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Although it is unlikely that we would be able to learn the full details <strong>of</strong> the <strong>of</strong>fense and subsequent criminal justice decision-making processes, it should be possible to take into account the ultimate disposition <strong>of</strong> the charge in future analyses. Conclusion This paper examined patterns <strong>of</strong> <strong>of</strong>fending severity in a sample <strong>of</strong> probationers in order to assess how risk can best be predicted and managed for the effective operation <strong>of</strong> a probation agency. As the use <strong>of</strong> probation continues to grow, and especially as <strong>of</strong>fenders who pose a significant threat <strong>of</strong> harm to society are placed under community supervision, creative resource allocation is required to manage these <strong>of</strong>fenders effectively. Risk prediction techniques have been used in probation and other criminal justice agencies for one-hundred years, with varying success. <strong>The</strong> Philadelphia Adult Probation and Parole Department and the University <strong>of</strong> Pennsylvania developed a new statistical risk prediction model that differs from other instruments in two main ways: it attempts to operationalize the clinical, informal decision rules already used by probation <strong>of</strong>ficers in the department, and it focuses only on predicting a handful <strong>of</strong> crimes considered to be the most detrimental to public safety and confidence in the criminal justice system. <strong>The</strong> ultimate goal in creating the model was to provide a tool for classifying the entire APPD caseload along risk-based lines, and channeling a majority <strong>of</strong> <strong>of</strong>fenders who posed little risk <strong>of</strong> serious harm into a large caseload receiving reduced intensity supervision. This 169
strategy allows probation <strong>of</strong>ficers to focus their time and resources on the highest-risk clients. We explored the ability <strong>of</strong> the model to correctly classify <strong>of</strong>fenders as low or nonlow risk, using the sample <strong>of</strong> <strong>of</strong>fenders who participated in the trial <strong>of</strong> low intensity supervision and an additional group <strong>of</strong> APPD clients who also received low or non-low risk predictions according to the model. We also examined the possibility that assignment to low-intensity supervision could interact with prior serious <strong>of</strong>fending to increase serious recidivism compared to regular probation. Our analyses revealed that the model is largely successful, perhaps needing just a slight adjustment the threshold for defining low-risk. In the context <strong>of</strong> the APPD’s goal <strong>of</strong> classifying the majority <strong>of</strong> its caseload as low risk, we also found that the crimes defined in the model as ‘serious’ provided a better indication <strong>of</strong> who the higher-risk <strong>of</strong>fenders were than did UCR Part I <strong>of</strong>fenses or a simple victim/non-victim crime status indicator. <strong>The</strong> majority <strong>of</strong> <strong>of</strong>fenders predicted by the model to be low risk had committed a ‘serious’ <strong>of</strong>fense by those definitions at some point in their careers, although their risk <strong>of</strong> an <strong>of</strong>fense in these categories remained lower than that <strong>of</strong> predicted non-low risk <strong>of</strong>fenders. Thus, we conclude that a statistical model <strong>of</strong> the type developed in Philadelphia would appear to be a useful <strong>of</strong>fender and resource management tool. Proceeding from the assumption that a majority <strong>of</strong> a probation agency’s caseload could be classified as low-risk for the most serious recidivism, we discovered that such <strong>of</strong>fenders respond just as well, if not better, to reduced supervision as they do to traditional probation. Offenders with no prior history <strong>of</strong> serious <strong>of</strong>fending appear to improve their outcomes regardless <strong>of</strong> probation’s input. This is a strong justification for 170
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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
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Table 2.3: Frequency of</st
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Table 2.5: Instrumental Variables M
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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 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: make up the majority of</st
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
Page 229 and 230: Study Reference Lasater et al. (n.d
Page 231 and 232: Study Reference Robertson (2000) Co
Page 233 and 234: 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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