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Making degreeness count in QCA: Problems with fuzzy sets and a linguistic solution
Rutten,Roel
Rutten,Roel
Abstract
QCA prefers fuzzy sets because they capture all empirical variation. However, fuzziness plays no role in minimizing the truth table, which follows threshold (crisp) logic also in fsQCA. Fuzzy set values only matter for calculating set-relationships, where they present a problem. Fuzzy set-relationships are calculated over all cases, whereas crisp set-relationships only consider cases that provide corroborating or contradicting within-case (i.e. crisp) causal evidence. When developing causal explanations, fsQCA thus dialogues incompatible within-case and cross-case causal evidence. In response, this paper suggests capturing degreeness with linguistic hedges – e.g. somewhat, moderately, considerably and very – but calculating crisp set-relationships. This avoids incompatible within-case and cross-case causal evidence. The crossover point sits between intensifying and diluting hedges, but researchers can make degreeness count by setting lower or higher crossover points to investigate (i) necessity in degree, (ii) explanatory power of individual conditions and (iii) differences between solutions for higher and lower crossover points.
Description
Date
2025-08-31
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Publisher
Research Projects
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Keywords
Calibration, QCA, linguistic hedges, variation, fuzzy sets
Citation
Rutten, R 2025, 'Making degreeness count in QCA : Problems with fuzzy sets and a linguistic solution', International Journal of Social Research Methodology. https://doi.org/10.1080/13645579.2025.2554825