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.
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. Case study researchers are often suspicious of large-sample research, which, they suspect, contains heterogeneous cases whose differences cannot easily be modeled.
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. 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.
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The major difficulty with this approach, however, is that the category in which these clients are classified most always becomes unmanageably heterogeneous. 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. 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.
If, in the quest to explain a particular phenomenon, each potential case offers only one observation and also p. 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.
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. To be sure, one can look—and ought to look—for empirical patterns among potential cases.
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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.
Advantages and Disadvantages of Case Study Research
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.
Many case study researchers feel that to compare societies with vastly different cultures and historical trajectories is meaningless. 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. 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. 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.
Regardless of whether the population is homogeneous or heterogeneous, causal relationships are easier to study if the causal effect is strong, rather than weak. 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. A necessary and sufficient cause accounts for all of the variation on Y.
Case Study Method in Psychology | Simply Psychology
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, p. 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. 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.
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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.
Advantages and Disadvantages of Case Studies
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 p. A good deal of criticism has been directed toward studies of this type, where findings are rarely robust.
The positive hypothesis, as well as the null hypothesis, is better approached in a sample rather than in a case. 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. 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.
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. 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 p. 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. 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. I have left the most prosaic factor for last. 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. An evidence-rich environment is one where all relevant factors are measurable, where p.
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.
What are the benefits and drawbacks of case study research?
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. I employ data, evidence, and information as synonyms in this section.