Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data

TL;DR

This tutorial guides researchers on implementing complex Dynamic Latent Class Structural Equation Models (DLCSEM) in Bayesian software, combining multiple modeling techniques for intensive longitudinal data analysis.

Contribution

It introduces a comprehensive, step-by-step tutorial for applying DLCSEM, integrating various models like DSEM, HMMs, and Bayesian methods for the first time.

Findings

Demonstrates DLCSEM application on clinical psychology data.

Provides practical implementation steps in JAGS.

Shows how to interpret complex longitudinal models.

Abstract

In this tutorial, we provide a hands-on guideline on how to implement complex Dynamic Latent Class Structural Equation Models (DLCSEM) in the Bayesian software JAGS. We provide building blocks starting with simple Confirmatory Factor and Time Series analysis, and then extend these blocks to Multilevel Models and Dynamic Structural Equation Models (DSEM). Subsequently, we introduce Hidden Markov Switching Models (HMSM) and demonstrate their integration with DSEM to yield DLCSEM. Leading through the tutorial is an example from clinical psychology using data on a generalized anxiety treatment that includes scales on anxiety symptoms and the Working Alliance Inventory that measures alliance between therapists and patients. Within each block, we provide an overview, specific hypotheses we want to test, the resulting model and its implementation, as well as an interpretation of the results. The aim of this tutorial is to provide a step-by-step guide for applied researchers that enables them to use this flexible DLCSEM framework for their own analyses.