Qualitative Comparative Analysis: Discovering Core Combinations of Conditions in Political Decision Making

Abstract

Qualitative Comparative Analysis (QCA) was launched in the late 1980s by Charles Ragin, as a research approach bridging case-oriented and variable-oriented perspectives. It conceives cases as complex combinations of attributes (i.e. configurations), is designed to process multiple cases, and enables one to identify, through minimization algorithms, the core equifinal combinations of conditions leading to an outcome of interest. It systematizes the analysis in terms of necessity and sufficiency, models social reality in terms of set-theoretic relations, and provides powerful logical tools for complexity reduction. It initially came along with one technique, crisp-set QCA (csQCA), requiring dichotomized coding of data. As it has expanded, the QCA field has been enriched by new techniques such as multi-value QCA (mvQCA) and especially fuzzy-set QCA (fsQCA), both of which enable finer-grained calibration. It has also developed further with diverse extensions and more advanced designs, including mixed- and multimethod designs in which QCA is sequenced with focused case studies or with statistical analyses. QCA’s emphasis on causal complexity makes it very fit to address various types of objects and research questions touching upon political decision making—and indeed QCA has been applied in multiple related social scientific fields. While QCA can be exploited in different ways, it is most frequently used for theory evaluation purposes, with a streamlined protocol including a sequence of core operations and good practices. Several reliable software options are also available to implement the core of the QCA procedure. However, given QCA’s case-based foundation, much researcher input is still required at different stages. As it has further developed, QCA has been subject to fierce criticism, especially from a mainstream statistical perspective. This has stimulated further innovations and refinements, in particular in terms of parameters of fit and robustness tests which also correspond to the growth of QCA applications in larger-n designs. Altogether the field has diversified and broadened, and different users may exploit QCA in various ways, from smaller-n case-oriented uses to larger-n more analytic uses, and following different epistemological positions regarding causal claims. This broader field can therefore be labeled as that of both “Configurational Comparative Methods” (CCMs) and “Set-Theoretic Methods” (STMs).