The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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independence <strong>of</strong> data (i.e., the model parameters were not derived from the same sample for which predictions would then be made). This helps to determine whether the relationships found in the initial sample are generalizable to other members <strong>of</strong> the same population. Once the model has been specified, it may then be used to derive risk predictions for probationers in current caseloads. <strong>The</strong> model assigns each probation case (not each <strong>of</strong>fender) a ‘reliability score’ to indicate its risk level. <strong>The</strong> reliability score is a value between 0 and 1. <strong>The</strong> selected threshold for low-risk cases was 0.5, so that cases with a reliability score greater than 0.5 were designated as low risk and scores equal to or less than 0.5 were not low risk. A specific <strong>of</strong>fender’s risk score is based on the average reliability score across all his or her active probation cases. However, even if the average reliability score exceeded 0.5, an <strong>of</strong>fender could not be designated low risk if any one <strong>of</strong> his or her active cases scored 0.5 or below. <strong>The</strong> Philadelphia model meets many <strong>of</strong> the recommendations set out by Bonta (2002) and Gottfredson and Moriarty (2006) for optimal risk prediction. Bonta suggests that risk assessments require predictive validity, direct relevance to criminal behavior and the correctional setting, and should adhere to the principle <strong>of</strong> the least restrictive alternative. <strong>The</strong> Philadelphia model is validated by its development on a training sample <strong>of</strong> cases for which outcomes were already known and its application to other members <strong>of</strong> the same population. <strong>The</strong> present paper attempts to further validate its ability to predict who will be low risk. <strong>The</strong> model’s focus on serious <strong>of</strong>fending is directly relevant to correctional priorities. Finally, the express purpose <strong>of</strong> the model is to ensure that the most intensive supervision and treatment is only reserved for those who need it most. 139
<strong>The</strong> Philadelphia model differs from other risk assessment and prediction instruments in several ways. Its focus on only the most serious <strong>of</strong>fenses is its primary distinguishing feature. Berk et al. (2009) argue that it is not helpful for the purposes <strong>of</strong> effective resource allocation to predict any type <strong>of</strong> re<strong>of</strong>fending, as many existing prediction instruments do. <strong>The</strong>re are both operational and political advantages to focusing only on the most extreme cases. Furthermore, the model uses charges rather than convictions as the outcome measure. While all recidivism outcome measures have well-documented advantages and disadvantages, charges are appropriate in this context because serious crimes are more likely to be pursued, but they do not all result in a conviction, <strong>of</strong>ten because <strong>of</strong> issues such as witness intimidation, which is a significant problem in Philadelphia (Berk et al., 2009, p. 194). Although the Philadelphia model is clearly statistical, it also respects the clinical decision making processes that are used by probation intake <strong>of</strong>ficers. <strong>The</strong> model uses only information routinely available to intake <strong>of</strong>ficers (demographic characteristics and criminal history) and already used by probation <strong>of</strong>ficers when making clinical judgments. While this may not fully assuage the ethical concerns about statistical prediction, it cannot be said that the model imposes constraints on decision making beyond standard practice. It simply makes these processes more transparent and replicable. <strong>The</strong> Low-<strong>Intensity</strong> Supervision Experiment <strong>The</strong> Philadelphia APPD Low Risk Experiment ran from October 2007 to October 2008. It tested the hypothesis that low-intensity supervision (LIS) would not cause a harmful increase in recidivism for low-risk <strong>of</strong>fenders compared to APPD’s existing 140
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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
Page 103 and 104: ecidivists in the sample, 77 commit
Page 105 and 106: (IRR = 1.41, p ≤ .107). SES did n
Page 107 and 108: probability of fai
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
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Page 115 and 116: an end. On the other hand, research
Page 117 and 118: low-intensity supervision would imp
Page 119 and 120: Conclusion The aim
Page 121 and 122: 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: statistical model that would predic
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 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
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E. DEPENDENT VARIABLE CODING SHEET
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F14. Comparison group sample size f
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weight w is then recalculated (6),
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Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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Study Minnesota ISR 1995 (Deschenes
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Study Clark WA 2003 (Barnoski, 2003
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Study Whatcom WA 2003 (Barnoski, 20
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Study Los Angeles CA 2005 (Zhang &
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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
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Angrist, J. D. (2006). Instrumental
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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
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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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