Evaluating covariate effects on ESM measurement model changes with latent markov factor analysis: A three-step approach

Leonie V.D.E. Vogelsmeier*, Jeroen K. Vermunt, AL (Anne) Bülow, Kim De Roover

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
39 Downloads (Pure)

Abstract

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects’ observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.
Original languageEnglish
Pages (from-to)262-291
Number of pages31
JournalMultivariate Behavioral Research
Volume58
Issue number2
Early online date16 Oct 2021
DOIs
Publication statusPublished - 2023

Bibliographical note

Funding:
The research leading to the results reported in this paper was sponsored by the Netherlands Organization for Scientific Research (NWO) [Research Talent grant 406.17.517; Veni grant 451.16.004; Vidi grant 452.17.011].

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