The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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<strong>of</strong>fending. This can be achieved through a low-intensity supervision model in which the need for low-risk <strong>of</strong>fenders to attend the probation department is minimal. Of course, a caveat to using this reasoning to justify low-intensity supervision is that research on DPC and related criminological theories has largely focused on juveniles and it is not clear whether the same mechanisms operate for adults. More importantly, we can limit exposure to other probationers at the probation <strong>of</strong>fice but it is not possible to control <strong>of</strong>fenders’ access to delinquent networks in their home neighborhoods. However, informal social controls may also operate there that do not exist in the probation waiting room – for example, family or a job opportunity – that allow <strong>of</strong>fenders to engage in prosocial activity without the encumbrance <strong>of</strong> regular probation visits. Lowenkamp and Latessa (2004) note that such pro-social networks play an important role in explaining why some <strong>of</strong>fenders remain at low risk for future criminal behavior. Subjecting low-risk <strong>of</strong>fenders to increased supervision may disrupt their positive social networks. A further theoretical mechanism by which low-intensity probation may successfully operate is through the principles <strong>of</strong> effective intervention (PEI). <strong>The</strong> PEI were introduced by Andrews, Bonta, and Hoge (1990) in response to the ‘nothing works’ attitude to correctional treatment that persisted in the 1970s and 1980s. <strong>The</strong> PEI state that correctional treatment can in fact be effective when programs are designed to be responsive to <strong>of</strong>fenders’ specific risk and need levels (the risk-need-responsivity, or RNR, model: see also Taxman & Thanner, 2006). Thus, high intensity interventions are best reserved for high-risk, high-need <strong>of</strong>fenders. Although the principles seem obvious, Andrews (1989) explains that thinking prior to the elucidation <strong>of</strong> the PEI held that treatment did not work for high-risk <strong>of</strong>fenders. Andrews calls this the “social work 63
paradox.” He suggests that ‘nothing works’ proponents did not consider the fact that high-risk <strong>of</strong>fenders, by definition, will always re<strong>of</strong>fend more than low-risk <strong>of</strong>fenders. <strong>The</strong>y mistakenly took higher recidivism rates to mean that treatments were not effective, but rigorous research comparing intensive treatment to non-intensive programs actually shows that intensive treatment programs can help to reduce re<strong>of</strong>fending for high-risk <strong>of</strong>fenders. Low-risk <strong>of</strong>fenders continue to re<strong>of</strong>fend at a lower rate than high-risk <strong>of</strong>fenders in both intensive and non-intensive treatment, but studies frequently find that their recidivism increases when they are subjected to intensive programs. Drawing on a wide body <strong>of</strong> Canadian research, Andrews (1989) concludes that “lower risk cases may be assigned safely to the least restrictive settings” (p. 15). Since then, numerous metaanalyses <strong>of</strong> correctional treatment have consistently shown that both treatment and supervision work better when the PEI (particularly the risk principle) are adhered to; that is, when a larger proportion <strong>of</strong> high-risk <strong>of</strong>fenders are served. More importantly for the present study, they have shown that low-risk <strong>of</strong>fenders tend to have less favorable outcomes when they receive higher-intensity programming or supervision (see Lowenkamp & Latessa, 2004 for a summary <strong>of</strong> the research). As we have seen, this finding is also borne out in studies <strong>of</strong> intensive probation (Erwin, 1986; Hanley, 2006). While the idea <strong>of</strong> placing high-risk <strong>of</strong>fenders in low-intensity supervision clearly seems inadvisable, the commonly-held notion that ‘more is better’ also means that the thought <strong>of</strong> reducing supervision <strong>of</strong> low-risk <strong>of</strong>fenders is not intuitive to policymakers or researchers. Nevertheless, we have presented several theories – deterrence/defiance, deviant peer contagion, and the principles <strong>of</strong> effective intervention – suggesting that assigning lower-level <strong>of</strong>fenders less supervision may be more appropriate. Low-intensity 64
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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
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 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: Barnes et al. (forthcoming) have su
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
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
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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
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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
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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
Page 271 and 272:
Taxman, F. S., & Thanner, M. (2006)
Page 273 and 274:
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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