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Replication data for: Fuzzy Sets on Shaky Ground: Parameter Sensitivity and Confirmation Bias in fsQCA

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for: Fuzzy Sets on Shaky Ground: Parameter Sensitivity and Confirmation Bias in fsQCA
 
Identifier https://doi.org/10.7910/DVN/27100
 
Creator Krogslund, Chris
Choi, Donghyun Danny
Poertner, Mathias
 
Publisher Harvard Dataverse
 
Description Scholars have increasingly turned to fuzzy set Qualitative Comparative Analysis (fsQCA) to conduct small- and medium-N studies, arguing that it combines the most desired elements of variable-oriented and case-oriented research. This paper demonstrates, however, that fsQCA is an extraordinarily sensitive method whose results are worryingly susceptible to minor parametric and model specification changes. We make two specific claims. First, the causal conditions identified by fsQCA as being sufficient for an outcome to occur are highly contingent upon the values of several key parameters selected by the user. Second, fsQCA results are subject to marked confirmation bias. Given its tendency towards finding complex connections between variables, the method is highly likely to identify as sufficient for an outcome causal combinations containing even randomly generated variables. To support these arguments, we replicate three articles utilizing fsQCA and conduct sensitivity analyses and Monte Carlo simulations to assess the impact of small changes in parameter values and the method's built-in confirmation bias on the overall conclusions about sufficient conditions.
 
Subject Fuzzy Set Qualitative Comparative Analysis
Sensitivity Analysis
Monte Carlo Simulation
 
Date 2014