This article aims to clarify the fundamental aspects of aggregating fuzzy scores of conditions with multiple attributes in fuzzy set qualitative comparative analysis (fsQCA). Fuzzy multiple attribute conditions (FMACs) are conditions that are built using different types of concepts. FMACs are flexible conditions that express the ontological nature of the concept and deals with the causal heterogeneity. In fact, researchers can add a new attribute to a concept in order to consider the concept’s meaning vis-à-vis to the outcome of interest instead of only considering the theory. In relation to FMAC fuzzy scores, we have individuated one problematic issue which is the aggregation strategy of attributes that are already calibrated that should be able to capture conceptual properties of membership and similarity. In this article, we will employ an empirical example in order to deal with causal heterogeneity and aggregation strategies. After discussing the disadvantages of the aggregation techniques used by QCA scholars, we individuate an axiomatic framework for defining logical conjunction operators that allows one to aggregate parts of concepts in accordance with membership and similarity. Then, we propose a technique to assign fuzzy scores to FMAC using the arithmetic mean–based compensatory fuzzy logic. This technique indirectly affects the solution formula(s) following the QCA and allows one to better locate cases in the XY plot during the post-QCA analysis.
Veri, F. (2020). Fuzzy Multiple Attribute Conditions in fsQCA: Problems and Solutions. Sociological Methods and Research, 49(2), 312–355. https://doi.org/10.1177/0049124117729693