The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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To what extent do the risk/need levels <strong>of</strong> program participants affect their response to changes in supervision intensity? Based on the principles <strong>of</strong> effective intervention (PEI), we speculated that supervision may be more effective if more intensive programs are targeted at the highest risk/need <strong>of</strong>fenders, and vice versa. <strong>The</strong>re are several impediments to assessing this question in great detail. First, we could not examine whether low-intensity probation is effective for low-risk probationers across a range <strong>of</strong> studies. Second, very few studies included needs assessments, and those that did lacked detail. Thus, we decided not to use the need variable in our analysis. Finally, many <strong>of</strong> the studies discussed the PEI or evaluated programs that had made an attempt to target higher-risk <strong>of</strong>fenders, so we do not have much variation in our data. We are only able to examine whether programs including either all or a majority <strong>of</strong> high-risk <strong>of</strong>fenders were more or less likely to prevent re<strong>of</strong>fending than those including <strong>of</strong>fenders <strong>of</strong> any risk level or not utilizing a risk assessment. A further caveat related to this final point is that programs that did not formally assess participants for risk may still have had inclusion criteria that targeted more serious <strong>of</strong>fenders. <strong>The</strong> results in Table 1.3 may lend some support to this caveat. <strong>The</strong>re is no difference between programs that targeted higher-risk <strong>of</strong>fenders and those that accepted any <strong>of</strong>fender type (Q B < .01, p ≤ .981), and neither risk category is associated with recidivism outcomes. <strong>The</strong> odds ratio for studies involving higher risk <strong>of</strong>fenders was .96 (N = 11, p ≤ .798), and for all risk levels .97 (N = 27, p ≤ .735). For technical violations, there is also very little variation in the odds <strong>of</strong> failure by risk level (Q B = .04, p ≤ .836; high risk: N = 10, OR = 1.59, p ≤ .092; mixed risk: N = 6, OR = 1.45, p ≤ .292). 31
Which other components <strong>of</strong> programs or <strong>of</strong>fender characteristics moderate the overall effect <strong>of</strong> supervision intensity on crime? We examined a range <strong>of</strong> other potential moderators <strong>of</strong> the effect <strong>of</strong> supervision intensity on recidivism and technical violations, the results <strong>of</strong> which are listed in Tables 1.3 and 1.4 respectively. As before, our analyses are somewhat limited by the reduced cell frequencies when studies are broken out by each level <strong>of</strong> the moderator variables. We group our outcomes into three main categories: study characteristics, program characteristics, and sample characteristics. <strong>The</strong> varied program characteristics are particularly important because <strong>of</strong> the range <strong>of</strong> different activities that constitutes ISP in each study. Among selected study characteristics (Table 1.3), we found that only the type <strong>of</strong> publication was significantly associated with ISP recidivism outcomes (Q B = 11.86, p ≤ .003). Of course, the publication type does not directly influence the outcome <strong>of</strong> a study, but these results are important because they show that our other results are not affected by publication bias (for example, non-publication <strong>of</strong> unfavorable or null-effect results). In government reports, ISP did not have any effect on recidivism on average (N = 13, OR = 1.01, p ≤ .949), but among the other unpublished papers we found, the odds <strong>of</strong> recidivism were significantly reduced by ISP programs (N = 9, OR = .70, p ≤ .002), while in academic articles there was a marginally significant increase (N = 16, OR = 1.20, p ≤ .085). If we had only examined the published literature we might have deduced that ISP does not work, and while these results are not exactly promising they do not lend themselves to such a drastic conclusion. We could not reliably assess different study settings because almost all <strong>of</strong> the studies were conducted in the U.S., and the five that 32
Page 1 and 2: 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: To what extent does program philoso
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 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
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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
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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
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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
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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