Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling.
2003/11/01 by David A. Cole, Scott E. Maxwell · 3,505 citations
Computer Science · Decision Sciences · Mathematics · Psychology · Social Sciences · #Advanced Statistical Modeling Techniques #CLARION #Causal model #Cognitive psychology #Computer science #Data mining #Econometrics #Longitudinal data #Longitudinal study #Mathematics #Psychology #Psychometric Methodologies and Testing #Social and Intergroup Psychology #Statistical hypothesis testing #Statistics #Structural equation modeling
paper · doi:10.1037/0021-843x.112.4.558
published in Journal of Abnormal Psychology 112(4), 558-577 (American Psychological Association)
openalex publication_date 2003/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects.
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