USING COVARIANCE STRUCTURE-ANALYSIS TO DETECT CORRELATES AND PREDICTORS OF INDIVIDUAL CHANGE OVER TIME

Recently, methodologists have shown how two disparate conceptual arenas - individual growth modeling and covariance structure analysis - can be integrated. The integration brings the flexibility of covariance analysis to bear on the investigation of systematic interindividual differences in change and provides another powerful data-analytic tool for answering questions about the relationship between individual true change and potential predictors of that change. The individual growth modeling framework uses a pair of hierarchical statistical models to represent (a) within-person true status as a function of time and (b) between-person differences in true change as a function of predictors. This article explains how these models can be reformatted to correspond, respectively, to the measurement and structural components of the general LISREL model with mean structures and illustrates, by means of worked example, how the new method can be applied to a sample of longitudinal panel data.