Results indicate that it is risky for parents to allow children to drink during early adolescence. When these findings are considered together with the risks associated with early onset of alcohol use, it is clear that parents can play an important role in prevention.
Summary Mixed effect models have become very popular, especially for the analysis of longitudinal data. One challenge is how to build a good enough mixed effects model. In this paper, we suggest a systematic strategy for addressing this challenge and introduce easily implemented practical advice to build mixed effect models. A general discussion of scientific strategies motivates the recommended five step procedure for model fitting. The need to model both the mean structure (the fixed effects) and the covariance structure (the random effects and residual error) creates the fundamental flexibility and complexity. Some very practical recommendations help conquer the complexity. Centering, scaling, and full-rank coding all predictor variables radically improves the chances of convergence, computing speed, and numerical accuracy. Applying computational and assumption diagnostics from univariate linear models to mixed model data greatly helps detect and solve related computational problems. Applying computational and assumption diagnostics from univariate linear models to mixed model data can radically improve the chances of convergence, computing speed, and numerical accuracy. The approach helps fit more general covariance models, a crucial step in selecting a credible covariance model needed for defensible inference. A detailed demonstration of the recommended strategy is based on data from a published study of a randomized trial of a multicomponent intervention to prevent young adolescents' alcohol use. The discussion highlights a need for additional covariance and inference tools for mixed models. The discussion also highlights the need for improving how scientists and statisticians teach and review the process of finding a good enough mixed model.
Intimate partner violence is a significant public health problem, as these behaviors have been associated with a number of negative health outcomes including illicit drug use, physical injury, chronic pain, sexually transmitted diseases, depression, and posttraumatic stress disorder. The current study examined the association between marijuana use and intimate partner violence using a longitudinal survey of adolescents and young adults ages 15 to 26 years. Data were obtained from 9,421 adolescents in the National Longitudinal Study of Adolescent Health (Add Health) Waves 1 through 4 (1995–2008). Marijuana use was measured in the past year at each wave and participants were categorized as “users” or “nonusers.” Partner violence was constructed using six items (three pertaining to victimization and three concerning perpetration) from Wave 4 (2007–2008). Using these six items, participants were categorized as “victims only,” “perpetrators only,” or “victims and perpetrators.” Survey multinomial regression was used to examine the relationship between marijuana use and intimate partner violence. Consistent use of marijuana during adolescence was most predictive of intimate partner violence (OR = 2.08, p < .001). Consistent marijuana use (OR = 1.85, p < .05) was related to an increased risk of intimate partner violence perpetration. Adolescent marijuana use, particularly consistent use throughout adolescence, is associated with perpetration or both perpetration of and victimization by intimate partner violence in early adulthood. These findings have implications for intimate partner violence prevention efforts, as marijuana use should be considered as a target of early intimate partner violence intervention and treatment programming.
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