ArticleField methods2024
Case-to-factor Ratios and Model Specification in Qualitative Comparative Analysis.
Article in Field methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- CORA: An Open-Source Software Tool for Combinational Regularity Analysis.Social science computer review · 2025Article
- Fewer Procedures, More Reflection: A Rejoinder to Duşa and Marx.Field methods · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.