ArticleBehavior research methods2023
How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.
Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Intensive Longitudinal Data Collection Methods in Health Research: Tutorial for Selecting Measures and Designing a Sampling Protocol.JMIR mHealth and uHealth · 2026Article
- Investigating dynamics in attentive and inattentive responding together with their contextual correlates using a novel mixture IRT model for intensive longitudinal data.The British journal of mathematical and statistical psychology · 2026Article
- A primer on intensive longitudinal psychometrics.Behavior research methods · 2026Review
- When our measurements are different every day: an ML-SEM simulation study on within-person nonuniform measurement bias in intensive longitudinal data.Frontiers in psychology · 2026Article
- The importance of examining measurement properties in ecological momentary assessment research: An illustrative example in suicide research.Psychological assessment · 2025Article
- Assessing and accounting for measurement in intensive longitudinal studies: current practices, considerations, and avenues for improvement.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
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3 authors.
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Abstract
Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.
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