Applying and Assessing Large-N QCA

Causality and Robustness From a Critical Realist Perspective

Datos Bibliográficos

ID2331027
AutoresRoel Rutten (0000-0002-5933-2996, Tilburg University, autor de correspondencia)
Año2022
Volumen51
Número3
Páginas1211-1243
Fecha de publicación2022-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSociological Methods & Research (JOURNAL)
Identificadores de la revistaISSN: 0049-1241 • E-ISSN: 1552-8294
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/0049124120914955
OpenAlexW3020192136
IdiomaEN
Citas recibidas44
Referencias citadas42

Applying qualitative comparative analysis (QCA) to large Ns 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

Econometrics · Machine learning · Qualitative comparative analysis · Robustness (evolution) · Computer Science · Mathematics · Political Influence and Corporate Strategies · Qualitative Comparative Analysis Research