case study method in international relations

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The Oxford Handbook of International Relations

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29 Case Study Methods

Andrew Bennett is Professor of Government at Georgetown University.

Colin Elman is Associate Professor of Political Science, The Maxwell School, Syracuse University.

Published: 02 September 2009
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This article focuses on a third generation of qualitative methods research. Third-generation qualitative methods provide a unique bridge between the single-logic-of-inference and interpretivist communities. Accepting comparison and intuitive regression as part of its underlying justification, the third-generation case study approach is readily compatible with large-n studies, as well as being accepting of many of the claims of the comparative advantages offered by quantitative methods. The article considers some of the ways in which the third generation has developed and suggest potentially fruitful directions for future research. It focuses on some key innovations in third-generation qualitative methods over the last decade regarding within-case analysis, comparative case studies, case selection, concepts and measurement, counterfactual analysis, typological theorizing, and Fuzzy Set analysis. It concludes with a discussion of promising avenues for future developments in qualitative methods.

As we have noted elsewhere ( Bennett and Elman 2007 a ), qualitative research methods are currently enjoying an almost unprecedented popularity and vitality in both the international relations and the comparative politics subfields. To be sure, this renaissance has not been fully reflected in contemporary studies of American politics, which continue to emphasize statistical analysis and formal modeling ( Bennett, Barth, and Rutherford 2003 ; Bennett and Elman 2007 b ; Mahoney 2007 ; Pierson 2007 ). Nevertheless, in the international relations subfield, qualitative methods are indisputably prominent, if not pre‐eminent. In a 2007 survey, 95 percent of US international relations scholars reported that qualitative analysis is their primary or secondary methodology, compared with 55 percent for quantitative analysis and 16 percent for formal modeling ( Maliniak et al. 2007, 37 ). Substantively, qualitative research has contributed to essentially every research program in international relations, including those on international political economy ( Odell 2004 ), the democratic peace ( George and Bennett 2005, 37–59 ), ethnic and civil conflicts ( Sambanis 2004 ; Bennett and Elman 2007 b ), the end of the cold war ( Wohlforth 1998 ), international environmental politics ( Mitchell and Bernauer 2004 ), and security studies ( Katzenstein 1996 ; Kacowicz 2004 ).

The present resurgence in qualitative research methods owes a significant debt to two previous generations of ground‐breaking scholars. A first generation of post‐Second World War case study methods made important contributions but eventually came to be seen as being too atheoretical, lacking in methodological rigor, and not conducive to cumulative theory‐building ( George 1979 ). A second generation of scholars developed more systematic procedures for qualitative research from the 1970s through the early 1990s, including Adam Przeworski and Henry Teune (1970) , Arend Lijphart (1971) , Harry Eckstein (1975) , Neil Smelser (1976) , Alexander George (1979) , Timothy McKeown ( George and McKeown 1985 ), Charles Ragin (1987) , and David Collier (1993) .

We focus in this chapter on a third generation of qualitative methods research. We argue that this third generation constitutes a renaissance in qualitative methods that has clarified their procedures, grounded them more firmly in the contemporary philosophy of science, illuminated their comparative advantages relative to quantitative and formal methods, and expanded the repertoire of qualitative techniques on conceptualization and measurement, case selection, and comparative and within‐case analysis. Qualitative methods have also become more deeply institutionalized than in prior periods. In 2003, the American Political Science Association (APSA) formed the Qualitative Methods section. Subsequently renamed Qualitative and Multi‐Method Research, as of February 2008 it was the second‐largest of APSA's thirty‐seven sections. In addition, the Consortium for Qualitative Research Methods (CQRM) was founded in 2001, and in 2007–8 it had roughly sixty member departments and research institutes. By January 2008 it had co‐organized seven Institutes for Qualitative and Multi‐Method Research, training more than 600 graduate students and faculty in state‐of‐the‐art qualitative methods ( Collier and Elman 2008 ). 1

An important dimension of the recent development of qualitative research methods has been the emergence of more pluralistic attitudes toward methodology. This pluralism is clearest in the increasing use of sophisticated multi‐method research designs. It is also evident within the qualitative research methods community itself, which has grown in size and variety to the point that it now incorporates a diverse range of views. One school of thought, influenced by quantitative research techniques, suggests that “the same underlying logic provides the framework for each research approach [statistical and qualitative]. This logic tends to be explicated and formalized clearly in discussions of quantitative research methods” ( King, Keohane, and Verba 1994, 3 ). In contrast to this single‐logic‐of‐inference approach, advocates of what we have termed the third generation of qualitative research argue that there are alternative ways to make inferences. These scholars often use within‐case methods to establish the presence of particular causal mechanisms and the conditions under which they operate. 2 The third‐generation group also emphasizes the value of theory development, as well as theory testing; and of historical explanation of individual cases, as well as generalized statements about causal mechanisms. In the view of third‐generation qualitative methodologists, different methods have different strengths and weaknesses, and they should be used with those trade‐offs in mind. From this perspective, quantitative methods are very useful and powerful, but they are not always the best choice for all inferential goals, even when many cases exist.

The qualitative research methods community also includes a variety of scholars engaged in interpretative approaches. Interpretivism involves an extraordinary range of modes of analysis, 3 as well as difficult issues arising from the variety of philosophical and linguistic traditions in which interpretative approaches have their roots. We find useful in the international relations subfield John Ruggie's distinction (1998) among neoclassical, postmodernist, and naturalistic constructivists. Neoclassical constructivists, among whom Ruggie counts himself, follow a pragmatic epistemology and are committed to the idea of a pluralistic social science even if its results are time‐bound and culturally contingent. Naturalistic constructivists, such as Alexander Wendt (1999) and David Dessler (1999 ; Dessler and Owen 2005 ), aspire to make valid inferences on the causal mechanisms that underlie social life, and see much in common between the epistemologies of the social and natural sciences. These two groups' approach to methods are largely consistent with those of third‐generation qualitative researchers. 4 For instance, Peter Katzenstein's neoclassical constructivism (2005, x–xi, 40) is open to an analytically eclectic combination of interpretative and other methods. In contrast, Ruggie's final category of postmodernist constructivists take a much more skeptical view. This group, which grounds its works in the writings of Friedrich Nietzsche, Michel Foucault, and Jacques Derrida, focuses on the linguistic construction of social reality. They “make a decisive epistemic break with the precepts and practices of modernism” ( Ruggie 1998, 881 ) and hence are pessimistic about the prospects for a legitimate social science or for justifiable causal inferences. 5

Third‐generation qualitative methods have the potential to occupy a pivotal position in the discourses among different qualitative approaches. In some respects they can be seen as providing a unique bridge between the single‐logic‐of‐inference and interpretivist communities. Accepting comparison and intuitive regression as part of its underlying justification, the third‐generation case study approach is readily compatible with large‐n studies, as well as being accepting of many of the claims of the comparative advantages offered by quantitative methods. On the other hand, its close attention to detail, narrative, and context gives the third generation a close compatibility with interpretative approaches, especially with the pragmatist and naturalist branches. For the rest of the chapter, we consider some of the ways in which the third generation has developed and suggest potentially fruitful directions for future research. We focus on some key innovations in third‐generation qualitative methods over the last decade regarding within‐case analysis, comparative case studies, case selection, concepts and measurement, counterfactual analysis, typological theorizing, and Fuzzy Set analysis. We conclude with a discussion of promising avenues for future developments in qualitative methods, including ways of combining qualitative methods with statistical and/or formal methods, means of assessing theories involving various forms of complexity, ways of adapting qualitative methods to address common inferential biases uncovered by cognitive research, means of increasing the replicability and accessibility of qualitative data and using qualitative knowledge to improve codings in statistical databases, and ways of generalizing from case studies.

1 Innovations in Third‐Generation Qualitative Methods

Third‐generation qualitative scholars have over the last fifteen years revised or added to essentially every aspect of traditional case study research methods. 6 Although these new and updated methods vary along more than one dimension, Figure 29.1 presents them along a spectrum from methods of within‐case analysis of single cases at the left end, through methods of implicit comparison and small‐n comparison in the middle, to multi‐case comparisons and Fuzzy Set/Qualitative Comparative Analysis (FS/QCA) on the right. We very briefly discuss each of these methods in turn below.

Qualitative methods by numbers of cases and modes of analysis

1.1 Process Tracing

Methods of within‐case analysis have a long pedigree, but scholars have recently clarified their procedures and illuminated their foundations in the contemporary philosophy of science. A central method of within‐case analysis, termed process tracing ( George 1979 ; George and Bennett 2005 ), involves the close examination of the observable implications of alternative hypothesized explanations for a historical case. 7 The researcher using process tracing continually asks “if this explanation is accurate in this case, what else must be true about the processes through which the hypothesized causal mechanisms unfolded in this case?” The investigator then tests these hypothesized intervening variables against evidence from the case. With its emphasis on testing hypothesized causal mechanisms, often at a lower or more detailed level of analysis than the independent and dependent variables, process tracing is consistent with the “scientific realism” school of thought in the philosophy of science ( George and Bennett 2005 ).

Process tracing can involve both inductive analysis to generate hypotheses about a case and deductive tests of potential explanations of a case. A hypothesis can even be developed in a case and tested in that same case if it is tested against evidence that is in some way independent of the evidence that gave rise to it. In these regards, process tracing is closely analogous to traditional historical methods, as well as methods of developing and testing explanations of individual cases in epidemiology, pathology, geology, evolutionary biology, and detective work. Good process tracing requires giving attention to alternative hypotheses and their observable implications, taking into account potential biases in the available evidence, incorporating diverse sources of information, and providing as continuous as possible an explanation of the key sequential steps in a hypothesized process.

The logic of process tracing is quite similar to that of Bayesian inference ( Bennett 2007 ). 8 Like Bayesian inference, process tracing uses evidence to affirm some explanations and to cast into doubt, through eliminative induction, explanations that do not fit the evidence. Although there can be a problem of indeterminacy if there is no accessible evidence to discriminate between two competing and incompatible explanations of a case, it is also possible for one or a few pieces of evidence strongly to increase confidence in one explanation while also calling into question many others. Hence, in contrast to the single‐logic‐of‐inference approach to qualitative methods, the third‐generation view is that, to the extent that case studies rely on within‐case methods, they are not necessarily vulnerable to the “degrees of freedom critique.” This is because within‐case methods provide evidence that bears on multiple testable implications of alternative theories within a single case ( George and Bennett 2005, 28–9 ; see also Campbell 1975 ). Although case studies (and indeed all methods) are vulnerable to the more general problem of underdetermination of theories by evidence, the presence and severity of this problem in any particular case study research design depends, not on the number of variables or cases, but on whether the evidence from the cases is suitable for discriminating between alternative explanations. There is thus no inherent “degrees of freedom” problem in using process tracing to test several potential explanations in a single case.

1.2 Implicit Comparisons: Deviant, Most‐likely, Least‐likely, Crucial, and Counterfactual Cases

Just as language and concepts are inherently comparative, all single case studies, even when not explicitly comparative, are implicitly so. Case studies that are at least implicitly comparative include deviant, most‐likely, least‐likely, crucial, and counterfactual cases. Methodologists have clarified the uses of each of these kinds of case study in the last decade. Deviant cases are cases whose outcomes either do not conform to theoretical expectations or do not fit the empirical patterns observed in a population of cases of which the deviant case is considered to be a member. Prior statistical work can be useful in identifying deviant cases through these cases' high error term ( Seawright and Gerring 2006 ). Deviant cases are often useful for generating new hypotheses through inductive process tracing ( Eckstein 1975 ; George and Bennett 2005 ). A hypothesis generated from a deviant case may prove to be applicable to that case only, or to broad populations of cases. It is impossible to predict how generalizable the explanation for a deviant case might be until one has studied the case, developed an explanation, and considered the conditions under which the newly hypothesized underlying mechanisms might apply.

Most‐likely cases are those in which a theory is likely to provide a good explanation if it applies to any cases at all, and least‐likely cases are “tough test” cases in which the theory in question is unlikely to provide a good explanation. A theory that fails to fit a most‐likely case is strongly impugned, while a theory that fits even a case in which it is least likely gains confidence. A “crucial” case is a tough test in both senses: It must fit one explanation if the explanation is true, and it must not fit any other explanations. Harry Eckstein (1975) developed the ideas of crucial, most‐likely, and least‐likely cases in the 1970s, but scholars have more recently clarified that whether a case is most likely or least likely for a theory should be judged, not just by its values on the variables of that theory, but on the values of variables pointed to by alternative theories as well ( George and Bennett 2005 ; see also Gerring 2007 b ).

Counterfactual analysis is another form of implicit comparison, one in which the researcher compares an extant case with a counterfactual case that differs in one or more key respects. Philip Tetlock and Aaron Belkin have devised a number of standards for judging counterfactuals, including the “miminal re‐write rule” (changing as few variables as possible to construct the counterfactual) and the prescription that to the extent possible counterfactuals should include projectible and testable implications for the real world ( Tetlock and Belkin 1996 ). Counterfac‐ tual reasoning also serves as a useful test of consistency in a researcher's thinking, as every causal or explanatory claim about the world has a logically equivalent counterfactual claim. If a researcher finds a causal claim convincing, but does not find the logically equivalent counterfactual claim equally convincing, the researcher needs to consider whether there are asymmetries or faults in their theorizing about a case ( Lebow 2000 ; George and Bennett 2005 ; for applications to international relations cases, see Goertz and Levy 2007 ).

1.3 Small‐n Comparisons: Most‐similar and Least‐similar Cases, Comparative Historical Analysis

Methodologists have updated two forms of pairwise comparison that have a long pedigree: most‐similar and least‐similar case comparisons. In a most‐similar case comparison, two cases are similar in all but one independent variable, and differ on the outcome variable. In a least‐similar case comparison, two cases are similar on only one independent variable and have the same value on the dependent variable. These comparisons draw, respectively, on John Stuart Mill's “method of difference” and “method of agreement” (the seeming confusion of the terms arises from the fact Mill named his methods for the cases' difference or agreement on the dependent variable, while the contemporary labels correspond to similarity or lack thereof on the independent variables).

As Mill himself noted, inferences from these kinds of comparisons are potentially flawed for a variety of reasons: cases rarely differ in only one or all but one variable, there may be alternative paths to the same outcome (equifinality), some variables may be left out from the comparison, or there may be measurement error. More recently, methodologists have reaffirmed these potential threats to inferences in pairwise comparisons, but at the same time they have emphasized that process tracing helps reduce the likelihood of these problems ( George and Bennett 2005 ). In a most‐similar cases design, for example, process tracing can supplement the comparative analysis by using within‐case analysis to test whether the independent variable that differs between the two cases is related to the outcome through the hypothesized processes. Researchers can also use process tracing in this design to test whether other residual differences in the two cases' independent variables are related to the differences in the cases' outcomes.

Several of the innovations discussed in this chapter have been developed and/or deployed in the comparative historical analysis tradition, which straddles the disciplines of both political science and sociology. It has a particularly strong following in the subfields of comparative politics and American political development. James Mahoney and Dietrich Rueschemeyer (2003, 6) suggest that the comparative historical analysis approach addresses substantially important outcomes, and is defined by “a concern with causal analysis, an emphasis on process over time, and the use of systematic and contextualized comparison.” Uncomfortable with either universal generalizations or idiographic explanations, comparative historical analysis typically focuses on configurational analysis and on making contingent generalizations.

1.4 Typological Theorizing

Typological theorizing often involves a number of cross‐case comparisons within a single research design. Such theorizing uses a combination of these comparisons and within‐case analysis to develop theories about different configurations of variables and the outcomes to which they lead. One of the distinctive features of such theories is that they treat cases inherently as configurations of variables ( Ragin 1987 ; George and Bennett 2005 ), thereby allowing for the possibility of different multivariate interaction effects within each configuration.

Depending on the state of development of theories on the phenomenon of interest, the development of a typological theory can begin with established theories or it can proceed more inductively from individual case studies. In either event, the researcher usually iterates between evidence from cases and development of the theoretical framework, seeking with each iteration to uncover “new facts” to guard against the dangers of post hoc anomaly‐solving ( Lakatos 1970 ; Elman and Elman 2002 ). To build a typological theory, the investigator begins with the variables earlier research has identified (if any) on the phenomenon of interest. Using categorical measures of these variables, often dichotomous or trichotomous ones, the researcher outlines the “typological space” (termed a “truth table” in philosophy) of all the possible combinations of the variables. If, for example, there are four dichotomous independent variables and a dichotomous dependent variable, there will be two to the power of five or thirty‐two potential combinations or types.

Next, the researcher arrays the cases from the relevant population into the types that they best fit based on preliminary knowledge of the values of the variables in each case. This process can contribute to changes to the theoretical framework. If there are cases classified in the same type that the researcher thinks of as being dissimilar cases in important respects, for example, this can stimulate further consideration of the differences between the cases, and of any associated variables that might need to be added to the typological space to separate the cases into different types. Similarly, if cases with the same combination of independent variables have different outcomes, this poses a potential anomaly that merits attention.

Even after the apparent anomalies have been resolved to the extent possible using preliminary knowledge about the cases, at this point the typological space can be complex and seemingly unwieldy. Fortunately, there are several ways to reduce the typological space ( Elman 2005 ). First, the variables can be rescaled to a less detailed level of measurement if fine‐grained distinctions are not essential to the theory. Secondly, variables might be indexed, or aggregated into composite variables. Thirdly, the researcher can use logical compression to eliminate any empirically empty cells that are theoretically unlikely ever to include actual cases. A fourth option is empirical compression, eliminating empty cells whether or not they seem unlikely. A fifth route is pragmatic compression of adjacent types when their division serves no theoretical purpose. A sixth is to set aside from further and more detailed analysis types of cases whose outcomes appear to be theoretically overdetermined and whose empirical examples do not deviate from the expected outcomes. Finally, the researcher can decide to focus on a more narrowly circumscribed set of specific cells or subtypes of the phenomenon of interest.

Alternatively, if the typological space appears to be oversimplified, a researcher can use expansion (sometimes called “substruction”) to add variables and/or more finely grained distinctions back into the theory. Once the typological space has been reduced or expanded to the desired degree, it can contribute directly to the selection of cases that serve alternative research designs. Cases in adjacent cells that differ in one independent variable and in the dependent variable, for example, can be used for most‐similar comparisons, and cases with different outcomes from those of the other cases within the same cell constitute deviant cases that might be examined to try to identify left‐out variables. Examples of typological theories include those on burden‐sharing in ad hoc security coalitions ( Bennett, Lepgold, and Unger 1997 ), military occupations ( Edelstein 2008 ), status quo and revisionist regimes ( Schweller 1994 ; 1998 ), and types of federalist states ( Ziblatt 2006 ).

1.5 Fuzzy Set Analysis

Fuzzy Set (FS) analysis is another recent innovation in qualitative methods, one that typically includes studies of about ten to fifty cases ( Ragin and Rihoux 2004 ). A full explication of FS is beyond present purposes (see Ragin 2000 ), and we focus only on a few of its features and its comparative advantages vis‐à‐vis other qualitative methods.

FS methods are a variation on Charles Ragin's (1987) Qualitative Comparative Analysis (QCA), which uses “crisp” categorical variables and Boolean algebra to reduce populations of cases in truth tables to logical statements of necessity and sufficiency consistent with these cases. In contrast to crisp set QCA, FS methods assign “degree of membership” values between zero and one to cases based on the extent to which they are “fully in” a specified concept or collection of attributes. For example, a state that is “fully in” the conceptually defined set of “democracies” would be assigned a score of 1.0, a state whose attributes place it “mostly in” this set might have a score of 0.75, one that is “more in than out” might be a 0.5, and so on. After a researcher has assigned FS values to the cases in a study, he or she can use statistical tests to assess whether the outcomes of a particular type of case are consistent enough to sustain a claim of (near) necessity or sufficiency (in contrast to QCA, FS methods can use probabilistic statements).

FS analysis is a comparative method that does not necessarily rely on within‐case analysis of individual cases, although it does require sufficient information about each case to assign it an FS value, and it is not incompatible with within‐case analysis. FS analysis differs from typological theorizing in that it tends to assume that outlier cases can arise by chance, whereas typological theorizing typically uses a default assumption that deviant cases are potential sources for identifying left‐out variables. In addition, FS analysis is best suited to subjects for which prior theories are well established, for which diversity of cases is not sharply limited, and for the goal of testing claims of necessity or sufficiency rather than that of generating new theories ( Bennett and Elman 2006 a ). Typological theorizing, on the other hand, can be used both for testing and generating theories and for explaining individual cases.

1.6 Innovations in Conceptual Analysis, Two‐level Theories, and Case Selection

There are three other important sets of recent innovations in case study methods that do not fit neatly on the spectrum from single to multiple case study designs but are applicable to many of these designs. First, methodologists have clarified the role and procedures of developing and refining concepts. Robert Adcock and David Collier (2001) have outlined the relationships among background concepts, systematized concepts, indicators, and scores on individual cases, noting that there are often iterative changes from one level to another in the course of research. Collier and Stephen Levitsky (1997) have pointed out the prevalence and uses of “diminished subtypes,” or conceptual categories that lack one or more of the attributes of full examples of the phenomenon in question. Collier, Hidalgo, and Maciuceanu (2006) have unpacked and updated the debate over essentially contested concepts. John Gerring (2001 ; Gerring and Barresi 2003 ) has clarified the trade‐offs among different desiderata of qualitative concepts and measures, as well as suggested guidelines for concept formation. Gary Goertz (2006) has distinguished between necessary/sufficient concepts, or concepts for which some component is necessary or sufficient, and family resemblance concepts, for which membership in a conceptual category is determined by having a specified minimal level of several substitutable attributes.

Secondly, Goertz and Mahoney (2005) have identified “two‐level theories” as an important pattern of theorizing common to many qualitative studies. Two‐ level theories can combine elements of necessity at one level that interact with family resemblance relationships at another level. Goertz and Mahoney illustrate this with Theda Skocpol's famous theory (1979) on social revolutions, in which state breakdown and peasant revolt are both necessary for social revolution, but either of these conditions can be achieved through several different substitutable routes. There are many possible kinds of two‐level theories, which can be depicted either as a flowchart diagram or as a typological space (for an example showing the correspondence of these two forms of presentation, see Bennett 1999, 109–10 ).

Thirdly, several methodologists have clarified the problems of case selection and selection bias in case study research designs. Proponents of a single logic of inference level strong criticisms at qualitative methods undertaken without a proper appreciation for what they consider to be universally applicable quantitative rules of inference. One common critique is that case study research designs often involve the investigator selecting cases for study based on prior knowledge of these cases' outcomes. The most common form of this critique is that the selection of cases on the basis of values of the dependent variable leads to an underestimation of the effects of the independent variable ( King, Keohane, and Verba 1994 ; Geddes 2003, 87 ).

Third‐generation methodologists, however, argue that the challenge of case selection in qualitative research is often misunderstood when it is viewed through the prism of case selection biases in observational statistical studies. Properly understood, case selection procedures in qualitative research designs could in some instances be more damaging to causal inference than the standard statistical critique suggests, but often these procedures are in fact well adapted to the inferential purposes for which qualitative researchers use them. Selection on the dependent‐ variable and no‐variance designs have important uses in case study research. A single deviant case, for example, can prove fruitful in identifying a new variable, even though such a case is selected on the dependent variable. As noted above, although a deviant case is seemingly a “no‐variance” design, it is chosen for implicit or explicit comparison to a theoretical or empirical pattern from which it varies. Moreover, no‐variance single cases selected on the dependent variable can test claims of necessity or sufficiency ( Dion 1998 ).

In addition, the statistical selection bias critique assumes a preconstituted population, but if the researcher has no such population in mind and is trying to learn more about similarities among positive cases before identifying the relevant underlying population, selection on the dependent variable is justifiable. Otherwise, “addressing the question of selection bias before establishing an appropriate population puts the cart before the horse” ( Collier, Mahoney, and Seawright 2004, 88 ). In addition, the selection bias critique does not apply to process tracing in the same way that it does to cross‐case comparative methods, as process tracing does not rely on cross‐case covariation ( Collier and Mahoney 1996 ; Collier, Mahoney, and Seawright 2004, 96 ). As noted above, even comparative cases research designs, such as the most‐similar cases design, draw much of their inferential power from process tracing. In short, although variance on the independent and dependent variables is essential for many kinds of case study research designs and inferential goals, it is not necessary or even useful for all such designs and goals.

Another area of innovation regarding case selection concerns an important issue that researchers using both qualitative and quantitative methods often overlook: the problem of defining and selecting negative cases of a phenomenon, or contexts in which the outcome of interest could have happened but did not. The inclusion of irrelevant cases in a statistical study, such as including in a study of inter‐state wars dyads of far distant countries with no capability or motivation to fight one another, can make a theory look stronger than it actually is. Mahoney and Goertz (2004) have suggested a “possibility principle” for identifying relevant cases by a “rule of inclusion,” in which cases are included if the value of at least one independent variable points to the outcome of interest, and a “rule of exclusion,” through which cases are excluded if they have a variable at a value known through previous studies to make the outcome of interest impossible. These authors note that these rules are in part theory dependent and should not be applied mechanically, and in fact there may be many variants on these rules depending on the nature of prior knowledge of the phenomenon in question. Whatever criteria one chooses for identifying negative cases, the task of identifying them as rigorously as possible is important for many studies.

2 New Frontiers in Qualitative Methods

Innovations in qualitative methods are ongoing, and five areas in particular deserve mention as current or potential subjects for further development. First, the development of multi‐method research designs is already well under way, led by empirical research examples rather than by systematic analyses by methodologists of alternative ways of combining different methods. There are several excellent examples of international relations research designs combining case study methods with formal models, statistical analysis or both. Other methods, including experiments and ethnographic research, can be combined with case studies as well. The great advantage of combining methods is that each approach offers the potential for at least partly offsetting the limitations of another. The challenge of multi‐method research, particularly for graduate students, is that a great deal of time and skill are required to develop expertise in more than one method and to gather the evidence each method requires. Scholars have only just begun addressing the question of how to combine methods more generally ( Lieberman 2005 ; Seawright and Gerring 2006 ; Bennett and Elman 2007 b ), and much more work on this subject remains to be done.

Secondly, qualitative methodologists have begun focusing on how to assess theories that involve different forms of complexity. Several have investigated issues related to path dependence and ways in which qualitative methods can address them ( Mahoney 2000 ; Bennett and Elman 2006 b ). Goertz and Mahoney (2005) address a different form of complexity in their work on various combinations of necessity and family resemblance relations in two‐level theories. Fuzzy Set analysis and typological theorizing are ways of addressing the related challenge of multivariate interaction effects. There is potential for further advances in these and other areas of complexity theory, perhaps drawing on work from other sciences that have confronted the problems of complexity, such as evolutionary biology.

Thirdly, qualitative methods need to keep pace with developments in the cognitive sciences. One role for rigorous methodological procedures is to safeguard against our own cognitive biases. Many procedures in both qualitative and quantitative methods, for example, are geared to guard against the dangers of confirmation bias, which have been amply demonstrated in laboratory experiments. Research in cognitive psychology and behavioral economics has pointed to many other kinds of inferential biases ( Kahneman, Slovic, and Tversky 1982 ), and studies in political psychology have demonstrated that political scientists are vulnerable to such biases ( Tetlock 2005 ). Recent work suggests that a few simple procedures, such as asking individuals to think counterfactually about the conditions under which their predictions might be proved wrong, can improve performance at inferential tasks like Bayesian updating ( Herrmann and Choi 2007 ). Qualitative methodologists, and methodologists more generally, need to mine the large and growing literature on cognitive biases and systematically develop procedures for addressing them.

A fourth area for further development is that of improving the access to and replicability of qualitative evidence. Qualitative researchers can make much greater use of improved technologies for gathering and storing audio and visual data and making these data web‐accessible. Field notes, audio and videotapes of interviews and events, photographs of symbols and artifacts, and other kinds of qualitative data can be made accessible and linked to publications. As more such evidence becomes available online, research organizations like the National Science Foundation need to address the question of whether they can play a role in providing storage space for such information, and to consider whether existing open‐source search engines are adequate for the task of enabling users easily to find what they need. Methodologists and communities of scholars also need to devise standards and protocols on the presentation and replicability of such qualitative data. There is a role as well for qualitative researchers with regional and functional expertise to contribute to the improvement of quantitative databases, and cumulatively to apply their knowledge toward making the codings in such databases more accurate ( Bowman, Lehoucq, and Mahoney 2005 ). Web‐based means of soliciting and vetting community input, similar to the process used by Wikipedia, may prove helpful here.

Finally, qualitative methodologists need to renew their focus on the challenge of generalizing from individual and comparative case studies. The findings of studies of deviant cases, and of studies that affirm a theory in a least‐likely case or undermine it in a most‐likely case, may be widely generalizable, or they may prove to be limited only to the case studied. The standards for assessing the generalizability of findings from such cases need to be clarified, and researchers need to be more precise in stating whether they think their findings apply only to the case under study, to some type or category of configurative cases of which it is member, or to broad populations sharing only one or a few features of the case studied. Put another way, qualitative researchers need to clarify the conditions under which they can claim different kinds of scope conditions for their theories based on the cases they study ( Goertz and Mahoney 2006 ). These five tasks pose important and potentially fruitful challenges for the next generation of qualitative researchers and methodologists.

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In 2007, the institute name was changed from the Institute for Qualitative Research Methods.

International relations scholars may already be familiar with some of the third‐generation works centrally located in their subfield—e.g. Tetlock and Belkin (1996) ; George and Bennett (2005) — but the larger canon also includes works by Bates et al. (1998) ; Ragin (2000) ; Elman and Elman (2001 ; 2003 ); Gerring (2001 ; 2007 a ) ; Goertz and Starr (2002) ; Mahoney and Rueschemeyer (2003) ; Brady and Collier (2004) ; Pierson (2004) ; Goertz (2006) ; and Goertz and Levy (2007) .

See, e. g., the list of thirty‐five varieties of interpretative research methods listed in table 1 in the introduction to Yanow and Schwartz‐Shea (2006) .

There is a substantial overlap here with scholars whom Christian Reus‐Smit (2002, 495) identifies as “methodological conventionalists.” These two categories also roughly coincide with what Andrew Hurd (this volume) identifies as “positivists,” though in our view many international relations scholars, whether constructivist or not, are methodologically conventional but do not subscribe to traditional positivist notions of “laws” and “falsifiability.”

See also Yanow (2006, 6) , and Yanow and Schwartz‐Shea (2006, xxxvi , n. 15). Similarly, Friedrich Kratochwil (this volume) raises several hermeneutic critiques of the most ambitious form of scientific realism, specifically the claim that theories in some sense “refer” in progressively more accurate ways to underlying “realities.” Kratochwil does not go so far as suggesting, however, that there is no basis for judging some interpretations to be superior to others, and it is not clear whether he objects as well to more modest forms of scientific realism that do not presume science is always progressive (for an analysis of different varieties of scientific realism, see Chernoff 2002 ).

The discussion below draws upon and further develops our previous separate and joint writings on qualitative methods, including George and Bennett (2005) ; and Bennett and Elman (2006 a ; 2006 b ; 2007 a ; 2007 b ).

See also Collier, Brady, and Seawright's discussion (2004, 252–5) of causal process observations.

The Bayesian approach to theory testing focuses on updating degrees of belief in the truth of alternative explanations. In other words, Bayesians treat statistical parameters probabilistically, attaching subjective probabilities to the likelihood that hypotheses are true and then updating these probabilities in the light of new evidence. In contrast, frequentist statistics attaches probabilities to the likelihood of getting similar results from repeated sampling of a population. Bayesian inference can apply to one or a few cases or pieces of evidence, whereas frequentist statistical analysis needs a higher number of cases to allow inferences, although the two forms of inference should converge on similar results as the number of cases or pieces of evidence grows. Process tracing follows a logic that is very similar to Bayesian reasoning, updating degrees of belief in alternative explanations of a case in light of evidence generated from within that case. For further discussion of the similarities between process tracing and Bayesian inference, see Bennett (2007) .

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Case Studies and Comparative Analysis

A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. Generally, a case study can highlight an individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation, but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study). Comparative analysis, on the other hand, is a method that compares two or more of anything (documents, data sets, political systems etc.) – though sometimes a form of comparative analysis is used to compare two or more cases studies, demonstrating the links between these two methods. Text adapted from Wikipedia .

The resources below have been curated by the E-International Relations team. You can find more resources on our methods homepage .

Types of Case Study by Graham R Gibbs (YouTube)

Comparative Politics: What and how to compare by Patrick Theiner (YouTube)

Comparative Case Studies – Avoid bias, ensure case studies represent population, not anomalies by Delwyn Goodrick (YouTube)

Websites Writing a Case Study by PLNU (Website).

Qualitative Comparative Analysis by Wendy Olsen (Website).

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