Modeling Qualitative Between-Person Heterogeneity in Time-Series using Latent Class Vector Autoregressive Models

Time-series data have become ubiquitous in psychological research because it allows us to study within-person dynamics and their heterogeneity across persons. Vector autoregressive (VAR) models have become a popular choice as a first approximation of within-person dynamics. The VAR model of each person and the heterogeneity across persons can be jointly modeled using a hierarchical model that captures heterogeneity as a latent distribution. Currently, the most popular choice for this is the multilevel VAR model, which models heterogeneity across persons as quantitative variation through a multivariate Gaussian distribution. Here, we discuss an alternative, the latent class VAR model, which models heterogeneity as qualitative variation using a number of discrete clusters. While this model has been introduced before, it has not been readily accessible to researchers. We change this with this paper, in which we provide an accessible introduction to latent class VAR models; evaluate, in a simulation study, how well this model can be estimated in situations resembling applied research; introduce a new R package ClusterVAR, which provides easy-to-use functions to estimate the model; and provide a fully reproducible tutorial on modeling emotion dynamics, which walks the reader through all steps of estimating, analyzing, and interpreting latent class VAR models.