Impact of temporal order selection on clustering intensive longitudinal data based on vector autoregressive models.

Abstract

When multivariate intensive longitudinal data are collected from a sample of individuals, the model-based clustering (e.g., vector autoregressive [VAR] based) approach can be used to cluster the individuals based on the (dis)similarity of their person-specific dynamics of the studied processes. To implement such clustering procedures, one needs to set the temporal order to be identical for all individuals; however, between-individual differences on temporal order have been evident for psychological and behavioral processes. One existing method is to apply the most complex structure or the highest order (HO) for all processes, while the other is to use the most parsimonious structure or the lowest order (LO). Up to date, the impact of these methods has not been well studied. In our simulation study, we examined the performance of HO and LO in conjunction with Gaussian mixture model (GMM) and k-means algorithms when a two-step VAR-based clustering procedure is implemented across various data conditions. We found that (a) the LO outperformed the HO in cluster identification, (b) the HO was more favorable than the LO in estimation of cluster-specific dynamics, (c) the GMM generally outperformed the k-means, and (d) the LO in conjunction with the GMM produced the best cluster identification outcome. We demonstrated the uses of the VAR-based clustering technique using the data collected from the "How Nuts are the Dutch" project. We then discussed the results from all our analyses, limitations of our study, and direction for future research, and meanwhile offered our recommendations on the empirical uses of the model-based clustering techniques. (PsycInfo Database Record (c) 2025 APA, all rights reserved).