PUB - Publikationen an der Universität Bielefeld

As the popularity of the experience sampling methodology rises, there is a growing need for suitable analytical procedures. These studies often aim to separate fleeting situation-specific from more enduring influences. Latent state-trait (LST) models can make this differentiation. This tutorial discusses wide-format LST models suitable for experience sampling data. We outline second-order and first-order model specifications, their (dis)advantages, and make the assumptions of first-order specifications explicit for the first time. These LST models are very flexible, allow for a variety of different models and for testing invariance assumptions. However, their specification is tedious and error-prone. This tutorial introduces a new user-friendly browser app and an R-function for experience sampling models in the R-package _lsttheory_. Extending on existing models, the software also allows to add covariates which can further explain the stable components. Throughout the tutorial, we answer exemplary research questions about well-being in everyday life with data from a five-day experience-sampling study. An autoregressive model with indicator-specific traits fitted the data best and revealed relatively high consistency, implying that well-being depends more strongly on the person than the current situation. Of the Big Five, extraversion and neuroticism are predictive of trait-well-being. We conclude with recommendations about model fit and comparisons.