The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Several reviews have indicated that some ISP programs, particularly those involving a treatment component, show more favorable results (e.g., Aos, Miller, & Drake, 2006; MacKenzie, 2006b). Our own analysis did not support this finding, but the individual point estimates in our forest graphs clearly show that some ISP programs were very successful. This suggests that further exploration <strong>of</strong> programs and designs may be needed to fully understand the potential benefits <strong>of</strong> ISP. Two recent successful ISP studies, the Maryland Proactive Community Supervision program (Taxman, Yancey, & Bilanin, 2006), and Hawaii’s HOPE (Hawken & Kleiman, 2009), appear very different on the surface, but share certain elements that might be the key to understanding the ‘optimal’ approach to intensive supervision. <strong>The</strong> Maryland program focuses on service brokerage and individual case planning by the probation <strong>of</strong>ficers: in the broad philosophical scheme, it is more treatment-based, although it includes surveillance and enforcement components too. In contrast, the Hawaii program is much more enforcement- and deterrence-focused. Probationers are notified daily whether or not they have been selected for random drug testing, and failed drug tests are met with swiftly delivered sanctions: a brief period <strong>of</strong> imprisonment (usually a weekend), the duration <strong>of</strong> which increases in response to further violations. However, multiple violators are also directed to residential drug treatment. Underlying both programs is a behavioral management model, which is articulated in the Maryland program and implicit in Hawaii’s approach. <strong>The</strong> principles <strong>of</strong> behavioral management include incentive/sanction schemes; a focus on criminogenic factors that leads to tailored, rather than mandated, treatment and services; and <strong>of</strong>fender accountability through behavioral contracts. In Hawaii, for example, <strong>of</strong>fenders who were sent to jail for 39
violations first went before a judge who reminded them <strong>of</strong> their responsibilities on probation in a manner that reinforced the desire <strong>of</strong> the whole criminal justice system to see <strong>of</strong>fenders succeed rather than fail. Criminal justice programming that emphasizes a combination <strong>of</strong> treatment and accountability, and incentive/sanction-based models, has shown promise in other settings, such as drug courts (Marlowe, 2003; MacKenzie, 2006b). Given the extent to which ISP programs have been directed toward druginvolved <strong>of</strong>fenders, it may be particularly informative to draw comparisons with drug <strong>of</strong>fending research. Gendreau, Goggin, and Fulton (2001) also lend more support to the general contention that such a balanced approach to <strong>of</strong>fender supervision, emphasizing relationship-building, incentives, and adherence to the PEI, is effective. One striking element <strong>of</strong> both programs just discussed is the lack <strong>of</strong> any emphasis <strong>of</strong> specific alterations to program intensity. Whereas prior experiments have searched for optimal caseload sizes or mandated certain numbers <strong>of</strong> contacts or drug tests, these more recent studies seem to focus on the content <strong>of</strong> supervision and responses to violations. This is in contrast to the surveillance-based programs in which (at their most extreme), probation <strong>of</strong>ficers “go out in the field actively looking for violations” (Pearson, 1988). This begs the larger question: what do probation <strong>of</strong>ficers actually do when required to supervise <strong>of</strong>fenders more closely? <strong>The</strong> present study has not brought us much closer to answering that question than when Clear and Hardyman (1990) considered it twenty years ago. A reduction in caseload size is not automatically accompanied by a guarantee that <strong>of</strong>ficers will actually be able to spend more time with their clients. Fewer cases do not necessarily equate to more intensive treatment <strong>of</strong> probationers. Indeed, for all the 40
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University of Penn
Page 3 and 4: In memory of Edwar
Page 5 and 6: Janel McCaffrey and Knakiya Hagans,
Page 7 and 8: traditional practice, we find no ev
Page 9 and 10: Methodology .......................
Page 11 and 12: LIST OF FIGURES Figure 1.1: Effect
Page 13 and 14: intensity makes no difference to re
Page 15 and 16: isk of committing
Page 17 and 18: impact of probatio
Page 19 and 20: which perhaps suggests that ISP is
Page 21 and 22: program intended to reduce the stra
Page 23 and 24: supervision with probation
Page 25 and 26: 3. To what extent do the risk/need
Page 27 and 28: 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: up in unpublished reports than thos
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 and 82: fatal shootings in Philadelphia are
Page 83 and 84: the proportions of
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
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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
Page 121 and 122:
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
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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
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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 (
Page 191 and 192:
Table 3.6: Predictive Ability at Al
Page 193 and 194:
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 &
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
Page 255 and 256:
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
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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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