Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes

Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the within-class parameter estimates obtained from these mod-els are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested. Over the last decade, random coefficient growth modeling has become a centerpiece of longitudinal data analysis. These models have been adopted en-thusiastically by applied psychological researchers in part because they provide a more dynamic analysis of repeated measures data than do many traditional tech-niques. However, these methods are not ideally suited for testing theories that posit the existence of qualita-tively different developmental pathways, that is, theo-ries in which distinct developmental pathways are thought to hold within subpopulations. One widely cited theory of this type is Moffitt’s (1993) distinction between “life-course persistent ” and “adolescent-limited ” antisocial behavior trajectories. Moffitt’s theory is prototypical of other developmental taxono-mies that have been proposed in such diverse areas as developmental psychopathology (Schulenberg