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Between complexity and generalization: Addressing evaluation challenges with QCA
84
Citations
16
References
2013
Year
Empirical Case StudyQuantitative MethodsEngineeringComputational Complexity TheoryGeneralizability TheoryLawEducationComputational ComplexityCausal InferenceComplexityAuditingStatisticsBetween ComplexityTheory TestingCausal ModelQualitative Comparative AnalysisAbstract ComplexityCausal PackagesComputer ScienceValidity TheoryCausal StructureCausal ReasoningReasoningComputational ScienceAutomated ReasoningFormal MethodsCase AnalysisCausality
This article argues that Qualitative Comparative Analysis can be a useful method in case-based evaluations for two reasons: a) it is aimed at causal inference and explanation, leading to theory development; b) it is strong on external validity and generalization, allowing for theory testing and refinement. After a brief introduction to QCA, the specific type of causality handled by QCA is discussed. QCA is shown to offer improvements over Mill’s methods by handling asymmetric and multiple-conjunctural causality in addition to counterfactual reasoning. It thereby allows the explicitly separate analysis of necessity and sufficiency, recognizing the relevance of causal packages as well as single causes and of multiple causal paths leading to the same outcome (equifinality). It is argued that QCA can generalize findings to a small, medium and large number of cases.
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