A Cross-Domain Model for Growth in Adolescent Alcohol Expectancies.

Methodologists have recently shown how the methods of individual growth modeling and covariance structure analysis can be integrated, bringing the flexibility of the latter to bear on the investigation of inter-individual differences in change. The individual growth-modeling framework uses a pair of hierarchical statistical models to represent: (a) individual status as a function of time, and (b) inter-individual differences in true change. Under the covariance structure approach, these level- I and level-2 models can be reformatted as the "measurement" and "structural" components of the general LISREL model with mean structures. Consequently, a covariance structure analysis of longitudinal panel data can provide maximum-likelihood estimates for all level-2 parameters. In this article, using longitudinal data drawn from a school-based alcohol prevention trial, we demonstrate how the new approach can be used to investigate the inter-relationships among simultaneous individual changes in two domains - positive arid negative alcohol expectancies - over the course of early to mid-adolescence, for both boys and girls. We represent individual change over time in positive expectancies with a piecewise growth model, and in negative expectancies with a straight-line growth model. Then, we use multi-sample covariance structure analysis to ask whether individual changes in positive and negative expectancies are related to each other and whether the pattern of inter-relationships differs by gender. Our approach can easily be generalized to more than two domains and has a variety of other advantages that we document in the discussion.