The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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agency’s jurisdiction. Table 1.1 shows some limited participant characteristics that could be generalized across studies. We found a mix <strong>of</strong> studies evaluating programs for either juvenile probationers, or youth (18 and over) and adults. Study samples comprised mostly male <strong>of</strong>fenders. Only two studies examined ISP versus SAU in exclusively female caseloads. 15 Most samples comprised high or mostly high risk <strong>of</strong>fenders (as assessed by classification instruments or <strong>of</strong>fending history), reflecting the fact that ISP has usually been used as a means <strong>of</strong> community supervision for more serious <strong>of</strong>fenders who might otherwise have gone to prison. Needs assessments were not frequently discussed so we do not present these results, but where needs assessments were carried out most ISP <strong>of</strong>fenders were classified as high need for services, <strong>of</strong>ten based on drug and alcohol dependencies. Overall mean effects <strong>of</strong> probation supervision intensity on recidivism Table 1.2 shows the results <strong>of</strong> the main analysis examining how probation supervision intensity is related to subsequent <strong>of</strong>fending and technical violations. Each row <strong>of</strong> the table, along with the estimated effect sizes for each included study, is visually represented in separate forest plots (Figures 1.1 to 1.7). Across the 47 studies, we obtained a total <strong>of</strong> 213 different outcome measures. <strong>The</strong> present study makes use <strong>of</strong> those that measure the prevalence <strong>of</strong> arrests, drug arrests, convictions, and technical violations. 16 <strong>The</strong> results reported in Table 1.2 are consistent with what we already know about intensive supervision. None <strong>of</strong> the mean effect sizes is statistically significant, and the 27
direction <strong>of</strong> effects is as we would expect given prior research. In the RCTs, assignment to intensive supervision made no difference to the prevalence <strong>of</strong> rearrest or reconviction (mean OR for arrests = .93; p ≤ .72; mean OR for convictions = .98, p ≤ .80). We also see no significant effect <strong>of</strong> ISP in the quasi-experiments, although note that the raw effect sizes (especially for convictions) show moderate reductions in recidivism associated with ISP and the small number <strong>of</strong> studies in these categories reduce the likelihood <strong>of</strong> a finding being statistically significant (mean OR for arrests = .83, p ≤ .10; mean OR for convictions = .60, p ≤ .10). <strong>The</strong> forest plot for conviction outcomes (Figure 1.4) suggests that there is a lot <strong>of</strong> uncertainty in this model: the confidence interval around the mean effect size is clearly very large. Our analyses also indicate an increase in technical violations associated with ISP. Across the RCTs, intensive supervision was associated with a 54 per cent increase in the odds <strong>of</strong> a technical violation (mean OR = 1.54, p ≤ .06). A smaller, non-significant increase <strong>of</strong> 29 per cent was observed across the quasi-experiments (mean OR = 1.29, p ≤ .22) but is again based on a much smaller subset <strong>of</strong> studies. Finally, we found no effect <strong>of</strong> ISP for the subset <strong>of</strong> studies reporting drug related effects (mean OR = 1.14, p ≤ .10). <strong>The</strong> mean effect sizes should be interpreted with caution given the very small number <strong>of</strong> events in some <strong>of</strong> the studies (see Figure 1.7). Table 1.2 indicates substantial heterogeneity across studies in four <strong>of</strong> our seven analyses, as evidenced by the highly significant Q statistics (RCT arrests: Q = 61.55, p < .001; RCT technical violations: Q = 120.11, p < .001; quasi-experiment technical violations: Q = 18.91, p ≤ .004; quasi-experiment convictions: Q = 22.74, p < .001). This indicates that there is more variability between studies than we would expect from the 28
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: were evaluated in the same report (
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 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
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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
Page 117 and 118:
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
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
Page 251 and 252:
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
Page 259 and 260:
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
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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