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Optimal Data Analysis





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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.


Author Posting © Optimal Data Analysis LLC, 2010. This article is posted here by permission of Optimal Data Analysis LLC for personal use, not for redistribution. The article was published in Optimal Data Analysis, Volume 1, September 2010,

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Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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