New Approaches to Studying Problem Behaviors: A Comparison of Methods for Modeling Longitudinal Categorical Adolescent Drinking Data

摘要

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this paper, we present several currently-available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model (HGLM), a growth mixture model (GMM), and a latent class growth analysis (LCGA). We also describe a longitudinal latent class analysis (LLCA), which requires fewer assumptions than the first three. Finally, we illustrate all of the models using actual longitudinal adolescent alcohol-use data. We guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention.