An Extension of the General Latent Variable Growth Modeling Framework to Four Levels of the Hierarchy

In recent years, a number of statistical techniques for the analysis of longitudinal multilevel data have become available, allowing researchers the opportunity to study behavior at different levels (e.g., individual, family, school, neighborhood). Although many of these statistical approaches are widely available, they are still relatively underused, and there is uncertainty among researchers as to the appropriateness and usefulness of different approaches for analyzing longitudinal data. The purpose of this article is to present an extension of the general latent variable growth curve modeling framework to 4 levels of the hierarchy. This 4-level extension merges 2 common analytic approaches used in answering longitudinal multilevel research questions: (a) a full information maximum likelihood (ML) latent growth modeling approach using an extension of a factor-of-curves model and (b) a limited information multilevel latent growth modeling (LGM) approach using Muthen's ML-based estimator. Data comprised r...