Dynamic Structural Equation Models

TLDR

Dynamic structural equation modeling (DSEM) is designed for intensive longitudinal data, integrating time‑series and structural equation approaches to analyze observed and latent variables across any duration and number of observations. The article introduces DSEM to study the evolution of observed and latent variables and structural equation models over time. DSEM is estimated via Bayesian MCMC using Gibbs and Metropolis–Hastings samplers, with simulation studies and model‑fit evaluation methods described. The authors detail the estimation algorithm in Mplus and demonstrate its performance through simulation studies.

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 Mplus 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.