The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

the risk ratio is bounded by zero at the lower end and has no upper bound. A risk ratio <strong>of</strong>
1 indicates no difference in risk between the groups. In the present study, a risk ratio <strong>of</strong> 2
would indicate that one group (‘exposure’: risk level or treatment status) is twice as likely
to have a serious <strong>of</strong>fense (‘disease’) than the other.
We use another epidemiological tool, sensitivity/specificity analysis, to assess the
effect <strong>of</strong> changing the model’s cut-<strong>of</strong>f point for classifying risk. We examine how many
<strong>of</strong>fenders were correctly classified as low-risk (having no serious <strong>of</strong>fense two years postrisk
assessment date) at each cut point. Sensitivity is defined as the proportion <strong>of</strong> ‘true
positives’ correctly identified by the model, or the proportion <strong>of</strong> <strong>of</strong>fenders without a
future serious <strong>of</strong>fense who had been predicted low risk. Specificity is defined as the
proportion <strong>of</strong> ‘true negatives’ identified: the proportion <strong>of</strong> serious recidivists who were
classified as non-low risk. Sensitivity or specificity <strong>of</strong> 100 per cent indicate that the
classification tool is able to identify all the true positives or all the true negatives,
respectively. In practice, most classification models require a trade-<strong>of</strong>f between one or
the other: no model will perfectly classify every case, so users must decide whether it is
more important to identify mostly true positives, or mostly true negatives. We also
present the positive and negative predictive values <strong>of</strong> the model. <strong>The</strong> positive predictive
value is the proportion <strong>of</strong> <strong>of</strong>fenders predicted to be low risk who are actually low risk,
and the negative predictive value is the proportion <strong>of</strong> <strong>of</strong>fenders predicted non-low risk
who go on to commit a serious <strong>of</strong>fense. Formulas for calculating each <strong>of</strong> these measures
are presented in Appendix J. We also define false positives as the proportion <strong>of</strong> predicted
low-risk <strong>of</strong>fenders committing serious <strong>of</strong>fenses, and false negatives as the proportion <strong>of</strong>
predicted non-low risk <strong>of</strong>fenders not committing serious <strong>of</strong>fenses.
151