Fitting single- and multiple-indicator STARTS models as dynamic structural equation models

The STARTS model is a structural equation model that decomposes individual differences in a longitudinally assessed variable into a time-invariant stable component, a time-varying autoregressive component, and a time-point specific state component. Typically, the model is estimated with Maximum Likelihood in standard SEM software. Here, we show how different STARTS models (the single-indicator univariate and multivariate and the multiple-indicator univariate model) can be incorporated into the Dynamic Structural Equation Model framework. We use the data of an experience sampling study to illustrate the expositions and we provide codes to fit the models in Mplus. As the DSEM parameters are estimated with a Bayesian approach, we also report the results of three simulation studies in which we examined the statistical properties of the parameter estimates obtained in the DSEM framework. Finally, we discuss how to estimate variants of the STARTS model and we make suggestions for future applied and methodological research.