How to Evaluate Causal Dominance Hypotheses in Lagged Effects Models

The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Researchers are interested in the question which of the lagged effects is causally dominant receives considerable attention. However, currently used methods do not allow for the evaluation of causal dominance hypotheses. This paper will show the performance of the Generalized Order-Restricted Information Criterion Approximation (GORICA), an extension of Akaike's Information Criterion (AIC), in the context of causal dominance hypotheses using a simulation study. The GORICA proves to be an adequate method to evaluate causal dominance in lagged effects models.

Automated summary

The (RI-)CLPM is a popular model used in psychology and related fields to assess longitudinal relationships between variables. Causal dominance in lagged effects is a key question researchers are interested in, but current methods do not allow for the evaluation of this. This paper explores the performance of the GORICA in evaluating causal dominance hypotheses in lagged effects models through a simulation study.