Comparison of Models for the Analysis of Intensive Longitudinal Data

• Published in: Structural equation modeling Vol. 27; no. 2; pp. 275 - 297
• Main Authors: Asparouhov, Tihomir, Muthén, Bengt
• Format: Journal Article
• Language: English
• Published: Hove Routledge 03.03.2020; Psychology Press
• Subjects: Bayesian Statistics; dynamic structural equation models; Factor Analysis; repeated measures longitudinal models; residual dynamic structural equation models; restricted maximum likelihood estimation; Structural equation modeling; Within-subjects design
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2019.1626733

We discuss the differences between several intensive longitudinal data models. The dynamic structural equation model (DSEM), the residual dynamic structural equation model (RDSEM) and the repeated measures longitudinal model are compared in several simulation studies. We show that the DIC can be used to select the correct modeling framework. We discuss the consequences of incomplete or incorrect modeling for the predictors in multilevel time series models. We also illustrate the advantages of the Bayesian estimation over the REML estimation for models with categorical data, subject-specific autocorrelations, and subject-specific residual variances. Dynamic factor analysis models are discussed where autoregressive relations occur not only for the factors but also for the residuals of the measurement variables. The models are also illustrated with an empirical example.