case study definition in government

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

John Gerring is Professor of Political Science, Boston University.

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

Two centuries after Le Play’s pioneering work, the various disciplines of the social sciences continue to produce a vast number of case studies, many of which have entered the pantheon of classic works. Judging by the large volume of recent scholarly output the case study research design plays a central role in anthropology, archeology, business, education, history, medicine, political science, psychology, social work, and sociology (Gerring 2007 a , ch. 1 ). Even in economics and political economy, fields not usually noted for their receptiveness to case-based work, there has been something of a renaissance. Recent studies of economic growth have turned to case studies of unusual countries such as Botswana, Korea, and Mauritius. 1 Debates on the relationship between trade and growth and the IMF and growth have likewise combined cross-national regression evidence with in-depth (quantitativ and qualitative) case analysis ( Srinivasan and Bhagwati 1999 ; Vreeland 2003 ). Work on ethnic politics and ethnic conflict has exploited within-country variation or small-N cross-country comparisons ( Abadie and Gardeazabal 2003 ; Chandra 2004 ; Posner 2004 ). By the standard of praxis, therefore, it would appear that the method of the case study is solidly ensconced, perhaps even thriving. Arguably, we are witnessing a movement away from a variable-centered approach to causality in the social sciences and towards a case-based approach.

Indeed, the statistical analysis of cross-case observational data has been subjected to increasing scrutiny in recent years. It no longer seems self-evident, even to nomothetically inclined scholars, that non-experimental data drawn from nation-states, cities, social movements, civil conflicts, or other complex phenomena should be treated in standard regression formats. The complaints are myriad, and oftreviewed. 2 They include: (a) the problem of arriving at an adequate specification of the causal model, given a plethora of plausible models, and the associated problem of modeling interactions among these covariates; (b) identification problems, which cannot always be corrected by instrumental variable techniques; (c) the problem of “extreme” counterfactuals, i.e. extrapolating or interpolating results from a general model where the extrapolations extend beyond the observable data points; (d) problems posed by influential cases; (e) the arbitrariness of standard significance tests; (f) the misleading precision of point estimates in the context of “curve-fitting” models; (g) the problem of finding an appropriate estimator and modeling temporal autocorrelation in pooled time series; (h) the difficulty of identifying causal mechanisms; and last, but certainly not least, (i) the ubiquitous problem of faulty data drawn from a variety of questionable sources. Most of these difficulties may be understood as the by-product of causal variables that offer limited variation through time and cases that are extremely heterogeneous.

A principal factor driving the general discontent with cross-case observational research is a new-found interest in experimental models of social scientific research. Following the pioneering work of Donald Campbell (1988 ; Cook and Campbell 1979 ) and Donald Rubin (1974) , methodologists have taken a hard look at the regression model and discovered something rather obvious but at the same time crucially important: this research bears only a faint relationship to the true experiment, for all the reasons noted above. The current excitement generated by matching estimators, natural experiments, and field experiments may be understood as a move toward a quasi-experimental, and frequently case-based analysis of causal relations. Arguably, this is because the experimental ideal is often better approximated by a small number of cases that are closely related to one another, or by a single case observed over time, than by a large sample of heterogeneous units.

A third factor militating towards case-based analysis is the development of a series of alternatives to the standard linear/additive model of cross-case analysis, thus establishing a more variegated set of tools to capture the complexity of social behavior (see Brady and Collier 2004 ). Charles Ragin and associates have shown us how to deal with situations where multiple causal paths lead to the same set of outcomes, a series of techniques known as Qualitative Comparative Analysis (QCA) (“Symposium: Qualitative Comparative Analysis” 2004). Andrew Abbott has worked out a method that maps causal sequences across cases, known as optimal sequence matching ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Bear Braumoeller, Gary Goertz, Jack Levy, and Harvey Starr have defended the importance of necessary-condition arguments in the social sciences, and have shown how these arguments might be analyzed ( Braumoeller and Goertz 2000 ; Goertz 2003 ; Goertz and Levy forthcoming; Goertz and Starr 2003 ). James Fearon, Ned Lebow, Philip Tetlock, and others have explored the role of counterfactual thought experiments in the analysis of individual case histories ( Fearon 1991 ; Lebow 2000 ; Tetlock and Belkin 1996 ). Colin Elman has developed a typological method of analyzing cases ( Elman 2005 ). David Collier, Jack Goldstone, Peter Hall, James Mahoney, and Dietrich Rueschemeyer have worked to revitalize the comparative and comparative-historical methods ( Collier 1993 ; Goldstone 1997 ; Hall 2003 ; Mahoney and Rueschemeyer 2003 ). And scores of researchers have attacked the problem of how to convert the relevant details of a temporally constructed narrative into standardized formats so that cases can be meaningfully compared (Abell 1987 , 2004 ; Abbott 1992 ; Buthe 2002 ; Griffin 1993 ). While not all of these techniques are, strictly speaking, case study techniques—since they sometimes involve a large number of cases—they do move us closer to a case-based understanding of causation insofar as they preserve the texture and detail of individual cases, features that are often lost in large-N cross-case analysis.

A fourth factor concerns the recent marriage of rational choice tools with case study analysis, sometimes referred to as an “analytic narrative” ( Bates et al. 1998 ). Whether the technique is qualitative or quantitative, scholars equipped with economic models are turning, increasingly, to case studies in order to test the theoretical predictions of a general model, investigate causal mechanisms, and/or explain the features of a key case.

Finally, epistemological shifts in recent decades have enhanced the attractiveness of the case study format. The “positivist” model of explanation, which informed work in the social sciences through most of the twentieth century, tended to downplay the importance of causal mechanisms in the analysis of causal relations. Famously, Milton Friedman (1953) argued that the only criterion of a model was to be found in its accurate prediction of outcomes. The verisimilitude of the model, its accurate depiction of reality, was beside the point. In recent years, this explanatory trope has come under challenge from “realists,” who claim (among other things) that causal analysis should pay close attention to causal mechanisms (e.g. Bunge 1997 ; Little 1998 ). Within political science and sociology, the identification of a specific mechanism—a causal pathway—has come to be seen as integral to causal analysis, regardless of whether the model in question is formal or informal or whether the evidence is qualitative or quantitative ( Achen 2002 ; Elster 1998 ; George and Bennett 2005 ; Hedstrom and Swedberg 1998 ). Given this new-found (or at least newly self-conscious) interest in mechanisms, it is not surprising that social scientists would turn to case studies as a mode of causal investigation.

For all the reasons stated above, one might intuit that social science is moving towards a case-based understanding of causal relations. Yet, this movement, insofar as it exists, has scarcely been acknowledged, and would certainly be challenged by many close observers—including some of those cited in the foregoing passages.

The fact is that the case study research design is still viewed by most methodologists with extreme circumspection. A work that focuses its attention on a single example of a broader phenomenon is apt to be described as a “mere” case study, and is often identified with loosely framed and non-generalizable theories, biased case selection, informal and undisciplined research designs, weak empirical leverage (too many variables and too few cases), subjective conclusions, non-replicability, and causal determinism. To some, the term case study is an ambiguous designation covering a multitude of “inferential felonies.” 3

The quasi-mystical qualities associated with the case study persist to this day. In the field of psychology, a gulf separates “scientists” engaged in cross-case research and “practitioners” engaged in clinical research, usually focused on several cases ( Hersen and Barlow 1976 , 21). In the fields of political science and sociology, case study researchers are acknowledged to be on the “soft” side of hard disciplines. And across fields, the persisting case study orientations of anthropology, education, law, social work, and various other fields and subfields relegate them to the non-rigorous, non-systematic, non-scientific, non-positivist end of the academic spectrum.

The methodological status of the case study is still, officially, suspect. Even among its defenders there is confusion over the virtues and vices of this ambiguous research design. Practitioners continue to ply their trade but have difficulty articulating what it is they are doing, methodologically speaking. The case study survives in a curious methodological limbo.

This leads to a paradox: although much of what we know about the empirical world has been generated by case studies and case studies continue to constitute a large proportion of work generated by the social science disciplines, the case study method is poorly understood.

How can we make sense of the profound disjuncture between the acknowledged contributions of this genre to the various disciplines of social science and its maligned status within these disciplines? If case studies are methodologically flawed, why do they persist? Should they be rehabilitated, or suppressed? How fruitful is this style of research?

In this chapter, I provide a reconstructed definition of the case study approach to research with special emphasis on comparative politics, a field that has been closely identified with this method since its birth. Based on this definition, I then explore a series of contrasts between case study and cross-case study research. These contrasts are intended to illuminate the characteristic strengths and weaknesses (“affinities”) of these two research designs, not to vindicate one or the other. The effort of this chapter is to understand this persisting methodological debate as a matter of tradeoffs. Case studies and cross-case studies explore the world in different ways. Yet, properly constituted, there is no reason that case study results cannot be synthesized with results gained from cross-case analysis, and vice versa. My hope, therefore, is that this chapter will contribute to breaking down the boundaries that have separated these rival genres within the subfield of comparative politics.

1 Definitions

The key term of this chapter is, admittedly, a definitional morass. To refer to a work as a “case study” might mean: that its method is qualitative, small-N; that the research is holistic, thick (a more or less comprehensive examination of a phenomenon); that it utilizes a particular type of evidence (e.g. ethnographic, clinical, non-experimental, non-survey based, participant observation, process tracing, historical, textual, or field research); that its method of evidence gathering is naturalistic (a “real-life context”); that the research investigates the properties of a single observation; or that the research investigates the properties of a single phenomenon, instance, or example. Evidently, researchers have many things in mind when they talk about case study research. Confusion is compounded by the existence of a large number of near-synonyms—single unit, single subject, single case, N = 1, case based, case control, case history, case method, case record, case work, clinical research, and so forth. As a result of this profusion of terms and meanings, proponents and opponents of the case study marshal a wide range of arguments but do not seem any closer to agreement than when this debate was first broached several decades ago.

Can we reconstruct this concept in a clearer, more productive fashion? In order to do so we must understand how the key terms—case and case study—are situated within a neighborhood of related terms. In this crowded semantic field, each term is defined in relation to others. And in the context of a specific work or research terrain, they all take their meaning from a specific inference. (The reader should bear in mind that any change in the inference, and the meaning of all the key terms will probably change.) My attempt here will be to provide a single, determinate, definition of these key terms. Of course, researchers may choose to define these terms in many different ways. However, for purposes of methodological discussion it is helpful to enforce a uniform vocabulary.

Let us stipulate that a case connotes a spatially delimited phenomenon (a unit) observed at a single point in time or over some period of time. It comprises the sort of phenomena that an inference attempts to explain. Thus, in a study that attempts to explain certain features of nation-states, cases are comprised of nation-states (across some temporal frame). In a study that attempts to explain the behavior of individuals, individuals comprise the cases. And so forth. Each case may provide a single observation or multiple (within-case) observations.

For students of comparative politics, the archetypal case is the dominant political unit of our time, the nation-state. However, the study of smaller social and political units (regions, cities, villages, communities, social groups, families) or specific institutions (political parties, interest groups, businesses) is equally common in other subfields, and perhaps increasingly so in comparative politics. Whatever the chosen unit, the methodological issues attached to the case study have nothing to do with the size of the individual cases. A case may be created out of any phenomenon so long as it has identifiable boundaries and comprises the primary object of an inference.

Note that the spatial boundaries of a case are often more apparent than its temporal boundaries. We know, more or less, where a country begins and ends, even though we may have difficulty explaining when a country begins and ends. Yet, some temporal boundaries must be assumed. This is particularly important when cases consist of discrete events—crises, revolutions, legislative acts, and so forth—within a single unit. Occasionally, the temporal boundaries of a case are more obvious than its spatial boundaries. This is true when the phenomena under study are eventful but the unit undergoing the event is amorphous. For example, if one is studying terrorist attacks it may not be clear how the spatial unit of analysis should be understood, but the events themselves may be well bounded.

A case study may be understood as the intensive study of a single case for the purpose of understanding a larger class of cases (a population). Case study research may incorporate several cases. However, at a certain point it will no longer be possible to investigate those cases intensively. At the point where the emphasis of a study shifts from the individual case to a sample of cases we shall say that a study is cross-case . Evidently, the distinction between a case study and cross-case study is a continuum. The fewer cases there are, and the more intensively they are studied, the more a work merits the appellation case study. Even so, this proves to be a useful distinction, for much follows from it.

A few additional terms will now be formally defined.

An observation is the most basic element of any empirical endeavor. Conventionally, the number of observations in an analysis is referred to with the letter N . (Confusingly, N may also be used to designate the number of cases in a study, a usage that I shall try to avoid.) A single observation may be understood as containing several dimensions, each of which may be measured (across disparate observations) as a variable. Where the proposition is causal, these may be subdivided into dependent (Y) and independent (X) variables. The dependent variable refers to the outcome of an investigation. The independent variable refers to the explanatory (causal) factor, that which the outcome is supposedly dependent on.

Note that a case may consist of a single observation (N = 1). This would be true, for example, in a cross-sectional analysis of multiple cases. In a case study, however, the case under study always provides more than one observation. These may be constructed diachronically (by observing the case or some subset of within-case units through time) or synchronically (by observing within-case variation at a single point in time).

This is a clue to the fact that case studies and cross-case usually operate at different levels of analysis. The case study is typically focused on within-case variation (if there a cross-case component it is probably secondary). The cross-case study, as the name suggests, is typically focused on cross-case variation (if there is also within-case variation, it is secondary in importance). They have the same object in view—the explanation of a population of cases—but they go about this task differently.

A sample consists of whatever cases are subjected to formal analysis; they are the immediate subject of a study or case study. (Confusingly, the sample may also refer to the observations under study, and will be so used at various points in this narrative. But at present, we treat the sample as consisting of cases.) Technically, one might say that in a case study the sample consists of the case or cases that are subjected to intensive study. However, usually when one uses the term sample one is implying that the number of cases is rather large. Thus, “sample-based work” will be understood as referring to large-N cross-case methods—the opposite of case study work. Again, the only feature distinguishing the case study format from a sample-based (or “cross-case”) research design is the number of cases falling within the sample—one or a few versus many. Case studies, like large-N samples, seek to represent, in all ways relevant to the proposition at hand, a population of cases. A series of case studies might therefore be referred to as a sample if they are relatively brief and relatively numerous; it is a matter of emphasis and of degree. The more case studies one has, the less intensively each one is studied, and the more confident one is in their representativeness (of some broader population), the more likely one is to describe them as a sample rather than a series of case studies. For practical reasons—unless, that is, a study is extraordinarily long—the case study research format is usually limited to a dozen cases or less. A single case is not at all unusual.

The sample rests within a population of cases to which a given proposition refers. The population of an inference is thus equivalent to the breadth or scope of a proposition. (I use the terms proposition , hypothesis , inference , and argument interchangeably.) Note that most samples are not exhaustive; hence the use of the term sample, referring to sampling from a population. Occasionally, however, the sample equals the population of an inference; all potential cases are studied.

For those familiar with the rectangular form of a dataset it may be helpful to conceptualize observations as rows, variables as columns, and cases as either groups of observations or individual observations.

2 What is a Case Study Good For? Case Study versus Cross-case Analysis

I have argued that the case study approach to research is most usefully defined as the intensive study of a single unit or a small number of units (the cases), for the purpose of understanding a larger class of similar units (a population of cases). This is put forth as a minimal definition of the topic. 4 I now proceed to discuss the non -definitional attributes of the case study—attributes that are often, but not invariably, associated with the case study method. These will be understood as methodological affinities flowing from a minimal definition of the concept. 5

The case study research design exhibits characteristic strengths and weaknesses relative to its large-N cross-case cousin. These tradeoffs derive, first of all, from basic research goals such as (1) whether the study is oriented toward hypothesis generating or hypothesis testing, (2) whether internal or external validity is prioritized, (3) whether insight into causal mechanisms or causal effects is more valuable, and (4) whether the scope of the causal inference is deep or broad. These tradeoffs also hinge on the shape of the empirical universe, i.e. (5) whether the population of cases under study is heterogeneous or homogeneous, (6) whether the causal relationship of interest is strong or weak, (7) whether useful variation on key parameters within that population is rare or common, and (8) whether available data are concentrated or dispersed.

Along each of these dimensions, case study research has an affinity for the first factor and cross-case research has an affinity for the second, as summarized in Table 51.1 . To clarify, these tradeoffs represent methodological affinities , not invariant laws. Exceptions can be found to each one. Even so, these general tendencies are often noted in case study research and have been reproduced in multiple disciplines and subdisciplines over the course of many decades.

It should be stressed that each of these tradeoffs carries a ceteris paribus caveat. Case studies are more useful for generating new hypotheses, all other things being equal . The reader must bear in mind that many additional factors also rightly influence a writer’s choice of research design, and they may lean in the other direction. Ceteris are not always paribus. One should not jump to conclusions about the research design appropriate to a given setting without considering the entire range of issues involved—some of which may be more important than others.

3 Hypothesis: Generating versus Testing

Social science research involves a quest for new theories as well as a testing of existing theories; it is comprised of both “conjectures” and “refutations.” 6 Regrettably, social science methodology has focused almost exclusively on the latter. The conjectural element of social science is usually dismissed as a matter of guesswork, inspiration, or luck—a leap of faith, and hence a poor subject for methodological reflection. 7 Yet, it will readily be granted that many works of social science, including most of the acknowledged classics, are seminal rather than definitive. Their classic status derives from the introduction of a new idea or a new perspective that is subsequently subjected to more rigorous (and refutable) analysis. Indeed, it is difficult to devise a program of falsification the first time a new theory is proposed. Path-breaking research, almost by definition, is protean. Subsequent research on that topic tends to be more definitive insofar as its primary task is limited: to verify or falsify a pre-existing hypothesis. Thus, the world of social science may be usefully divided according to the predominant goal undertaken in a given study, either hypothesis generating or hypothesis testing . There are two moments of empirical research, a lightbulb moment and a skeptical moment, each of which is essential to the progress of a discipline. 8

Case studies enjoy a natural advantage in research of an exploratory nature. Several millennia ago, Hippocrates reported what were, arguably, the first case studies ever conducted. They were fourteen in number. 9 Darwin’s insights into the process of human evolution came after his travels to a few select locations, notably Easter Island. Freud’s revolutionary work on human psychology was constructed from a close observation of fewer than a dozen clinical cases. Piaget formulated his theory of human cognitive development while watching his own two children as they passed from childhood to adulthood. Lévi-Strauss’s structuralist theory of human cultures built on the analysis of several North and South American tribes. Douglass North’s neo-institutionalist theory of economic development was constructed largely through a close analysis of a handful of early developing states (primarily England, the Netherlands, and the United States). 10 Many other examples might be cited of seminal ideas that derived from the intensive study of a few key cases.

Evidently, the sheer number of examples of a given phenomenon does not, by itself, produce insight. It may only confuse. How many times did Newton observe apples fall before he recognized the nature of gravity? This is an apocryphal example, but it illustrates a central point: case studies may be more useful than cross-case studies when a subject is being encountered for the first time or is being considered in a fundamentally new way. After reviewing the case study approach to medical research, one researcher finds that although case reports are commonly regarded as the lowest or weakest form of evidence, they are nonetheless understood to comprise “the first line of evidence.” The hallmark of case reporting, according to Jan Vandenbroucke, “is to recognize the unexpected.” This is where discovery begins. 11

The advantages that case studies offer in work of an exploratory nature may also serve as impediments in work of a confirmatory/disconfirmatory nature. Let us briefly explore why this might be so. 12

Traditionally, scientific methodology has been defined by a segregation of conjecture and refutation. One should not be allowed to contaminate the other. 13 Yet, in the real world of social science, inspiration is often associated with perspiration. “Lightbulb” moments arise from a close engagement with the particular facts of a particular case. Inspiration is more likely to occur in the laboratory than in the shower.

The circular quality of conjecture and refutation is particularly apparent in case study research. Charles Ragin notes that case study research is all about “casing”—defining the topic, including the hypothesis(es) of primary interest, the outcome, and the set of cases that offer relevant information vis-à-vis the hypothesis. 14 A study of the French Revolution may be conceptualized as a study of revolution, of social revolution, of revolt, of political violence, and so forth. Each of these topics entails a different population and a different set of causal factors. A good deal of authorial intervention is necessary in the course of defining a case study topic, for there is a great deal of evidentiary leeway. Yet, the “subjectivity” of case study research allows for the generation of a great number of hypotheses, insights that might not be apparent to the cross-case researcher who works with a thinner set of empirical data across a large number of cases and with a more determinate (fixed) definition of cases, variables, and outcomes. It is the very fuzziness of case studies that grants them an advantage in research at the exploratory stage, for the single-case study allows one to test a multitude of hypotheses in a rough-and-ready way. Nor is this an entirely “conjectural” process. The relationships discovered among different elements of a single case have a prima facie causal connection: they are all at the scene of the crime. This is revelatory when one is at an early stage of analysis, for at that point there is no identifiable suspect and the crime itself may be difficult to discern. The fact that A , B , and C are present at the expected times and places (relative to some outcome of interest) is sufficient to establish them as independent variables. Proximal evidence is all that is required. Hence, the common identification of case studies as “plausibility probes,” “pilot studies,” “heuristic studies,” “exploratory” and “theory-building” exercises. 15

A large-N cross-study, by contrast, generally allows for the testing of only a few hypotheses but does so with a somewhat greater degree of confidence, as is appropriate to work whose primary purpose is to test an extant theory. There is less room for authorial intervention because evidence gathered from a cross-case research design can be interpreted in a limited number of ways. It is therefore more reliable. Another way of stating the point is to say that while case studies lean toward Type 1 errors (falsely rejecting the null hypothesis), cross-case studies lean toward Type 2 errors (failing to reject the false null hypothesis). This explains why case studies are more likely to be paradigm generating, while cross-case studies toil in the prosaic but highly structured field of normal science.

I do not mean to suggest that case studies never serve to confirm or disconfirm hypotheses. Evidence drawn from a single case may falsify a necessary or sufficient hypothesis, as discussed below. Additionally, case studies are often useful for the purpose of elucidating causal mechanisms, and this obviously affects the plausibility of an X/Y relationship. However, general theories rarely offer the kind of detailed and determinate predictions on within-case variation that would allow one to reject a hypothesis through pattern matching (without additional cross-case evidence). Theory testing is not the case study’s strong suit. The selection of “crucial” cases is at pains to overcome the fact that the cross-case N is minimal. Thus, one is unlikely to reject a hypothesis, or to consider it definitively proved, on the basis of the study of a single case.

Harry Eckstein himself acknowledges that his argument for case studies as a form of theory confirmation is largely hypothetical. At the time of writing, several decades ago, he could not point to any social science study where a crucial case study had performed the heroic role assigned to it. 16 I suspect that this is still more or less true. Indeed, it is true even of experimental case studies in the natural sciences. “We must recognize,” note Donald Campbell and Julian Stanley,

that continuous, multiple experimentation is more typical of science than once-and-for-all definitive experiments. The experiments we do today, if successful, will need replication and cross-validation at other times under other conditions before they can become an established part of science … [E]ven though we recognize experimentation as the basic language of proof … we should not expect that “crucial experiments” which pit opposing theories will be likely to have clear-cut outcomes. When one finds, for example, that competent observers advocate strongly divergent points of view, it seems likely on a priori grounds that both have observed something valid about the natural situation, and that both represent a part of the truth. The stronger the controversy, the more likely this is. Thus we might expect in such cases an experimental outcome with mixed results, or with the balance of truth varying subtly from experiment to experiment. The more mature focus…avoids crucial experiments and instead studies dimensional relationships and interactions along many degrees of the experimental variables. 17

A single case study is still a single shot—a single example of a larger phenomenon.

The tradeoff between hypothesis generating and hypothesis testing helps us to reconcile the enthusiasm of case study researchers and the skepticism of case study critics. They are both right, for the looseness of case study research is a boon to new conceptualizations just as it is a bane to falsification.

4 Validity: Internal versus External

Questions of validity are often distinguished according to those that are internal to the sample under study and those that are external (i.e. applying to a broader—unstudied—population). Cross-case research is always more representative of the population of interest than case study research, so long as some sensible procedure of case selection is followed (presumably some version of random sampling). Case study research suffers problems of representativeness because it includes, by definition, only a small number of cases of some more general phenomenon. Are the men chosen by Robert Lane typical of white, immigrant, working-class, American males? 18 Is Middletown representative of other cities in America? 19 These sorts of questions forever haunt case study research. This means that case study research is generally weaker with respect to external validity than its cross-case cousin.

The corresponding virtue of case study research is its internal validity. Often, though not invariably, it is easier to establish the veracity of a causal relationship pertaining to a single case (or a small number of cases) than for a larger set of cases. Case study researchers share the bias of experimentalists in this regard: they tend to be more disturbed by threats to within-sample validity than by threats to out-of-sample validity. Thus, it seems appropriate to regard the tradeoff between external and internal validity, like other tradeoffs, as intrinsic to the cross-case/single-case choice of research design.

5 Causal Insight: Causal Mechanisms versus Causal Effects

A third tradeoff concerns the sort of insight into causation that a researcher intends to achieve. Two goals may be usefully distinguished. The first concerns an estimate of the causal effect ; the second concerns the investigation of a causal mechanism (i.e. pathway from X to Y).

By causal effect I refer to two things: (a) the magnitude of a causal relationship (the expected effect on Y of a given change in X across a population of cases) and (b) the relative precision or uncertainty associated with that point estimate. Evidently, it is difficult to arrive at a reliable estimate of causal effects across a population of cases by looking at only a single case or a small number of cases. (The one exception would be an experiment in which a given case can be tested repeatedly, returning to a virgin condition after each test. But here one faces inevitable questions about the representativeness of that much-studied case.) 20 Thus, the estimate of a causal effect is almost always grounded in cross-case evidence.

It is now well established that causal arguments depend not only on measuring causal effects, but also on the identification of a causal mechanism. 21   X must be connected with Y in a plausible fashion; otherwise, it is unclear whether a pattern of covariation is truly causal in nature, or what the causal interaction might be. Moreover, without a clear understanding of the causal pathway(s) at work in a causal relationship it is impossible to accurately specify the model, to identify possible instruments for the regressor of interest (if there are problems of endogeneity), or to interpret the results. 22 Thus, causal mechanisms are presumed in every estimate of a mean (average) causal effect.

In the task of investigating causal mechanisms, cross-case studies are often not so illuminating. It has become a common criticism of large-N cross-national research—e.g. into the causes of growth, democracy, civil war, and other national-level outcomes—that such studies demonstrate correlations between inputs and outputs without clarifying the reasons for those correlations (i.e. clear causal pathways). We learn, for example, that infant mortality is strongly correlated with state failure; 23 but it is quite another matter to interpret this finding, which is consistent with a number of different causal mechanisms. Sudden increases in infant mortality might be the product of famine, of social unrest, of new disease vectors, of government repression, and of countless other factors, some of which might be expected to impact the stability of states, and others of which are more likely to be a result of state instability.

Case studies, if well constructed, may allow one to peer into the box of causality to locate the intermediate factors lying between some structural cause and its purported effect. Ideally, they allow one to “see” X and Y interact—Hume’s billiard ball crossing the table and hitting a second ball. 24 Barney Glaser and Anselm Strauss point out that in fieldwork “general relations are often discovered in vivo ; that is, the field worker literally sees them occur.” 25 When studying decisional behavior case study research may offer insight into the intentions, the reasoning capabilities, and the information-processing procedures of the actors involved in a given setting. Thus, Dennis Chong uses in-depth interviews with a very small sample of respondents in order to better understand the process by which people reach decisions about civil liberties issues. Chong comments:

One of the advantages of the in-depth interview over the mass survey is that it records more fully how subjects arrive at their opinions. While we cannot actually observe the underlying mental process that gives rise to their responses, we can witness many of its outward manifestations. The way subjects ramble, hesitate, stumble, and meander as they formulate their answers tips us off to how they are thinking and reasoning through political issues. 26

Similarly, the investigation of a single case may allow one to test the causal implications of a theory, thus providing corroborating evidence for a causal argument. This is sometimes referred to as pattern matching ( Campbell 1988 ).

Dietrich Rueschemeyer and John Stephens offer an example of how an examination of causal mechanisms may call into question a general theory based on cross-case evidence. The thesis of interest concerns the role of British colonialism in fostering democracy among postcolonial regimes. In particular, the authors investigate the diffusion hypothesis, that democracy was enhanced by “the transfer of British governmental and representative institutions and the tutoring of the colonial people in the ways of British government.” On the basis of in-depth analysis of several cases the authors report:

We did find evidence of this diffusion effect in the British settler colonies of North America and the Antipodes; but in the West Indies, the historical record points to a different connection between British rule and democracy. There the British colonial administration opposed suffrage extension, and only the white elites were “tutored” in the representative institutions. But, critically, we argued on the basis of the contrast with Central America, British colonialism did prevent the local plantation elites from controlling the local state and responding to the labor rebellion of the 1930s with massive repression. Against the adamant opposition of that elite, the British colonial rulers responded with concessions which allowed for the growth of the party– union complexes rooted in the black middle and working classes, which formed the backbone of the later movement for democracy and independence. Thus, the narrative histories of these cases indicate that the robust statistical relation between British colonialism and democracy is produced only in part by diffusion. The interaction of class forces, state power, and colonial policy must be brought in to fully account for the statistical result. 27

Whether or not Rueschemeyer and Stephens are correct in their conclusions need not concern us here. What is critical, however, is that any attempt to deal with this question of causal mechanisms is heavily reliant on evidence drawn from case studies. In this instance, as in many others, the question of causal pathways is simply too difficult, requiring too many poorly measured or unmeasurable variables, to allow for accurate cross-sectional analysis. 28

To be sure, causal mechanisms do not always require explicit attention. They may be quite obvious. And in other circumstances, they may be amenable to cross-case investigation. For example, a sizeable literature addresses the causal relationship between trade openness and the welfare state. The usual empirical finding is that more open economies are associated with higher social welfare spending. The question then becomes why such a robust correlation exists. What are the plausible interconnections between trade openness and social welfare spending? One possible causal path, suggested by David Cameron, 29 is that increased trade openness leads to greater domestic economic vulnerability to external shocks (due, for instance, to changing terms of trade). If so, one should find a robust correlation between annual variations in a country’s terms of trade (a measure of economic vulnerability) and social welfare spending. As it happens, the correlation is not robust and this leads some commentators to doubt whether the putative causal mechanism proposed by David Cameron and many others is actually at work. 30 Thus, in instances where an intervening variable can be effectively operationalized across a large sample of cases it may be possible to test causal mechanisms without resorting to case study investigation. 31

Even so, the opportunities for investigating causal pathways are generally more apparent in a case study format. Consider the contrast between formulating a standardized survey for a large group of respondents and formulating an in-depth interview with a single subject or a small set of subjects, such as that undertaken by Dennis Chong in the previous example. In the latter situation, the researcher is able to probe into details that would be impossible to delve into, let alone anticipate, in a standardized survey. She may also be in a better position to make judgements as to the veracity and reliability of the respondent. Tracing causal mechanisms is about cultivating sensitivity to a local context. Often, these local contexts are essential to cross-case testing. Yet, the same factors that render case studies useful for micro-level investigation also make them less useful for measuring mean (average) causal effects. It is a classic tradeoff.

6 Scope of Proposition: Deep versus Broad

The utility of a case study mode of analysis is in part a product of the scope of the causal argument that a researcher wishes to prove or demonstrate. Arguments that strive for great breadth are usually in greater need of cross-case evidence; causal arguments restricted to a small set of cases can more plausibly subsist on the basis of a single-case study. The extensive/intensive tradeoff is fairly commonsensical. 32 A case study of France probably offers more useful evidence for an argument about Europe than for an argument about the whole world. Propositional breadth and evidentiary breadth generally go hand in hand.

Granted, there are a variety of ways in which single-case studies can credibly claim to provide evidence for causal propositions of broad reach—e.g. by choosing cases that are especially representative of the phenomenon under study (“typical” cases) or by choosing cases that represent the most difficult scenario for a given proposition and are thus biased against the attainment of certain results (“crucial” cases). Even so, a proposition with a narrow scope is more conducive to case study analysis than a proposition with a broad purview, all other things being equal. The breadth of an inference thus constitutes one factor, among many, in determining the utility of the case study mode of analysis. This is reflected in the hesitancy of many case study researchers to invoke determinate causal propositions with great reach—“covering laws,” in the idiom of philosophy of science.

By the same token, one of the primary virtues of the case study method is the depth of analysis that it offers. One may think of depth as referring to the detail, richness, completeness, wholeness, or the degree of variance in an outcome that is accounted for by an explanation. The case study researcher’s complaint about the thinness of cross-case analysis is well taken; such studies often have little to say about individual cases. Otherwise stated, cross-case studies are likely to explain only a small portion of the variance with respect to a given outcome. They approach that outcome at a very general level. Typically, a cross-case study aims only to explain the occurrence/non-occurrence of a revolution, while a case study might also strive to explain specific features of that event—why it occurred when it did and in the way that it did. Case studies are thus rightly identified with “holistic” analysis and with the “thick” description of events. 33

Whether to strive for breadth or depth is not a question that can be answered in any definitive way. All we can safely conclude is that researchers invariably face a choice between knowing more about less, or less about more. The case study method may be defended, as well as criticized, along these lines. 34 Indeed, arguments about the “contextual sensitivity” of case studies are perhaps more precisely (and fairly) understood as arguments about depth and breadth. The case study researcher who feels that cross-case research on a topic is insensitive to context is usually not arguing that nothing at all is consistent across the chosen cases. Rather, the case study researcher’s complaint is that much more could be said—accurately—about the phenomenon in question with a reduction in inferential scope. 35

Indeed, I believe that a number of traditional issues related to case study research can be understood as the product of this basic tradeoff. For example, case study research is often lauded for its holistic approach to the study of social phenomena in which behavior is observed in natural settings. Cross-case research, by contrast, is criticized for its construction of artificial research designs that decontextualize the realm of social behavior by employing abstract variables that seem to bear little relationship to the phenomena of interest. 36 These associated congratulations and critiques may be understood as a conscious choice on the part of case study researchers to privilege depth over breadth.

7 The Population of Cases: Heterogeneous versus Homogeneous

The choice between a case study and cross-case style of analysis is driven not only by the goals of the researcher, as reviewed above, but also by the shape of the empirical universe that the researcher is attempting to understand. Consider, for starters, that the logic of cross-case analysis is premised on some degree of cross-unit comparability (unit homogeneity). Cases must be similar to each other in whatever respects might affect the causal relationship that the writer is investigating, or such differences must be controlled for. Uncontrolled heterogeneity means that cases are “apples and oranges;” one cannot learn anything about underlying causal processes by comparing their histories. The underlying factors of interest mean different things in different contexts (conceptual stretching) or the X/Y relationship of interest is different in different contexts (unit heterogeneity).

Case study researchers are often suspicious of large-sample research, which, they suspect, contains heterogeneous cases whose differences cannot easily be modeled. “Variable-oriented” research is said to involve unrealistic “homogenizing assumptions.” 37 In the field of international relations, for example, it is common to classify cases according to whether they are deterrence failures or deterrence successes. However, Alexander George and Richard Smoke point out that “the separation of the dependent variable into only two subclasses, deterrence success and deterrence failure,” neglects the great variety of ways in which deterrence can fail. Deterrence, in their view, has many independent causal paths (causal equifinality), and these paths may be obscured when a study lumps heterogeneous cases into a common sample. 38

Another example, drawn from clinical work in psychology, concerns heterogeneity among a sample of individuals. Michel Hersen and David Barlow explain:

Descriptions of results from 50 cases provide a more convincing demonstration of the effectiveness of a given technique than separate descriptions of 50 individual cases. The major difficulty with this approach, however, is that the category in which these clients are classified most always becomes unmanageably heterogeneous. “Neurotics,” [for example], …may have less in common than any group of people one would choose randomly. When cases are described individually, however, a clinician stands a better chance of gleaning some important information, since specific problems and specific procedures are usually described in more detail. When one lumps cases together in broadly defined categories, individual case descriptions are lost and the ensuing report of percentage success becomes meaningless. 39

Under circumstances of extreme case heterogeneity, the researcher may decide that she is better off focusing on a single case or a small number of relatively homogeneous cases. Within-case evidence, or cross-case evidence drawn from a handful of most-similar cases, may be more useful than cross-case evidence, even though the ultimate interest of the investigator is in a broader population of cases. (Suppose one has a population of very heterogeneous cases, one or two of which undergo quasi-experimental transformations. Probably, one gains greater insight into causal patterns throughout the population by examining these cases in detail than by undertaking some large-N cross-case analysis.) By the same token, if the cases available for study are relatively homogeneous, then the methodological argument for cross-case analysis is correspondingly strong. The inclusion of additional cases is unlikely to compromise the results of the investigation because these additional cases are sufficiently similar to provide useful information.

The issue of population heterogeneity/homogeneity may be understood, therefore, as a tradeoff between N (observations) and K (variables). If, in the quest to explain a particular phenomenon, each potential case offers only one observation and also requires one control variable (to neutralize heterogeneities in the resulting sample), then one loses degrees of freedom with each additional case. There is no point in using cross-case analysis or in extending a two-case study to further cases. If, on the other hand, each additional case is relatively cheap—if no control variables are needed or if the additional case offers more than one useful observation (through time)—then a cross-case research design may be warranted. 40 To put the matter more simply, when adjacent cases are unit homogeneous the addition of more cases is easy, for there is no (or very little) heterogeneity to model. When adjacent cases are heterogeneous additional cases are expensive, for every added heterogeneous element must be correctly modeled, and each modeling adjustment requires a separate (and probably unverifiable) assumption. The more background assumptions are required in order to make a causal inference, the more tenuous that inference is; it is not simply a question of attaining statistical significance. The ceteris paribus assumption at the core of all causal analysis is brought into question. In any case, the argument between case study and cross-case research designs is not about causal complexity per se (in the sense in which this concept is usually employed), but rather about the tradeoff between N and K in a particular empirical realm, and about the ability to model case heterogeneity through statistical legerdemain. 41

Before concluding this discussion it is important to point out that researchers’ judgements about case comparability are not, strictly speaking, matters that can be empirically verified. To be sure, one can look—and ought to look—for empirical patterns among potential cases. If those patterns are strong then the assumption of case comparability seems reasonably secure, and if they are not then there are grounds for doubt. However, debates about case comparability usually concern borderline instances. Consider that many phenomena of interest to social scientists are not rigidly bounded. If one is studying democracies there is always the question of how to define a democracy, and therefore of determining how high or low the threshold for inclusion in the sample should be. Researchers have different ideas about this, and these ideas can hardly be tested in a rigorous fashion. Similarly, there are longstanding disputes about whether it makes sense to lump poor and rich societies together in a single sample, or whether these constitute distinct populations. Again, the borderline between poor and rich (or “developed” and “undeveloped”) is blurry, and the notion of hiving off one from the other for separate analysis questionable, and unresolvable on purely empirical grounds. There is no safe (or “conservative”) way to proceed. A final sticking point concerns the cultural/historical component of social phenomena. Many case study researchers feel that to compare societies with vastly different cultures and historical trajectories is meaningless. Yet, many cross-case researchers feel that to restrict one’s analytic focus to a single cultural or geographic region is highly arbitrary, and equally meaningless. In these situations, it is evidently the choice of the researcher how to understand case homogeneity/heterogeneity across the potential populations of an inference. Where do like cases end and unlike cases begin?

Because this issue is not, strictly speaking, empirical it may be referred to as an ontological element of research design. An ontology is a vision of the world as it really is, a more or less coherent set of assumptions about how the world works, a research Weltanschauung analogous to a Kuhnian paradigm. 42 While it seems odd to bring ontological issues into a discussion of social science methodology it may be granted that social science research is not a purely empirical endeavor. What one finds is contingent upon what one looks for, and what one looks for is to some extent contingent upon what one expects to find. Stereotypically, case study researchers tend to have a “lumpy” vision of the world; they see countries, communities, and persons as highly individualized phenomena. Cross-case researchers, by contrast, have a less differentiated vision of the world; they are more likely to believe that things are pretty much the same everywhere, at least as respects basic causal processes. These basic assumptions, or ontologies, drive many of the choices made by researchers when scoping out appropriate ground for research.

8 Causal Strength: Strong versus Weak

Regardless of whether the population is homogeneous or heterogeneous, causal relationships are easier to study if the causal effect is strong, rather than weak. Causal “strength,” as I use the term here, refers to the magnitude and consistency of X’s effect on Y across a population of cases. (It invokes both the shape of the evidence at hand and whatever priors might be relevant to an interpretation of that evidence.) Where X has a strong effect on Y it will be relatively easy to study this relationship. Weak relationships, by contrast, are often difficult to discern. This much is commonsensical, and applies to all research designs.

For our purposes, what is significant is that weak causal relationships are particularly opaque when encountered in a case study format. Thus, there is a methodological affinity between weak causal relationships and large-N cross-case analysis, and between strong causal relationships and case study analysis.

This point is clearest at the extremes. The strongest species of causal relationships may be referred to as deterministic , where X is assumed to be necessary and/or sufficient for Y’s occurrence. A necessary and sufficient cause accounts for all of the variation on Y. A sufficient cause accounts for all of the variation in certain instances of Y. A necessary cause accounts, by itself, for the absence of Y. In all three situations, the relationship is usually assumed to be perfectly consistent, i.e. invariant. There are no exceptions.

It should be clear why case study research designs have an easier time addressing causes of this type. Consider that a deterministic causal proposition can be dis proved with a single case. 43 For example, the reigning theory of political stability once stipulated that only in countries that were relatively homogeneous, or where existing heterogeneity was mitigated by cross-cutting cleavages, would social peace endure. 44 Arend Lijphart’s case study of the Netherlands, a country with reinforcing social cleavages and very little social conflict, disproved this deterministic theory on the basis of a single case. 45 (One may dispute whether the original theory is correctly understood as deterministic. However, if it is , then it has been decisively refuted by a single case study.) Proving an invariant causal argument generally requires more cases. However, it is not nearly as complicated as proving a probabilistic argument for the simple reason that one assumes invariant relationships; consequently, the single case under study carries more weight.

Magnitude and consistency—the two components of causal strength—are usually matters of degree. It follows that the more tenuous the connection between X and Y, the more difficult it will be to address in a case study format. This is because the causal mechanisms connecting X with Y are less likely to be detectable in a single case when the total impact is slight or highly irregular. It is no surprise, therefore, that the case study research design has, from the very beginning, been associated with causal arguments that are deterministic, while cross-case research has been associated with causal arguments that are assumed to be minimal in strength and “probabilistic” in consistency. 46 (Strictly speaking, causal magnitude and consistency are independent features of a causal relationship. However, because they tend to covary, and because we tend to conceptualize them in tandem, I treat them as components of a single dimension.)

Now, let us now consider an example drawn from the other extreme. There is generally assumed to be a weak relationship between regime type and economic performance. Democracy, if it has any effect on economic growth at all, probably has only a slight effect over the near-to-medium term, and this effect is probably characterized by many exceptions (cases that do not fit the general pattern). This is because many things other than democracy affect a country’s growth performance and because there may be a significant stochastic component in economic growth (factors that cannot be modeled in a general way). Because of the diffuse nature of this relationship it will probably be difficult to gain insight by looking at a single case. Weak relationships are difficult to observe in one instance. Note that even if there seems to be a strong relationship between democracy and economic growth in a given country it may be questioned whether this case is actually typical of the larger population of interest, given that we have already stipulated that the typical magnitude of this relationship is diminutive and irregular. Of course, the weakness of democracy’s presumed relationship to growth is also a handicap in cross-case analysis. A good deal of criticism has been directed toward studies of this type, where findings are rarely robust. 47 Even so, it seems clear that if there is a relationship between democracy and growth it is more likely to be perceptible in a large cross-case setting. The positive hypothesis, as well as the null hypothesis, is better approached in a sample rather than in a case.

9 Useful Variation: Rare versus Common

When analyzing causal relationships we must be concerned not only with the strength of an X/Y relationship but also with the distribution of evidence across available cases. Specifically, we must be concerned with the distribution of useful variation —understood as variation (temporal or spatial) on relevant parameters that might yield clues about a causal relationship. It follows that where useful variation is rare—i.e. limited to a few cases—the case study format recommends itself. Where, on the other hand, useful variation is common, a cross-case method of analysis may be more defensible.

Consider a phenomenon like social revolution, an outcome that occurs very rarely. The empirical distribution on this variable, if we count each country-year as an observation, consists of thousands of non-revolutions (0) and just a few revolutions (1). Intuitively, it seems clear that the few “revolutionary” cases are of great interest. We need to know as much as possible about them, for they exemplify all the variation that we have at our disposal. In this circumstance, a case study mode of analysis is difficult to avoid, though it might be combined with a large-N cross-case analysis. As it happens, many outcomes of interest to social scientists are quite rare, so the issue is by no means trivial. 48

By way of contrast, consider a phenomenon like turnover, understood as a situation where a ruling party or coalition is voted out of office. Turnover occurs within most democratic countries on a regular basis, so the distribution of observations on this variable (incumbency/turnover) is relatively even across the universe of country-years. There are lots of instances of both outcomes. Under these circumstances a cross-case research design seems plausible, for the variation across cases is regularly distributed.

Another sort of variation concerns that which might occur within a given case. Suppose that only one or two cases within a large population exhibit quasi-experimental qualities: the factor of special interest varies, and there is no corresponding change in other factors that might affect the outcome. Clearly, we are likely to learn a great deal from studying this particular case—perhaps a lot more than we might learn from studying hundreds of additional cases that deviate from the experimental ideal. But again, if many cases have this experimental quality, there is little point in restricting ourselves to a single example; a cross-case research design may be justified.

A final sort of variation concerns the characteristics exhibited by a case relative to a particular theory that is under investigation. Suppose that a case provides a “crucial” test for a theory: it fits that theory’s predictions so perfectly and so precisely that no other explanation could plausibly account for the performance of the case. If no other crucial cases present themselves, then an intensive study of this particular case is de rigueur. Of course, if many such cases lie within the population then it may be possible to study them all at once (with some sort of numeric reduction of the relevant parameters).

The general point here is that the distribution of useful variation across a population of cases matters a great deal in the choice between case study and cross-case research designs.

10 Data Availability: Concentrated versus Dispersed

I have left the most prosaic factor for last. Sometimes, one’s choice of research design is driven by the quality and quantity of information that is currently available, or could be easily gathered, on a given question. This is a practical matter, and is distinct from the actual (ontological) shape of the world. It concerns, rather, what we know about the former at a given point in time. 49 The question of evidence may be posed as follows: How much do we know about the cases at hand that might be relevant to the causal question of interest, and how precise, certain, and case comparable is that data? An evidence-rich environment is one where all relevant factors are measurable, where these measurements are relatively precise, where they are rendered in comparable terms across cases, and where one can be relatively confident that the information is, indeed, accurate. An evidence-poor environment is the opposite.

The question of available evidence impinges upon choices in research design when one considers its distribution across a population of cases. If relevant information is concentrated in a single case, or if it is contained in incommensurable formats across a population of cases, then a case study mode of analysis is almost unavoidable. If, on the other hand, it is evenly distributed across the population—i.e. we are equally well informed about all cases—and is case comparable, then there is little to recommend a narrow focus. (I employ data, evidence, and information as synonyms in this section.)

Consider the simplest sort of example, where information is truly limited to one or a few cases. Accurate historical data on infant mortality and other indices of human development are currently available for only a handful of countries (these include Chile, Egypt, India, Jamaica, Mauritius, Sri Lanka, the United States, and several European countries). 50 This data problem is not likely to be rectified in future years, as it is exceedingly difficult to measure infant mortality except by public or private records. Consequently, anyone studying this general subject is likely to rely heavily on these cases, where in-depth analysis is possible and profitable. Indeed, it is not clear whether any large-N cross-case analysis is possible prior to the twentieth century. Here, a case study format is virtually prescribed, and a cross-case format proscribed.

Other problems of evidence are more subtle. Let us dwell for the moment on the question of data comparability. In their study of social security spending, Mulligan, Gil, and Sala-i-Martin note that

although our spending and design numbers are of good quality, there are some missing observations and, even with all the observations, it is difficult to reduce the variety of elderly subsidies to one or two numbers. For this reason, case studies are an important part of our analysis, since those studies do not require numbers that are comparable across a large number of countries. Our case study analysis utilizes data from a variety of country-specific sources, so we do not have to reduce “social security” or “democracy” to one single number. 51

Here, the incommensurability of the evidence militates towards a case study format. In the event that the authors (or subsequent analysts) discover a coding system that provides reasonably valid cross-case measures of social security, democracy, and other relevant concepts then our state of knowledge about the subject is changed, and a cross-case research design is rendered more plausible.

Importantly, the state of evidence on a topic is never entirely fixed. Investigators may gather additional data, recode existing data, or discover new repositories of data. Thus, when discussing the question of evidence one must consider the quality and quantity of evidence that could be gathered on a given question, given sufficient time and resources. Here it is appropriate to observe that collecting new data, and correcting existing data, is usually easier in a case study format than in a large-N cross-case format. It will be difficult to rectify data problems if one’s cases number in the hundreds or thousands. There are simply too many data points to allow for this.

One might consider this issue in the context of recent work on democracy. There is general skepticism among scholars with respect to the viability of extant global indicators intended to capture this complex concept (e.g. by Freedom House and by the Polity IV data project). 52 Measurement error, aggregation problems, and questions of conceptual validity are rampant. When dealing with a single country or a single continent it is possible to overcome some of these faults by manually recoding the countries of interest. 53 The case study format often gives the researcher an opportunity to fact-check, to consult multiple sources, to go back to primary materials, and to overcome whatever biases may affect the secondary literature. Needless to say, this is not a feasible approach for an individual investigator if one’s project encompasses every country in the world. The best one can usually manage, under the circumstances, is some form of convergent validation (by which different indices of the same concept are compared) or small adjustments in the coding intended to correct for aggregation problems or measurement error. 54

For the same reason, the collection of original data is typically more difficult in cross-case analysis than in case study analysis, involving greater expense, greater difficulties in identifying and coding cases, learning foreign languages, traveling, and so forth. Whatever can be done for a set of cases can usually be done more easily for a single case.

It should be kept in mind that many of the countries of concern to anthropologists, economists, historians, political scientists, and sociologists are still terra incognita. Outside the OECD, and with the exception of a few large countries that have received careful attention from scholars (e.g. India, Brazil, China), most countries of the world are not well covered by the social science literature. Any statement that one might wish to make about, say, Botswana, will be difficult to verify if one has recourse only to secondary materials. And these—very limited—secondary sources are not necessarily of the most reliable sort. Thus, if one wishes to say something about political patterns obtaining in roughly 90 percent of the world’s countries and if one wishes to go beyond matters that can be captured in standard statistics collected by the World Bank and the IMF and other agencies (and these can also be very sketchy when lesser-studied countries are concerned) one is more or less obliged to conduct a case study. Of course, one could, in principle, gather similar information across all relevant cases. However, such an enterprise faces formidable logistical difficulties. Thus, for practical reasons, case studies are sometimes the most defensible alternative when the researcher is faced with an information-poor environment.

However, this point is easily turned on its head. Datasets are now available to study many problems of concern to the social sciences. Thus, it may not be necessary to collect original information for one’s book, article, or dissertation. Sometimes in-depth single-case analysis is more time consuming than cross-case analysis. If so, there is no informational advantage to a case study format. Indeed, it may be easier to utilize existing information for a cross-case analysis, particularly when a case study format imposes hurdles of its own—e.g. travel to distant climes, risk of personal injury, expense, and so forth. It is interesting to note that some observers consider case studies to be “relatively more expensive in time and resources.” 55

Whatever the specific logistical hurdles, it is a general truth that the shape of the evidence—that which is currently available and that which might feasibly be collected by an author—often has a strong influence on an investigator’s choice of research designs. Where the evidence for particular cases is richer and more accurate there is a strong prima facie argument for a case study format focused on those cases. Where, by contrast, the relevant evidence is equally good for all potential cases, and is comparable across those cases, there is no reason to shy away from cross-case analysis. Indeed, there may be little to gain from case study formats.

11 Conclusions

At the outset, I took note of the severe disjuncture that has opened up between an often-maligned methodology and a heavily practiced method. The case study is disrespected but nonetheless regularly employed. Indeed, it remains the workhorse of most disciplines and subfields in the social sciences. How, then, can one make sense of this schizophrenia between methodological theory and praxis?

The torment of the case study begins with its definitional penumbra. Frequently, this key term is conflated with a set of disparate methodological traits that are not definitionally entailed. My first objective, therefore, was to craft a narrower and more useful concept for purposes of methodological discussion. The case study, I argued, is best defined as an intensive study of a single case with an aim to generalize across a larger set of cases. It follows from this definition that case studies may be small-or large-N, qualitative or quantitative, experimental or observational, synchronic or diachronic. It also follows that the case study research design comports with any macrotheoretical framework or paradigm—e.g. behavioralism, rational choice, institutionalism, or interpretivism. It is not epistemologically distinct. What differentiates the case study from the cross-case study is simply its way of defining observations, not its analysis of those observations or its method of modeling causal relations. The case study research design constructs its observations from a single case or a small number of cases, while cross-case research designs construct observations across multiple cases. Cross-case and case study research operate, for the most part, at different levels of analysis.

The travails of the case study are not simply definitional. They are also rooted in an insufficient appreciation of the methodological tradeoffs that this method calls forth. At least eight characteristic strengths and weaknesses must be considered. Ceteris paribus, case studies are more useful when the strategy of research is exploratory rather than confirmatory/disconfirmatory, when internal validity is given preference over external validity, when insight into causal mechanisms is prioritized over insight into causal effects, when propositional depth is prized over breadth, when the population of interest is heterogeneous rather than homogeneous, when causal relationships are strong rather than weak, when useful information about key parameters is available only for a few cases, and when the available data are concentrated rather than dispersed.

Although I do not have the space to discuss other issues in this venue, it is worth mentioning that other considerations may also come into play in a researcher’s choice between a case study and cross-case study research format. However, these additional issues—e.g. causal complexity and the state of research on a topic—do not appear to have clear methodological affinities. They may augur one way, or the other.

My objective throughout this chapter is to restore a greater sense of meaning, purpose, and integrity to the case study method. It is hoped that by offering a narrower and more carefully bounded definition of this method the case study may be rescued from some of its most persistent ambiguities. And it is hoped that the characteristic strengths of this method, as well as its limitations, will be more apparent to producers and consumers of case study research. The case study is a useful tool for some research objectives, but not all.

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Acemoglu, Johnson, and Robinson (2003) , Chernoff and Warner (2002) , Rodrik (2003) . See also studies focused on particular firms or regions, e.g. Coase ( 1959 ; 2000 ).

For general discussion of the following points see Achen (1986) , Freedman (1991) , Kittel ( 1999 , 2005 ), Kittel and Winner (2005) , Manski (1993) , Winship and Morgan (1999) , Winship and Sobel (2004) .

Achen and Snidal ( 1989 , 160). See also Geddes ( 1990 ; 2003 ), Goldthorpe (1997) , King, Keohane, and Verba (1994) , Lieberson ( 1985 , 107–15; 1992 ; 1994 ), Lijphart ( 1971 , 683–4), Odell (2004) , Sekhon (2004) , Smelser ( 1973 , 45, 57). It should be noted that these writers, while critical of the case study format, are not necessarily opposed to case studies per se (that is to say, they should not be classified as opponents of the case study).

My intention is to include only those attributes commonly associated with the case study method that are always implied by our use of the term, excluding those attributes that are sometimes violated by standard usage. Thus, I chose not to include “ethnography” as a defining feature of the case study, since many case studies (so called) are not ethnographic. For further discussion of minimal definitions see Gerring (2001 , ch. 4 ), Gerring and Barresi (2003) , Sartori (1976) .

These additional attributes might also be understood as comprising an ideal-type (“maximal”) definition of the topic ( Gerring 2001 , ch. 4 ; Gerring and Barresi 2003 ).

Popper (1969) .

Karl Popper (quoted in King, Keohane, and Verba 1994 , 14) writes: “there is no such thing as a logical method of having new ideas…Discovery contains ‘an irrational element,’ or a ‘creative intuition.”’ One recent collection of essays and interviews takes new ideas as its special focus ( Munck and Snyder 2007 ), though it may be doubted whether there are generalizable results.

Gerring (2001 , ch. 10 ). The tradeoff between these two styles of research is implicit in Achen and Snidal (1989) , who criticize the case study for its deficits in the latter genre but also acknowledge the benefits of the case study along the former dimension (1989, 167–8). Reichenbach also distinguished between a “context of discovery,” and a “context of justification.” Likewise, Peirce’s concept of abduction recognizes the importance of a generative component in science.

Bonoma ( 1985 , 199). Some of the following examples are discussed in Patton (2002 , 245).

North and Weingast (1989) ; North and Thomas (1973) .

Vandenbroucke ( 2001 , 331).

For discussion of this trade-off in the context of economic growth theory see Temple ( 1999 , 120).

Geddes (2003) , King, Keohane, and Verba (1994) , Popper ( 1934 /1968).

Ragin (1992) .

Eckstein (1975) , Ragin ( 1992 ; 1997 ), Rueschemeyer and Stephens (1997) .

Eckstein (1975) .

Campbell and Stanley ( 1963 , 3).

Lane (1962) .

Lynd and Lynd (1929/1956) .

Note that the intensive study of a single unit may be a perfectly appropriate way to estimate causal effects within that unit . Thus, if one is interested in the relationship between welfare benefits and work effort in the United States one might obtain a more accurate assessment by examining data drawn from the USA alone, rather than crossnationally. However, since the resulting generalization does not extend beyond the unit in question it is not a case study in the usual sense.

Achen (2002) , Dessler (1991) , Elster (1998) , George and Bennett (2005) , Gerring (2005) , Hedstrom and Swedberg (1998) , Mahoney (2001) , Tilly (2001) .

In a discussion of instrumental variables in two-stage least-squares analysis, Angrist and Krueger ( 2001 : 8) note that “good instruments often come from detailed knowledge of the economic mechanism, institutions determining the regressor of interest.”

Goldstone et al. (2000) .

This has something to do with the existence of process-tracing evidence, a matter discussed below. But it is not necessarily predicated on this sort of evidence. Sensitive time-series data, another specialty of the case study, is also relevant to the question of causal mechanisms.

Glaser and Strauss ( 1967 , 40).

Chong ( 1993 , 868). For other examples of in-depth interviewing see Hochschild (1981) , Lane (1962) .

Rueschemeyer and Stephens ( 1997 , 62).

Other good examples of within-case research that shed light on a broader theory can be found in Martin (1992) ; Martin and Swank (2004) ; Thies (2001) ; Young (1999) .

Cameron (1978) .

Alesina, Glaeser, and Sacerdote (2001) .

For additional examples of this nature, see Feng (2003) ; Papyrakis and Gerlagh (2003) ; Ross (2001) .

Eckstein ( 1975 , 122).

I am using the term “thick” in a somewhat different way than in Geertz (1973) .

See Ragin ( 2000 , 22).

Ragin (1987 , ch. 2 ). Herbert Blumer’s (1969 , ch. 7 ) complaints, however, are more far-reaching.

Orum, Feagin, and Sjoberg ( 1991 , 7).

Ragin ( 2000 , 35). See also Abbott (1990) ; Bendix (1963) ; Meehl (1954) ; Przeworski and Teune ( 1970 , 8–9); Ragin ( 1987 ; 2004 , 124); Znaniecki (1934 , 250–1).

George and Smoke (1974 , 514).

Hersen and Barlow (1976 , 11).

Shalev (1998) .

To be sure, if adjacent cases are identical , the phenomenon of interest is invariant then the researcher gains nothing at all by studying more examples of a phenomenon, for the results obtained with the first case will simply be replicated. However, virtually all phenomena of interest to social scientists have some degree of heterogeneity (cases are not identical), some stochastic element. Thus, the theoretical possibility of identical, invariant cases is rarely met in practice.

Gutting (1980) ; Hall (2003) ; Kuhn ( 1962 /1970); Wolin (1968) .

Dion (1998) .

Almond (1956) ; Bentley ( 1908 /1967); Lipset ( 1960 /1963); Truman (1951) .

Lijphart (1968) ; see also Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965) ; Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; discussion in Rogowski (1995) .

Znaniecki (1934) . See also discussion in Robinson (1951) .

Kittel ( 1999 ; 2005 ); Kittel and Winner (2005) ; Levine and Renelt (1992) ; Temple (1999) .

Consider the following topics and their—extremely rare—instances of variation: early industrialization (England, the Netherlands), fascism (Germany, Italy), the use of nuclear weapons (United States), world war (WWI, WWII), single non-transferable vote electoral systems (Jordan, Taiwan, Vanuatu, pre-reform Japan), electoral system reforms within established democracies (France, Italy, Japan, New Zealand, Thailand). The problem of “rareness” is less common where parameters are scalar, rather than dichotomous. But there are still plenty of examples of phenomena whose distributions are skewed by a few outliers, e.g. population (China, India), personal wealth (Bill Gates, Warren Buffett), ethnic heterogeneity (Papua New Guinea).

Of course, what we know about the potential cases is not independent of the underlying reality; it is, nonetheless, not entirely dependent on that reality.

Gerring (2007 b ) .

Mulligan, Gil, and Sala-i-Martin (2002 , 13).

Bollen (1993) ; Bowman, Lehoucq, and Mahoney (2005) ; Munck and Verkuilen (2002) ; Treier and Jackman (2005) .

Bowman, Lehoucq, and Mahoney (2005) .

Bollen (1993) ; Treier and Jackman (2005) .

Stoecker ( 1991 , 91).

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A case study focuses on a particular unit - a person, a site, a project. It often uses a combination of quantitative and qualitative data.

Case studies can be particularly useful for understanding how different elements fit together and how different elements (implementation, context and other factors) have produced the observed impacts.

There are different types of case studies, which can be used for different purposes in evaluation. The GAO (Government Accountability Office) has described six different types of case study:

1.  Illustrative : This is descriptive in character and intended to add realism and in-depth examples to other information about a program or policy. (These are often used to complement quantitative data by providing examples of the overall findings).

2.  Exploratory : This is also descriptive but is aimed at generating hypotheses for later investigation rather than simply providing illustration.

3.  Critical instance : This examines a single instance of unique interest, or serves as a critical test of an assertion about a program, problem or strategy.

4.  Program implementation . This  investigates operations, often at several sites, and often with reference to a set of norms or standards about implementation processes.

5.  Program effects . This examines the causal links between the program and observed effects (outputs, outcomes or impacts, depending on the timing of the evaluation) and usually involves multisite, multimethod evaluations.

6.  Cumulative . This brings together findings from many case studies to answer evaluative questions. 

The following guides are particularly recommended because they distinguish between the research design (case study) and the type of data (qualitative or quantitative), and provide guidance on selecting cases, addressing causal inference, and generalizing from cases.

This guide from the US General Accounting Office outlines good practice in case study evaluation and establishes a set of principles for applying case studies to evaluations.

This paper, authored by Edith D. Balbach for the California Department of Health Services is designed to help evaluators decide whether to use a case study evaluation approach.

This guide, written by Linda G. Morra and Amy C. Friedlander for the World Bank, provides guidance and advice on the use of case studies.

Expand to view all resources related to 'Case study'

• Broadening the range of designs and methods for impact evaluations
• Case study evaluations - US General Accounting Office
• Case study evaluations - World Bank
• Designing and facilitating creative conversations & learning activities
• Estudo de caso: a avaliação externa de um programa
• Evaluation tools
• Methods for monitoring and evaluation
• Reflections on innovation, assessment and social change processes: A SPARC case study, India
• Toward a listening bank: A review of best practices and the efficacy of beneficiary assessment
• Using case studies to do program evaluation

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• Week 32: Better use of case studies in evaluation

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Thinking Like a Policy Analyst pp 227–257 Cite as

Case Study Method and Policy Analysis

• Leslie A. Pal  

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Case studies are a good part of the backbone of policy analysis and research. This chapter illustrates case study methodology with a specific example drawn from the author’s current research on Internet governance.

Real-world problems are embedded in complex systems, in specific institutions, and are viewed differently by different policy actors. The case study method contributes to policy analysis in two ways. First, it provides a vehicle for fully contextualized problem definition. For example, in dealing with rising crime rates in a given city, the case approach allows the analyst to develop a portrait of crime in that city, for that city, and for that city’s decision makers. Second, case studies can illuminate policy-relevant questions (more as research than analysis) and can eventually inform more practical advice down the road. The chapter reviews the relationship between case study research and the aspirations of more nomothetic (law-like generalizations) social science. To study a case is not to study a unique phenomenon, but one that provides insight into a broader range of phenomena.

The author’s example of ICANN illustrates issues pertaining to globalization, global governance, and the internationalization of policy processes.

• comparative case study approach
• critical test
• generalization
• idiographic
• Internet Corporation for Assigned Names and Numbers (ICANN)
• unit of analysis

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Pal, L.A. (2005). Case Study Method and Policy Analysis. In: Geva-May, I. (eds) Thinking Like a Policy Analyst. Palgrave Macmillan, New York. https://doi.org/10.1057/9781403980939_12

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3.4: You Decide- Case Studies in Federalism

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Imagine you are a policymaker deciding each of the following policy issues. A crucial question is how much freedom local and state governments should have. Think about your responsibilities based on your particular role in each of the following case studies, and then decide what you will do. Each section concludes with a discussion of what actually happened.

Case Study One: Shall all California high school students be required to take an Ethnic Studies Course?

You are the governor of California. The California legislature passed a bill mandating all high school students complete a one-semester ethnic studies course. The course emphasizes the history and culture of four groups: African Americans, Asian Americans and Pacific Islanders, Latina/o/x Americans, and Native Americans. Should you sign the bill?

Advocates for the course argue that traditional social studies do not sufficiently address multicultural history, leaving students ignorant of our state's rich heritage. They also say that ethnic studies help students have more pride in their own histories, improve general academic performance, increase intercultural understanding, and contribute to a more educated citizenry better able to understand the challenges we face as a diverse society.

Opponents of the course favor teaching multicultural history, but they are concerned that the course omits many other groups, such as Armenian and Jewish Americans. Additionally, they are worried that the curriculum may overly emphasize oppression as a theme and demand that white students confess their "privilege." They argue that existing social studies courses already use a multicultural approach. At best, the class should be an elective.

Under dual federalism, the state government establishes the public school curriculum. Your choices are to sign the bill and make ethnic studies a requirement or veto it. You will be expected to justify your actions in your signing statement or veto message. What will you do and why?

What Happened?

In 2020, Governor Newsom vetoed the ethnic studies bill ("Veto Message"). Newsom expressed support for an ethnic studies curriculum, pointing out that he had already approved a bill requiring the course for California State University students. However, he asked that the curriculum be revised so that it "achieves balance, fairness and is inclusive of all communities." For the next several months, the State Board of Education revised the curriculum and included the experiences of many more ethnic groups. In 2021, a bill reflecting these changes made its way through the California legislature, and Governor Newsom signed it into law (Fensterwald).

Case Study Two: Shall all states be required to license marriage as between two people?

You are a U.S. Supreme Court justice. You are one of nine responsible for deciding whether the laws and practices of government are constitutional. It is 2015. The case before you is regarding marriage. Shall states be able to define the nature of marriage as a union between only a man and a woman, or should the U.S. Supreme Court tell states that they must offer marriage licenses to any two people?

Traditionally, the states have defined the nature of marriage. States respect the legitimacy of the marriage contracts of other states under the Full Faith and Credit Clause of the U.S. Constitution (Article IV). There are precedents for the Supreme Court to intervene. In the nineteenth century, the Court ruled that marriage shall be monogamous in all the states ( Reynolds v. United States , 1878), outlawing a man having multiple wives. More recently, the Court ruled that a state ban on interracial marriage was unconstitutional based on the Fourteenth Amendment's equal protection clause (Loving v. Virginia , 1967).

There are also precedents for the Supreme Court about gay rights. In 1986, the Court affirmed the right of a state to ban homosexual relations ( Bowers v. Hardwick ). It then reversed this decision in Lawrence v. Texas (2003), with the majority arguing that the right to privacy includes a right to consensual sex between two people.

State laws about marriage were rapidly changing beginning in the mid-1990s. Some states legalized same-sex marriage; others defined marriage as only between a man and a woman. A few had a middle ground of "civil union" that gives states, but not federal, marriage rights to same-sex couples without using the word marriage. The U.S. Congress passed the Defense of Marriage Act (1996), permitting states to refuse to recognize same-sex marriages from other states.

California voters passed Proposition 22 in 2003, which defined marriage as only between a man and a woman. The California Supreme Court declared this proposition unconstitutional because it violated California equal protection laws. Then California voters passed Proposition 8 in 2008, which added an amendment to the California Constitution, again defining marriage as solely between a man and a woman. The drama continued with gay rights advocates turning to the federal courts, which ruled Proposition 8 unconstitutional. Meanwhile, similar messes were brewing in the rest of the country with a mishmash of laws and conflicting Court rulings causing legal and practical confusion.

Now, let's move forward to 2015. The Supreme Court has consolidated several cases from multiple federal appeals courts to focus on whether the states shall be required to legalize same-sex marriage. The question before us is also very much a question regarding federalism. Shall the federal government impose its will on the states regarding marriage? If so, this nation-centered approach is an example of cooperative federalism. Alternatively, the Court may defer to the states and let their legislative or judicial authorities resolve the matter, an example of dual federalism.

Advocates for requiring states to license same-sex marriages make two arguments based on the Fourteenth Amendment and prior Court cases that provide the precedents for promoting privacy and equal protection of the law. First, lesbian and gay people are entitled to equal dignity before the law. Dignity means that states respect the autonomy and privacy of two people of the same sex to marry. The due process and equal protection clauses of the Fourteenth Amendment provide this fundamental right to dignity concerning marriage. Second, particular costs burden gays and lesbians and their children if marriage is limited to heterosexual couples. Health insurance and family leave may be inaccessible. Hospital visits and next-of-kin medical decisions are off-limits. Property laws leave partners destitute in the event of the breakup of relationships. Marriage gives the children and spouses in same-sex families the same rights as those in heterosexual unions.

Opponents of same-sex marriage make two general arguments, one substantive and the other procedural. First, they argue that states have traditionally defined marriage as between a man and a woman. It is in the child's interest to receive care and financial support from both their mother and father. Hence, the institution of marriage is central for one generation to raise the next. Second, procedurally, opponents argue that the Supreme Court should not have jurisdiction over this matter. Instead, elected officials, whether at the state or the national level, are the proper authorities to address this question. The Supreme Court should avoid establishing fundamental rights not clearly enumerated in the Constitution.

You are a Supreme Court justice. Redefining marriage will force all states to change their laws to increase liberty and equality. On the other hand, retaining the absence of a federal definition of marriage respects dual federalism. It leaves the states to address the issue through the democratic process, allowing for a diversity of choices among the states. How will you rule?

In 2015, in Obergefell v. Hodges, the U.S. Supreme Court ruled 5-4 to legalize same-sex marriage. The majority decision, authored by Justice Anthony Kennedy, argued that the Fourteenth Amendment requires that same-sex marriage be protected under law to extend equal dignity, or marriage equality, to the same-sex couple:

"No union is more profound than marriage, for it embodies the highest ideal of love, fidelity, devotion, sacrifice, and family. In forming a marital union, two people become something greater than once they were. As some of the petitioners in these cases demonstrate, marriage embodies a love that may endure even past death. It would misunderstand these men and women to say they disrespect the idea of marriage. Their plea is that they do respect it, respect it so deeply that they seek to find its fulfillment for themselves. They hope not to be condemned to loneliness, excluded from one of civilization's oldest institutions. They ask for equal dignity in the eyes of the law. The Constitution grants them that right."

Chief Justice Roberts, in one of the dissenting opinions, argued that the Supreme Court was exceeding its jurisdiction with its decision, that elected officials rather than justices should decide this issue:

"If you are among the many Americans—of whatever sexual orientation—who favor expanding same-sex marriage, by all means celebrate today's decision. Celebrate the achievement of a desired goal. Celebrate the opportunity for a new expression of commitment to a partner. Celebrate the availability of new benefits. But do not celebrate the Constitution. It had nothing to do with it."

After the Supreme Court decision, a single policy for all Americans applied: states must allow same-sex couples to marry, and they will enjoy the same state and federal rights and benefits as opposite-sex couples. The marriage contract and all other marriage-related laws changed to reflect the new view about our fundamental rights as Americans.

Case Study Three: Should the Affordable Care Act be repealed?

It is 2017. You are a Republican member of the United States House of Representatives representing the 25th district of California (in 2022, this district, encompassing the Santa Clarita and Antelope valleys, was renamed the 27th district as part of the redistricting process). The 25th district is what political scientists call a swing district, meaning that the district is evenly divided between Democrats and Republican voters, with candidates winning with less than 55% of the vote. In 2016, you won reelection with 53% of the vote ("California's 25th Congressional District Election"). However, the Democratic presidential candidate, Hillary Clinton, won the district by 50.3%, suggesting that some voters engaged in split-ticket voting, voting for both Republican and Democratic candidates ("Presidential Election in California").

The President, Mr. Trump, is a Republican. One of Mr. Trump's central campaign platforms was to repeal the Affordable Care Act (the "ACA") passed under his predecessor, President Obama. You must decide whether to vote to repeal the ACA, which will improve your support among Republicans, especially the President, or reject the repeal to avoid alienating yourself from moderates and Democrats in your district.

Some background about the Affordable Care Act and the arguments by supporters and critics will help you decide this issue. The ACA is often called "Obamacare" for short because President Obama's central campaign platform in 2008 was to help Americans with health care by improving existing health insurance coverage and expanding coverage to reach uninsured people. It was quite a political battle to push it through a very polarized Congress. When it finally passed in 2010, Obamacare had become the most significant change in healthcare policy in more than a generation. It also represented a substantial shift in the relations between the federal government and states. Some aspects of the ACA expand national power, hence representing a deepening of cooperative federalism. Other parts allow states to set their own policies, representing a deepening of new federalism.

The reach of the federal government increased in many ways. Some of the most significant were: first, employers with fifty or more full-time employees were required to provide health insurance; second, individuals were mandated to buy health insurance and received subsidies from the federal government if their income was up to 400% of the poverty level, and third, insurance companies had to cover preexisting conditions and preventative care. In addition, the ACA preempted, or displaced, state health insurance regulations and hence is an example of cooperative federalism where the national government takes control over a policy area and mandates changes in state policies.

However, aspects of the ACA gave states some freedom to implement the law and are therefore consistent with new federalism. First, states were allowed to create state health insurance exchanges for their residents to buy private insurance. If they chose not to, their residents would have to buy from the federal health insurance exchange. As of 2022, fourteen states and Washington D.C. have set up their own exchanges, including California ( CoveredCalifornia.com ). The state exchanges allow states to have greater autonomy. Second, the ACA expanded Medicaid, the existing public health insurance plan for low-income people established in the 1960s, to cover people who make up to 133% of the federal poverty level. However, because both the federal and state governments fund Medicaid, the U.S. Supreme Court ruled ( National Federation of Independent Business v. Sebelius , 2012) that states were not required to expand Medicaid eligibility. Hence, eligibility and income requirements for Medicaid vary from one state to another. These variances in ACA policies and programs among the states illustrate the signature characteristic of New Federalism: giving states flexibility based on state political preferences (Health Reform).

Now, we return to your dilemma as a representative. Should you vote to abolish and replace the Affordable Care Act with the American Health Care Act? Most prominently, this 2017 bill ends the expansion of the Medicaid program and income-based subsidies, saving the federal government hundreds of billions of dollars but causing approximately fifteen million people to lose coverage.

Advocates of replacing the ACA are motivated by ideological and partisan reasons. Ideologically, conservatives are skeptical of further government involvement in the healthcare sector of the economy. Seeing health care as an optional consumer product, individual consumers, businesses, and health insurance companies should not be subject to government coercion. Historically, states have been in control of their insurance markets. The ACA undermines state autonomy. Second, for many years, the health care debate has become intensely partisan, with this issue having a prominent role in the platforms of each party. Republicans had invested much importance in defeating President Obama's program. Mr. Trump promised he would succeed in this regard when other Republicans had failed.

Supporters of the Affordable Care Act argue that millions more Americans have health insurance coverage; health insurance coverage is better, and, in the long term, these improvements will lead to a healthier population. Further, they argue that the federal government's appropriate role is to devise a program that provides affordable health care for all. It has long been noted that the U.S. spends far more per capita with far worse health outcomes than other countries and is the only high-income country without some form of universal health care ("U.S. Health Care from a Global Perspective"). The ACA is a significant step to remedy this situation.

Public opinion is split along ideological and partisan lines in your district and the country. However, given that more people in your congressional district voted for Hillary Clinton than Donald Trump, it is likely that a vote in favor of repeal will make you less popular. On the other hand, if you vote to keep the ACA, you will likely be ostracized by Republicans in Congress and publicly criticized by the President, making it harder for you to accomplish anything else. Will you vote to repeal and replace Obamacare?

This case study has assigned you the role of Representative Steve Knight. He was elected in 2014 to represent California's 25th district, encompassing the Antelope and Santa Clarita valleys and a portion of Simi Valley. Representative Knight chose to vote with the Republican majority. The vote was intensely partisan and highly visible. Along party lines, the bill passed the House, 217-213, but then a similar bill failed in the Senate, 49-51. Nevertheless, the Affordable Care Act survived.

As a representative, Mr. Knight found that he was in an increasingly difficult position. As a Republican in a swing district, he tried to chart a moderate course. Still, because of the increasingly polarized nature of party politics and the shift of the Republican party to the right, this was increasingly difficult. As a result, in 2018, Knight was defeated by his Democratic opponent.

Transforming government in a new era

Never before have governments and their workforces been asked to do so much, so fast. As a result, public-sector leaders are seeking transformational improvement in citizen services, policy outcomes, and regulation. But government transformation is hard to pull off in a context of fiscal challenges, public mistrust, and workforce fatigue. McKinsey’s recent survey of public-sector leaders finds that nearly 80 percent of major change efforts fall short of meeting their objectives. (see sidebar, “About this study”).

That makes it critical to pinpoint the common success factors of transformations that do deliver. Our survey finds that the success rate is triple among programs that apply the following five disciplines of government transformation identified in McKinsey’s previous research :

• committed leadership
• clear purpose and priorities
• compelling communication
• capability for change
• cadence and coordination in delivery

About this study

The findings presented here draw on comprehensive and longitudinal evidence on what makes government transformations succeed. McKinsey surveyed 1,360 leaders and managers involved in public-sector transformation initiatives from 2019 to 2022. These transformation leaders were located across the globe—Australia, Brazil, Canada, Germany, India, Japan, Singapore, Sweden, the United Kingdom, and the United States (exhibit). Respondents came from government at all levels (federal, state, and local), state-owned enterprises, and the social sector. The survey findings were complemented with 18 interviews with leaders of successful government transformations and 30 case studies drawn from diverse settings and regions. This research builds on an earlier survey and interview series conducted by McKinsey in 2017 and 2018, allowing us to track valuable insights on the elements of successful government transformations that have proved enduring in very different contexts. Insights on the factors that have increased in importance with recent changes—COVID-19 and the “Great Attrition” as two examples—were also identified.

Our new research shows that the impact of these “five Cs” is amplified by two cross-cutting imperatives: first, meaningful engagement of public-sector employees; and second, effective use of digital tools.

In leadership, there is now greater emphasis on compassion and care for employees’ well-being, while purpose and priorities need to be co-defined with teams and made meaningful to individuals. Digital tools and techniques can now drive more engaging communication, as well as better cadence and coordination in delivery. And today, capability for change is underpinned by powerful learning journeys for employees and an understanding of how data and analytics can help drive innovation.

Our latest analysis also shows that governments that consistently apply the five Cs with an explicit focus on employee engagement and digital technologies are more resilient to shocks—and are better able to adapt and evolve their change programs when faced with disruption.

To step up delivery and face the challenges of the future, governments can seek out ways to connect with their employees’ sense of purpose and harness digital tools to strengthen innovation, collaboration, and delivery.

Many governments are struggling to transform—and to engage their workforces

There are many examples of how the COVID-19 pandemic prompted far-reaching government transformations and brought out the best in the public sector. HMRC, the United Kingdom Tax and Customs Agency, needed to build technology and operational solutions rapidly during the pandemic—in one example, it worked in a partnership with a private-sector consortium to build and launch a national digital customs service in 12 weeks to enable Northern Ireland businesses to trade with both the Great Britain mainland and the European Union. 1 “Written evidence submitted by the Trader Support Service (NIP00250),” Trader Support Service, UK Parliament, April 21, 2021. The Australian Federal Government undertook the largest mobilization of staff in working memory with the redeployment of more than 2,000 public servants across the areas of greatest need during the pandemic. 2 “Management of the Australian public service’s workforce response to COVID-19,” Australian National Audit Office, December 1, 2020. And numerous countries achieved impressive rates of vaccination in previously unimaginable timeframes.

However, our survey findings show that relatively few government transformation efforts achieve such breakthroughs. Of the change programs in our sample, 22 percent delivered their objectives fully and on time—virtually at the same rate as in our previous survey, when 20 percent of programs reported success (Exhibit 1). Our survey also found that transformation in the public sector is substantially less effective than in the private sector, where the success rate is around 30 percent.

Our survey went further to identify challenges that have been amplified by recent events. For example, three-quarters of respondents said employees were concerned about the nature of hybrid work, and more than 70 percent said they were facing labor shortages and skills mismatches between jobs and availability (Exhibit 2). Almost every respondent to the survey—94 percent—said they were experiencing at least one of these challenges. And less than a third were confident that they could handle these issues successfully.

Many governments, like their counterparts in the private sector, are facing the Great Attrition, which could hamper governmental transformation efforts and broader organizational health. In Australia, for example, 35 percent of public-sector employees surveyed by McKinsey in 2022 said they were at least somewhat likely to quit their jobs in the next three to six months. The drivers of this disengagement include work that does not feel meaningful, lack of potential for career development, and leadership that fails to inspire.

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How to drive change: meaningfully engage employees and enable them with digital tools.

In the light of these challenges, how can public-sector leaders give themselves the best chance of successfully driving positive change? The research finds that engaging employees is more important than ever. Public servants are searching for renewed purpose and meaning, better career-development opportunities, and more inspiration and care from their leaders.

Today, successful leaders of transformation engage employees around the larger purpose of their work, link that purpose to day-to-day activities, and give people autonomy in initiative design. As one former leader of a large services delivery department put it, “because the culture and values of our organization were about helping people, I conveyed constantly, consistently, and meaningfully to people that the changes underway were about helping people—because of this connection, people went above and beyond to deliver.”

It is particularly striking, at a time when many public servants are experiencing fatigue and burnout, that a focus on mental wellness has become one of the strongest markers of successful transformations. In recent years, there has been a surge in research and investment into how employers and leaders can support this priority. Key actions that leading employers (both public and private) are taking include use of better assessments of employee stress, promoting open discussions and clear processes to support mental wellness, and broadening mental health coverage for workforces. 3 “ Addressing employee burnout: Are you solving the right problem? ” McKinsey, May 27, 2022.

The other shift relates to the use of digital tools and enablers. The most successful government transformations are much more likely to use real-time data than other programs (Exhibit 3), and to deploy cutting-edge digital tools such as hybrid work platforms to strengthen their collaboration, communication, and decision making. These can improve the speed and effectiveness of decision making, according to ministers and public servants. As Noureddine Boutayeb, Morocco’s former minister of interior, put it, “Speed matters more than ever. We no longer talk about changes that take years, we talk about months or even less.”

A senior civil servant who served in the governor’s office in a US state noted: “The use of real-time intelligence was dramatically accelerated by the COVID-19 challenge. We established a COVID-19 collaboration cell across our state government agencies, and we also included outside stakeholders from the state’s healthcare system. This approach enabled transparency on information and a common operating picture to drive decisions.”

The evolving themes of employee engagement and digital enablement are common to all the enduring five Cs of successful transformations: committed leadership, clear purpose and priorities, compelling communication, capability for change, and cadence and coordination in delivery. Today, as in our 2018 report, our research finds that government transformations are three times more likely to succeed when all the five Cs are applied (Exhibit 4). They are seen as universal—each being a key driver of transformation successes regardless of the geography, trigger, scope, or structure of the change effort.

Our survey, along with our interviews with government leaders from around the world, highlights the key people-centric and digital interventions that make a difference in government transformations—in each of the five Cs.

Committed leadership: Leading with empathy, humility, and adaptability

Previous research has made it clear that the most successful transformations are driven by extraordinary leaders who make personal and professional commitments to achieve the targeted outcomes. Our new research underscores this finding and adds an extra dimension: committed leaders who displayed compassion, care, and adaptability were the most important factor for ensuring successful transformations and for ensuring that those transformations are resilient to future shocks.

General Sir Nicholas Carter, former chief of the UK defence staff, said in an interview with McKinsey, “To have an effect as a transformational leader, it’s so important that you care for and motivate those that you’re leading … you’ve got to have empathy and humility.” Often, this kind of leadership needs to be shaped through in-depth development programs. In the UK military, he told us, this involved “the creation of an army leadership center, a leadership doctrine, and a whole philosophy of trying to get people to look downwards rather than upwards.”

Role-modeling behavior changes can be crucial, as can be effective resource allocation to support the implementation of change program initiatives to avoid workforce fatigue and burnout. As David Thodey, former chair of the Independent Review of the Australian Public Service, told us, “We need to stare into the challenges of working in the public service and understand our future needs—and then be willing to fund and invest in that change.” Our survey found that allocating enough people to get the job done was an action 1.9 times more prevalent in successful transformations than in their unsuccessful counterparts.

Clear purpose and priorities: Shared definition of success and making change meaningful to the people delivering it

Successful transformations have crystal-clear purpose and priorities, which translate into a few measurable outcomes. During the pandemic response, clearly articulated purpose helped to galvanize government response. Kristina Murrin CBE, former director of implementation at Number 10 Downing Street in the UK Government, told us, “I focus heavily on purpose—and we managed to get people to just do extraordinary things during the COVID-19 period because it mattered.”

Our latest findings bring an important new dimension: the most consequential action to support the success of transformations is now ensuring that purpose is translated into individual meaning. This can involve co-designing the organization’s purpose with employees, and then linking their incentives to it. Our survey finds that programs that align individual incentives to purpose are nearly twice as likely to succeed as other transformation efforts.

A recent example of co-designing purpose can be seen in a large US government department. Through a series of working sessions, employees explored the organization’s imperatives and desired shifts. Together employees crafted an overarching purpose statement that translated into a series of focus areas and ultimately a series of tangible metrics for success.

Sarah Webber, COO of the state of Arizona in the United States, described the value of employees owning purpose, not only in delivering government transformations but in retention: “Besides just resources, for people to keep showing up to work you have to provide purpose: allowing folks to feel that they can make that impact and take control of that, is critical.”

Compelling communication: Harnessing digital tools to engage and listen

A compelling future vision, communicated to teams by visible leaders, is a key component of successful transformations. Our latest survey underlines the importance of engaging employees’ hearts and minds—the communication of a meaningful change story by senior leaders across their organization is an action 1.5 times more prevalent in successful transformations.

However, methods of communicating are increasingly disrupted by new patterns of work and digital delivery. Communication must now be omni-directional and multi-channel: new digital tools give leaders new ways to communicate with employees, but also open up new mechanisms to listen and demonstrate authenticity. As one former head of a major government financial agency emphasized: “You’re most successful if you’re listening, if you can authenticate your mission with staff. I was a leader that used Twitter—it allowed me to give a little bit of myself and to listen.”

Today’s most effective government transformation programs are deploying digital tools in imaginative ways, both to communicate progress and to generate support. In the German federal government’s drive to digitize public services, for example, the transformation team created a digitization-laboratory demonstration that allowed citizens, journalists, and public servants to experience the new approach. It also invited ministers to take part in user tests of digital prototypes. 4 Matthias Daub, Axel Domeyer, Abdulkader Lamaa, and Frauke Renz, “ Digital public services: How to achieve fast transformation at scale ,” McKinsey, July 15, 2020.

Our survey respondents confirmed the importance of compelling communication. “Engaging employees more through two-way communication” and “focusing more on engaging the front line” were two of the top three actions that leaders of unsuccessful transformations wished they had focused on more (Exhibit 5).

Capability for change: Building adaptive, digitally enabled talent

Successful transformations actively invest in building public servants’ talents with the skills needed to deliver change, and to respond to the unexpected. These include capabilities in digital and data analytics as well as adaptive leadership—defined by Ronald Heifetz of the Harvard Kennedy School as “the practice of mobilizing people to tackle tough challenges and thrive.” 5 Alexander Grashow, Ronald Heifetz, and Martin Linsky, “The practice of adaptive leadership,” Harvard Business Press , 2009.

Many governments are investing to create unique learning experiences and journeys—development opportunities that cannot be accessed elsewhere and that cultivate these essential capacities. Her Excellency Huda AlHashimi, the United Arab Emirates’ deputy minister of cabinet affairs for strategic affairs put it this way: “Training’s not the right word. It’s changing the mindset and providing the right methodologies and tools to employees at all levels. The main thing is that we are asking them to constantly learn. And this constantly learning is critical.”

Consider the example of Namyangju, a city in South Korea, that launched an initiative to train all its staff on the use of a smart-city platform to drive operations. Led by the mayor, the program of employee training and education supported multiple innovative new projects on citizen convenience and efficiency via improved data collection and analysis. 6 Michael J. Ahn, Younhee Kim, and Suenghwan Myeong, “Smart city strategies—technology push or pull? A case study exploration of Gimpo and Namyangju, South Korea,” MDPI, December 24, 2020.

Finally, our survey shows that the staffing of transformation programs can itself be a powerful engine for capability building. The most effective transformations assign high-potential employees or managers to lead the change: those who do so are 1.5 times more effective than those who don’t.

Cadence and coordination in delivery: Agility in transformation

Akin to a rowing team with a coxswain calling a regular rhythm of progress, effective transformations have highly collaborative teams and a central point of coordination. For example, our survey found that dedicated central teams charged with coordinating all change-related activities were 1.5 times more prevalent in successful transformations. Programs that harness dynamic digital tools—such as live dashboards—are also more likely to achieve effective coordination.

Many government transformations coordinate across multiple government agencies using agile approaches such as cross-functional teams. As the COVID-19 pandemic showed, governments can be very effective at cross-agency coordination during crises—but this is challenging to maintain beyond the immediate emergency.

One leader who has marshalled such cross-government coordination is G. Edward DeSeve, who oversaw several government-wide agile efforts. He reflected, “We had to use agile techniques along the way with a lot of customer involvement, a lot of teams, a lot of deadlines, and things like that.” This, he told us, was key to the program achieving results according to an aggressive schedule.

An increasingly common feature of successful transformations is the use of simulation planning and piloting of initiatives before they are scaled up. This was the approach followed in the digital transformation of Canada’s social services. John Knubley, the former deputy minister of innovation, science and economic development of Canada, told us, “Social services needed to be much more digital and accessible, but they didn’t do it all at once—they tested and piloted, and then they kept their long-term goals in mind when scaling.”

Building resilience to the challenges of the future

One of the clearest lessons of the COVID-19 pandemic, according to our survey, is that it is very difficult for governments to anticipate how future changes might impact on their priorities and change programs. Indeed, increasing global uncertainty driven by pandemic risks, cyber incidents, and unforeseen events underline the importance of building resilience as a core business of government (Exhibit 6).

Many governments recognize that they need to build resilience to external shocks and uncertain futures—and our research offers insights on how they might do so.

Even given the COVID-19 pandemic, government transformation programs that embedded the five disciplines set out in this article experienced greater resilience than those that did not. Importantly, our survey also suggests that consistent application of these transformation disciplines can improve organizational resilience against a broader set of challenges such as supply-chain disruptions, price increases, and labor shortages (Exhibit 7).

Of the five Cs, committed leadership was the most important factor in predicting resilience. We defined one in five of the transformation programs in our study as “very resilient”—and among those, 72 percent applied the discipline of committed leadership.

To promote resilience, government leaders can cultivate an “adaptive mindset”—in themselves and their teams—by recognizing that complex and changing environments will require repeated iteration and problem solving in both policy and delivery. Adaptiveness can help move people from simply enduring a challenge to thriving beyond it. 7 “ Future proof: Solving the ‘adaptability paradox’ for the long term ,” McKinsey, August 2, 2021.

As Douglas Millican, chief executive of Scottish Water, observed, this investment can create a virtuous cycle: “Investing heavily in leadership development drives employee engagement, which gets people on board to deliver great performance.”

The leaders interviewed emphasized that such actions not only support successful transformations, but also improve organizational health and employee engagement across government. As David Thodey put it: “There are many challenges. But if you create a great place for people to work, if you are purpose driven, and the quality of the work you do is impactful, and people are valued for who they are—if all these attributes are present, then it will be a great place to work—and in that environment, people don’t leave easily.”

Faced with the disruptions of COVID-19, many governments found ways to unlock new capabilities—such as digital tools and real-time data—that today can position them to drive the next transformations of public services. Many public servants are in need of reconnecting and re-energizing after two years of navigating the pandemic. Governments that succeed in engaging their people in meaningful change efforts, and bring real care to their mental well-being, can galvanize their organizations to tackle their societies’ most pressing challenges.

Governments’ experiences of COVID-19 have underlined just how important it is for public-sector change programs to inspire their workforces with compelling purpose, nurture adaptive leadership, and focus their efforts on building the capabilities of the future. Governments that can “bottle the best” lessons of recent years—and focus with renewed vigor on supporting talented public servants—will be better placed to deliver the quality services that citizens require, and the innovations that a fast-changing world demands.

Roland Dillon is a partner in McKinsey’s Melbourne office, where Elizabeth Murray is an associate partner; Scott Blackburn is a senior partner in the Washington, DC, office, and Neil Christie is a partner in the Stockholm office.

The authors wish to thank Miguel Aleluia, Michael Bucy, Jonathan Dimson, Christian Habla, and Solveigh Hieronimus for their contributions to this article.

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Understanding Experimentation and Implementation : A Case Study of China’s Government Transparency Policy

Jieun Kim is a Postdoctoral Fellow at the Center for the Study of Contemporary China, University of Pennsylvania, Philadelphia, USA and will be an Assistant Professor at NYU Shanghai beginning in the Fall of 2022. Email: < [email protected] >

Kevin J. O’Brien is the Alann P. Bedford Professor of Asian Studies and Political Science at the University of California, Berkeley. We thank John Yasuda for his feedback on an earlier draft, and the scholars and staff at the Center for Public Participation Studies and Support, Beijing University, for their help in arranging Jieun Kim’s interviews in China. We also acknowledge generous support from the Center for Chinese Studies, University of California, Berkeley. Email: < [email protected] >

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Jieun Kim , Kevin J. O’Brien; Understanding Experimentation and Implementation : A Case Study of China’s Government Transparency Policy . Asian Survey 1 August 2021; 61 (4): 591–614. doi: https://doi.org/10.1525/as.2021.61.4.591

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Studies of local governance in China often point to nimble experimentation but problematic implementation. To reconcile these competing images, it is useful to clarify the concepts of experimentation and implementation and see how they unfolded in one policy area. The history of China’s Open Government Information (OGI) initiative shows that the experimentation stage sometimes proceeds well and produces new policy options, but may falter if local leaders are unwilling to carry out an experiment. And the implementation stage often poses challenges, but may improve if the Center initiates new, small-scale experiments and encourages local innovation. This suggests that the experimentation and implementation stages are not so different when officials in Beijing and the localities have diverging interests and the Center is more supportive of a measure than local officials. The ups and downs of OGI, and also village elections, can be traced to the policy goal of monitoring local cadres, the central–local divide, and the pattern of support and opposition within the state.

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• v.7(2); 2019

Fundamentals of case study research in family medicine and community health

Sergi fàbregues.

1 Department of Psychology and Education, Universitat Oberta de Catalunya, Barcelona, Spain

Michael D Fetters

2 Department of Family Medicine, University of Michigan, Ann Arbor, Michigan, USA

The aim of this article is to introduce family medicine researchers to case study research, a rigorous research methodology commonly used in the social and health sciences and only distantly related to clinical case reports. The article begins with an overview of case study in the social and health sciences, including its definition, potential applications, historical background and core features. This is followed by a 10-step description of the process of conducting a case study project illustrated using a case study conducted about a teaching programme executed to teach international family medicine resident learners sensitive examination skills. Steps for conducting a case study include (1) conducting a literature review; (2) formulating the research questions; (3) ensuring that a case study is appropriate; (4) determining the type of case study design; (5) defining boundaries of the case(s) and selecting the case(s); (6) preparing for data collection; (7) collecting and organising the data; (8) analysing the data; (9) writing the case study report; and (10) appraising the quality. Case study research is a highly flexible and powerful research tool available to family medicine researchers for a variety of applications.

Significance statement

Given their potential for answering ‘how’ and ‘why’ questions about complex issues in their natural setting, case study designs are being increasingly used in the health sciences. Conducting a case study can, however, be a complex task because of the possibility of combining multiple methods and the need to choose between different types of case study designs. In order to introduce family medicine and community health researchers to the fundamentals of case study research, this article reviews its definition, potential applications, historical background and main characteristics. It follows on with a practical, step-by-step description of the case study process that will be useful to researchers interested in implementing this research design in their own practice.

Introduction

This article provides family medicine and community health researchers a concise resource to conduct case study research. The article opens with an overview of case study in the social and health sciences, including its definition, potential applications, historical background and core features. This is followed by a 10-step description of the process of conducting a case study project, as described in the literature. These steps are illustrated using a case study about a teaching programme executed to teach international medical learners sensitive examination skills. The article ends with recommendations of useful articles and textbooks on case study research.

Origins of case study research

Case study is a research design that involves an intensive and holistic examination of a contemporary phenomenon in a real-life setting. 1–3 It uses a variety of methods and multiple data sources to explore, describe or explain a single case bounded in time and place (ie, an event, individual, group, organisation or programme). A distinctive feature of case study is its focus on the particular characteristics of the case being studied and the contextual aspects, relationships and processes influencing it. 4 Here we do not include clinical case reports as these are beyond the scope of this article. While distantly related to clinical case reports commonly used to report unusual clinical case presentations or findings, case study is a research approach that is frequently used in the social sciences and health sciences. In contrast to other research designs, such as surveys or experiments, a key strength of case study is that it allows the researcher to adopt a holistic approach—rather than an isolated approach—to the study of social phenomena. As argued by Yin, 3 case studies are particularly suitable for answering ‘how’ research questions (ie, how a treatment was received) as well as ‘why’ research questions (ie, why the treatment produced the observed outcomes).

Given its potential for understanding complex processes as they occur in their natural setting, case study increasingly is used in a wide range of health-related disciplines and fields, including medicine, 5 nursing, 6 health services research 1 and health communication. 7 With regard to clinical practice and research, a number of authors 1 5 8 have highlighted how insights gained from case study designs can be used to describe patients’ experiences regarding care, explore health professionals’ perceptions regarding a policy change, and understand why medical treatments and complex interventions succeed or fail.

In anthropology and sociology, case study as a research design was introduced as a response to the prevailing view of quantitative research as the primary way of undertaking research. 9 From its beginnings, social scientists saw case study as a method to obtain comprehensive accounts of social phenomena from participants. In addition, it could complement the findings of survey research. Between the 1920s and 1960s, case study became the predominant research approach among the members of the Department of Sociology of the University of Chicago, widely known as ‘The Chicago School’. 10 11 During this period, prominent sociologists, such as Florian Znaniecki, William Thomas, Everett C Hughes and Howard S Becker, undertook a series of innovative case studies (including classical works such as The Polish peasant in Europe and America or Boys in White ), which laid the foundations of case study designs as implemented today.

In the 1970s, case study increasingly was adopted in the USA and UK in applied disciplines and fields, such as education, programme evaluation and public policy research. 12 As a response to the limitations of quasi-experimental designs for undertaking comprehensive programme evaluations, researchers in these disciplines saw in case studies—either alone or in combination with experimental designs—an opportunity to gain additional insights into the outcomes of programme implementation. In the mid-1980s and early 1990s, the case study approach became recognised as having its own ‘logic of design’ (p46). 13 This period coincides with the publication of a considerable number of influential articles 14–16 and textbooks 4 17 18 on case study research.

These publications were instrumental in shaping contemporary case study practice, yet they reflected divergent views about the nature of case study, including how it should be defined, designed and implemented (see Yazan 19 for a comparison of the perspectives of Yin, Merriam and Stake, three leading case study methodologists). What these publications have in common is that case study revolves around four key features.

First, case study examines a specific phenomenon in detail by performing an indepth and intensive analysis of the selected case. The rationale for case study designs, rather than more expansive designs such as surveys, is that the researcher is interested in investigating the particularity of a case, that is, the unique attributes that define an event, individual, group, organisation or programme. 2 Second, case study is conducted in natural settings where people meet, interact and change their perceptions over time. The use of the case study design is a choice in favour of ‘maintaining the naturalness of the research situation and the natural course of events’ (p177). 20

Third, case study assumes that a case under investigation is entangled with the context in which it is embedded. This context entails a number of interconnected processes that cannot be disassociated from the case, but rather are part of the study. The case study researcher is interested in understanding how and why such processes take place and, consequently, uncovering the interactions between a case and its context. Research questions concerning how and why phenomena occur are particularly appropriate in case study research. 3

Fourth, case study encourages the researcher to use a variety of methods and data types in a single study. 20 21 These can be solely qualitative, solely quantitative or a mixture of both. The latter option allows the researcher to gain a more comprehensive understanding of the case and improve the accuracy of the findings. The four above-mentioned key features of case study are shown in table 1 , using the example of a mixed methods case study evaluation. 22

Key features of case study as presented by Shultz et al 22

There are many potential applications for case study research. While often misconstrued as having only an exploratory role, case study research can be used for descriptive and explanatory research (p7–9). 3 Family medicine and community health researchers can use case study research for evaluating a variety of educational programmes, clinical programmes or community programmes.

Case study illustration from family medicine

In the featured study, Japanese family medicine residents received standardised patient instructor-based training in female breast, pelvic, male genital and prostate examinations as part of an international training collaboration to launch a new family medicine residency programme. 22 From family medicine residents, trainers and staff, the authors collected and analysed data from post-training feedback, semistructured interviews and a web-based questionnaire. While the programme was perceived favourably, they noted barriers to reinforcement in their home training programme, and taboos regarding gender-specific healthcare appear as barriers to implementing a similar programme in the home institution.

A step-by-step description of the process of carrying out a case study

As shown in table 2 and illustrated using the article by Shultz et al , 22 case study research generally includes 10 steps. While commonly conducted in this order, the steps do not always occur linearly as data collection and analysis may occur over several iterations or implemented with a slightly different order.

Ten steps for conducting a case study

SPI, standardised patient instructor.

Step 1. Conduct a literature review

During the literature review, researchers systematically search for publications, select those most relevant to the study’s purpose, critically appraise them and summarise the major themes. The literature review helps researchers ascertain what is and is not known about the phenomenon under study, delineate the scope and research questions of the study, and develop an academic or practical justification for the study. 23

Step 2. Formulate the research questions

Research questions critically define in operational terms what will be researched and how. They focus the study and play a key role in guiding design decisions. Key decisions include the case selection and choice of a case study design most suitable for the study. According to Fraenkel et al , 24 the key attributes of good research questions are (1) feasibility, (2) clarity, (3) significance, (4) connection to previous research identified in the literature and (5) compliance with ethical research standards.

Step 3. Ensure that a case study is appropriate

Before commencing the study, researchers should ensure that case study design embodies the most appropriate strategy for answering the study questions. The above-noted four key features—in depth examination of phenomena, naturalness, a focus on context and the use of a combination of methods—should be reflected in the research questions as well as subsequent design decisions.

Step 4. Determine the type of case study design

Researchers need to choose a specific case study design. Sometimes, researchers may define the case first (step 5), for example, in a programme evaluation, and the case may need to be defined before determining the type. Yin’s 3 typology is based on two dimensions, whether the study will examine a single case or multiple cases, and whether the study will focus on a single or multiple units of analysis. Figure 1 illustrates these four types of design using a hypothetical example of a programme evaluation. Table 3 shows an example of each type from the literature.

Examples of published studies using the four types of case study designs suggested by Yin 3

Types of case study designs. 3 21

In type 1 holistic single case design , researchers examine a single programme as the sole unit of analysis. In type 2 embedded single case design , the interest is not exclusively in the programme, but also in its different subunits, including sites, staff and participants. These subunits constitute the range of units of analysis. In type 3 holistic multiple case design , researchers conduct a within and cross-case comparison of two or more programmes, each of which constitutes a single unit of analysis. A major strength of multiple case designs is that they enable researchers to develop an in depth description of each case and to identify patterns of variation and similarity between the cases. Multiple case designs are likely to have stronger internal validity and generate more insightful findings than single case designs. They do this by allowing ‘examination of processes and outcomes across many cases, identification of how individual cases might be affected by different environments, and the specific conditions under which a finding may occur’ (p583). 25 In type 4 embedded multiple case design , a variant of the holistic multiple case design, researchers perform a detailed examination of the subunits of each programme, rather than just examining each case as a whole.

Step 5. Define the boundaries of the case(s) and select the case(s)

Miles et al 26 define a case as ‘a phenomenon of some sort occurring in a bounded context’ (p28). What is and is not the case and how the case fits within its broader context should be explicitly defined. As noted in step 4, this step may occur before choice of the case study type, and the process may actually occur in a back-and-forth fashion. A case can entail an individual, a group, an organisation, an institution or a programme. In this step, researchers delineate the spatial and temporal boundaries of the case, that is, ‘when and where it occurred, and when and what was of interest’ (p390). 9 Aside from ensuring the coherence and consistency of the study, bounding the case ensures that the planned research project is feasible in terms of time and resources. Having access to the case and ensuring ethical research practice are two central considerations in case selection. 1

Step 6. Prepare to collect data

Before beginning the data collection, researchers need a study protocol that describes in detail the methods of data collection. The protocol should emphasise the coherence between the data collection methods and the research questions. According to Yin, 3 a case study protocol should include (1) an overview of the case study, (2) data collection procedures, (3) data collection questions and (4) a guide for the case study report. The protocol should be sufficiently flexible to allow researchers to make changes depending on the context and specific circumstances surrounding each data collection method.

Step 7. Collect and organise the data

While case study is often portrayed as a qualitative approach to research (eg, interviews, focus groups or observations), case study designs frequently rely on multiple data sources, including quantitative data (eg, surveys or statistical databases). A growing number of authors highlight the ways in which the use of mixed methods within case study designs might contribute to developing ‘a more complete understanding of the case’ (p902), 21 shedding light on ‘the complexity of a case’ (p118) 27 or increasing ‘the internal validity of a study’ (p6). 1 Guetterman and Fetters 21 explain how a qualitative case study can also be nested within a mixed methods design (ie, be considered the qualitative component of the design). An interesting strategy for organising multiple data sources is suggested by Yin. 3 He recommends using a case study database in which different data sources (eg, audio files, notes, documents or photographs) are stored for later retrieval or inspection. See guidance from Creswell and Hirose 28 for conducting a survey and qualitative data collection in mixed methods and DeJonckheere 29 on semistructured interviewing.

Step 8. Analyse the data

Bernard and Ryan 30 define data analysis as ‘the search for patterns in data and for ideas that help explain why these patterns are there in the first place’ (p109). Depending on the case study design, analysis of the qualitative and quantitative data can be done concurrently or sequentially. For the qualitative data, the first step of the analysis involves segmenting the data into coding units, ascribing codes to data segments and organising the codes in a coding scheme. 31 Depending on the role of theory in the study, an inductive, data-driven approach can be used where meaning is found in the data, or a deductive, concept-driven approach can be adopted where predefined concepts derived from the literature, or previous research, are used to code the data. 32 The second step involves searching for patterns across codes and subsets of respondents, so major themes are identified to describe, explain or predict the phenomenon under study. Babchuk 33 provides a step-by-step guidance for qualitative analysis in this issue. When conducting a single case study, the within-case analysis yields an in depth, thick description of the case. When the study involves multiple cases, the cross-comparison analysis elicits a description of similarities and divergence between cases and may generate explanations and theoretical predictions regarding other cases. 26

For the quantitative part of the case study, data are entered in statistical software packages for conducting descriptive or inferential analysis. Guetterman 34 provides a step-by-step guidance on basic statistics. In case study designs where both data strands are analysed simultaneously, analytical techniques include pattern matching, explanation building, time-series analysis and creating logic models (p142–167). 3

Step 9. Write the case study report

The case study report should have the following three characteristics. First, the description of the case and its context should be sufficiently comprehensive to allow the reader to understand the complexity of the phenomena under study. 35 Second, the data should be presented in a concise and transparent manner to enable the reader to question, or to re-examine, the findings. 36 Third, the report should be adapted to the interests and needs of its primary audience or audiences (eg, academics, practitioners, policy-makers or funders of research). Yin 3 suggests six formats for organising case study reports, namely linear-analytic, comparative, chronological, theory building, suspense and unsequenced structures. To facilitate case transferability and applicability to other similar contexts, the case study report must include a detailed description of the case.

Step 10. Appraise quality

Although presented as the final step of the case study process, quality appraisal should be considered throughout the study. Multiple criteria and frameworks for appraising the quality of case study research have been suggested in the literature. Yin 3 suggests the following four criteria: construct validity (ie, the extent to which a study accurately measures the concepts that it claims to investigate), internal validity (ie, the strength of the relationship between variables and findings), external validity (ie, the extent to which the findings can be generalised) and reliability (ie, the extent to which the findings can be replicated by other researchers conducting the same study). Yin 37 also suggests using two separate sets of guidelines for conducting case study research and for appraising the quality of case study proposals. Stake 4 presents a 20-item checklist for critiquing case study reports, and Creswell and Poth 38 and Denscombe 39 outline a number of questions to consider. Since these quality frameworks have evolved from different disciplinary and philosophical backgrounds, the researcher’s approach should be coherent with the epistemology of the study. Figure 2 provides a quality appraisal checklist adapted from Creswell and Poth 38 and Denscombe. 39

Checklist for evaluating the quality of a case study. 38 39

The challenges to conducting case study research include rationalising the literature based on literature review, writing the research questions, determining how to bound the case, and choosing among various case study purposes and designs. Factors held in common with other methods include analysing and presenting the findings, particularly with multiple data sources.

Other resources

Resources with more in depth guidance on case study research include Merriam, 17 Stake 4 and Yin. 3 While each reflects a different perspective on case study research, they all provide useful guidance for designing and conducting case studies. Other resources include Creswell and Poth, 38 Swanborn 2 and Tight. 40 For mixed methods case study designs, Creswell and Plano Clark, 27 Guetterman and Fetters, 21 Luck et al , 6 and Plano Clark et al 41 provide guidance. Byrne and Ragin’s 42 The SAGE Handbook of Case-Based Methods and Mills et al ’s 43 Encyclopedia of case study research provide guidance for experienced case study researchers.

Conclusions

Family medicine and community health researchers engage in a wide variety of clinical, educational, research and administrative programmes. Case study research provides a highly flexible and powerful research tool to evaluate rigorously many of these endeavours and disseminate this information.

Acknowledgments

The authors would like to acknowledge the help of Dick Edelstein and Marie-Hélène Paré in editing the final manuscript.

Correction notice: This article has been corrected. Reference details have been updated.

Contributors: SF and MDF conceived and drafted the manuscript, and approved the final version of the manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; internally peer reviewed.