Analyzing relationships of necessity is important for both scholarly and applied research questions in the social sciences. An often-used technique for identifying such relationships—fuzzy set Qualitative Comparative Analysis (fsQCA)—has limited ability to make the most out of the data used. The set-theoretical technique fsQCA makes statements in kind (e.g., “a condition or configuration is necessary or not for an outcome”), thereby ignoring the variation in degree. We propose to apply a recently developed technique for identifying relationships of necessity that can make both statements in kind and in degree, thus making full use of variation in the data: Necessary Condition Analysis (NCA). With its ability to also make statements in degree (“a specific level of a condition is necessary or not for a specific level of the outcome”), NCA can complement the in kind analysis of necessity with fsQCA.

中文翻译：

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不仅在种类上而且在程度上分析必要性的关系：用 NCA 补充 fsQCA

分析必要性关系对于社会科学中的学术和应用研究问题都很重要。用于识别此类关系的常用技术——模糊集定性比较分析 (fsQCA)——在充分利用所用数据方面的能力有限。集合论技术 fsQCA 做出实物陈述（例如，“一个条件或配置对于结果是必要的还是不必要的”），从而忽略了程度的变化。我们建议应用一种最近开发的技术来识别必要性关系，该技术可以在种类和程度上做出陈述，从而充分利用数据中的变化：必要条件分析（NCA）。它还能够在程度上做出陈述（“特定级别的条件对于特定级别的结果是必要的还是不需要的”），