Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond

摘要

Studies of human development require longitudinal data analysis methods that describe within-and between-individual variation in developmental and behavioral trajectories. This article reviews life-course data analysis methods for modeling these trajectories, as well as their application in studies of antisocial behavior and of crime in childhood, in adolescence, and throughout life. We set the stage by introducing growth curve (hierarchical linear) models. We focus our review on finite mixture models for life-course data, known as group-based trajectory and growth mixture models. We then discuss how these models are applied within criminology and developmental psychology, recent controversies over their substantive use and interpretation, and important issues of statistical practice and the challenges they raise. Building on the critical literature, we offer several recommendations for the applied users of the models. Finally, we present the most recent method of examining behavioral trajectories in criminology, the unimodal curve registration (UCR) approach. We briefly contrast the UCR model with growth curve and finite mixture models for life-course data analysis.