Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data
Year: 2020
Type: article
Abstract: 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.
Institutions: Tilburg University, Erasmus University Rotterdam
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
Awards: Lack of measurement invariance in multilevel data: A cluster-based solution for making valid attribute comparisons., ADAPT: Assessing the Dynamics Between Parenting and Adaptation in Teens, Understanding between- and within-person differences in experience sampling measurements using mixture factor analysis
OpenAlex ID: W3111031121