Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-13T04:28:12.205Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ambühl, MBaumgartner, M (2018) ‘cna: Causal Modeling With Coincidence Analysis [Computer Program]. R Package Version 2.1.1’. Available at https://cran.r-project.org/package=cna accessed 26 September 2018.Google Scholar
Baumgartner, M (2009) Inferring Causal Complexity. Sociological Methods & Research 38, 71–101.CrossRefGoogle Scholar
Baumgartner, M (2013) A Regularity Theoretic Approach to Actual Causation. Erkenntnis 78, 85–109.CrossRefGoogle Scholar
Baumgartner, M (2015) Parsimony and Causality. Quality & Quantity 49, 839–856.CrossRefGoogle Scholar
Baumgartner, MThiem, A (2017) ‘Often Trusted But Never (Properly) Tested: Evaluating Qualitative Comparative Analysis’. Sociological Methods & Research https://doi.org/10.1177/0049124117701487.CrossRefGoogle Scholar
Baumgartner, MFalk, C (2018) ‘Boolean Difference-Making: A Modern Regularity Theory of Causation’. PhilSci Archive. Available at http://philsci-archive.pitt.edu/id/eprint/14876 accessed 26 September 2018.Google Scholar
Cronqvist, LBerg-Schlosser, D (2009) Multi-Value QCA (mvQCA). In Rihoux B and Ragin CC (eds), Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques, 69–86. London: Sage Publications.CrossRefGoogle Scholar
Downing, BM (1992) The Military Revolution and Political Change: Origins of Democracy and Autocracy in Early Modern Europe. Princeton, NJ: Princeton University Press.Google Scholar
Duşa, A (2007) User Manual for the QCA(GUI) Package in R. Journal of Business Research 60(5), 576–586.CrossRefGoogle Scholar
Goertz, G (2006) Social Science Concepts: A User’s Guide. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Graßhoff, GMay, M (2001) Causal Regularities. In Spohn W, Ledwig M and Esfeld M (eds), Current Issues in Causation, 85–114. Paderborn: Mentis.Google Scholar
Hájek, P (1998) Metamathematics of Fuzzy Logic. Dordrecht: Kluwer.CrossRefGoogle Scholar
Hume, D (1999 (1748)) An Enquiry Concerning Human Understanding. Oxford: Oxford University Press.Google Scholar
Lucas, SRSzatrowski, A (2014) Qualitative Comparative Analysis in Critical Perspective. Sociological Methodology 44(1), 1–79.CrossRefGoogle Scholar
Mackie, JL (1974) The Cement of the Universe. A Study of Causation. Oxford: Clarendon Press.Google Scholar
Ragin, CC (1987) The Comparative Method. Berkeley, CA: University of California Press.Google Scholar
Ragin, CC (2006) Set Relations in Social Research: Evaluating their Consistency and Coverage. Political Analysis 14(3), 291–310.CrossRefGoogle Scholar
Ragin, CC (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Ragin, CC (2009) Qualitative Comparative Analysis Using Fuzzy Sets (fsQCA). In Rihoux B and Ragin CC (eds), Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques, 87–121. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
Suppes, P (1970) A Probabilistic Theory of Causality. Amsterdam: North Holland.Google Scholar
Thiem, A (2015) Using Qualitative Comparative Analysis for Identifying Causal Chains in Configurational Data: A Methodological Commentary on Baumgartner and Epple. Sociological Methods & Research 44(4), 723–736.CrossRefGoogle Scholar
Thiem, A (2018) ‘QCApro: Advanced Functionality for Performing and Evaluating Qualitative Comparative Analysis [Computer Program]. R Package Version 1.1-2.’ Available at http://www.alrik-thiem.net/software/.Google Scholar
Supplementary material: PDF

Baumgartner and Ambühl supplementary material

Online Appendix

Download Baumgartner and Ambühl supplementary material(PDF)
PDF 201.5 KB
Supplementary material: Link

Baumgartner and Ambühl Dataset

Dataset

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