How to Evaluate Causal Dominance Hypotheses in Lagged Effects Models

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 consi...

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Bibliographic Details
Published in:Structural equation modeling Vol. 31; no. 3; pp. 404 - 419
Main Authors: Sukpan, Chuenjai, Kuiper, Rebecca M.
Format: Journal Article
Language:English
Published: Hove Routledge 03-05-2024
Psychology Press
Subjects:
Causal dominance
Causality
Hypothesis testing
informative hypotheses
model selection
order restrictions
Psychology
Simulation
Online Access:Get full text
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Summary: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.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2023.2265065