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Causal modeling with multi-value and fuzzy-set Coincidence Analysis

Published online by Cambridge University Press:  05 November 2018

Michael Baumgartner  and
Mathias Ambühl
Michael Baumgartner*
Affiliation:
Department of Philosophy, University of Bergen, Norway
Mathias Ambühl
Affiliation:
Consult AG, Switzerland
*
*Corresponding author. Email: michael.baumgartner@uib.no
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Abstract

Coincidence Analysis (CNA) is a configurational comparative method of causal data analysis that is related to Qualitative Comparative Analysis (QCA) but, contrary to the latter, is custom-built for analyzing causal structures with multiple outcomes. So far, however, CNA has only been capable of processing dichotomous variables, which greatly limited its scope of applicability. This paper generalizes CNA for multi-value variables as well as continuous variables whose values are interpreted as membership scores in fuzzy sets. This generalization comes with a major adaptation of CNA’s algorithmic protocol, which, in an extended series of benchmark tests, is shown to give CNA an edge over QCA not only with respect to multi-outcome structures but also with respect to the analysis of non-ideal data stemming from single-outcome structures. The inferential power of multi-value and fuzzy-set CNA is made available to end users in the newest version of the R package cna.

Type
Original Articles
Information
Political Science Research and Methods , Volume 8 , Issue 3 , July 2020 , pp. 526 - 542
Copyright
Copyright © The European Political Science Association, 2018

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Baumgartner and Ambühl Dataset

Dataset

https://doi.org/10.7910/DVN/YIEAF1
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