Optimal Data Analysis
Optimal Data Analysis LLC
This study explored multiple variables that influence the development of juvenile delinquency. Two datasets of the National Youth Survey, a longitudinal study of delinquency and drug use among youths from 1976 and 1978, were used: 166 predictors were selected from the 1976 dataset, and later self-reported delinquency was selected from the 1978 dataset. Optimal data analysis was then used to construct a hierarchical classification tree model tracing the causal roots of juvenile delinquency and non-delinquency. Five attributes entered the final model and provided 70.37% overall classification accuracy: prior self-reported delinquency, exposure to peer delinquency, exposure to peer alcohol use, attitudes toward marijuana use, and grade level in school. Prior self-reported delinquency was the strongest predictor of later juvenile delinquency. These results highlight seven distinct profiles of juvenile delinquency and non-delinquency: lay delinquency, unexposed chronic delinquency, exposed chronic delinquency, unexposed non-delinquency, exposed non-delinquency, unexposed reformation, and exposed reformation.
Bryant, Fred B.; Suzuki, Hideo; and Edwards, John D.. Tracing Prospective Profiles of Juvenile Delinquency and Non-Delinquency: An Optimal Classification Tree Analysis. Optimal Data Analysis, 1, : 125-143, 2010. Retrieved from Loyola eCommons, Psychology: Faculty Publications and Other Works,
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