An Examination of Hierarchical Bayesian Dynamic Structural Equation Models in Stan

Dynamic Structural Equation Models (DSEMs) can be used to model complex multilevel relationships between multiple variables over time and have thus a wide applicability in many fields of psychological science. Mplus is a widely used and powerful software program for estimating DSEMs, but it has some limitations in terms of flexibility and scalability. To overcome these limitations, we have implemented the DSEM framework in Stan, a Bayesian modeling language which provides a flexible and efficient platform for developing complex models. Here we highlight the most important aspects from our upcoming tutorial paper: A theoretical introduction to DSEM, fitting a base-model (i.e., a bivariate lag-1 model) and some possible model extensions (i.e., latent variable modeling, mediation analysis), and finally a comparison between Mplus and Stan in functionality and parameter recovery. Overall, we want to present our tutorial as a clear and practical guide for researchers who want to take advantage of Stan as a powerful toolbox to specify and fit DSEMs.