How to Use Residual Dynamic Structural Equation Modeling to Study Individual Differences and Intraindividual Variability in Experimental Factorial Designs: A Tutorial

Multivariate Behavioral Research, vol. 61, pp. 806–829

Abstract

This article demonstrates the application of residual dynamic structural equation modeling (RDSEM) for analyzing custom contrasts in experimental factorial designs. Previous applications of RDSEM have often focused on ecological momentary assessment and daily diary data. However, RDSEM was explicitly developed for intensive longitudinal data more generally, including settings with very short time intervals between observations. Beyond these types of studies, RDSEM is also well suited for analyzing data from laboratory studies such as eye-tracking or reaction time experiments. We compare three analytic approaches, namely analysis of variance, linear mixed models, and RDSEM, emphasizing the unique advantages of RDSEM. Although often applied to momentary assessment data, RDSEM proves highly effective for experimental analysis, offering the ability to integrate both time-varying and time-invariant covariates, model autoregressive effects, and capture interindividual differences in residual variances / intraindividual variability. These strengths arise from RDSEM's integration of time-series, multilevel, and latent variable modeling, all implemented through Bayesian estimation.

Authors 5

1. Benedikt Langenberg corresponding

   Maastricht University

   Affiliation as printed

   Maastricht University

2. Jonathan Lee Helm

   San Diego State University

   Affiliation as printed

   San Diego State University

3. Connor J. McCabe

   University of Washington

   Affiliation as printed

   University of Washington

4. Thomas Günther Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Axel Mayer

   Bielefeld University

   Affiliation as printed

   Bielefeld University,