Limited diversity as overspecification : assessing the explanatory power of single conditions in QCA

Limited diversity has long been seen as a source of threats to the credibility of causal ascription in Qualitative Comparative Analysis. To rule out such threats, strategies have been developed that question the counterfactual nature of unobserved configurations, their explanatory merit, and the causal structure entailed in the algorithm for ascription. A lesser explored line considers limited diversity to be the consequence of model overspecification. In contributing to this latter line, this article builds on the established theoretical criteria that a distribution must meet for an explanatory claim to be held true, and it advances two gauges – “import” and “essentiality” – to assess the difference-making power of single conditions and mold proper models before analysis. Their application in prominent studies suggests solutions from Standard Analysis may be more sound than is often conceded.