducted when developed theory offers precise, testable hypotheses.

Two other types of case studies are also common in the literature, but they are not designed for theory building, although they can contribute indirectly to it (Lijphart Reference Lijphart 1971 , 691-692). These are Eckstein’s “configurative-ideographic” and “disciplined-configurative” cases, both of which are primarily descriptive. The latter explicitly builds on theoretical propositions in interpreting a case, but it does not use the case to examine the propositions themselves.

A typology of paired comparisons is analogous (see George Reference George and Gorden Lauren. 1979 ). If they conform to one of the first four types outlined previously, comparisons of two cases can arguably offer more leverage on theory development than single cases, but not all comparisons of two cases are designed to contribute directly to theory building. Many studies that explicitly use “paired” comparisons are of the configurative-ideographic and disciplined-configurative type. The rest of this article focuses on paired comparisons for theory development only.

In his System of Logic , Mill described four methods of “experimental inquiry,” the first two, the methods of agreement and difference, constitute the core logics of paired comparisons for the development of causal theories (Mill 1882). To these two, we add Przeworski and Teune ( Reference Przeworski and Teune 1970 )’s “most different systems” analysis, an important variation on the method of agreement.

A large literature already exists on comparative methods, which includes detailed examples (e.g., Collier Reference Collier and Finifter 1993 ; Geddes Reference Geddes 1990 ; George Reference George and Gorden Lauren. 1979 ; Przeworski and Teune Reference Przeworski and Teune 1970 ; Skocpol Reference Skocpol 1979 ; Slater and Ziblatt Reference Slater and Ziblatt 2013 ; Tarrow Reference Tarrow 2010 ; Van Evera 1997). Without rehashing those discussions here, this article builds on them to focus on describing the three core logics of paired comparison for theory development, using a simple framework, with figures. This framework facilitates more intentional consideration of research design options, as explored in the final section of this article. Note that in defending the method of agreement as a legitimate approach to paired comparison for theory development, this discussion shares the interpretation of, for instance, Tarrow ( Reference Tarrow 2010 ), but differs with Lijphart ( Reference Lijphart 1975 ) and King, Keohane, and Verba (1994). In highlighting the similarities between most different systems analysis and the method of agreement, it shares the interpretation of, for instance, Gerring ( Reference Gerring 2007 ), but differs with Przeworski and Teune ( Reference Przeworski and Teune 1970 ) and Meckstroth ( Reference Meckstroth 1975 ).

For simplicity, the term “variable” is used to describe the factors that may be considered in the design of paired comparisons (e.g., w, x, y, and z in the figures). However, these factors can also be understood as combinations of variables, or as discrete processes or mechanisms; indeed, some of the best paired comparisons use the logics described here to conduct causal-process analysis (see Brady and Collier Reference Brady and Collier 2004 ).

Comparable Cases and the Method of Difference

The most accepted method of paired comparison is the comparable case strategy (see Abadie, Diamond, and Hainmueller 2012; Lijphart Reference Lijphart 1975 ). The logic of comparable cases is summarized by Mill (1882, 483) in terms of the method of difference: “If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance in common save one, that one occurring only in the former; the circumstance in which alone the two instances differ, is the effect, or the cause, or an indispensable part of the cause, of the phenomenon.” Figure 1 illustrates with a simple example in which four IVs ( w, x, y, and z ) and the DV are all binary values. The IVs are either present (“+”) or absent (“-”). The DV is either positive (“◼”) or negative (“◻”). As shown, all IVs, except the circled causal variable, w , share the same value, and w varies with the DV.

Figure 1 Comparable Cases (Mill’s Method of Difference)

More generally, comparable case analysis may match more subtle variations in IVs and DVs, which are not binary values. Strictly speaking, such analysis follows Mill’s “method of concomitant variations,” but the core logic is that of the method of difference for as Mill (1882, 496) notes, “the concomitance itself must be proved by the Method of Difference” (see Lijphart Reference Lijphart 1971 , 688).

Przeworski and Teune ( Reference Przeworski and Teune 1970 ) discuss this method in terms of the “most similar systems” approach, highlighting its use at the “system” level, by which they generally mean the country level. In their view, because there are so many differences across countries, even when carefully selected as comparable cases, outcomes are over-determined and “experimental variables cannot be singled out” using this approach (Przeworski and Teune Reference Przeworski and Teune 1970 , 34). (This criticism is related to a more general criticism of paired comparisons, insufficient degrees of freedom, which is discussed more fully in the text that follows.) Although the method of difference is often used at the national level consistent with a most similar systems approach, it need not be. The comparable case strategy can be used, for instance, with reference to diachronic comparisons within a single country, comparison of subnational units in the same country, and comparison of subnational units in several countries.

Although the method of difference is often used at the national level consistent with a most similar systems approach, it need not be. The comparable case strategy can be used, for instance, with reference to diachronic comparisons within a single country, comparison of subnational units in the same country, and comparison of subnational units in several countries.

Method of Agreement

The method of agreement is the converse of the method of difference. Mill (1882, 482) summarizes its logic as follows: “If two or more instances of the phenomenon under investigation have only one circumstance in common, the circumstance in which alone all the instances agree, is the cause (or effect) of the given phenomenon.” Figure 2 offers an example using the same IVs and DVs as figure 1 . As shown, all IVs, except w and the DV have different values, suggesting the presence of w as the cause of the DV.

Figure 2 Mill’s Method of Agreement

Many analysts reject the use of the method of agreement in theory development because it involves selecting on “extreme” values of the DV, thus introducing selection bias (see Collier 1995, 464). King, Keohane, and Verba (1994, 129) argue that “When observations are selected on the basis of a particular value of the dependent variable, nothing whatsoever can be learned about the causes of the dependent variable without taking into account other instances when the dependent variable takes on other values.” Geddes (1990, 149) elaborates:

This is not to say that studies of cases selected on the dependent variable have no place in comparative politics. They are ideal for digging into the details of how phenomena come about and for developing insights. They identify plausible causal variables. They bring to light anomalies that current theories cannot accommodate. In so doing, they contribute to building and revising theories. By themselves, however, they cannot test the theories they propose and, hence, cannot contribute to the accumulation of theoretical knowledge … [italics mine]

Many other methodologists, however, disagree—the position taken here (see, e.g., Brady and Collier Reference Brady and Collier 2004 ; Collier Reference Collier and Finifter 1993 ; Van Evera 1997). Indeed, several of the ways in which comparative analysis based on the method of agreement can contribute to theoretical knowledge are precisely noted in Geddes ( Reference Geddes 1990 ), which arguably adopts too narrow a view of theory development. In light of Eckstein (1975)’s and Lijphart (1971)’s typologies of cases, at least three contributions can be identified:

First, comparisons selected on the DV and paired on the basis of the method of agreement can identify causal variables and build theories, in the manner of hypothesis-building cases or plausibility probes. Second, they can “bring to light anomalies that current theories cannot accommodate” and use them to refine theories, in the manner of deviant cases (Geddes Reference Geddes 1990 , 149).

Third, they can be used as theory-infirming cases with reference to theories about necessary conditions (Braumoeller and Goertz Reference Braumoeller and Goertz 2000 ; Collier Reference Collier and Finifter 1993 ; Dion Reference Dion 1998 ; Jervis Reference Jervis 1989 ). This latter view is consistent with Mill’s interpretation that the method could not demonstrate causation, but could help to eliminate causal factors (see Collier Reference Collier and Finifter 1993 , 464). In thinking about testing theories of necessary conditions, Dion (1998, 141) offers the useful example of how to test the hypothesis that state crisis is a necessary condition for social revolution, which would involve obtaining a list of all social revolutions, selecting a random sample, and identifying cases preceded by state crisis. “One would not gather a list of state crises and then see whether they resulted in social revolutions or obtain a biased selection of the social revolutions” (Dion Reference Dion 1998 , 133). As the example suggests, the method of agreement is most powerful for theory testing when multiple cases are examined; two cases may not provide enough information, but they could. For instance, the method could infirm the hypothesis in Dion’s example if state crisis were missing from one or both of the cases of social revolution selected for analysis. (In contrast, evidence of state crisis in both cases—or even many cases, but not the entire population—would not provide definitive confirmation of the hypothesis, but it would lend further support.)

Most Different Systems

Finally, Przeworski and Teune (1970)’s most different systems approach offers a fourth way in which the method of agreement can contribute to theory development. It hinges on exploiting different levels of analysis, which is not part of the standard method of agreement although the underlying logic is similar (Gerring Reference Gerring 2007 , 139). Figure 2 illustrates if we think of x, y , and z as system variables and w , the causal variable, as operating at a subsystem variable (“the level of individual actors [or]… the level of groups, local communities, social classes, or occupations”) (Przeworski and Teune Reference Przeworski and Teune 1970 , 34). This approach can eliminate irrelevant systemic factors in explanations of behavior at the subsystem level and thus infirm theories that operate at the systemic level (Meckstroth Reference Meckstroth 1975 , 137).

Effectively, most different systems analysis combines the method of agreement with multicase analysis: cases are paired at the systemic level, and multiple cases are examined at the subsystem (i.e., subnational) level. Strictly speaking, in analyzing the behavior of multiple individuals or groups at the subnational level, the approach uses the statistical method and does not suffer from the innate “few cases, small n” problem characteristic of case studies and paired comparisons (Lijphart Reference Lijphart 1975 , 164).

Indeterminate Designs

Each of the three approaches to paired comparison described previously can contribute to the identification of causal variables, theory testing, and theory building (see Tarrow Reference Tarrow 2010 , 235). Some comparisons of cases, however, are generally inferior for this purpose. These involve research designs in which the IVs and DVs of both cases are either both the same or both vary as shown in the examples in figure 3 . On the one hand, cells A and D, which are the same as figures 1 and 2 , offer examples of the methods of difference and agreement. As we have seen, assuming that we have identified all of the relevant variables—a big assumption, as discussed later—both of these methods can identify w as the causal variable or as a necessary condition in a causal process. The examples in cells B and C, on the other hand, are indeterminate in the sense that even if we have identified all of the relevant variables, they cannot pinpoint which of the IVs causes, or is a necessary condition for, the outcome beyond something in the set of [ x , y , or z ]. Footnote 2

Figure 3 Determinate and Indeterminate Paired Comparisons for Identifying Causal Variables

Note, however, that cases of the sort depicted in cells B and C may infirm some specific hypotheses about necessary conditions, which could be useful in testing of some well-developed theories. For instance, in the examples here, the cases in cell B show to be false the hypothesis that the presence of w is always necessary for the outcome ◻ (it results even when w is absent). Similarly, cells B and C both show to be false the hypothesis that the presence of w precludes the outcome ◻ (it results both when it is present and when it is absent).

A Brief Note on Combining Comparisons

Combining paired comparisons with each other, or with multicase comparative analysis, can draw on these same logics and provide more traction on causal inference for theory development. For instance, Mill’s “joint method of agreement and difference” operates as follows: “If two or more instances in which the phenomenon occurs have only one circumstance in common, while two or more instances in which it does not occur have nothing in common save the absence of that circumstance, the circumstance in which alone the two sets of instances differ, is the effect, or the cause, of an indispensable part of the cause, of the phenomenon” (Mill 1882, 489). Or, consider Collier and Collier (1991)’s research strategy, which begins with analysis of eight Latin American countries “roughly matched on a number of broad dimensions” and then turns to analyze “pairs of countries that are nonetheless markedly different” (Collier Reference Collier and Finifter 1993 , 112). Researchers using paired comparisons should consider such approaches building on the three core logics outlined here. However, even if the research strategy is ultimately to combine comparisons, the researcher still needs a way to think about the selection of each individual paired set, and especially of the first paired set. The rest of this article thus focuses on the selection of single paired comparisons and leaves discussion of combining comparisons to other work.

The literature highlights various cautions and guidelines for paired comparisons. One of the core guidelines, not selecting on the DV , is discussed previously. Four others are well summarized by Tarrow (2010, 246-253):

• Beware “insufficient degrees of freedom:” The methods described previously assume all relevant variables are known and can be observed, which is a large assumption. Additional variables not controlled for in paired comparison imply insufficient degrees of freedom, that is, more uncontrolled for variables than the number of cases. This problem is inevitable, but not fatal. Our best strategy is to construct comparisons carefully, building on theory and taking advantage of new statistical methods for creative matching where possible (e.g., Seawright and Gerring Reference Seawright and Gerring 2008 ). However, it does imply that a paired comparison is not the best strategy if we want to test several rival hypotheses about the causal effects of specific variables against each other.

• Beware “non-representativeness”: Selected cases may not be representative of the population, particularly if they are selected because of their extreme values. This is a common criticism, but the degree to which it is problematic is debatable. Some classic theorists consider cases useful for theory testing precisely because they have extreme values (on crucial cases, see, e.g., Eckstein Reference Eckstein, Greenstein and Polsby 1975 ; Gerring Reference Gerring 2007 ; Lijphart Reference Lijphart 1971 ).

• Beware “atheoretical case selection”: In particular, paired comparisons are often selected “atheoretically” from one world region because of the researcher’s interest in that particular region. This is inadvisable if careful attention is not also paid to research design. However, drawing comparable cases from the same region is not necessarily a bad approach as countries in the same region may share many characteristics.

• Do not ignore scope conditions: A paired comparison may not contribute to theory development if scope conditions are ignored in case selection. This is a problem for comparative research in general. Paired comparison may address it as progressively conducting multiple paired comparisons is one promising way of testing scope conditions (see Samuels Reference Samuels 1999 ).

Several other general cautions and guidelines are suggested by other scholars. Przeworski and Teune (1970, 36), advise researchers to use the most different systems approach and begin at the lowest level of analysis, “most often individuals.” This advice is taken to heart by some scholars (e.g., those working within a rational choice framework), but not so much by others (e.g., historical institutionalists or international relations theorists). King, Keohane, and Verba (1994) offer another guideline: select on the IV and especially avoid selecting on the IV and the DV together . As King, Keohane, and Verba (1994, 143) note: “The most egregious error is to select observations in which the explanatory and dependent variables vary together in ways that are known to be consistent with the hypotheses that the research purports to test” (see also Lijphart Reference Lijphart 1975 , 164). Presenting dishonest hypothesis “tests” of this sort is obviously to be avoided, but with only two cases considered, completely ignoring the values of the IV and DV also poses major dilemmas for analysts of paired comparisons. In particular, there is a good chance of an indeterminate research design, as explored next.

The methods described previously assume all relevant variables are known and can be observed, which is a large assumption. Additional variables not controlled for in paired comparison imply insufficient degrees of freedom, that is, more uncontrolled for variables than the number of cases. This problem is inevitable, but not fatal.

Several studies have mapped innovative ways to select cases for comparative analysis using new statistical methods (Abadie, Diamond, and Hainmueller 2012; Seawright and Gerring Reference Seawright and Gerring 2008 ). These methods, however, assume significant knowledge about the population from which cases are selected. As Seawright and Gerring (2008, 296) note, “if nothing—or very little—is known about the population, the methods described in this study cannot be implemented.”

What if knowledge about the population is incomplete? One answer is that the researcher should buckle down and compile all the necessary data on the population before proceeding (see Lieberman Reference Lieberman 2005 ). This can be a good strategy for some topics, but it is not a promising one for all: for many topics that we care about in political science (including ethnic conflict, new political parties, protests, and social movements), compiling a comprehensive database can be an impossible task, with the results inherently suffering from problems of selection bias (Hug Reference Hug 2003 ). Such data collection thus may involve considerable time and effort only to result in a problematic inventory. Challenges may be compounded for some countries or regions, such as much of sub-Saharan Africa, where basic data and sources such as newspaper archives may be incomplete or take considerable work to compile (see Jerven Reference Jerven 2013 ). Thus, in addressing some research questions, the best strategy for contributing to the development of causal theories may begin with paired comparison, even if cases must be selected with an incomplete view of the population. This section explores several scenarios for case selection in low information settings in light of the logics of paired comparison mapped earlier.

In some situations, significant research may be needed to determine the values of the DV. If this seems unlikely for DVs with binary values like ◼ and ◻, do not forget topics in which the DV requires more nuanced assessment. For instance, how does the quality of public institutions compare across post-civil war African countries? Or how have political parties played the ethnic card in campaign messages, including through the use of “coded” appeals (see Chandra Reference Chandra, Abdelal and Herrera 2009 ; Mendelberg Reference Mendelberg 2001 ). Measuring the DV in such situations may be a major component of a research project itself, meaning that cases must be initially chosen with incomplete knowledge about outcomes.

This would seem an ideal situation for adherents of King, Keohane, and Verba (1994): One cannot but follow their advice to select on the IV of interest and not to select on the IV and the DV together. But it also poses major risks for researchers. Consider figure 3 but imagine that all the DVs noted are unknown. In selecting cases, researchers hope to have a determinate research design (i.e., to be in cells A or D), but because they do not know the values of the DV, they may end up with an indeterminate research design (in cells B or C). King et al.’s advice points to the adoption of a strategy corresponding to the first row of figure 3 , that is, to choose cases with variation in the (hypothesized) causal variable and with otherwise “similar” characteristics. If it turns out that our researchers are operating in cell A, they have a determinate research design, but they could be operating in cell B.

On the contrary, the approach introduced here suggests that cases should be selected and their selection justified—not with reference to such general guidelines—but based explicitly on the types of theoretical propositions under examination and the known characteristics of the possible cases. For instance:

Suppose our hypothesis is that the presence of w (e.g., national unity) is a necessary condition for the outcome ◼ (e.g., successful democratic transition). Such a hypothesis might build on theories of democracy that highlight national unity as the single precondition for democratic transition (Rustow Reference Rustow 1970 ). As discussed previously, in addressing hypotheses about necessary conditions, the method of agreement is better suited than the method of difference. In this instance, our hypothesis gives us a clear prediction for the DV when w is absent (◼ will not occur), but not when it is present (either ◼ or ◻ could occur, i.e., according to our hypothesis national unity may be present in countries that do not transition to democracy, although it is also a necessary precondition for countries that do transition). Thus, it seems our preferred option here is to select two cases in which w is absent in both and all other IVs are the same. This would be like operating in the second row of figure 4 . ( Figure 4 is the same as figure 3 , except that the w ’s are absent in the second row for the purposes of simplicity in this example.) If we are “lucky,” we have a research design that provides a definitive test of our hypothesis and infirms it—for example, we find, as in cells C or D in figure 4 , that ◼ can be the outcome even when w is absent. If we had instead operated in the first row of figure 4 , we would have found in this particular example that cell A suggests support for our hypothesis (as ◼ occurs when w is present and not when it is absent), while cell B does not tell us anything about our hypothesis as ◼ does not occur.

Figure 4 Testing Hypotheses about Necessary Conditions

On the one hand, we might consider selecting two cases in which w is present in both and all other IVs are the same. This would be like operating in the second row of figure 3 . In this example, we find that none of the possible outcomes is a determinate research strategy: If we find that ◼ is the outcome in both cases (as in cell D), our data suggest support for our hypothesis (but not a definitive test). Cell C also provides some support as ◼ occurs when w is present (and it also shows than ◻ can occur when w is present).

Suppose, on the other hand, that we are interested in testing a theory about how w (e.g., modernization) “causes” ◼ (e.g., democratic transition). Our theory also implies that ◼ should not result when w is absent and that ◻ (i.e., failure to transition) will not result when w is present. Here, the standard advice to select paired cases with variation in the causal variable w and no variation in other IVs (i.e., to operate in the first row of figure 3 ) seems appropriate. If it turns out that the DV varies (as in cell A), we have a determinate research design that also lends support to our hypothesis. If instead the DVs are the same (as in cell B), this approach would help to infirm our hypothesis—but would not allow us to identify one single causal variable from among the other IVs. Here, selecting for variation in w is generally a better strategy than selecting for similarity in w . In this particular example, if it turns out that the DVs are the same and both ◼ (cell D in figure 3 ), our analysis lends partial support to our hypothesis, but does not speak to instances in which w is absent. (In contrast, in a different example in which the DVs were both ◻, our analysis would have helped to infirm our hypothesis.) If it turns out that DVs vary (cell C), Case II would be theory-infirming, but the cases together would not identify the causal variable.

In all of these scenarios, the possibility that ◼ is a rare occurrence with little chance of occurring when we select cases with no attention to the DV, raises particular challenges for our research strategy. War is one example of a rare event (see King and Zeng Reference King and Zeng 2001 ). In the first scenario, in particular, if the outcomes in the second row of figure 4 had all been ◻, we would not have learned anything new about our hypothesis. If we think it is likely that the DV of interest is a rare event, choosing cases when our incomplete knowledge suggests that it occurs would be advisable. If it turns out that we are wrong, we can still use the case studies for developing insights and perhaps in theory building, being careful to note their limits in theory testing. (Alternatively, we may also reconsider the project of compiling comprehensive information about the instances in which ◼ occurs, even if the resulting database is likely to suffer from selection bias.)

When the IVs Are Unknown

In many situations, we know the values of the DV, but we have incomplete information about the explanatory and control variables. This may result either because we have identified all of the relevant variables but we do not know all of their values, or because theory is relatively weak and we are unsure of whether we have identified all of the relevant variables. Here, too, cases would need to be chosen initially with incomplete information. In this situation, the researcher is unable to follow the standard advice of selecting on the IVs. This dilemma is depicted in figure 5 , which is the same as figure 3 except that cells A and B have opposite outcomes for the purposes of this example.

Figure 5 When the IVs are Unknown

In this situation, the best strategy also depends on the types of theoretical propositions under examination and the particular characteristics of possible cases: suppose, as in the first instance described above, that our hypothesis is that the presence of w is a necessary condition for the outcome ◼. Here, the best option is to select two cases in which ◼ occurs (the second column of figure 5 ). In this particular example, our analysis will either provide support for the hypothesis (cell D), or it will infirm the hypothesis for these cases (cell B). Cell B, unfortunately, will not shed additional light for us on which of the IVs x , y , and z is the causal variable or necessary condition. Alternatively, selecting cases in which the DV varies gives us basically one less shot at infirming our hypothesis. In this particular example, however, Case I in cell A happens to be theory-infirming as ◼ occurs even if w is absent, while cell C suggests some partial support for the hypothesis.

Suppose then, as in the second situation discussed earlier, that we are interested in testing a theory about how w “causes” ◼. Here it is advisable to select paired cases with variation on the DV (i.e., the first column of figure 5 ). In this particular example, cell A suggests exactly the opposite relationship to our hypothesis and is theory-infirming. Cell C is also inconsistent with it. If we had instead chosen for similar DVs, cell B would also have been inconsistent with our hypothesis (but provided no additional traction on the causal variable), while cell D would have suggested some support.

More broadly, this article suggests the need for appropriate modesty in describing what can be learned with reference to theory from a single paired comparison, however elegantly it is constructed.

Incomplete Information about Both IVs and DVs

In reality, at the start of their work researchers often face a situation that looks more like figure 6 , with some data but overall incomplete information about both IVs and DVs. In such situations, the advisable strategy, still seems to depend on the type of hypotheses one is testing and the available data. However, particularly when information is this incomplete, researchers should be prepared to temper their claims and ambitions of theory testing through paired comparison, unless they luckily happen on a design that can definitively test their hypotheses.

Figure 6 Incomplete Information about IVs and DVs

Building on the literature, and especially on Tarrow ( Reference Tarrow 2010 )’s points toward a theory of practice, this article identifies three logics underlying the use of paired comparisons for theory development and presents a simple approach, with visualizations, that might help researchers to build on these logics to develop and defend more intentional research designs using paired comparisons. The final section of the article illustrates how this approach can be used to explore the implications for case selection of various scenarios in low information settings, where knowledge about IVs and/or DVs is incomplete. Such situations, are relatively common for political scientists addressing core questions in the field. This discussion suggests no easy solutions. In general, it underscores, among other points, the need to add a number of qualifications to the “rule” of not selecting on the DV.

More broadly, this article suggests the need for appropriate modesty in describing what can be learned with reference to theory from a single paired comparison, however elegantly it is constructed. Both the challenges of constructing paired comparisons well, and the limits to a number of comparative strategies are illustrated. Indeed, particularly in low information settings, the discussion suggests that it is the rare research project that will uncover a paired comparison that can provide a definitive test of a theory. Indeed, without a full view of the population from which the cases are selected in many instances, it can be unclear even whether these cases are “extreme” or “representative.” However, when compared to other options like building a large-n dataset, paired comparison may still provide the best strategy for theory development on many low information topics.

Finally, as briefly noted, a paired comparison is a problematic approach in terms of degrees of freedom if the researcher seeks to test multiple theories against each other. In other words, implicit in this discussion is the notion that paired comparative research will be guided primarily by focus on a single working theory. However, combined with each other or other data, paired comparison can be a core component of a research strategy designed to test multiple theories. For instance, multiple paired comparisons might be used together to triangulate the set of causal variables that explain a particular phenomenon.

ACKNOWLEDGMENTS

This article was first drafted for a collaborative project, “Aid and Institution-Building in Fragile States: Findings from Comparative Cases,” that I developed at UNU-WIDER and its support for this research is gratefully acknowledged. I thank the project participants for their insights, Omar McDoom for comments on an earlier draft, and Anu Laakso for preparing the figures used in this article.

Rachel M. Gisselquist is a research fellow with the United Nations University, World Institute for Development Economics Research (UNU-WIDER). Her research focuses on the comparative politics of the developing world, with particular attention to ethnic politics, governance, and state fragility in sub-Saharan Africa. She holds a PhD in political science from MIT. She can be reached at [email protected] .

1. For further discussion about the definition of a case see, e.g., Gerring ( Reference Gerring 2004 ); Ragin and Becker ( Reference Ragin and Becker 1992 ).

2. A critical reader might note that the outcome could be caused by more than one variable, e.g., in cell C, the outcome could be caused by a process involving x , y , and z . This criticism is somewhat misleading: In the example w, x , y , and z represent all of the ways in which cases I and II might meaningfully be compared. It is unlikely that “almost everything” explains the DV. In addition, as noted above, w , x , y , and z are described as variables purely for descriptive simplicity; they might equally well be understood as combinations of multiple variables or processes themselves.

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• Volume 47, Issue 2
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• DOI: https://doi.org/10.1017/S1049096514000419

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The Role of Case Study Research in Political Science: Evidence for Causal Claims

2012, Philosophy of Science

Political science research, particularly in international relations and comparative politics,has increasingly become dominated by statistical and formal approaches. The promise of these approaches shifted the methodological emphasis away from case study research. In response, supporters of case study research argue that case studies provide evidence for causal claims that is not available through statistical and formal research methods, and many have advocated multimethod research. I propose a way of understanding the integration of multiple methodologies in which the causes sought in case studies are treated as singular causation and contingent on a theoretical framework.

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This article argues that International Relations (IR) researchers concerned with why-questions about the state’s external behaviour ought to employ a multicausal approach attentive to the interrelated relationship between external structures and internal agents, presenting the (meta-)theoretical rationales underlying its argument. Here the author suggests ‘a rich/bold ontology’ regarding foreign policy behaviour. Then the article elaborates on detailed and explicit guidelines on how to traverse the bridge that connects the insights of that rich ontology to the empirical research necessary to make claims about the real world of any one moment. In a related vein, the article claims that a multicausal approach should be established using what the author calls ‘loose-knit deductive reasoning’ through which epistemological and methodological openness can be preserved in a manageable way. More importantly, this article discusses the role of theory for IR scholarship and the standards for judging theoretical contributions and progress in the field of IR. Ultimately, the author argues that a complex and flexible approach – both as a useful mode of explanation and as a progressive model of theory construction – can make important contributions to a better understanding of foreign policy and world politics, not only because it enables researchers to become keenly sensitive to the complex reality underlying a nation’s foreign policy and to the interrelated relationship between structures and agents in international relations, but also because it can serve to provide a secure base for the progressive accumulation of the evidence closely associated with multiple causation on which any adequate explanation about complex foreign policy behaviour must surely be founded and without which general theory cannot really flourish.

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This article reviews recent attempts to develop multi-method social scientific frameworks. The article starts by discussing the ontological and epistemological foundations underlying case studies and variance-based approaches, differentiating approaches into bottom-up, case-based and top-down, variance-based approaches. Case-based approaches aim to learn how a causal process works within a case, whereas variance-based approaches assess mean causal effects across a set of cases. However, because of the different fundamental assumptions, it is very difficult for in-depth studies of individual cases to communicate meaningfully with claims about mean causal effects across a large set of cases. The conclusions discuss the broader challenges this distinction has for the study of comparative politics more broadly.

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This article makes the case for process patterns as an alternative to causal mechanisms. Causal mechanisms are explanatory tools to unpack the " black boxes " separating the input and output of models. Unlike causal mechanisms, process patterns do not require such a black box. They refer to recurrent sequences of interaction observed across any number of empirical domains. Scholars can apply them across disciplines when similar processes occur in different domains. The article provides examples from International Relations where scholars have sometimes studied process patterns in all but name.

Edgar Ojeda

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FOUR. Case Study and Theory in Political Science

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100 Best Political Science Research Topics

June 3, 2024

Political science is an incredibly broad and variated discipline, containing numerous subfields that attract immense amounts of research. Researchers in the field can utilize diverse empirical or theoretical methodologies, sometimes in combination. There are specialized fields based on geographic areas, time periods, political forms and institutions, and bodies of thought. Researchers might aim to impact policy, inspire advocacy, or produce knowledge about the nature of political ideas, systems, and processes. There are even sub-disciplines that focus separately on either relations or comparisons between nation-states (international relations and comparative politics, respectively). Given this expansiveness, it can be challenging to quickly pick out compelling political science research topics.

This article provides a broad sample of political science topics. Most of the topics are already the subject of intensive research, which is ultimately something to look for when identifying a topic. Understanding the themes and subfields that already structure study in the field can ultimately help toward locating a unique research interest. From there, it’s a matter of refining interests into specific topics and, eventually, questions that drive research.

Picking Political Science Research Topics

Finding political science topics usually involves doing two things, sometimes simultaneously. The first requires understanding what prominent topics already exist in the field(s). After all, this discipline has been around for many decades, with incredible amounts of published research each year. It’s best to not try to wholly reinvent the wheel in this case, or risk selecting a topic for which there is no available research. The second entails reflecting on what feels important to research. A topic could be significant because it is remarkably timely or because there is some pragmatic outcome in mind. The researcher might even have a personal connection with the topic on top of these other kinds of factors. Pursuing research that feels important is an evergreen recommendation – otherwise, research can be produced without any clear purpose or benefit in mind.

So, when picking political science research topics, consider these processes and resources:

1) Start with what interests you. Take inventory of your personal and intellectual experiences and how they might intersect with what you know about political research. How would you map your experiences geographically or historically? What themes in the political sphere grab your attention? In what ways have you and others around you interacted with formal politics? Understanding what you are motivated to learn about makes the process of selecting among political science topics more organic.

Picking Political Science Research Topics (Cont.)

2) Consider what feels urgent. Political science has a good deal of overlap with the discipline of history. But perhaps even more than historians, political scientists are committed to understanding and improving politics in the present. A great deal of tremendous scholarship is sparked from this presentist impulse. What news items are persistently interesting or seem to revolve around intractably complex problems? A topic connected to widely recognized issues in the present will almost immediately justify the resulting research.

3) Survey field categories and literature. Usually, topics that are timely will already be drawing attention from other researchers. It’s also possible to spark curiosity through understanding how others have framed topics and questions. However, the most important reason for this step is to ensure that a research topic will have enough published writing around it to warrant attention. There are many ways to complete a survey within or across political science fields. To start, fields like comparative politics have field-specific journals and anthologies with useful descriptions and citations. There is the Journal of Comparative Politics , a journal called Comparative Political Studies , and a big anthology called Comparative Politics: A Practical Guide . Another extremely helpful resource is the research database known as Oxford Bibliographies . There, political science scholars compile bibliographic entries that define subfields and provide key citations.

Again, these are not strictly sequential steps. Often, it’s necessary to engage existing topics and research questions and use them to inspire reflection on what feels significant. Or it’s likely that specific interests will help delimit subfields, making it easier to comb through topics.

It’s important to narrow topics so that a researcher could reasonably become an expert on. In Oxford Bibliographies, many times they break very broad topics into multiple subtopics, each with its own body of literature. But it can also be useful to think about the research question that might stem from the broad topic. What specifically is it that needs to be known about this topic? What causes and effects are involved in phenomena we can see and describe but have not yet analyzed? Or what can be critiqued, evaluated, improved or replaced? Finding how a broad topic can lead to descriptive, analytical, or evaluative research questions is the most effective way to identify compelling and specific topics.

The List – Political Science Research Topics (1-30)

African politics.

1) African Ethnic, Linguistic, and Religious Minority Political Representation

2) Authoritarianism in the Politics of Sub-Saharan Africa

3) Comparative Political Structures of African Urban Governments

4) Democratization Policies and Political Movements in Africa

5) Ethnic and Religious Plurality in the Politics of Nigeria

6) National and Transnational LGBTQ Political Movements in Africa

7) Political Organization Around Climate Crises in Africa

8) Public Opinion on International NGOs in Africa

9) South Africa’s Governmental Policies for COVID Vaccination

10) Women in African Politics

Asian Politics

11) Competing Claims to Sovereignty in the South China Sea

12) Economic and Cultural Effects of China’s One-Child Policy

13) Labor Politics in China

14) Media Politics and Expression in Asian Countries

15) Nationalism and Religious Minorities in India

16) Philippine Politics and the Illicit Drug Economy

17) Social Movements in East Asia

18) South Korean Policies Addressing Demographic Decline

19) Special Administrative Region of Hong Kong

20) Youth Protest Movements in Taiwan

Comparative Politics

21) Authoritarian Populism in Brazil, Hungary, and India

22) Countries that Host Foreign-Owned Nuclear Weapons

23) Diasporas and National Politics in South and East Asian Countries

24) Differences in Environmental Politics Between the Global North and Global South

25) Elections in Sierra Leone and Their Differences from Other Countries

26) Governmental Responses to Gang Violence in Different Latin American Countries

27) Income Inequality in Advanced Democracies

28) Indigenous Rights and Politics in Canada, Australia, and New Zealand

29) Laws Surrounding Abortion in the U.S. and the U.K.

30) State Legitimacy and Authority in Botswana and Somalia

Political Science Topics (31-60)

European politics.

31) Authoritarianism and Media Politics in Turkey

32) Austerity, White Identity Politics, and Brexit

33) Ethnic and Religious Minority Representation in the European Union

34) European Public Opinion on Russia’s Invasion of Ukraine

35) Far-Right Political Mobilization in Europe

36) France’s Political Opposition to Multicultural Pluralism

37) Immigration, Islamophobia, and European Union Political Campaigns

38) Political Challenges and Opportunities for European Integration

39) Regime Transitions in Post-Communist Eastern Europe

40) Welfare State Development in Western Europe

International Relations

41) Border Security Politics, Immigration, and International Relations

42) Efficacy of International Criminal Justice Bodies

43) Energy Politics of International Relations

44) Global Economy of Illicit Drugs

45) Global Politics of Intellectual Property

46) International Conflict Management

47) International NGOs and Their Effects on National Politics

48) Political Responses to Transboundary Pollution

49) Preferential Trade Agreements and Tariff Wars

50) Regulation of International Private Corporations

Latin American Politics

51) Authoritarianism and Democratization in Brazil’s Political History

52) Christian Transnational Political Mobilization in Latin America

53) Electoral Volatility and Suppression in Venezuela

54) Government Responses to Organized Crime in Mexico and Central America

55) Guerilla Insurgencies in Latin America

56) Neoliberalism and Democracy in Latin America

57) Military Government in Latin America, 1959-1990

58) Populism’s Role in Shaping Latin American Politics

59) Poverty, Inequality, and Development in Latin America

60) Regional Economic, Political, and Cultural Integration in Latin America

Political Science Research Topics (61-90)

Methodology.

61) Accuracy and Reliability of Models Predicting Election Outcomes

62) Applying Game Theory to Understand Political Strategies

63) Development of Survey Methods and Research

64) Ethical Problems Surrounding the Use of Experiments in Political Research

65) Experimental Methods for Promoting Voter Turnout

66) Impact of Survey Design and Sampling Techniques on Validity in Public Opinion Polls

67) Integrating Qualitative and Quantitative Methods in Political Research

68) Machine Learning Techniques and Political Science

69) Researching Politics Through Big Data

70) Using Ethnography to Study Political Behavior and Institutions

Middle Eastern Politics

71) Comparative Politics of the Middle East and North Africa

72) Democracy, Statehood, and the Israel-Palestine Peace Process

73) Immigrant Labor Politics in Oil-Rich Middle East Countries

74) Mobilization and Oppression Through Media Control in the Arab Spring

75) Oil Wealth, Political Stability, and Economic Development in the Middle East

76) Political Islam, National, and International Politics in the Middle East

77) Postcolonialism and the Political Development of Iran

78) S. Foreign Policy and Its Effects on Stability and Governance in the Middle East

79) Women’s Political Participation in the Middle East and North Africa

80) Water Politics and Their Impact on Middle Easter International Relations and Security

Political Theory

81) Critical Political Theory and the Frankfurt School

82) Development of Political Thought in Ancient China

83) Effects of New Media (i.e., TVs, CDs, social messaging platforms) in Politics

84) Feminist Interventions in Political Thought

85) Hegelian and Counter-Hegelian Political Thought

86) Intersectionality in Political Theory

87) Nationalist and Cosmopolitan Political Thought

88) Religion in Classical and Contemporary Political Thought

89) Political Representation and Policy Responsiveness to Public Opinion

90) Postcolonial and Decolonial Political Theory

Political Science Topics (91-100)

U.s. politics.

91) Age and Generational Differences in U.S. Politics

92) Asian American Politics and Political Movements

93) Campaign Advertising in U.S. Elections

94) Celebrity Influence on Political Campaigns in the U.S.

95) Interest Groups and Modern U.S. Conservativism

96) Litigation Strategies in Political Contests Over Abortion

97) Politics of Electoral Redistricting in the U.S.

98) Politics of U.S. Healthcare System and Reform

99) S. Politics and Policies of Mass Incarceration

100) Voter Opinion on Women Political Candidates

Final Thoughts on Political Science Research Topics

Political science topics are rarely one-size-fits-all. Like many humanistic and other modes of research, topics typically suit the specific interests and motivations of the researcher. Do you want to improve a method or practical process? Are you interested in the politics of a specific region, or motivated to learn about something especially urgent? Where is the conversation within or across fields, and where might you have the most to contribute? Combining careful self-assessment and knowledge of research fields is the best way to effectively carve out your own chunk of the gigantic discipline that is political science.

Political Science Research Topics – Additional Resources

• Good Persuasive Speech Topics
• Debate Topics
• Argumentative Essay Topics
• Social Issues Topics
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• 62 Research Opportunities for High School Students 
• 150 Journal Prompts for Kids, Middle, and High School Students 
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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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