Edited by B. Guy Peters and Guillaume Fontaine
Chapter 15: Qualitative Comparative Analysis for comparative policy analysis
This chapter discusses different research approaches to Qualitative Comparative Analysis (QCA) and their application to comparative public policy analysis. QCA is a case-sensitive, set-theoretic method that allows researchers to model complexity in order to answer causes-of-effects types of research questions. There seems to be a preferential connection between QCA and public policy analysis in terms of research design and the actual needs and goals of policy-oriented research. Moreover, both small-N and large-N, as well as exploratory and theory-led approaches to QCA have developed, which prioritize either the parsimony of the results, or their substantive interpretability. Through a selective review of recent applications, this chapter illustrates the usefulness and limitations of different QCA approaches in analysing important research questions at all stages of the policy process. Key to a successful QCA application is coherence in methodological choices. The chapter helps policy researchers identify the most useful QCA approach for a given analytic goal in order to capitalize on the remarkable flexibility of the QCA technique. While the case-oriented approach achieves in-depth descriptions, explanations, or evaluations, condition-oriented QCA enables policy researchers to identify complex patterns across a range of cases, or a broad understanding of different types of cases. Exploratory QCA analyses are attractive for public policy scholars who want to comprehensively understand why some hitherto unexplored outcome occurs. Conversely, more theory-led QCA applications allow scholars to assess and refine the various theories of the policy process or to evaluate policy intervention models. Finally, limited empirical diversity can cause QCA results to err in different directions. Policy scholars and practitioners face a trade-off between ensuring the completeness of sufficient configurations at the cost of including causally irrelevant conditions, and ensuring causal relevance of identified conditions, at the cost of potentially incomplete configurations. Intermediate solutions are designed to avoid both over- and under-simplification.
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