Understanding Random Effects in Group-Based Trajectory Modeling: An Application of Moffitt's Developmental Taxonomy

Abstract

The group-based trajectory modeling approach is a systematic way of categorizing subjects into different groups based on their developmental trajectories using formal and objective statistical criteria. With the recent advancement in methods and statistical software, modeling possibilities are almost limitless; however, parallel advances in theory development have not kept pace. This paper examines some of the modeling options that are becoming more widespread and how they impact both empirical and theoretical findings. The key issue that is explored is the impact of adding random effects to the latent growth factors and how this alters the meaning of a group. The paper argues that technical specification should be guided by theory, and Moffitt's developmental taxonomy is used as an illustration of how modeling decisions can be matched to theory.