occurs. The use of multiple sources of evidence, as opposed to strictly predefined data elements, helps to ensure construct and context validity and provides checks and balances that protect the inquiry from contamination by methodological artifacts related to reliance on single sources of evidence.

One of the most common failures in attempting to traverse the perilous gulf between correlation and causation is that of ignoring a previous causal factor that explains the association of interest. Because case studies treat the boundary between phenomenon and context as inherently problematic and consult multiple sources of evidence in order to discover as many potentially important factors as possible, the danger of ignoring important factors in this way is reduced.

Inferring relationships in a single case from aggregate patterns in a large number of cases sharing some but not all the features of the case in point is not inherently superior. Inference downward from the aggregate to the single case is not illegitimate either, but it runs the well-known risk of ecological fallacy (Robinson, 1950). The danger of generalizing inappropriately from the aggregate to the individual is no less than that of moving in the other direction (Sullivan, 1998). And yet, even a single well-documented case can refute absolutely a potentially overly broad theory such as “they are all victims of bullying.”

The second objection, that a single case cannot be generalized to others without additional scientific operations, is not incorrect. Rather, it misses the point in a situation in which there are as yet few viable hypotheses to generalize. Here we return to Campbell’s assertion of the centrality to the scientific method of examining rival hypotheses. Case studies are excellent tools for pruning extraneous hypotheses and generating potentially viable ones. Objecting to case studies in the name of generalization and science ignores the variety of important functions they play in scientific inquiry along with the complexity of the process of scientific generalization.


Case studies are inherently naturalistic, conducted by means of gathering data from within the naturally existing social fields in which the phenomena of interest are located (Lincoln and Gruba, 1985). This naturalistic perspective is well suited to the systematic discovery of both important factors, “variables” in other terminology, as well as the processes that connect these factors, or varying conditions, in the unfolding of social action (Abbott, 1992). Considering the potentially infinite number of factors that might be related to any given social phenomenon, a systematic

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