Class Enumeration and Parameter Bias in Growth Mixture Models with Misspecified Time-Varying Covariates: A Monte Carlo Simulation Study
Description:
Growth mixture modeling (GMM) is a useful tool for examining both between- and within-persons change over time and uncovering unobserved heterogeneity in growth trajectories. Importantly, the correct extraction of latent classes and parameter recovery can be dependent upon the type of covariates used. Time-varying covariates (TVCs) can influence class membership but are scarcely included in GMMs as predictors. Other times, TVCs are incorrectly modeled as time-invariant covariates (TICs). Additi…
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Date:
December 2020
Creator:
Palka, Jayme M.
Partner:
UNT Libraries