Logistic Regression: Predicting Recidivism Rates Based on Demographic and Criminal History
Background
The criminal justice system aims to reduce recidivism, but many individuals re-offend within a few years of release. Understanding the factors that contribute to recidivism can inform policy changes and rehabilitation programs.
Possible Research Questions
- What factors (e.g., age, prior convictions, education level) are associated with a higher likelihood of reoffending?
- Does participation in rehabilitation programs reduce recidivism?
- Are certain types of offenses more predictive of recidivism?
Possible Data Sources
Key Variables
Dependent Variable: Recidivism (binary: 1 = reoffended, 0 = did not reoffend)
Independent Variables: Age at release, number of prior convictions, type of offense, education level, participation in job training or rehabilitation, race/ethnicity, etc.
Methods
- Perform logistic regression to classify individuals as likely or unlikely to reoffend.
- Check odds ratios for each predictor variable.
- Evaluate the model using AUC-ROC curves and confusion matrices to measure classification performance.