Detecting Causal Chains in Small-n Data. Issue 1 (February 2013)

• Record Type: Journal Article
• Authors: Baumgartner, Michael
• Is Part Of: Field methods. Volume 25:Issue 1(2013)
• Page Start: 3
• Page End: 24
• Publication Date: 2013-02
• Subjects: causal modeling -- small-n data -- causal chains -- qualitative comparative analysis -- coincidence analysis; Ethnology -- Methodology -- Periodicals; Anthropology -- Methodology -- Periodicals; Anthropologie -- Méthodologie -- Périodiques; 306.072
• DOI: 10.1177/1525822X12462527
• Languages: English
• ISSNs: 1525-822X

Abstract

The first part of this article shows that qualitative comparative analysis (QCA)—also in its most recent form as inRagin (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-n data.