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Case Selection in Public Management Research: Problems and Solutions

Case selection is ubiquitous in public management research. Rarely do scholars have access to entire populations of interest. Yet, the manner by which scholars select samples to conduct their analyses can have profound consequences on their ability both to draw valid causal inferences and to estimate accurate relationships. In this article, we review the basic threats to inference that are likely to emerge in the presence of non-random case selection, with specific attention to their manifestation in empirical public management research. The article first reviews the threats to causal inference presented by case selection, focusing on their implications for internal and external validity. We then summarize a standard set of solutions to address potential problems for empirical models caused by non-random case selection. As part of this discussion, we review recent articles published in this journal to illustrate the prevalence of selection issues in contemporary public management studies, and then illustrate several techniques that have been developed to overcome specific problems to show their utility for public management research.