Coincidence Analysis (CNA) traditionally analyzes dichotomous variables. This paper extends its capabilities to multi-value and fuzzy-set data, offering enhanced inferential power for complex political phenomena.
Dataset Expansion: The generalized approach accommodates continuous variables interpreted as membership scores in fuzzy sets.
Multi-Outcome Advantage: CNA now excels at identifying causal structures across multiple outcomes—a capability QCA lacks.
Methodological Comparison: This adaptation provides superior analytical tools even for single-outcome scenarios, where QCA typically operates.
Practical Application: The new techniques are implemented in the R package cna's latest version.
| Causal Modeling with Multi-Value and Fuzzy-Set Coincidence Analysis was authored by Michael Baumgartner and Mathias Ambühl. It was published by Cambridge in PSR&M in 2020. |
