How to explore within‑person and between‑person measurement model differences in intensive longitudinal data with the R package lmfa

Leonie V.D.E. Vogelsmeier*, Jeroen K. Vermunt, Kim De Roover

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
77 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)2387-2422
JournalBehavior Research Methods
Volume55
Issue number5
DOIs
Publication statusPublished - 2023

Keywords

  • CHULL
  • CONTINUOUS-TIME
  • EMOTION DIFFERENTIATION
  • ESM
  • EXPERIENCE
  • Factor analysis
  • Intensive longitudinal data
  • LATENT MARKOV-MODELS
  • Latent Markov modeling
  • Measurement invariance
  • NUMBER
  • OF-THE-ART
  • PANEL-DATA
  • R
  • Software package
  • Three-step approach
  • VALENCE FOCUS

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