American Journal of Criminal Justice

, Volume 40, Issue 1, pp 47–74 | Cite as

Assessing the Predictive Utility of Logistic Regression, Classification and Regression Tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

  • Fawn T. Ngo
  • Ramakrishna Govindu
  • Anurag Agarwal
Article

Abstract

This study assesses the relative utility of a traditional regression approach - logistic regression (LR) - and three classification techniques - classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)—in predicting inmate misconduct. The four models were tested using a sample of inmates held in state and federal prisons and predictors derived from the importation model on inmate adaptation. Multi-validation procedure and multiple evaluation indicators were used to evaluate and report the predictive accuracy. The overall accuracy of the four models varied between 0.60 and 0.66 with an overall AUC range of 0.60–0.70. The LR and MLPNN methods performed significantly better than the CART and CHAID techniques at identifying misbehaving inmates and the CHAID method outperformed the CART approach in classifying defied inmates. The MLPNN method performed significantly better than the LR technique in predicting inmate misconduct among the training samples.

Keywords

Actuarial risk assessment techniques Comparative statistical techniques Logistic regression Classification and regression tree Chi-squared automatic interaction detection Neural networks Importation model Inmate misconduct 

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Copyright information

© Southern Criminal Justice Association 2014

Authors and Affiliations

  • Fawn T. Ngo
    • 1
  • Ramakrishna Govindu
    • 2
  • Anurag Agarwal
    • 2
  1. 1.College of Arts and SciencesUniversity of South Florida Sarasota-ManateeSarasotaUSA
  2. 2.College of BusinessUniversity of South Florida Sarasota-ManateeSarasotaUSA

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