#3206. Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach

October 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Statistics and Probability;
Arts and Humanities (miscellaneous);
Experimental and Cognitive Psychology;
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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. In this research, the scholars simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: obtain states with mixture factor analysis while treating repeated measures as independent, assign observations to the states, and use these states in a discrete- or continuous-time latent Markov model taking into account classification errors.
Keywords:
Experience sampling; factor analysis; latent Markov modeling; measurement invariance; three-step approach

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