PUB - Publikationen an der Universität Bielefeld

The investigation of developmental trajectories is a central goal of educational science. However, modeling and predicting complex trajectories in the context of large-scale panel studies poses multiple challenges. Statistical models oftentimes need to take into account a) potentially nonlinear shapes of trajectories, b) multiple levels of analysis (e.g., individual level, university level) and c) measurement models for the typically unobservable latent constructs. In this paper, we develop a new approach, termed the multilevel latent growth components model (ML-LGCoM) that can adequately address all three challenges simultaneously. A key feature of this new approach is that it allows researchers to test contrasts of interest among latent variables in a multilevel study. In our illustrative example, we used data from the National Educational Panel Study to model the (non-linear) development of students’ satisfaction with their academic success over four years while taking into account cluster- and individual-level trajectories and measurement error.