Making Multimethod Latent State-Trait Models for Random and Fixed Situations Accessible: A Tutorial

As more researchers employ longitudinal research designs, which integrate multiple methods and multiple (fixed) situations, the need for appropriate analytical methods arises.

Multimethod latent state-trait models for random and fixed situations (MM-LST-RF; Hintz et al. 2019) provide a means with which person characteristics, (fixed) situation, and method effects, as well as their interactions can be studied. While these models are very versatile, their complexity poses a significant hurdle to their implementation.

This tutorial helps facilitate the application of MM-LST-RF models. First, we present two simpler methodological approaches in which the full MM-LST-RF model is broken down into its (a) multimethod and (b) random and fixed situation components. Key parameters and model coefficients are highlighted using a motivational example. Second, we present a user-friendly shiny app based on a newly developed R function. Users are walked through the process of specifying, estimating, and interpreting an MM-LST-RF model guided by detailed explanations of all specification options and practical use recommendations.

The shiny app facilitates the analysis of data from longitudinal study designs implementing multiple methods and (fixed) situations, helping researchers gain a deeper understanding of psychological constructs.