The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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sampling error within each study alone. This suggests that other unique characteristics <strong>of</strong> each study, such as program and <strong>of</strong>fender characteristics and study setting (all <strong>of</strong> which could also be confounded with each other), might explain the heterogeneity. Thus, the overall effects <strong>of</strong> ISP on recidivism compared to SAU may be moderated by some <strong>of</strong> these explanatory variables. Moderator analyses We focus only on arrests/convictions and technical violations for the experimental sample in these analyses. We only have a maximum <strong>of</strong> 9 quasi-experiments, so frequently have insufficient observations in each category <strong>of</strong> the moderator variables to conduct a meaningful analysis. We justify combining arrests and convictions for the recidivism analysis because it substantially increases our sample size (seventeen RCTs reported only conviction outcomes) and our main effects analysis showed that there was no difference in the effects <strong>of</strong> ISP on either arrests or convictions. Furthermore, we did not observe significant heterogeneity across conviction outcomes, so we have no reason to believe that the effect <strong>of</strong> ISP on convictions could be moderated by other factors that are not related to arrest outcomes. <strong>The</strong> overall mean effect size and Q-statistic for the combined RCT arrest/conviction studies were very similar to those for RCT arrests. 17 29
To what extent does program philosophy influence the effect <strong>of</strong> changes in supervision intensity on recidivism? Table 1.3 indicates no effect <strong>of</strong> program philosophy on recidivism (arrests and convictions) in the 38 RCTs included in the study, based on the meta-analytic analog to the ANOVA (Q B = 1.73, p ≤ .421). Most <strong>of</strong> the studies were <strong>of</strong> surveillance/treatment hybrid programs (N = 17). In these programs, the odds <strong>of</strong> failure in the treatment group were marginally smaller than in the treatment group, but no real effect is observed (mean OR = .93, p ≤ .525). Similar results were observed in the 15 surveillance-based programs (mean OR = .92, p ≤ .475). Interestingly, <strong>of</strong>fenders in treatment-based programs did have 20 per cent greater odds <strong>of</strong> recidivism than their counterparts in regular probation, but the effect size is not statistically significant, and its reliability is questionable because it is only based on 6 studies, 5 <strong>of</strong> which were conducted by the same evaluators (mean OR = 1.20, p ≤ .314). Unfortunately, we were unable to build a good picture <strong>of</strong> the moderating effect <strong>of</strong> supervision philosophy on ISP and technical violations because none <strong>of</strong> the studies reporting technical violation outcomes followed a treatment-based model (Table 1.4). As we would expect, technical violations were higher in both controlbased (N = 13) and hybrid (N = 3) programs. For both program types the odds <strong>of</strong> a technical violation was about fifty per cent greater in the treatment groups than the control groups (mean OR for control-based: 1.55, p ≤ .091; mean OR for hybrid: 1.46, p ≤ .481). However, the statistical difference between these estimates is negligible (Q B = .01, p ≤ .923) . 30
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: direction of effec
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 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
Page 203 and 204:
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),
Page 211 and 212:
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
Page 239 and 240:
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
Page 251 and 252:
Cook, T. D., & Campbell, D. T. (197
Page 253 and 254:
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
Page 259 and 260:
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
Page 269 and 270:
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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