Growth mixture models (GMMs) can be used to uncover the heterogeneity in individual growth trajectories, using the discrete latent class variable to predict between class differences of growth...Show moreGrowth mixture models (GMMs) can be used to uncover the heterogeneity in individual growth trajectories, using the discrete latent class variable to predict between class differences of growth patterns in the continuous growth factors. GMMs can incorporate the distal outcomes, defined as the consequence of individuals in latent classes, allowing researchers to explore the consequence of the growth trajectories. Prior work on continuous latent growth variables as predictors of the distal outcome has shown that this prediction is sensitive to the time coding choices in the latent curve models (LCMs). However, the GMM allows categorical latent class variables to directly predict the distal outcome rather than the continuous latent growth factors in the LCMs, and may be less susceptible to the issues in the continuous case. Here, we examine the effect of the time coding approaches on the predictive relationship between categorical latent variables and the distal outcomes, and explore the role of estimation approaches, measurement quality, latent class proportions and sample sizes in determining the reliable recovery of the distal outcome and the latent classes. The results suggest that, the one-step approach, is robust across simulation conditions despite yielding downward biased estimates in standard error, but that the two-step approach results convergence issues, biased parameter recovery, and biased class proportions when the measurement model is weak. We demonstrate that similar issue occurs in both simulated and empirical data. In sum, there is no evidence for an influence of time coding choices on predicting the distal outcomes in the GMMs.Show less
