Dynamic Structural Equation Models. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Asparouhov, Tihomir; Hamaker, Ellen L.; Muthén, Bengt
• Is Part Of: Structural equation modeling. Volume 25:Issue 3(2018)
• Page Start: 359
• Page End: 388
• Publication Date: 2018-05-04
• Subjects: Baysian methods -- dynamic factor analysis -- intensive longitudinal data -- time series analysis; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1406803
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the M plus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.