Level: Intermediate
Structural equation modeling (SEM) is a framework that allows for the study of associations between observed and unobserved latent variables. Many applications of SEM use cross-sectional data, and the framework is also very flexible for modeling longitudinal data.
This presentation describes latent growth curve modeling (LGCM) as a flexible tool for characterizing growth trajectories over time. We begin by briefly explaining the basic concepts of SEM, followed by an introduction to how mean values can be incorporated into analyses to test growth theories. This study illustrates LGCM by fitting the model to data on individual-reported anxiety and health complaints during the COVID-19 pandemic. Our analysis reveals that resilience predicts unique patterns of change in the trajectories of anxiety and generalized symptoms.
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