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e put into assessing and addressing those clients’ needs. In order to do so, the lowest risk <strong>of</strong>fenders in the agency needed to be assigned to large caseloads with minimal supervision, so that <strong>of</strong>ficers would be freed up to work more closely with high-risk clients. This represented a departure from the initial ‘one size fits all’ approach to supervision previously used by the agency (see Fig. 2.1). A risk prediction model was developed to assess which <strong>of</strong>fenders were at low and high risk <strong>of</strong> <strong>of</strong>fending. <strong>The</strong> Low Risk Experiment then randomly assigned <strong>of</strong>fenders predicted to be low risk to a lowintensity model <strong>of</strong> supervision (‘LIS’) or the normal model <strong>of</strong> supervision as described above (supervision as usual: ‘SAU’). <strong>The</strong> results <strong>of</strong> the experiment indicated that LIS can safely be used with low-risk <strong>of</strong>fenders without increasing the risk <strong>of</strong> serious recidivism. APPD next plans to test the allocation <strong>of</strong> high-risk <strong>of</strong>fenders to high-intensity or regular supervision. Forecasting model <strong>The</strong> statistical model used to forecast the risk <strong>of</strong> serious <strong>of</strong>fending is described in full in Berk et al. (2009). Random forests methods were applied to a dataset <strong>of</strong> all probation and parole cases in Philadelphia between 2002 and 2004 to predict the risk <strong>of</strong> being charged with a new serious crime 2 within two years <strong>of</strong> the probation or parole case start date. <strong>The</strong> prediction was based only on the type <strong>of</strong> data that would be available to probation <strong>of</strong>ficers at intake. 3 At the request <strong>of</strong> APPD, the model was designed to stratify 61 per cent <strong>of</strong> cases as low risk, with the remainder either high risk (approximately 10 per cent) or neither low nor high (approximately 30 per cent) (Fig. 2.1). APPD also deemed 67
the proportions <strong>of</strong> false positives and false negatives expected in the final model to be operationally acceptable. <strong>The</strong> proportion <strong>of</strong> false positives (<strong>of</strong>fenders erroneously identified as low risk) was set at 5 per cent, and the proportion <strong>of</strong> false negatives (<strong>of</strong>fenders erroneously identified as high risk) was 20 per cent. A higher false negative rate was accepted given the lesser public safety concerns around this type <strong>of</strong> error. Experimental design Selection <strong>of</strong> Cases APPD selected the West and Northeast regional supervision units as the sites from which experimental participants would be drawn. All cases active on probation in these two units on July 27, 2007 were extracted. <strong>The</strong> random forests model was applied to each case to produce an individual risk assessment (some probationers had multiple cases) in the form <strong>of</strong> a ‘reliability score.’ <strong>The</strong> reliability score is a number between 0 and 1. Cases with a reliability score above 0.5 were designated as low risk. From this assessment, 2,859 <strong>of</strong>fenders were serving probation terms for low-risk <strong>of</strong>fenses. <strong>The</strong>y were pre-screened for eligibility for the experiment. Low-risk cases were excluded from the random assignment pool if any one <strong>of</strong> the following factors made them ineligible for low-intensity supervision: <strong>The</strong> case was due to expire within thirty days <strong>of</strong> the extraction date. <strong>The</strong> <strong>of</strong>fender was placed under the supervision <strong>of</strong> a specialized unit by court order after the extraction date. <strong>The</strong> <strong>of</strong>fender was in an existing low-risk caseload. 4 68
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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
Page 29 and 30:
Types of Outcomes
Page 31 and 32: Description of met
Page 33 and 34: actual behavior. We analyze technic
Page 35 and 36: Treatment of quali
Page 37 and 38: treatment group, and those to the r
Page 39 and 40: Discussion of Resu
Page 41 and 42: were evaluated in the same report (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47 and 48: Which other components of</
Page 49 and 50: .450; technical violations: Q B < .
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
Page 57 and 58: 2 One U.S. estimate indicates that
Page 59 and 60: Tables Table 1.1: Characteristics <
Page 61 and 62: Table 1.3: Moderator Variable <stro
Page 63 and 64: Table 1.4: Moderator Variable <stro
Page 65 and 66: Figures Figure 1.1: Effect
Page 67 and 68: Figure 1.5: Effect of</stro
Page 69 and 70: CHAPTER 2. ‘Low-Intensity
Page 71 and 72: What Works in Probation Supervision
Page 73 and 74: to do with it. Furthermore, the mor
Page 75 and 76: to the New York City and Oregon stu
Page 77 and 78: Barnes et al. (forthcoming) have su
Page 79 and 80: paradox.” He suggests that ‘not
Page 81: fatal shootings in Philadelphia are
Page 85 and 86: Interventions and Follow-Up Probati
Page 87 and 88: officers more <str
Page 89 and 90: 1. Does the effect of</stro
Page 91 and 92: all, resulting in a large number <s
Page 93 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97 and 98: effect of LIS on r
Page 99 and 100: incarcerated must also be removed f
Page 101 and 102: 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
Page 113 and 114: twice the risk of
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
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Table 2.9: Time to Failure (Violent
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Table 2.12: Treatment Effect by Sub
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Table 2.14: Frequency of</s
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Table 2.16: Instrumental Variables
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Figures Figure 2.1: Risk-Based Allo
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Figure 2.5: Survival Time for LIS E
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CHAPTER 3. Risk Prediction for Effe
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sensitivity of the
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Programs should be designed to adhe
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weight information in an appropriat
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statistical model that would predic
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The Philadelphia m
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assignment pool. Backfill probation
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Little research has been done on th
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already predicted to be at low risk
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It assumes that if of</stro
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involved. Crimes that obviously met
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We examine the potential interactio
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age is based on this date regardles
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obberies. The only
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of low-intensity s
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classified as low risk. The
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All the risk ratios in Table 3.9 in
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0.55. Raising the threshold results
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are partly selected on the basis <s
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make up the majority of</st
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strategy allows probation o
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Notes 1 In this study, murder, atte
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Table 3.2: Sample Characteristics (
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Table 3.6: Predictive Ability at Al
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Table 3.9: Post-Random Assignment O
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APPENDICES Appendix A: Systematic R
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combination. The s
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B. ELIGIBILITY CHECKLIST B1. First
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Other: txcxloth C6d. Additional pro
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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
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
Page 265 and 266:
Clear, T. R., & Hardyman, P. L. (19
Page 267 and 268:
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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