Empirical researchers using qualitative comparative analysis (QCA) can work with crisp, multivalue, and fuzzy sets. The relative advantages of crisp and multivalue sets have been discussed in the QCA literature. There has been little reflection on the more frequent decision between crisp and fuzzy sets for which there often is no theoretical guidance. A review shows that researchers often prefer fuzzy over crisp sets, sometimes because they contain more information. This meets with the argument that fuzzy sets produce more conservative consistency measures and constitute tougher tests. In my article, I demonstrate analytically and with data from published QCA studies that the relationship between crisp sets, fuzzy sets, and the consistency score is ambiguous. It depends on the distribution of cases whether the consistency value is more or less conservative for fuzzy sets than for crisp sets. I outline the implications of the ambiguous relationship for empirical research.

中文翻译：

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定性比较分析中 Crisp 和 Fuzzy 集的选择以及寻找一致集关系的模糊后果

使用定性比较分析 (QCA) 的实证研究人员可以处理清晰、多值和模糊集。QCA 文献中讨论了清晰和多值集的相对优势。几乎没有对清晰和模糊集之间更频繁的决策进行反思，因为这通常没有理论指导。一项评论表明，研究人员通常更喜欢模糊集而不是清晰集，有时是因为它们包含更多信息。这符合模糊集产生更保守的一致性度量并构成更严格的测试的论点。在我的文章中，我用已发表的 QCA 研究的数据进行了分析，证明了清晰集、模糊集和一致性分数之间的关系是不明确的。模糊集的一致性值是否比清晰集更保守，这取决于个案的分布。我概述了这种模糊关系对实证研究的影响。