Abstract

Based on the INUS theory of causality, the search target of qualitative comparative analysis (QCA) is to find all the minimally sufficient conditions for the outcome’s occurrence in a data set, where the condition’s sufficiency, the necessity of the condition’s components, and the completeness of the solution are three core requirements. However, QCA’s current top-down approach, which relies on a truth table and Boolean minimization, cannot meet the main objective of QCA. Conditions generated by the top-down approach can be insufficient for the outcome or contain unnecessary components that can be removed. We found evidence supporting our arguments by examining the correctness of top-down QCA in Study 1. Then, we show that QCA can also proceed with a “bottom-up” search strategy in sufficiency analysis, similar to coincidence analysis (CNA). We contrast solutions of the top-down and bottom-up QCA approaches by analyzing a simulated crisp-set data set in Study 2 and a real-world fuzzy-set data set in Study 3. Both results show that only the bottom-up approach can produce all the minimally sufficient conditions. We contribute to the ongoing debate pertain QCA solution types and QCA algorithms by critically evaluating the limitations of QCA’s top-down approach and introducing a bottom-up approach for QCA.

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