Accounting for Time-Varying Inter-Individual Differences in Trajectories when Assessing Cross-Lagged Models. Issue 3 (4th May 2021)

• Record Type: Journal Article
• Authors: Scott, Paul Wesley
• Is Part Of: Structural equation modeling. Volume 28:Issue 3(2021)
• Page Start: 365
• Page End: 375
• Publication Date: 2021-05-04
• Subjects: Cross-lagged panel model -- general cross-lagged model -- random intercept cross-lagged panel model -- autoregressive latent trajectory model -- latent growth curve model with structured residuals; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1819815
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: This paper explores relationships amongst cross-lagged models allowing trajectories to be freely estimated, some accounting for time-varying differences amongst individuals (Autoregressive Latent Trajectory (ALT), General Cross-lagged Model (GCLM), and Latent Growth Curve Model with Structured Residuals and Unspecified Growth Trajectory (LGCM-SR-UGT)) and some not (Cross-lagged Panel Model (CLPM), Random Intercept Cross-lagged Panel Model (RI-CLPM), and Mean Stationary GCLM). An applied example using LSAY data demonstrates these models. Simulations examine (1) fit indices assessing "good" fit and Bayes Factor for model selection; (2) consequences of ignoring variability in trajectories on cross-lagged estimates. Findings were (1) RMSEA discerned "good" fit and Bayes Factor tended to select models closely related to true model over less related models; (2) various patterns of bias in path estimates and standard errors are found, in particular, causal dominance in conjunction with time-variant between-person variance and covariance were notably influential on bias in path estimates.