Combinational regularity analysis (CORA): An introduction for psychologists

Increasingly, psychologists make use of modern configurational comparative methods (CCMs), such as qualitative comparative analysis (QCA) and coincidence analysis (CNA), to infer regularity-theoretic causal structures from psychological data. At the same time, existing CCMs remain unable to reveal such structures in the presence of complex effects. Given the strong emphasis configurational methodology generally puts on the notion of complex causation, and the ubiquity of multieffect problems in psychological research, such as multimorbidity and polypharmacy, this limitation is severe. In this article, we introduce psychologists to combinational regularity analysis (CORA)-a new member in the family of CCMs-with which regularity-theoretic causal structures that may include complex effects can be uncovered. To this end, CORA draws on algorithms originally developed in electrical engineering for the analysis of multioutput switching circuits, which regulate the behavior of electrical signals between a set of inputs and a set of outputs. After having situated CORA within the landscape of modern CCMs, we present its technical foundations. Subsequently, we demonstrate the method's analytical and graphical capabilities by means of artificial and empirical data. To facilitate familiarization, we use the concept of the "method game" to compare CORA with QCA and CNA. Through CORA, configurational analyses of complex effects come into the analytical reach of CCMs. CORA thus represents a useful addition to the methodological toolkit of psychologists who want to analyze their data from a configurational perspective. (PsycInfo Database Record (c) 2025 APA, all rights reserved).