Latent Variable Modeling of Longitudinal and Multilevel Substance Use Data

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Abstract
This article demonstrates the use of a general model for latent variable growth analysis which takes into account cluster sampling. Multilevel Latent Growth Modeling (MLGR4) was used to analyze longitudinal and multilevel data for adolescent and parent substance use measured at four annual time points. An associative LGM model was tested for alcohol, marijuana, and cigarette use with a sample of 435 families. Hypotheses concerning the shape of the growth curve and the extent of individual differences in the common trajectory over time were tested. The effects of marital and family status and socio-economic status on family levels of substance use were also examined. Findings are discussed in terms of family-level substance use and similarities in developmental trajectories across substances, and the impact of contextual factors on family levels of substance use and development.