Case study
A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context.[1][2] For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.
Generally, a case study can highlight nearly any individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation (N=1), but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study).[3][4][5][6] Research projects involving numerous cases are frequently called cross-case research, whereas a study of a single case is called within-case research.[5][7]
Case study research has been extensively practiced in both the social and natural sciences.[8][9]: 5–6 [10][11]
Definition[edit]
There are multiple definitions of case studies, which may emphasize the number of observations (a small N), the method (qualitative), the thickness of the research (a comprehensive examination of a phenomenon and its context), and the naturalism (a "real-life context" is being examined) involved in the research.[12] There is general agreement among scholars that a case study does not necessarily have to entail one observation (N=1), but can include many observations within a single case or across numerous cases.[3][4][5][6] For example, a case study of the French Revolution would at the bare minimum be an observation of two observations: France before and after a revolution.[13] John Gerring writes that the N=1 research design is so rare in practice that it amounts to a "myth".[13]
The term cross-case research is frequently used for studies of multiple cases, whereas within-case research is frequently used for a single case study.[5][7]
John Gerring defines the case study approach as an "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)".[14] According to Gerring, case studies lend themselves to an idiographic style of analysis, whereas quantitative work lends itself to a nomothetic style of analysis.[15] He adds that "the defining feature of qualitative work is its use of noncomparable observations—observations that pertain to different aspects of a causal or descriptive question", whereas quantitative observations are comparable.[15]
According to John Gerring, the key characteristic that distinguishes case studies from all other methods is the "reliance on evidence drawn from a single case and its attempts, at the same time, to illuminate features of a broader set of cases".[13] Scholars use case studies to shed light on a "class" of phenomena.
Uses[edit]
Case studies have commonly been seen as a fruitful way to come up with hypotheses and generate theories.[21][22][36][25][37][15] Case studies are useful for understanding outliers or deviant cases.[38] Classic examples of case studies that generated theories includes Darwin's theory of evolution (derived from his travels to the Easter Island), and Douglass North's theories of economic development (derived from case studies of early developing states, such as England).[37]
Case studies are also useful for formulating concepts, which are an important aspect of theory construction.[39] The concepts used in qualitative research will tend to have higher conceptual validity than concepts used in quantitative research (due to conceptual stretching: the unintentional comparison of dissimilar cases).[25] Case studies add descriptive richness,[40][34] and can have greater internal validity than quantitative studies.[41] Case studies are suited to explain outcomes in individual cases, which is something that quantitative methods are less equipped to do.[33]
Case studies have been characterized as useful to assess the plausibility of arguments that explain empirical regularities.[42] Case studies are also useful for understanding outliers or deviant cases.[38]
Through fine-gained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large-N study.[43][40][44][21][45][38] In terms of identifying "causal mechanisms", some scholars distinguish between "weak" and "strong chains". Strong chains actively connect elements of the causal chain to produce an outcome whereas weak chains are just intervening variables.[46]
Case studies of cases that defy existing theoretical expectations may contribute knowledge by delineating why the cases violate theoretical predictions and specifying the scope conditions of the theory.[21] Case studies are useful in situations of causal complexity where there may be equifinality, complex interaction effects and path dependency.[25][47] They may also be more appropriate for empirical verifications of strategic interactions in rationalist scholarship than quantitative methods.[48] Case studies can identify necessary and insufficient conditions, as well as complex combinations of necessary and sufficient conditions.[25][33][49] They argue that case studies may also be useful in identifying the scope conditions of a theory: whether variables are sufficient or necessary to bring about an outcome.[25][33]
Qualitative research may be necessary to determine whether a treatment is as-if random or not. As a consequence, good quantitative observational research often entails a qualitative component.[15]
Limitations[edit]
Designing Social Inquiry (also called "KKV"), an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba, primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research.[22][50][39] The authors' recommendation is to increase the number of observations (a recommendation that Barbara Geddes also makes in Paradigms and Sand Castles),[35] because few observations make it harder to estimate multiple causal effects, as well as increase the risk that there is measurement error, and that an event in a single case was caused by random error or unobservable factors.[22] KKV sees process-tracing and qualitative research as being "unable to yield strong causal inference" due to the fact that qualitative scholars would struggle with determining which of many intervening variables truly links the independent variable with a dependent variable. The primary problem is that qualitative research lacks a sufficient number of observations to properly estimate the effects of an independent variable. They write that the number of observations could be increased through various means, but that would simultaneously lead to another problem: that the number of variables would increase and thus reduce degrees of freedom.[39] Christopher H. Achen and Duncan Snidal similarly argue that case studies are not useful for theory construction and theory testing.[51]
The purported "degrees of freedom" problem that KKV identify is widely considered flawed; while quantitative scholars try to aggregate variables to reduce the number of variables and thus increase the degrees of freedom, qualitative scholars intentionally want their variables to have many different attributes and complexity.[52][25] For example, James Mahoney writes, "the Bayesian nature of process of tracing explains why it is inappropriate to view qualitative research as suffering from a small-N problem and certain standard causal identification problems."[53] By using Bayesian probability, it may be possible to makes strong causal inferences from a small sliver of data.[54][55]
KKV also identify inductive reasoning in qualitative research as a problem, arguing that scholars should not revise hypotheses during or after data has been collected because it allows for ad hoc theoretical adjustments to fit the collected data.[56] However, scholars have pushed back on this claim, noting that inductive reasoning is a legitimate practice (both in qualitative and quantitative research).[57]
A commonly described limit of case studies is that they do not lend themselves to generalizability.[22] Due to the small number of cases, it may be harder to ensure that the chosen cases are representative of the larger population.[41] Some scholars, such as Bent Flyvbjerg, have pushed back on that notion.[36]
As small-N research should not rely on random sampling, scholars must be careful in avoiding selection bias when picking suitable cases.[21] A common criticism of qualitative scholarship is that cases are chosen because they are consistent with the scholar's preconceived notions, resulting in biased research.[21][36] Alexander George and Andrew Bennett also note that a common problem in case study research is that of reconciling conflicting interpretations of the same data.[25] Another limit of case study research is that it can be hard to estimate the magnitude of causal effects.[58]