Abstract
Longitudinal data are necessary to study how individuals change over time. Latent change score (LCS) models are a collection of longitudinal models used to study within-person change and dynamics. This chapter reviews the LCS framework, describes its specification in the structural equation modeling framework, and discusses how to specify LCS models in the nonlinear multilevel modeling framework using NLMIXED, and in the Bayesian hierarchical framework using JAGS. Using NLMIXED or JAGS for LCS models enables researchers to be more precise about the time metric, which allows for the idiosyncracies of each longitudinal dataset. When discussing time metrics, there are some time metrics whose values are discrete, where the time metric takes on a relatively small number of distinct values. One of the main advantages of the LCS model framework is the flexibility of which it can be adapted to the idiosyncracies of both research questions and datasets.
Published Version
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