Making Multimethod Latent State–Trait Models for Random and Fixed Situations Accessible: A Tutorial
Year: 2025
Type: article
Abstract: OBJECTIVE: As more researchers employ longitudinal research designs, which integrate multiple methods and multiple (fixed) situations, the need for appropriate analytical methods arises. METHOD: 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. RESULTS: 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. CONCLUSION: 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.
Source: Journal of Personality
DOI: 10.1111/jopy.13031
Authors: Dora L. Tinhof, Axel Mayer
Institution: Bielefeld University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,780
OpenAlex ID: W4411352632