Abstract
Abstract Longitudinal data offer the possibility for a systematic analysis of stability and change over time and thus are a powerful tool to examine processes underlying social phenomena and the causal relation between different constructs. One prominent and powerful method is latent growth curve models (LGM) as one variant of structural equation modeling. With LGM it is possible to analyze individual trajectories and interindividual differences in these individual trajectories. LGM can easily be extended by using multiple indicator latent factors to model measurement error, integrating predictors of change as well as mediators, and testing moderating influences of measures. An empirical example demonstrates the usefulness of LGM.
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