Combinational regularity analysis (CORA): An introduction for psychologists.

Area of Science:

• Psychology
• Methodology
• Causal Inference

Background:

• Psychologists increasingly use configurational comparative methods (CCMs) like QCA and CNA.
• Existing CCMs struggle with complex causal structures and multi-effect problems prevalent in psychology (e.g., multimorbidity).

Purpose of the Study:

• Introduce Combinational Regularity Analysis (CORA), a novel CCM.
• Enable the uncovering of regularity-theoretic causal structures, including complex effects, in psychological data.

Main Methods:

• CORA utilizes algorithms from electrical engineering for multi-output switching circuit analysis.
• The study situates CORA within existing CCMs and details its technical foundations.
• Demonstrates CORA's analytical and graphical capabilities with artificial and empirical data.

Main Results:

• CORA effectively analyzes regularity-theoretic causal structures with complex effects.
• CORA expands the reach of CCMs to address multi-effect problems in psychology.
• Comparative analysis via the "method game" highlights CORA's utility against QCA and CNA.

Conclusions:

• CORA is a valuable addition to the methodological toolkit for psychologists.
• Facilitates configurational analysis of complex causal relationships in psychological research.
• Enhances the ability to infer causal structures from psychological data, especially in complex scenarios.