Investigating Individual Change and Development: The Multilevel Model for Change and the Method of Latent Growth Modeling

Abstract

Although controversy has always surrounded the measurement of change, it is now known that change can indeed be measured well if a longitudinal perspective is adopted. A sample of individuals must be followed over time and measured repeatedly at sensibly spaced intervals, and an explicit multilevel model for change must be postulated during data analysis. The latter contains two submodels: (a) an individual growth model (Level 1) and (b) a model for systematic interindividual differences in change (Level 2). Recently, methodologists have shown how the multilevel model for change can be mapped onto the general covariance structure model (as implemented in LISREL, say), thereby empowering the use of covariance structure analysis in the measurement of change-a strategy now known as latent growth modeling. Although the statistical model is identical under both approaches, and parameter estimates are generally very close, latent growth modeling offers advantages. It permits the modeling of change simultaneously in several domains, the modeling of change in a construct, the modeling of change as part of a network of relations, and the testing of explicit hypotheses about error covariance structure. In this article, I use longitudinal data on the early reading scores of 1740 children from the Early Childhood Longitudinal Study (National Center for Education Statistics, 2002) to introduce latent growth modeling.