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  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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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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Multiple Case Research Design

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This chapter addresses the peculiarities, characteristics, and major fallacies of multiple case research designs. The major advantage of multiple case research lies in cross-case analysis. A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting more (second, third, etc.) case studies. Rather, it is the next step in developing a theory about factors driving differences and similarities. Also, researchers find relevant information on how to write a multiple case research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2021). Multiple Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_9

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Using qualitative comparative analysis to understand and quantify translation and implementation

Heather kane.

RTI International, 3040 Cornwallis Road, Research Triangle Park, P.O. Box 12194, Durham, NC 27709 USA

Megan A Lewis

Pamela a williams, leila c kahwati.

Understanding the factors that facilitate implementation of behavioral medicine programs into practice can advance translational science. Often, translation or implementation studies use case study methods with small sample sizes. Methodological approaches that systematize findings from these types of studies are needed to improve rigor and advance the field. Qualitative comparative analysis (QCA) is a method and analytical approach that can advance implementation science. QCA offers an approach for rigorously conducting translational and implementation research limited by a small number of cases. We describe the methodological and analytic approach for using QCA and provide examples of its use in the health and health services literature. QCA brings together qualitative or quantitative data derived from cases to identify necessary and sufficient conditions for an outcome. QCA offers advantages for researchers interested in analyzing complex programs and for practitioners interested in developing programs that achieve successful health outcomes.


In this paper, we describe the methodological features and advantages of using qualitative comparative analysis (QCA). QCA is sometimes called a “mixed method.” It refers to both a specific research approach and an analytic technique that is distinct from and offers several advantages over traditional qualitative and quantitative methods [ 1 – 4 ]. It can be used to (1) analyze small to medium numbers of cases (e.g., 10 to 50) when traditional statistical methods are not possible, (2) examine complex combinations of explanatory factors associated with translation or implementation “success,” and (3) combine qualitative and quantitative data using a unified and systematic analytic approach.

This method may be especially pertinent for behavioral medicine given the growing interest in implementation science [ 5 ]. Translating behavioral medicine research and interventions into useful practice and policy requires an understanding of the implementation context. Understanding the context under which interventions work and how different ways of implementing an intervention lead to successful outcomes are required for “T3” (i.e., dissemination and implementation of evidence-based interventions) and “T4” translations (i.e., policy development to encourage evidence-based intervention use among various stakeholders) [ 6 , 7 ].

Case studies are a common way to assess different program implementation approaches and to examine complex systems (e.g., health care delivery systems, interventions in community settings) [ 8 ]. However, multiple case studies often have small, naturally limited samples or populations; small samples and populations lack adequate power to support conventional, statistical analyses. Case studies also may use mixed-method approaches, but typically when researchers collect quantitative and qualitative data in tandem, they rarely integrate both types of data systematically in the analysis. QCA offers solutions for the challenges posed by case studies and provides a useful analytic tool for translating research into policy recommendations. Using QCA methods could aid behavioral medicine researchers who seek to translate research from randomized controlled trials into practice settings to understand implementation. In this paper, we describe the conceptual basis of QCA, its application in the health and health services literature, and its features and limitations.


QCA has its foundations in historical, comparative social science. Researchers in this field developed QCA because probabilistic methods failed to capture the complexity of social phenomena and required large sample sizes [ 1 ]. Recently, this method has made inroads into health research and evaluation [ 9 – 13 ] because of several useful features as follows: (1) it models equifinality , which is the ability to identify more than one causal pathway to an outcome (or absence of the outcome); (2) it identifies conjunctural causation , which means that single conditions may not display their effects on their own, but only in conjunction with other conditions; and (3) it implies asymmetrical relationships between causal conditions and outcomes, which means that causal pathways for achieving the outcome differ from causal pathways for failing to achieve the outcome.

QCA is a case-oriented approach that examines relationships between conditions (similar to explanatory variables in regression models) and an outcome using set theory; a branch of mathematics or of symbolic logic that deals with the nature and relations of sets. A set-theoretic approach to modeling causality differs from probabilistic methods, which examines the independent, additive influence of variables on an outcome. Regression models, based on underlying assumptions about sampling and distribution of the data, ask “what factor, holding all other factors constant at each factor’s average, will increase (or decrease) the likelihood of an outcome .” QCA, an approach based on the examination of set, subset, and superset relationships, asks “ what conditions —alone or in combination with other conditions—are necessary or sufficient to produce an outcome .” For additional QCA definitions, see Ragin [ 4 ].

Necessary conditions are those that exhibit a superset relationship with the outcome set and are conditions or combinations of conditions that must be present for an outcome to occur. In assessing necessity, a researcher “identifies conditions shared by cases with the same outcome” [ 4 ] (p. 20). Figure  1 shows a hypothetical example. In this figure, condition X is a necessary condition for an effective intervention because all cases with condition X are also members of the set of cases with the outcome present; however, condition X is not sufficient for an effective intervention because it is possible to be a member of the set of cases with condition X, but not be a member of the outcome set [ 14 ].

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Necessary and sufficient conditions and set-theoretic relationships

Sufficient conditions exhibit subset relationships with an outcome set and demonstrate that “the cause in question produces the outcome in question” [ 3 ] (p. 92). Figure  1 shows the multiple and different combinations of conditions that produce the hypothetical outcome, “effective intervention,” (1) by having condition A present, (2) by having condition D present, or (3) by having the combination of conditions B and C present. None of these conditions is necessary and any one of these conditions or combinations of conditions is sufficient for the outcome of an effective intervention.


The term “QCA” is sometimes used to refer to the comparative research approach but also refers to the “analytic moment” during which Boolean algebra and set theory logic is applied to truth tables constructed from data derived from included cases. Figure  2 characterizes this distinction. Although this figure depicts steps as sequential, like many research endeavors, these steps are somewhat iterative, with respecification and reanalysis occurring along the way to final findings. We describe each of the essential steps of QCA as an approach and analytic technique and provide examples of how it has been used in health-related research.

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QCA as an approach and as an analytic technique

Operationalizing the research question

Like other types of studies, the first step involves identifying the research question(s) and developing a conceptual model. This step guides the study as a whole and also informs case, condition (c.f., variable), and outcome selection. As mentioned above, QCA frames research questions differently than traditional quantitative or qualitative methods. Research questions appropriate for a QCA approach would seek to identify the necessary and sufficient conditions required to achieve the outcome. Thus, formulating a QCA research question emphasizes what program components or features—individually or in combination—need to be in place for a program or intervention to have a chance at being effective (i.e., necessary conditions) and what program components or features—individually or in combination—would produce the outcome (i.e., sufficient conditions). For example, a set theoretic hypothesis would be as follows: If a program is supported by strong organizational capacity and a comprehensive planning process, then the program will be successful. A hypothesis better addressed by probabilistic methods would be as follows: Organizational capacity, holding all other factors constant, increases the likelihood that a program will be successful.

For example, Longest and Thoits [ 15 ] drew on an extant stress process model to assess whether the pathways leading to psychological distress differed for women and men. Using QCA was appropriate for their study because the stress process model “suggests that particular patterns of predictors experienced in tandem may have unique relationships with health outcomes” (p. 4, italics added). They theorized that predictors would exhibit effects in combination because some aspects of the stress process model would buffer the risk of distress (e.g., social support) while others simultaneously would increase the risk (e.g., negative life events).

Identify cases

The number of cases in a QCA analysis may be determined by the population (e.g., 10 intervention sites, 30 grantees). When particular cases can be chosen from a larger population, Berg-Schlosser and De Meur [ 16 ] offer other strategies and best practices for choosing cases. Unless the number of cases relies on an existing population (i.e., 30 programs or grantees), the outcome of interest and existing theory drive case selection, unlike variable-oriented research [ 3 , 4 ] in which numbers are driven by statistical power considerations and depend on variation in the dependent variable. For use in causal inference, both cases that exhibit and do not exhibit the outcome should be included [ 16 ]. If a researcher is interested in developing typologies or concept formation, he or she may wish to examine similar cases that exhibit differences on the outcome or to explore cases that exhibit the same outcome [ 14 , 16 ].

For example, Kahwati et al. [ 9 ] examined the structure, policies, and processes that might lead to an effective clinical weight management program in a large national integrated health care system, as measured by mean weight loss among patients treated at the facility. To examine pathways that lead to both better and poorer facility-level weight loss, 11 facilities from among those with the largest weight loss outcomes and 11 facilities from among those with the smallest were included. By choosing cases based on specific outcomes, Kahwati et al. could identify multiple patterns of success (or failure) that explain the outcome rather than the variability associated with the outcome.

Identify conditions and outcome sets

Selecting conditions relies on the research question, conceptual model, and number of cases similar to other research methods. Conditions (or “sets” or “condition sets”) refer to the explanatory factors in a model; they are similar to variables. Because QCA research questions assess necessary and sufficient conditions, a researcher should consider which conditions in the conceptual model would theoretically produce the outcome individually or in combination. This helps to focus the analysis and number of conditions. Ideally, for a case study design with a small (e.g., 10–15) or intermediate (e.g., 16–100) number of cases, one should aim for fewer than five conditions because in QCA a researcher assesses all possible configurations of conditions. Adding conditions to the model increases the possible number of combinations exponentially (i.e., 2 k , where k = the number of conditions). For three conditions, eight possible combinations of the selected conditions exist as follows: the presence of A, B, C together, the lack of A with B and C present, the lack of A and lack of B with C present, and so forth. Having too many conditions will likely mean that no cases fall into a particular configuration, and that configuration cannot be assessed by empirical examples. When one or more configurations are not represented by the cases, this is known as limited diversity, and QCA experts suggest multiple strategies for managing such situations [ 4 , 14 ].

For example, Ford et al. [ 10 ] studied health departments’ implementation of core public health functions and organizational factors (e.g., resource availability, adaptability) and how those conditions lead to superior and inferior population health changes. They operationalized three core public functions (i.e., assessment of environmental and population public health needs, capacity for policy development, and authority over assurance of healthcare operations) and operationalized those for their study by using composite measures of varied health indicators compiled in a UnitedHealth Group report. In this examination of 41 state health departments, the authors found that all three core public health functions were necessary for population health improvement. The absence of any of the core public health functions was sufficient for poorer population health outcomes; thus, only the health departments with the ability to perform all three core functions had improved outcomes. Additionally, these three core functions in combination with either resource availability or adaptability were sufficient combinations (i.e., causal pathways) for improved population health outcomes.

Calibrate condition and outcome sets

Calibration refers to “adjusting (measures) so that they match or conform to dependably known standards” and is a common way of standardizing data in the physical sciences [ 4 ] (p. 72). Calibration requires the researcher to make sense of variation in the data and apply expert knowledge about what aspects of the variation are meaningful. Because calibration depends on defining conditions based on those “dependably known standards,” QCA relies on expert substantive knowledge, theory, or criteria external to the data themselves [ 14 ]. This may require researchers to collaborate closely with program implementers.

In QCA, one can use “crisp” set or “fuzzy” set calibration. Crisp sets, which are similar to dichotomous categorical variables in regression, establish decision rules defining a case as fully in the set (i.e., condition) or fully out of the set; fuzzy sets establish degrees of membership in a set. Fuzzy sets “differentiate between different levels of belonging anchored by two extreme membership scores at 1 and 0” [ 14 ] (p.28). They can be continuous (0, 0.1, 0.2,..) or have qualitatively defined anchor points (e.g., 0 is fully out of the set; 0.33 is more out than in the set; 0.66 is more in than out of the set; 1 is fully in the set). A researcher selects fuzzy sets and the corresponding resolution (i.e., continuous, four cutoff points, six cutoff) based on theory and meaningful differences between cases and must be able to provide a verbal description for each cutoff point [ 14 ]. If, for example, a researcher cannot distinguish between 0.7 and 0.8 membership in a set, then a more continuous scoring of cases would not be useful, rather a four point cutoff may better characterize the data. Although crisp and fuzzy sets are more commonly used, new multivariate forms of QCA are emerging as are variants that incorporate elements of time [ 14 , 17 , 18 ].

Fuzzy sets have the advantage of maintaining more detail for data with continuous values. However, this strength also makes interpretation more difficult. When an observation is coded with fuzzy sets, a particular observation has some degree of membership in the set “condition A” and in the set “condition NOT A.” Thus, when doing analyses to identify sufficient conditions, a researcher must make a judgment call on what benchmark constitutes recommendation threshold for policy or programmatic action.

In creating decision rules for calibration, a researcher can use a variety of techniques to identify cutoff points or anchors. For qualitative conditions, a researcher can define decision rules by drawing from the literature and knowledge of the intervention context. For conditions with numeric values, a researcher can also employ statistical approaches. Ideally, when using statistical approaches, a researcher should establish thresholds using substantive knowledge about set membership (thus, translating variation into meaningful categories). Although measures of central tendency (e.g., cases with a value above the median are considered fully in the set) can be used to set cutoff points, some experts consider the sole use of this method to be flawed because case classification is determined by a case’s relative value in regard to other cases as opposed to its absolute value in reference to an external referent [ 14 ].

For example, in their study of National Cancer Institutes’ Community Clinical Oncology Program (NCI CCOP), Weiner et al. [ 19 ] had numeric data on their five study measures. They transformed their study measures by using their knowledge of the CCOP and by asking NCI officials to identify three values: full membership in a set, a point of maximum ambiguity, and nonmembership in the set. For their outcome set, high accrual in clinical trials, they established 100 patients enrolled accrual as fully in the set of high accrual, 70 as a point of ambiguity (neither in nor out of the set), and 50 and below as fully out of the set because “CCOPs must maintain a minimum of 50 patients to maintain CCOP funding” (p. 288). By using QCA and operationalizing condition sets in this way, they were able to answer what condition sets produce high accrual, not what factors predict more accrual. The advantage is that by using this approach and analytic technique, they were able to identify sets of factors that are linked with a very specific outcome of interest.

Obtain primary or secondary data

Data sources vary based on the study, availability of the data, and feasibility of data collection; data can be qualitative or quantitative, a feature useful for mixed-methods studies and systematically integrating these different types of data is a major strength of this approach. Qualitative data include program documents and descriptions, key informant interviews, and archival data (e.g., program documents, records, policies); quantitative data consists of surveys, surveillance or registry data, and electronic health records.

For instance, Schensul et al. [ 20 ] relied on in-depth interviews for their analysis; Chuang et al. [ 21 ] and Longest and Thoits [ 15 ] drew on survey data for theirs. Kahwati et al. [ 9 ] used a mixed-method approach combining data from key informant interviews, program documents, and electronic health records. Any type of data can be used to inform the calibration of conditions.

Assign set membership scores

Assigning set membership scores involves applying the decision rules that were established during the calibration phase. To accomplish this, the research team should then use the extracted data for each case, apply the decision rule for the condition, and discuss discrepancies in the data sources. In their study of factors that influence health care policy development in Florida, Harkreader and Imershein [ 22 ] coded contextual factors that supported state involvement in the health care market. Drawing on a review of archival data and using crisp set coding, they assigned a value of 1 for the presence of a contextual factor (e.g., presence of federal financial incentives promoting policy, unified health care provider policy position in opposition to state policy, state agency supporting policy position) and 0 for the absence of a contextual factor.

Construct truth table

After completing the coding, researchers create a “truth table” for analysis. A truth table lists all of the possible configurations of conditions, the number of cases that fall into that configuration, and the “consistency” of the cases. Consistency quantifies the extent to which cases that share similar conditions exhibit the same outcome; in crisp sets, the consistency value is the proportion of cases that exhibit the outcome. Fuzzy sets require a different calculation to establish consistency and are described at length in other sources [ 1 – 4 , 14 ]. Table  1 displays a hypothetical truth table for three conditions using crisp sets.

Sample of a hypothetical truth table for crisp sets

1 fully in the set, 0 fully out of the set


The research steps to this point fall into QCA as an approach to understanding social and health phenomena. Analysis of the truth table is the sine qua non of QCA as an analytic technique. In this section, we provide an overview of the analysis process, but analytic techniques and emerging forms of analysis are described in multiple texts [ 3 , 4 , 14 , 17 ]. The use of computer software to conduct truth table analysis is recommended and several software options are available including Stata, fsQCA, Tosmana, and R.

A truth table analysis first involves the researcher assessing which (if any) conditions are individually necessary or sufficient for achieving the outcome, and then second, examining whether any configurations of conditions are necessary or sufficient. In instances where contradictions in outcomes from the same configuration pattern occur (i.e., one case from a configuration has the outcome; one does not), the researcher should also consider whether the model is properly specified and conditions are calibrated accurately. Thus, this stage of the analysis may reveal the need to review how conditions are defined and whether the definition should be recalibrated. Similar to qualitative and quantitative research approaches, analysis is iterative.

Additionally, the researcher examines the truth table to assess whether all logically possible configurations have empiric cases. As described above, when configurations lack cases, the problem of limited diversity occurs. Configurations without representative cases are known as logical remainders, and the researcher must consider how to deal with those. The analysis of logical remainders depends on the particular theory guiding the research and the research priorities. How a researcher manages the logical remainders has implications for the final solution, but none of the solutions based on the truth table will contradict the empirical evidence [ 14 ]. To generate the most conservative solution term, a researcher makes no assumptions about truth table rows with no cases (or very few cases in larger N studies) and excludes them from the logical minimization process. Alternately, a researcher can choose to include (or exclude) rows with no cases from analysis, which would generate a solution that is a superset of the conservative solution. Choosing inclusion criteria for logical remainders also depends on theory and what may be empirically possible. For example, in studying governments, it would be unlikely to have a case that is a democracy (“condition A”), but has a dictator (“condition B”). In that circumstance, the researcher may choose to exclude that theoretically implausible row from the logical minimization process.

Third, once all the solutions have been identified, the researcher mathematically reduces the solution [ 1 , 14 ]. For example, if the list of solutions contains two identical configurations, except that in one configuration A is absent and in the other A is present, then A can be dropped from those two solutions. Finally, the researcher computes two parameters of fit: coverage and consistency. Coverage determines the empirical relevance of a solution and quantifies the variation in causal pathways to an outcome [ 14 ]. When coverage of a causal pathway is high, the more common the solution is, and more of the outcome is accounted for by the pathway. However, maximum coverage may be less critical in implementation research because understanding all of the pathways to success may be as helpful as understanding the most common pathway. Consistency assesses whether the causal pathway produces the outcome regularly (“the degree to which the empirical data are in line with a postulated subset relation,” p. 324 [ 14 ]); a high consistency value (e.g., 1.00 or 100 %) would indicate that all cases in a causal pathway produced the outcome. A low consistency value would suggest that a particular pathway was not successful in producing the outcome on a regular basis, and thus, for translational purposes, should not be recommended for policy or practice changes. A causal pathway with high consistency and coverage values indicates a result useful for providing guidance; a high consistency with a lower coverage score also has value in showing a causal pathway that successfully produced the outcome, but did so less frequently.

For example, Kahwati et al. [ 9 ] examined their truth table and analyzed the data for single conditions and combinations of conditions that were necessary for higher or lower facility-level patient weight loss outcomes. The truth table analysis revealed two necessary conditions and four sufficient combinations of conditions. Because of significant challenges with logical remainders, they used a bottom-up approach to assess whether combinations of conditions yielded the outcome. This entailed pairing conditions to ensure parsimony and maximize coverage. With a smaller number of conditions, a researcher could hypothetically find that more cases share similar characteristics and could assess whether those cases exhibit the same outcome of interest.

At the completion of the truth table analysis, Kahwati et al. [ 9 ] used the qualitative data from site interviews to provide rich examples to illustrate the QCA solutions that were identified, which explained what the solutions meant in clinical practice for weight management. For example, having an involved champion (usually a physician), in combination with low facility accountability, was sufficient for program success (i.e., better weight loss outcomes) and was related to better facility weight loss. In reviewing the qualitative data, Kahwati et al. [ 9 ] discovered that involved champions integrate program activities into their clinical routines and discuss issues as they arise with other program staff. Because involved champions and other program staff communicated informally on a regular basis, formal accountability structures were less of a priority.


Because translational (and other health-related) researchers may be interested in which intervention features—alone or in combination—achieve distinct outcomes (e.g., achievement of program outcomes, reduction in health disparities), QCA is well suited for translational research. To assess combinations of variables in regression, a researcher relies on interaction effects, which, although useful, become difficult to interpret when three, four, or more variables are combined. Furthermore, in regression and other variable-oriented approaches, independent variables are held constant at the average across the study population to isolate the independent effect of that variable, but this masks how factors may interact with each other in ways that impact the ultimate outcomes. In translational research, context matters and QCA treats each case holistically, allowing each case to keep its own values for each condition.

Multiple case studies or studies with the organization as the unit of analysis often involve a small or intermediate number of cases. This hinders the use of standard statistical analyses; researchers are less likely to find statistical significance with small sample sizes. However, QCA draws on analyses of set relations to support small-N studies and to identify the conditions or combinations of conditions that are necessary or sufficient for an outcome of interest and may yield results when probabilistic methods cannot.

Finally, QCA is based on an asymmetric concept of causation , which means that the absence of a sufficient condition associated with an outcome does not necessarily describe the causal pathway to the absence of the outcome [ 14 ]. These characteristics can be helpful for translational researchers who are trying to study or implement complex interventions, where more than one way to implement a program might be effective and where studying both effective and ineffective implementation practices can yield useful information.

QCA has several limitations that researchers should consider before choosing it as a potential methodological approach. With small- and intermediate-N studies, QCA must be theory-driven and circumscribed by priority questions. That is, a researcher ideally should not use a “kitchen sink” approach to test every conceivable condition or combination of conditions because the number of combinations increases exponentially with the addition of another condition. With a small number of cases and too many conditions, the sample would not have enough cases to provide examples of all the possible configurations of conditions (i.e., limited diversity), or the analysis would be constrained to describing the characteristics of the cases, which would have less value than determining whether some conditions or some combination of conditions led to actual program success. However, if the number of conditions cannot be reduced, alternate QCA techniques, such as a bottom-up approach to QCA or two-step QCA, can be used [ 14 ].

Another limitation is that programs or clinical interventions involved in a cross-site analysis may have unique programs that do not seem comparable. Cases must share some degree of comparability to use QCA [ 16 ]. Researchers can manage this challenge by taking a broader view of the program(s) and comparing them on broader characteristics or concepts, such as high/low organizational capacity, established partnerships, and program planning, if these would provide meaningful conclusions. Taking this approach will require careful definition of each of these concepts within the context of a particular initiative. Definitions may also need to be revised as the data are gathered and calibration begins.

Finally, as mentioned above, crisp set calibration dichotomizes conditions of interest; this form of calibration means that in some cases, the finer grained differences and precision in a condition may be lost [ 3 ]. Crisp set calibration provides more easily interpretable and actionable results and is appropriate if researchers are primarily interested in the presence or absence of a particular program feature or organizational characteristic to understand translation or implementation.

QCA offers an additional methodological approach for researchers to conduct rigorous comparative analyses while drawing on the rich, detailed data collected as part of a case study. However, as Rihoux, Benoit, and Ragin [ 17 ] note, QCA is not a miracle method, nor a panacea for all studies that use case study methods. Furthermore, it may not always be the most suitable approach for certain types of translational and implementation research. We outlined the multiple steps needed to conduct a comprehensive QCA. QCA is a good approach for the examination of causal complexity, and equifinality could be helpful to behavioral medicine researchers who seek to translate evidence-based interventions in real-world settings. In reality, multiple program models can lead to success, and this method accommodates a more complex and varied understanding of these patterns and factors.


Practice : Identifying multiple successful intervention models (equifinality) can aid in selecting a practice model relevant to a context, and can facilitate implementation.

Policy : QCA can be used to develop actionable policy information for decision makers that accommodates contextual factors.

Research : Researchers can use QCA to understand causal complexity in translational or implementation research and to assess the relationships between policies, interventions, or procedures and successful outcomes.


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