Navigating the Complexities of Qualitative Comparative Analysis

Thiem, Alrik

doi:10.1177/0193841x14550863

Cited by 50 publications

(4 citation statements)

References 52 publications

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“…[28,29]). In this connection, our argument also updates [30] and [31] insofar as we hold that it is the objective function given to an optimization algorithm, not the algorithm per se, that may induce bias.…”

Section: Qca: Foundations Diffusion Analytical Approachmentioning

confidence: 99%

Algorithmic bias in social research: A meta-analysis

et al. 2020

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Both the natural and the social sciences are currently facing a deep "reproducibility crisis". Two important factors in this crisis have been the selective reporting of results and methodological problems. In this article, we examine a fusion of these two factors. More specifically, we demonstrate that the uncritical import of Boolean optimization algorithms from electrical engineering into some areas of the social sciences in the late 1980s has induced algorithmic bias on a considerable scale over the last quarter century. Potentially affected are all studies that have used a method nowadays known as Qualitative Comparative Analysis (QCA). Drawing on replication material for 215 peer-reviewed QCA articles from across 109 highprofile management, political science and sociology journals, we estimate the extent this problem has assumed in empirical work. Our results suggest that one in three studies is affected, one in ten severely so. More generally, our article cautions scientists against letting methods and algorithms travel too easily across disparate disciplines without sufficient prior evaluation of their suitability for the context in hand.

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Section: Qca: Foundations Diffusion Analytical Approachmentioning

confidence: 99%

Algorithmic bias in social research: A meta-analysis

et al. 2020

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“…4.It is not clear why the authors argue that “csQCA aims to develop explanatory models on the basis of a systematic comparison of a limited number of cases ( N < 100)” (p. 104), but then carry out simulations for up to 300 cases. We would argue that QCA works with any number of cases (see Thiem 2014:491–92).…”

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confidence: 99%

Case-to-factor Ratios and Model Specification in Qualitative Comparative Analysis

Thiem

Mkrtchyan

2023

Field Methods

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Qualitative comparative analysis (QCA) is an empirical research method that has gained some popularity in the social sciences. At the same time, the literature has long been convinced that QCA is prone to committing causal fallacies when confronted with non-causal data. More specifically, beyond a certain case-to-factor ratio, the method is believed to fail in recognizing real data. To reduce that risk, some authors have proposed benchmark tables that put a limit on the number of exogenous factors given a certain number of cases. Many applied researchers looking for methodological guidance have since adhered to these tables. We argue that fears of inferential breakdown in QCA due to an “unfavorable” case-to-factor ratio are without foundation. What is more, we demonstrate that these benchmarks induce more fallacious inferences than they prevent. For valid causal inference, researchers are better off relying on the current state of knowledge in their respective fields.

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“…Finally, this study selects the 'standard analysis' option over the 'specify analysis,' as suggested in the literature (Thiem, 2014;Baumgartner & Thiem, 2017). Due to the required limited diversity for a neat solution, QCA introduces the Quine-McCluskey algorithm to logically reduce redundancies based on minimal disjunctions in solution models when applying the 'specify analysis' option (Ragin, 2008).…”

Section: Analysis Of Sufficient Conditionsmentioning

confidence: 99%