CNA literature and software
This page collects relevant literature and software links.
- Indianapolis (via Zoom), Aug. 3-5, 2021, Introduction to Causal Modeling with Coincidence Analysis
- Indianapolis (via Zoom), 2020, Advanced Topics in Configurational Causal Data Analysis
- Indianapolis (via Zoom), 2020, Beginners’ Introduction to Configurational Causal Data Analysis
- 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
Base package cna
The cna package provides all the functionalities required to analyze data by means of CNA.
- On 05/28/2021, version 3.1.0 of the cna R-package has been released on CRAN.
- On 11/06/2020, update 3.0.1 has been released on CRAN.
- On 09/13/2020, version 3.0.0 of the cna R-package has been released on CRAN.
- On 05/20/2020, update 2.2.3 has been released on CRAN.
- On 06/12/2019, update 2.2.2 has been released on CRAN.
- On 08/09/2019, update 2.2.1 has been released on CRAN.
- On 04/13/2019, update 2.2.0 has been released on CRAN.
- On 06/13/2018, update 2.1.1 has been released on CRAN.
- On 06/01/2018, version 2.1.0 of the cna R-package has been released on CRAN.
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 09/14/2020, update 0.2.0 has been released on CRAN.
- 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.
The details of the most recent version of CNA, which is implemented in the cna package, are described here:
M. Baumgartner and M. Ambühl (2020), 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.
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]
This paper provides a theoretical introduction the functions of the cnaOpt package:
- M. Baumgartner and M. Ambühl (2021), Optimizing Consistency and Coverage in Configurational Causal Modeling, Sociological Methods & Research. [penultimate draft]; Replication material.
This paper introduces a robustness measure for CNA:
- V.P. Parkkinen and M. Baumgartner (2021), Robustness and Model Selection in Configurational Causal Modeling, Sociological Methods & Research. [penultimate draft]
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 (2009), Uncovering Deterministic Causal Structures: A Boolean Approach, Synthese 170, 71-96. [Springer Nature SharedIt] [penultimate draft]
Deborah J. Cohen, Shannon M. Sweeney, William L. Miller, et al., Miguel Marino (2021), Improving smoking and blood pressure outcomes: The interplay between operational changes and local context, The Annals of Family Medicine, May 2021, 19 (3) 240-248; doi: 10.1370/afm.2668
J. Coury, E. Miech, P. Styer et al. (2021), What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach. Implement Sci Commun 2, 5 https://doi.org/10.1186/s43058-020-00104-7
R.G. Whitaker, N. Sperber, M. Baumgartner, et al. (2020), Coincidence analysis: a new method for causal inference in implementation science, Implementation Science 15, 108 (2020). https://doi.org/10.1186/s13012-020-01070-3
Petrik, A., B. Green, J. Schneider et al. (2020), Factors influencing implementation of a colorectal cancer screening improvement program in community health centers: an applied use of configurational comparative methods, Journal of General Internal Medicine, doi: 10.1007/s11606-020- 06186-2.
Sydney M. Dy et al. (2020), Association of Implementation and Social Network Factors With Patient Safety Culture in Medical Homes. A Coincidence Analysis, The Journal of Patient Safety, doi: 10.1097/PTS.0000000000000752
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 & Research43, 280-312. [penultimate draft]