Moderation and Mediation in Interindividual Longitudinal Analysis

Abstract Developmental psychopathology research often involves hypotheses about multiple variables interacting and affecting each other over time to produce adaptive and maladaptive behavior and thus analytic methods for examining moderation and mediation in longitudinal data are particularly germane in this area. This chapter describes methods for testing moderation and mediation in cross sectional data, outlines two frameworks frequently used for the analysis of longitudinal data, and describes a number of models for testing longitudinal moderation and mediation, including ax lag as moderator model, multilevel models with time‐varying covariate interactions, a model of mediation change over time, autoregressive panel models, latent growth curve models, latent change score models, and exponential decay models. Methods for combining moderation and mediation and causal inference in longitudinal data are also discussed. Illustrative analyses are conducted using example data on kindergarten through fifth‐graders' attention‐deficit/hyperactivity disorder (ADHD) symptoms, interpersonal skills, academic performance, and depression variables from the ECLS‐K data set.