The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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although we should be cautious about reading too much into the stratum-specific risk ratios due to the small number <strong>of</strong> events, it is clear that low-intensity supervision was more effective than treatment as usual for <strong>of</strong>fenders without a prior history <strong>of</strong> serious <strong>of</strong>fending, than it was for <strong>of</strong>fenders who had committed a serious <strong>of</strong>fense. Discussion Our first three research questions examined the sensitivity <strong>of</strong> the prediction model used by Philadelphia APPD in classifying <strong>of</strong>fenders by risk across several different definitions <strong>of</strong> severity. <strong>The</strong> model appears to successfully categorize probationers into low and non-low risk. Overall, the probability that an <strong>of</strong>fender in our sample had been charged with a serious <strong>of</strong>fense (according to the model definition: murder, attempted murder, aggravated assault, robbery and sexual <strong>of</strong>fenses) was substantially lower if they received a low risk prediction than if they did not. <strong>The</strong> average ‘reliability score’ assigned by the model to each <strong>of</strong>fender across all <strong>of</strong> his or her probation cases also appeared to be linearly related to the <strong>of</strong>fender’s likelihood <strong>of</strong> serious <strong>of</strong>fending: in general, the higher the score (higher scores represent the lowest risk levels), the less likely an <strong>of</strong>fender was to have been charged with a serious <strong>of</strong>fense. <strong>The</strong> threshold used to distinguish predicted low risk <strong>of</strong>fenders from predicted non-low risk <strong>of</strong>fenders in the model, an average reliability score <strong>of</strong> 0.5, largely appears to be an appropriate cut-<strong>of</strong>f point in the present sample. However, the model performs slightly better in terms <strong>of</strong> avoiding the most serious errors – <strong>of</strong>fenders who were predicted to be low risk but committed serious <strong>of</strong>fenses – if the threshold is raised to 163
0.55. Raising the threshold results in a slight loss in the ability to predict who will not commit a serious <strong>of</strong>fense, but the trade-<strong>of</strong>f is small and favors increased public safety. This alternative threshold is close enough to the original 0.5 cut-<strong>of</strong>f that the results <strong>of</strong> the present study are unlikely to be greatly affected, and as a matter <strong>of</strong> policy any change would depend on the extent to which the probation agency was willing to trade considerable resource savings for a small potential decrease in false positives. Twelve per cent <strong>of</strong> the full sample (323 <strong>of</strong>fenders) had risk scores between 0.5 and 0.55, and would not have been eligible for low-intensity supervision at the higher classification threshold. One limitation <strong>of</strong> these diagnostic tests is the very low prevalence <strong>of</strong> serious recidivism post-risk assessment in the sample as a whole, which led to a large number <strong>of</strong> false negatives. This in turn reduces the ability <strong>of</strong> the model to predict true positives (true low-risk cases), which is the ultimate goal <strong>of</strong> the risk assessment process. This is an issue that is unlikely to be easily overcome, because serious <strong>of</strong>fenses like homicide and rape will always be relatively rare events. However, the prediction model can be applied to any new probationer entering the Philadelphia APPD, so the potential exists for more data to be collected on low and non-low risk clients, their serious <strong>of</strong>fending, and the performance <strong>of</strong> low risk <strong>of</strong>fenders under low-intensity supervision. <strong>The</strong>se data could be added to an analysis like those presented here, in order to conduct continuous validation and refinement <strong>of</strong> the model. Our analysis shows that the model is also somewhat successful in ensuring that <strong>of</strong>fenders who might not have committed one <strong>of</strong> the most serious <strong>of</strong>fenses, but could be considered serious <strong>of</strong>fenders by other standards, are not channeled into receiving less 164
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University of Penn
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In memory of Edwar
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Janel McCaffrey and Knakiya Hagans,
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traditional practice, we find no ev
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Methodology .......................
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LIST OF FIGURES Figure 1.1: Effect
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intensity makes no difference to re
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isk of committing
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impact of probatio
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which perhaps suggests that ISP is
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program intended to reduce the stra
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supervision with probation
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3. To what extent do the risk/need
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Methods Scale (SMS: Farrington et a
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Types of Outcomes
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Description of met
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actual behavior. We analyze technic
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Treatment of quali
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treatment group, and those to the r
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Discussion of Resu
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were evaluated in the same report (
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direction of effec
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To what extent does program philoso
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Which other components of</
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.450; technical violations: Q B < .
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it should be noted that those progr
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up in unpublished reports than thos
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violations first went before a judg
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2 One U.S. estimate indicates that
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Tables Table 1.1: Characteristics <
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Table 1.3: Moderator Variable <stro
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Table 1.4: Moderator Variable <stro
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Figures Figure 1.1: Effect
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Figure 1.5: Effect of</stro
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CHAPTER 2. ‘Low-Intensity
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What Works in Probation Supervision
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to do with it. Furthermore, the mor
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to the New York City and Oregon stu
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Barnes et al. (forthcoming) have su
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paradox.” He suggests that ‘not
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fatal shootings in Philadelphia are
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the proportions of
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Interventions and Follow-Up Probati
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officers more <str
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1. Does the effect of</stro
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all, resulting in a large number <s
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Differential treatment take-up and
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control group members who received
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effect of LIS on r
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incarcerated must also be removed f
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offenders in our s
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ecidivists in the sample, 77 commit
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(IRR = 1.41, p ≤ .107). SES did n
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probability of fai
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The count model ou
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estimate is also practically identi
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twice the risk of
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an end. On the other hand, research
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low-intensity supervision would imp
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Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
Page 127 and 128: Table 2.3: Frequency of</st
Page 129 and 130: Table 2.5: Instrumental Variables M
Page 131 and 132: Table 2.7: Prevalence of</s
Page 133 and 134: Table 2.9: Time to Failure (Violent
Page 135 and 136: Table 2.12: Treatment Effect by Sub
Page 137 and 138: Table 2.14: Frequency of</s
Page 139 and 140: Table 2.16: Instrumental Variables
Page 141 and 142: Figures Figure 2.1: Risk-Based Allo
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: sensitivity of the
Page 149 and 150: Programs should be designed to adhe
Page 151 and 152: weight information in an appropriat
Page 153 and 154: statistical model that would predic
Page 155 and 156: The Philadelphia m
Page 157 and 158: assignment pool. Backfill probation
Page 159 and 160: Little research has been done on th
Page 161 and 162: already predicted to be at low risk
Page 163 and 164: It assumes that if of</stro
Page 165 and 166: involved. Crimes that obviously met
Page 167 and 168: We examine the potential interactio
Page 169 and 170: age is based on this date regardles
Page 171 and 172: obberies. The only
Page 173 and 174: of low-intensity s
Page 175 and 176: classified as low risk. The
Page 177: All the risk ratios in Table 3.9 in
Page 181 and 182: are partly selected on the basis <s
Page 183 and 184: make up the majority of</st
Page 185 and 186: strategy allows probation o
Page 187 and 188: Notes 1 In this study, murder, atte
Page 189 and 190: Table 3.2: Sample Characteristics (
Page 191 and 192: Table 3.6: Predictive Ability at Al
Page 193 and 194: Table 3.9: Post-Random Assignment O
Page 195 and 196: APPENDICES Appendix A: Systematic R
Page 197 and 198: combination. The s
Page 199 and 200: B. ELIGIBILITY CHECKLIST B1. First
Page 201 and 202: Other: txcxloth C6d. Additional pro
Page 203 and 204: D. SAMPLE LEVEL CODING SHEET Instru
Page 205 and 206: E. DEPENDENT VARIABLE CODING SHEET
Page 207 and 208: F14. Comparison group sample size f
Page 209 and 210: weight w is then recalculated (6),
Page 211 and 212: Study Sheffield (Males) 1976 (Folka
Page 213 and 214: Study Dallas TX 1992 (Turner & Pete
Page 215 and 216: Study Minnesota ISR 1995 (Deschenes
Page 217 and 218: Study Clark WA 2003 (Barnoski, 2003
Page 219 and 220: Study Whatcom WA 2003 (Barnoski, 20
Page 221 and 222: Study Los Angeles CA 2005 (Zhang &
Page 223 and 224: Fed. Prob., 56, 12-17. Turner, Pete
Page 225 and 226: Study Reference Benekos & Sonnenber
Page 227 and 228: Study Reference Giblin (2002) Using
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Study Reference Lasater et al. (n.d
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Study Reference Robertson (2000) Co
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Study Reference Turner & Greene (19
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Appendix E: Philadelphia APPD Low-<
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Prevalence of Reci
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Frequency of Recid
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Instrumental Variables Model: Preva
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Instrumental Variables Model: Preva
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Post-RA Months in Jail and Probabil
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Scaled Schoenfeld Residuals Time =
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BIBLIOGRAPHY Chapter 1 ** Denotes t
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Cook, T. D., & Campbell, D. T. (197
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Glaze, L. E, & Bonczar, T. P. (2009
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MacKenzie, D. L. (2006a). What work
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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safety first: 13 strategies for suc
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
Page 265 and 266:
Clear, T. R., & Hardyman, P. L. (19
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period266.pdf Lowenkamp, C. T., Lat
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Rosch, J. (2006). Deviant peer cont
Page 271 and 272:
Taxman, F. S., & Thanner, M. (2006)
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statistical learning. Journal <stro
Page 275 and 276:
Federal Bureau of
Page 277 and 278:
Marsh, K., & Fox, C., (2008). <stro
Page 279:
Sherman, L. W. (1993). Defiance, de
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