The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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appears to be a good classification threshold. Here, the model’s sensitivity and specificity are most balanced compared to other cut-<strong>of</strong>f points (Sn = 63.0%; Sp = 65.8%). This means that the probability that a non-serious <strong>of</strong>fender received a low risk prediction and the probability that a serious <strong>of</strong>fender received a non-low risk prediction are roughly the same. Of the <strong>of</strong>fenders receiving a low-risk prediction, 96.6 per cent were in fact low-risk. <strong>The</strong> ‘worst case scenario’ false positive rate (low-risk <strong>of</strong>fenders who committed serious <strong>of</strong>fenses) is very low, at 3.4 per cent. Table 3.5 suggests that the cut-<strong>of</strong>f point should not be set below 0.5. Although the positive predictive value remains high at thresholds <strong>of</strong> 0.45 and below, there is a considerable loss <strong>of</strong> specificity at the expense <strong>of</strong> the less important sensitivity (Sn = 71.3%; Sp = 55.1% at 0.45 threshold). <strong>The</strong> probability <strong>of</strong> finding a false positive also begins to increase. On the other hand, there may be a case for increasing the cut-<strong>of</strong>f point to 0.55, but no more. At 0.55, the positive predictive value increases to 97.3 per cent, specificity increases to 78.5 per cent, and the likelihood <strong>of</strong> a false positive drops to 2.7 per cent. However, the sensitivity drops to just over 50 per cent, which starts to raise questions about the model’s ability to meet its purpose. Thus, a threshold between 0.5 and 0.55 appears to provide the best trade-<strong>of</strong>f between all the factors discussed above. Tables 3.6 and 3.7 show the same analyses repeated for UCR Part I and victim/damage <strong>of</strong>fenses. Note that the model is not designed to predict these <strong>of</strong>fense types (as the slightly lower positive predictive values in these two tables suggest), so the results from Table 3.5 should be taken as the definitive examination <strong>of</strong> the threshold. However, we use these additional outcomes to examine whether the cut-<strong>of</strong>f point allows too many <strong>of</strong>fenders who might be considered ‘serious’ by alternative standards to be 159
classified as low risk. <strong>The</strong> 0.5 threshold again performed reasonably well. This cut-point gave the best balance <strong>of</strong> sensitivity and specificity for both measures, but specificity was lower than sensitivity (UCR: Sn = 63.0%, Sp = 52.1%; victim/damage: Sn = 63.7%, Sp = 57.4%). Again, increasing the cut point to 0.55 improved specificity for both measures, at the expense <strong>of</strong> a reasonable degree <strong>of</strong> sensitivity (UCR: Sn = 50.6%, Sp = 63.9%; victim/damage: Sn = 51.5%, Sp = 70.3%). At the 0.55 threshold the positive predictive values increase slightly and the proportion <strong>of</strong> false positives is reduced. We see higher rates <strong>of</strong> false positives in these analyses compared to Table 3.5 because there is a higher prevalence <strong>of</strong> <strong>of</strong>fending in these categories. Does low-intensity probation supervision alter the propensity for serious <strong>of</strong>fending? This analysis focuses only on the experimental sample: 1,559 predicted low risk <strong>of</strong>fenders who participated in the Low Risk Experiment and were randomly assigned to low-intensity supervision (LIS) or supervision as usual (SAU). Regardless <strong>of</strong> their lowrisk status, we would expect that those <strong>of</strong>fenders who had been involved in serious <strong>of</strong>fending prior to random assignment (RA) would be more likely to continue to do so. 10 However, because the ultimate practical purpose <strong>of</strong> the predictive model is to identify low-risk <strong>of</strong>fenders so that they can be diverted to LIS, it is very important to ensure that low-risk supervision itself does not increase the likelihood that <strong>of</strong>fenders will engage in serious recidivism during or after supervision, over and above the extent to which we would expect given their past behavior. Table 3.8 shows the proportion <strong>of</strong> the sample that were charged with a serious <strong>of</strong>fense post-RA. Because <strong>of</strong> the small number <strong>of</strong> 160
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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</
Page 123 and 124: 7 LIS participants who were transfe
Page 125 and 126: 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: of low-intensity s
Page 177 and 178: All the risk ratios in Table 3.9 in
Page 179 and 180: 0.55. Raising the threshold results
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
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Study Reference Benekos & Sonnenber
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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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