Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data.

ABSTRACT: Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates ma

SUBMITTER: Vogelsmeier LVDE 

PROVIDER: S-EPMC7907986 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature