時系列データにおける潜在クラスベクトル自己回帰モデルを用いた質的個人間異質性のモデリング

Time-series data have become ubiquitous in psychological research, allowing us to study detailed within-person dynamics and their heterogeneity across persons. Vector autoregressive (VAR) models have become a popular choice as a first approximation of these dynamics. The VAR model for each person and heterogeneity across persons can be jointly modeled using a hierarchical model that treats 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. Here we address this issue by providing an accessible introduction to latent class VAR models; a simulation evaluating how well this model can be estimated in situations resembling applied research; introducing a new R package ClusterVAR, which provides easy-to-use functions to estimate the model; and providing a fully reproducible tutorial on modeling emotion dynamics, which walks the reader through all steps of estimating, analyzing, and interpreting latent class VAR models.