Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.

2003/01/01 by Daniel J. Bauer, Patrick J. Curran · 8 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Computer Science · #Genetic and phenotypic traits in livestock #Statistical Methods and Bayesian Inference #Advanced Statistical Modeling Techniques

paper · doi:10.1037/1082-989x.8.3.338

openalex publication_date 2003/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Abstract

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 models 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.

Citations

• Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions
• A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories
• Testing the number of components in a normal mixture
• Bayesian Density Estimation and Inference Using Mixtures
• Integrating Person‐Centered and Variable‐Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes
• Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy.

Cited by

• The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities.
• Diagnostic Procedures for Detecting Nonlinear Relationships Between Latent Variables
• Observations on the Use of Growth Mixture Models in Psychological Research
• Modeling Variability in Individual Development: Differences of Degree or Kind?
• Latent Variable Modeling of Differences and Changes with Longitudinal Data
• On Joining a Signal Detection Choice Model with Response Time Models
• Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches
• Daniel J. Bauer [wikipedia]