Applying and Assessing Large-N QCA: Causality and Robustness From a Critical Realist Perspective

Roel Rutten

Sociological Methods & Research, 2022, vol. 51, issue 3, 1211-1243

Abstract: Applying qualitative comparative analysis (QCA) to large N s relaxes researchers’ case-based knowledge. This is problematic because causality in QCA is inferred from a dialogue between empirical, theoretical, and case-based knowledge. The lack of case-based knowledge may be remedied by various robustness tests. However, being a case-based method, QCA is designed to be sensitive to such tests, meaning that also large- N QCA robustness tests must be evaluated against substantive knowledge. This article connects QCA’s substantive-interpretation approach of causality to critical realism. From that perspective, it identifies relevant robustness tests and applies them to a real-data large- N QCA study. Robustness test findings are visualized in a robustness table, and this article develops criteria to substantively interpret them. The robustness table is introduced as a tool to substantiate the validity of causal claims in large- N QCA studies.

Keywords: qualitative comparative analysis (QCA); large-N; robustness; validity; causality; critical realism (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:51:y:2022:i:3:p:1211-1243

DOI: 10.1177/0049124120914955

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