Identifying subtypes in persons, situations and person-situation interactions: Categorical latent state-trait modelling approaches.

The latent state-trait theory posits that a psychological construct may reflect stable influences specific to a person (i.e., trait), ephemeral influences from situations (i.e., state), and interactions between them (i.e., state-trait interactions). Researchers conventionally apply mixture modelling to explore heterogeneity in variables by identifying homogenous classes with respect to the measured variable, yet rarely distinguishing between person- and situation-specific classes. The current study introduces novel categorical latent state-trait models to identify subgroups in states and traits, quantifying the effects of person-specific classes, situation-specific classes, and person-situation interactions. The proposed models are applied to an empirical dataset. We discuss statistical inference, effect size measures, and model visualization for the proposed models. Based on realistic parameter values from the empirical dataset, preliminary simulation studies were conducted to investigate models' performances. Bayesian estimation in the proposed models allows flexible testing of a wide range of hypotheses related to state, trait, and interaction effects. We discuss limitations and future directions.