Observations on the Use of Growth Mixture Models in Psychological Research

Psychologists are applying growth mixture models at an increasing rate. This arti-cle argues that most of these applications are unlikely to reproduce the underlying taxonic structure of the population. At a more fundamental level, in many cases there is probably no taxonic structure to be found. Latent growth classes then cate-gorically approximate the true continuum of individual differences in change. This approximation, although in some cases potentially useful, can also be problem-atic. The utility of growth mixture models for psychological science thus remains in doubt. Some ways in which these models might be more profitably used are suggested. Growth mixture models (GMMs) are designed to separate a general population of individuals into subgroups characterized by qualitatively distinct patterns of change over time. In this article, I offer a few observations on the application of these models in psychological science. Like many, I was initially excited about the potential of GMMs. After several years of evaluating these models and reviewing applications, however, I am now skeptical that they will meaningfully advance our understanding of psychosocial development. In what follows, I outline key methodological and theoretical concerns that I have with current applications of GMMs. Correspondence concerning this article should be addressed to Daniel Bauer, Department of