Data Imbalances in Coincidence Analysis: A Simulation Study

Martyna Daria Swiatczak and Michael Baumgartner

Sociological Methods & Research, 2025, vol. 54, issue 2, 739-771

Abstract: In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis (CNA). We further examine how such imbalances relate to fragmentation and noise in data. We show that even extreme data imbalances, when not combined with fragmentation or noise, do not negatively affect CNA’s performance. However, an extended series of simulation experiments on fuzzy-set data reveals that, when mixed with fragmentation or noise, data imbalances may substantially impair CNA’s performance. Furthermore, we find that the performance impairment is higher when endogenous factors are imbalanced than when exogenous factors are concerned. Our results allow us to quantify these impacts and demarcate degrees at which data imbalances should be considered as problematic. Thus, applied researchers can use our demarcation guidelines to enhance the validity of their studies.

Keywords: configurational causal modeling; configurational comparative methods; Coincidence Analysis; distributional imbalances; skewness; membership ratio; method benchmarking (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:54:y:2025:i:2:p:739-771

DOI: 10.1177/00491241241227039

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