Affiliation:
1. Institute of Cognitive Science, University of Osnabrück, Germany
Abstract
The first part of this article shows that qualitative comparative analysis (QCA)—also in its most recent form as in Ragin (2008) —does not correctly analyze data generated by causal chains. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a , 2009b ) has introduced a Boolean methodology, termed coincidence analysis (CNA), which is related to QCA, yet, contrary to the latter, does not eliminate redundancies by means of Quine-McCluskey optimization. The second part of the article applies CNA to chain-generated data. It turns out that CNA successfully detects causal chains in small-[Formula: see text] data.
Cited by
43 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Modeling Contingency in Veteran Community Reintegration: A Mixed Methods Approach;Journal of Mixed Methods Research;2022-02-24
2. Index;Qualitative Comparative Analysis Using R;2021-09-30
3. References;Qualitative Comparative Analysis Using R;2021-09-30
4. Summary and Outlook;Qualitative Comparative Analysis Using R;2021-09-30
5. Post-QCA Tools;Qualitative Comparative Analysis Using R;2021-09-30