Detecting Causal Chains in Small-n Data

Autor(en): Baumgartner, Michael
Stichwörter: Anthropology; causal chains; causal modeling; coincidence analysis; COMPARATIVE-ANALYSIS QCA; qualitative comparative analysis; small-n data; Social Sciences - Other Topics; Social Sciences, Interdisciplinary
Erscheinungsdatum: 2013
Herausgeber: SAGE PUBLICATIONS INC
Journal: FIELD METHODS
Volumen: 25
Ausgabe: 1
Startseite: 3
Seitenende: 24
Zusammenfassung: 
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-n data.
ISSN: 1525822X
DOI: 10.1177/1525822X12462527

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