Coincidence Analysis (CNA)
Coincidence Analysis (CNA) is a configurational comparative method (CCM) of causal inference and data analysis that is custom-built for multi-outcome structures.
Training
- September 21-25, 2020, Michael Baumgartner and Alrik Thiem (University of Lucerne) will hold a seminar on configurational causal modeling with a heavy focus on CNA at the Regenstrief Institute in Indianapolis.
previous
- Arlington, 2019: Coincidence Analysis (CNA) Workshop at the D&I Conference
- Indianapolis, 2017: Configurational Research with QCA and CNA
- Konstanz, 2016: Configurational Research with QCA and CNA
- Geneva, 2015: Configurational Research with QCA and CNA
Software
Base package cna
The cna package provides all the functionalities required to analyze data by means of CNA.
Add-on package cnaOpt
The cnaOpt package provides various functions for optimizing consistency and coverage scores of models of configurational comparative methods as CNA and QCA.
- On 09/12/2019, update 0.1.1 has been released on CRAN.
- On 21/10/2019, version 0.1.0 of the cnaOpt R-package has been released on CRAN.
Literature
Methodological
The details of the most recent version of CNA, which is implemented in the cna package, are described here:
M. Baumgartner and M. Ambühl (2018), Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis, Political Science Research and Methods. Replication material at https://doi.org/10.7910/DVN/YIEAF1; [penultimate draft]
M. Baumgartner and M. Ambühl (2019), cna: An R Package for Configurational Causal Inference and Modeling. R package vignette: The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/cna/vignettes/cna_vignette.pdf.
The following draft provides a theoretical introduction the functions of the cnaOpt package:
- M. Baumgartner and M. Ambühl (manuscript), Optimizing Consistency and Coverage in Configurational Causal Modeling.
Older CNA versions are introduced/discussed here:
M. Baumgartner and A. Thiem (2015), Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis, The R Journal 7, 176-184. [penultimate draft]
M. Baumgartner (2013), Detecting Causal Chains in Small-N Data, Field Methods 25, 3-24. [penultimate draft]
M. Baumgartner (2009), Uncovering Deterministic Causal Structures: A Boolean Approach, Synthese 170, 71-96. [Springer Nature SharedIt] [penultimate draft]
M. Baumgartner (2009), Inferring Causal Complexity, Sociological Methods & Research 38, 71-101. [penultimate draft]
Applied
S. E. Hickman, E. Miech et al. (2020), Identifying the Implementation Conditions Associated With Positive Outcomes in a Successful Nursing Facility Demonstration Project , The Gerontologist, doi: 10.1093/geront/gnaa041
V. Yakovchenko, E. Miech et al. (2020), Strategy Configurations Directly Linked to Higher Hepatitis C Virus Treatment Starts. An Applied Use of Configurational Comparative Methods, Medical Care 58(5), p. e31-e38, doi: 10.1097/MLR.0000000000001319
W. Moret, and L. Lorenzetti (2020), Realistic expectations: exploring the sustainability of graduation outcomes in a program for children affected by HIV in Kenya’s Northern Arid Lands. Vulnerable Children and Youth Studies. doi: 10.1080/17450128.2020.1738022
T. Haesebrouck (2019), Who follows whom? A coincidence analysis of military action, public opinion and threats, Journal of Peace Research, doi: 10.1177/0022343319854787
R. Epple and S. Schief (2016), Fighting (for) gender equality: the roles of social movements and power resources, Interface: a journal for and about social movements, Vol. 8, No.2., 394- 432.
M. Baumgartner and R. Epple (2014), A Coincidence Analysis of a Causal Chain: The Swiss Minaret Vote, Sociological Methods & Research 43, 280-312. [penultimate draft]