The first part of this paper shows that Qualitative Comparative Analysis
(QCA)—also in its most recent form as presented 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,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that is related to QCA,
yet, contrary to the latter, does not eliminate redundancies by means of
Quine-McCluskey optimization. The second part of the paper applies CNA to chain-generated data. It will turn out that CNA successfully detects causal chains in small-N data.
