A General Framework for the Inclusion of Time-Varying and Time-Invariant Covariates in Latent State Trait Models

Latent state-trait (LST) models are increasingly applied in psychology. However, existing LST models are limited and do not allow researchers to relate time-varying or time-invariant covariates, or a combination of both, to key parameters in LST models. We present a general framework for the inclusion of nominal and/or continuous time-varying and time-invariant covariates in LST models. The new framework builds on modern LST theory and Bayesian moderated nonlinear factor analysis and is termed moderated nonlinear LST (MN-LST) framework. The MN-LST framework offers new modeling possibilities and allows for a fine-grained analysis of trait change, synergistic interaction effects, as well as inter- or intra- individual variability. The new MN-LST approach is compared to multiple-indicator latent growth curve models. The advantages of the MN-LST are illustrated in an empirical application examining dyadic coping in romantic relationships. Finally, the advantages and limitations of the approach are discussed, and practical recommendations are provided.