The influx of intensive longitudinal data creates a pressing need for complex modeling tools
that help enrich our understanding of how individuals change over time. Multilevel vector …

With the rising popularity of intensive longitudinal research, the modeling techniques for
such data are increasingly focused on individual differences. Here we present mixture …

Multilevel autoregressive models are especially suited for modeling between-person
differences in within-person processes. Fitting these models with Bayesian techniques …

The use of multilevel VAR (1) models to unravel within-individual process dynamics is
gaining momentum in psychological research. These models accommodate the structure of …

With the growing popularity of intensive longitudinal research, the modeling techniques and
software options for such data are also expanding rapidly. Here we use dynamic multilevel …

Intensive longitudinal research designs are increasingly used to study personality
processes. The resulting data can be highly informative in ways that other data cannot, but …

The focus of this article is on Markov models for the analysis of panel data and, more
specifically, on data obtained from repeated measurements of one categorical variable at …

Markov modeling presents an attractive analytical framework for researchers who are
interested in state-switching processes occurring within a person, dyad, family, group, or …

Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD)
analysis framework. DSEM models intraindividual changes over time on Level 1 and allows …

Researchers often collect multiple observations from many individuals. For example, in
research examining the relationship between stress and mood, a research participant may …