Latent Growth Modeling and Developmental Psychopathology

Abstract

We review variations of latent growth modeling and their applications in developmental psychopathology. We begin by reviewing core principles of developmental psychopathology. We then review basic assumptions of longitudinal studies and key issues in latent growth modeling. We present the history of the development of latent growth modeling as well as a series of specific latent growth models that are commonly considered in psychopathology. We then illustrate latent growth modeling studies for understanding developmental pathways in terms of continuity/discontinuity, equifinality/multifinality, and contextual influences. We describe latent change score modeling and growth mixture modeling studies and illustrate studies using these techniques to examine risk and protective mechanisms as predictors, mediators, and moderators for understanding cross‐population differences, contextual influences, cross‐domain relations, and prevention and intervention effects. Finally, we identify future directions for developmental psychopathology research that will benefit from latent growth modeling approaches.

Structural Home Environment Effects on Developmental Trajectories of Self-Control and Adolescent Risk Taking

“…Growth mixture modeling (GMM) was used to identify the smallest number of trajectory classes that described the repeatedly measured self-control variable well using MPlus statistical software version 7.4 (Muthén & Muthén, 2012). GMM estimates latent factors for intercepts and slopes of developmental trajectories on a univariate outcome, and tests whether there are two or more distinct classes of individuals in order to capture the heterogeneity in growth trajectories and determines the optimal class membership for each individual (Kim-Spoon & Grimm, 2016). As a result, each class made up of individuals with similar growth trajectories has uniquely estimated values for intercept and slope.…”

Gender disparities in students’ motivational experiences in high school science classrooms

“…We constructed the class day (time) variable by consecutively numbering each class session during the unit starting with zero. We opted to use class session as the time metric, as opposed to calendar days or school days elapsed, given Kim‐Spoon and Grimm's () recommendation to consider the dominant reasons for why changes in the outcome might occur when selecting a time metric. In our investigation, the dominant reason student perceptions, need satisfaction, and engagement in science class were expected to vary is because of their experiences during science class sessions.…”

Automated sensing of daily activity: A new lens into development

“…Additionally, methods papers by Granic and colleagues and others (Granic & Hollenstein, ; Granic et al, ; de Barbaro, Johnson, Forster, & Deak, ) detail the discovery and quantification of theoretically meaningful structure in high‐density multimodal repeated measures data with consideration of longitudinal outcomes. Finally, sophisticated tools have been developed for longitudinal analyses of high‐density repeated‐measures data (Bolger & Laurenceau, ; Kim‐Spoon & Grimm, ; Krull, Cheong, Fritz, & MacKinnon, ). A combination of such analysis techniques will likely be required to quantitively characterize complex longitudinal trajectories.…”

“…repeated-measures data (Bolger & Laurenceau, 2013;Kim-Spoon & Grimm, 2016;Krull, Cheong, Fritz, & MacKinnon, 2016). A combination of such analysis techniques will likely be required to quantitively characterize complex longitudinal trajectories.…”