A Factor Analytic Model of Drug-Related Behavior in Adolescence and Its Impact on Arrests at Multiple Stages of the Life Course
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Recognizing the inherent variability of drug-related behaviors, this study develops an empirically-driven and holistic model of drug-related behavior during adolescence using factor analysis to simultaneously model multiple drug behaviors.
The factor analytic model uncovers latent dimensions of drug-related behaviors, rather than patterns of individuals. These latent dimensions are treated as empirical typologies which are then used to predict an individual’s number of arrests accrued at multiple phases of the life course. The data are robust enough to simultaneously capture drug behavior measures typically considered in isolation in the literature, and to allow for behavior to change and evolve over the period of adolescence.
Results show that factor analysis is capable of developing highly descriptive patterns of drug offending, and that these patterns have great utility in predicting arrests. Results further demonstrate that while drug behavior patterns are predictive of arrests at the end of adolescence for both males and females, the impacts on arrests are longer lasting for females.
The various facets of drug behaviors have been a long-time concern of criminological research. However, the ability to model multiple behaviors simultaneously is often constrained by data that do not measure the constructs fully. Factor analysis is shown to be a useful technique for modeling adolescent drug involvement patterns in a way that accounts for the multitude and variability of possible behaviors, and in predicting future negative life outcomes, such as arrests.
KeywordsDrug use Drug selling Factor analysis Arrests
Support for the Rochester Youth Development Study has been provided by the Office of Juvenile Justice and Delinquency Prevention (86-JN-CX-0007), the National Institute on Drug Abuse (R01DA005512), the National Science Foundation (SBR-9123299), and the National Institute of Mental Health (R01MH63386). Work on this project was also aided by grants to the Center for Social and Demographic Analysis at the University at Albany from NICHD (P30HD32041) and NSF (SBR-9512290). We are most grateful to the many helpful comments and suggestions given by the anonymous reviewers on earlier versions of this paper.
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