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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


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 may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present “latent Markov latent trait analysis” (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents’ affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.

Original languageEnglish
Pages (from-to)61-76
Number of pages16
JournalEvaluation and the Health Professions
Issue number1
Early online date11 Dec 2020
Publication statusPublished - 1 Mar 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: 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]. Funding for data collection came from Utrecht University, Dynamics of Youth seed project, awarded to Loes Keijsers, Manon Hillegers et al.

Publisher Copyright:
© The Author(s) 2020.

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