MODELING INCOMPLETE LONGITUDINAL SUBSTANCE USE DATA USING LATENT VARIABLE GROWTH CURVE METHODOLOGY

Longitudinal data sets typically suffer from attrition and other forms of missing data. When this common problem occurs, several researchers have demonstrated that correct maximum likelihood estimation with mis sing data can be obtained under mild assumptions concerning the missin g data mechanism. With reasonable substantive theory, a mixture of cro ss-sectional and longitudinal methods developed within multiple-group structural equation modeling can provide a strong basis for inference about development change. Using an approach to the analysis of missing data, the present study investigated developmental trends in adolesce nt (N = 759) alcohol, marijuana, and cigarette use across a 5-year per iod using multiple-group latent growth modeling. An associative model revealed that common developmental trends existed for all three substa nces. Age and gender were included in the model as predictors of initi al status and developmental change. Findings discuss the utility of la tent variable structural equation modeling techniques and missing data approaches in the study of developmental change.