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Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies
Vogelsmeier,Leonie V.D.E. Vermunt,Jeroen K. van Roekel,Eeske De Roover,Kim
Vogelsmeier,Leonie V.D.E.
Vermunt,Jeroen K.
van Roekel,Eeske
De Roover,Kim
Abstract
When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.
Description
Date
2019
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Publisher
Research Projects
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Keywords
experience sampling, measurement invariance, factor analysis, latent Markov modeling, EXPLORATORY FACTOR-ANALYSIS, CROSS-CULTURAL RESEARCH, WEAK FACTOR LOADINGS, MAXIMUM-LIKELIHOOD, MONTE-CARLO, COMPONENT ANALYSIS, ANALYTIC ROTATION, RESPONSE STYLE, SAMPLE-SIZE, DAILY-LIFE
Citation
Vogelsmeier, L V D E, Vermunt, J K, van Roekel, E & De Roover, K 2019, 'Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies', Structural Equation Modeling, vol. 26, no. 4, pp. 557-575. https://doi.org/10.1080/10705511.2018.1554445