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Coincidence Analysis

Coincidence Analysis (CNA) is a configurational comparative method of causal inference and data analysis that models the Boolean dimensions of causal structures. This project further develops CNA. It is co-funded by the University of Bergen and the Bergen research foundation as part of the Toppforsk-programme.

Since the late 1980ies Boolean methods of causal modeling--also known as set-theoretic or configurational comparative methods--have gradually been added to the toolkit for causal data analysis in disciplines as diverse as political science, sociology, business administration, management, environmental science, evaluation science, and public health. The most prominent Boolean method is Qualitative Comparative Analysis (QCA) (Ragin 2008). QCA, however, is unsuited to analyze causal structures with more than one endogenous variable, e.g. structures with common causes or causal chains. To overcome that restriction, Baumgartner (forthcoming2009a2009b) developed an alternative Boolean method called Coincidence Analysis (CNA), which is meanwhile available as software package for the R environment.

This project has three objectives. The first is to fill all remaining gaps in the methodological protocol of CNA and to complement the CNA R-package accordingly. In particular, tools for robustness tests of CNA models and strategies for reducing model ambiguities shall be developed. The second objective is to systematically test the inferential potential of CNA by applying it to real-life studies from varying disciplines and, thereby, to explore the applicability of CNA outside of the standard domain of Boolean methods, for example, in biology, medicine or psychology. The third objective is to analyze the relationship between CNA and methods from other theoretical traditions--in particular Bayes-nets methods (cf. Spirtes et al. 2000; Pearl 2000) and regression-analytical methods (Gelman and Hill 2007). Are there substantive points of contact between these methodological traditions? Are there ways to fruitfully integrate them in multi-method studies? What are the conditions that determine what method is best suited to investigate a given phenomenon or to answer a given research question?