Specifying and Interpreting Latent State–Trait Models With Autoregression: An Illustration

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Abstract
Latent state–trait models are valuable tools for representing the longitudinal stability and variability of individuals’ relative standing on a construct (e.g., a behavior or psychological process). Specifically, state–trait models partition construct variance into time-varying and time-invariant components, enabling one to examine the relations between these components and other variables. Such partitioning of construct variance has a number of valuable applications including the improvement of risk-outcome research. The trait–state–occasion (TSO) model and latent state–trait model with autoregression (LST–AR) are ideal for use with constructs with relative stability that decreases with increasing durations, but relative stability that does not decrease to 0 even over long durations. Despite the fact that this pattern of relative stability expression is observed for a wide variety of constructs, there are relatively few applications of the TSO and LST–AR models. Thus, this article describes the TSO and LST–AR models and illustrates application of these models.