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.
Highlights
Intensive longitudinal data (ILD; e.g., Hamaker & Wichers, 2017) allow one to investigate the dynamics over time of latent psychological constructs
Since each adolescent may have a different measurement model (MM) at different measurement occasions, we examined adolescents’ transitions from one state to another
As motivated above, we investigated (1) whether adolescents differed in their state- memberships by classifying the adolescents based on their transition patterns into latent classes that could differ across the three waves, (2) whether the wavespecific covariate depression had an influence on this class membership, and (3) whether the time-varying social context covariates affected the transitions between the states and whether these effects differed across classes
Summary
Intensive longitudinal data (ILD; e.g., Hamaker & Wichers, 2017) allow one to investigate the dynamics over time of latent (i.e., unobservable) psychological constructs. Studies are being conducted on dynamics in emotions and behaviors related to mental health (e.g., Myin-Germeys et al, 2018; Snippe et al, 2016), and ILD can be used to tailor interventions to the subject’s real-time dynamics of negative affect (van Roekel et al, 2017). Such data is efficiently gathered by means of Experience Sampling Methodology (ESM; Scollon et al, 2003), in which subjects repeatedly rate questionnaire items over several weeks, say five times a day, at randomized time-points. There is an urgent need to develop novel analytical methods
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