
🔎 The problem
Qualitative Comparative Analysis (QCA) techniques are growing in popularity for modeling causal complexity and identifying necessary or sufficient conditions in medium-N settings. Because QCA is not designed as a statistical technique, it provides no built-in way to estimate the probability that uncovered patterns arose by chance. In addition, the multiple hypothesis tests implicit in QCA workflows can inflate false positive rates, a consequence that is not widely appreciated in practice.
🧪 A tailored permutation test for QCA
A simple permutation test is tailored to the specific requirements of QCA users and is paired with an adjustment to the Type I error rate that accounts for the multiple hypothesis tests inherent in QCA. Key features of the approach:
📊 Empirical reexamination: Arab Spring protest success
An empirical application revisits a published QCA study of protest-movement success during the Arab Spring. This reanalysis demonstrates the practical importance of the permutation test: even QCA solutions that appear very strong can plausibly be generated by chance once the test and Type I error adjustment are applied.
✅ Key findings and implications
📌 Why it matters
Researchers using QCA in medium-N studies gain a practical, implementable tool to reduce false positives and increase confidence that discovered configurations reflect substantive patterns rather than chance.

| Guarding Against False Positives in Qualitative Comparative Analysis was authored by Bear F. Braumoeller. It was published by Cambridge in Pol. An. in 2015. |
