Case study

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. 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. John Gerring writes that the N=1 research design is so rare in practice that it amounts to a "myth". According to Gerring, case studies lend themselves to an idiographic style of analysis, whereas quantitative work lends itself to a nomothetic style of analysis. 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. 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". Scholars use case studies to shed light on a "class" of phenomena. == Research design ==

As with other social science methods, no single research design dominates case study research. Case studies can use at least four types of designs. First, there may be a "no theory first" type of case study design, which is closely connected to Kathleen M. Eisenhardt's methodological work. A second type of research design highlights the distinction between single- and multiple-case studies, following Robert K. Yin's guidelines and extensive examples. Finally, the design rationale for a case study may be to identify "anomalies". A representative scholar of this design is Michael Burawoy. Each of these four designs may lead to different applications, and understanding their sometimes unique ontological and epistemological assumptions becomes important. However, although the designs can have substantial methodological differences, the designs also can be used in explicitly acknowledged combinations with each other. While case studies can be intended to provide bounded explanations of single cases or phenomena, they are often intended to raise theoretical insights about the features of a broader population. Random selection of cases may produce unrepresentative cases, as well as uninformative cases. For example, outlier cases (those which are extreme, deviant or atypical) can reveal more information than the potentially representative case. A case may also be chosen because of the inherent interest of the case or the circumstances surrounding it. Alternatively, it may be chosen because of researchers' in-depth local knowledge; where researchers have this local knowledge they are in a position to "soak and poke" as Richard Fenno put it, and thereby to offer reasoned lines of explanation based on this rich knowledge of setting and circumstances. Beyond decisions about case selection and the subject and object of the study, decisions need to be made about the purpose, approach, and process of the case study. Gary Thomas thus proposes a typology for the case study wherein purposes are first identified (evaluative or exploratory), then approaches are delineated (theory-testing, theory-building, or illustrative), then processes are decided upon, with a principal choice being between whether the study is to be single or multiple, and choices also about whether the study is to be retrospective, snapshot or diachronic, and whether it is nested, parallel or sequential. In a 2015 article, John Gerring and Jason Seawright list seven case selection strategies: • Typical cases are cases that exemplify a stable cross-case relationship. These cases are representative of the larger population of cases, and the purpose of the study is to look within the case rather than compare it with other cases. • Diverse cases are cases that have variations on the relevant X and Y variables. Due to the range of variation on the relevant variables, these cases are representative of the full population of cases. • Extreme cases are cases that have an extreme value on the X or Y variable relative to other cases. • Deviant cases are cases that defy existing theories and common sense. They not only have extreme values on X or Y (like extreme cases) but defy existing knowledge about causal relations. • Influential cases are cases that are central to a model or theory (for example, Nazi Germany in theories of fascism and the far-right). • Most similar cases are cases that are similar on all the independent variables, except the one of interest to the researcher. • Most different cases are cases that are different on all the independent variables, except the one of interest to the researcher. For theoretical discovery, Jason Seawright recommends using deviant cases or extreme cases that have an extreme value on the X variable. • Atheoretical (or configurative idiographic) case studies aim to describe a case very well, but not to contribute to a theory. • Interpretative (or disciplined configurative) case studies aim to use established theories to explain a specific case. • Hypothesis-generating (or heuristic) case studies aim to inductively identify new variables, hypotheses, causal mechanisms, and causal paths. • Theory testing case studies aim to assess the validity and scope conditions of existing theories. • Plausibility probes, aim to assess the plausibility of new hypotheses and theories. • Building block studies of types or subtypes, aim to identify common patterns across cases. Aaron Rapport reformulated "least-likely" and "most-likely" case selection strategies into the "countervailing conditions" case selection strategy. The countervailing conditions case selection strategy has three components: • The chosen cases fall within the scope conditions of both the primary theory being tested and the competing alternative hypotheses. • For the theories being tested, the analyst must derive clearly stated expected outcomes. • In determining how difficult a test is, the analyst should identify the strength of countervailing conditions in the chosen cases. In terms of case selection, Gary King, Robert Keohane, and Sidney Verba warn against "selecting on the dependent variable". They argue for example that researchers cannot make valid causal inferences about war outbreaks by only looking at instances where war did happen (the researcher should also look at cases where war did not happen). ==Uses==

Case studies have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Classic examples of case studies that generated theories includes Darwin's theory of evolution (derived from his travels to the Galapagos Islands), and Douglass North's theories of economic development (derived from case studies of early developing states, such as England). 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). and can have greater internal validity than quantitative studies. Case studies are suited to explain outcomes in individual cases, which is something that quantitative methods are less equipped to do. Case studies have been characterized as useful to assess the plausibility of arguments that explain empirical regularities. By emphasizing context across cases, case studies can be useful in identifying scope conditions and evaluating to what extent concepts and theories apply across cases. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large-N study. 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. They may also be more appropriate for empirical verifications of strategic interactions in rationalist scholarship than quantitative methods. Case studies can identify necessary and insufficient conditions, as well as complex combinations of necessary and sufficient conditions. 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. 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. ==Limitations==

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. 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. 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. By using Bayesian probability, it may be possible to make strong causal inferences from a small sliver of data. 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. However, scholars have pushed back on this claim, noting that inductive reasoning is a legitimate practice (both in qualitative and quantitative research). A commonly described limit of case studies is that they do not lend themselves to generalizability. ==Teaching case studies==

Teachers may prepare a case study that will then be used in classrooms in the form of a "teaching" case study (also see case method and casebook method). For instance, as early as 1870 at Harvard Law School, Christopher Langdell departed from the traditional lecture-and-notes approach to teaching contract law and began using cases pled before courts as the basis for class discussions. By 1920, this practice had become the dominant pedagogical approach used by law schools in the United States. Outside of law, teaching case studies have become popular in many different fields and professions, ranging from business education to science education. The Harvard Business School has been among the most prominent developers and users of teaching case studies. Teachers develop case studies with particular learning objectives in mind. Additional relevant documentation, such as financial statements, time-lines, short biographies, and multimedia supplements (such as video-recordings of interviews) often accompany the case studies. Similarly, teaching case studies have become increasingly popular in science education, covering different biological and physical sciences. The National Center for Case Studies in Teaching Science has made a growing body of teaching case studies available for classroom use, for university as well as secondary school coursework. ==See also==