The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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above. <strong>The</strong> random assignment sequence was designed so that the West and Northeast LIS groups each contained 400 <strong>of</strong>fenders. <strong>The</strong> control group contained slightly more cases from the West than the Northeast (52.8 per cent <strong>of</strong> the control group was from the West regional unit). Table 2.1 shows basic demographic and <strong>of</strong>fending history characteristics for the two groups. <strong>The</strong>re are no statistically significant differences between the treatment and control groups on any measure, indicating successful random assignment. <strong>The</strong> sample is predominantly male (67.0%), nonwhite (60.1%), and on average 41 years old at the time <strong>of</strong> random assignment. <strong>The</strong> majority (91.8%) <strong>of</strong> <strong>of</strong>fenders lived in zip code areas with an average household income <strong>of</strong> $20,000 or more. As we might expect from a sample already predicted to be low-risk, prior <strong>of</strong>fending rates are low. Approximately 12 per cent (N = 193) members <strong>of</strong> the sample were incarcerated at any time post-random assignment, for an average <strong>of</strong> 1.1 months. Members <strong>of</strong> both groups committed 1.3 <strong>of</strong>fenses per month at risk on average in the year prior to random assignment, with much lower rates for violent and drug <strong>of</strong>fending. Post-RA, the marginal first year difference between the treatment and control groups reported above disappears by year two. Control group members tended to engage in violent recidivism more than the treatment group in the first year post-RA (4.1% vs. 2.9%), but by the second year the gap has begun to close (5.4% vs. 4.5%). Treatment and control group participants committed new drug <strong>of</strong>fenses in similar proportions in the first year post-RA (treatment: 6.4% vs. control: 6.5%), but slightly more <strong>of</strong> the control group had failed by the second year (control: 10.1% vs. treatment: 8.9%). In all, 21.5 per cent (N = 335) <strong>of</strong> the 1,559 <strong>of</strong>fenders in the sample committed a new <strong>of</strong>fense <strong>of</strong> any kind two years post-RA. Among the 335 87
ecidivists in the sample, 77 committed violent <strong>of</strong>fenses and 148 committed drug <strong>of</strong>fenses. <strong>The</strong> violent and drug <strong>of</strong>fender samples are not independent: some engaged in both types <strong>of</strong> <strong>of</strong>fending during the follow-up period. Results Table 2.2 shows the odds ratios (OR) from the logistic regression model for the effect <strong>of</strong> assigned treatment on <strong>of</strong>fending participation two years post-random assignment. <strong>The</strong> results indicate that when other <strong>of</strong>fender characteristics are controlled, there is no notable difference in the odds <strong>of</strong> recidivism between the LIS and SAU groups (OR = 1.05, p ≤ .707). Several other <strong>of</strong>fender characteristics appear to have a greater impact on the odds <strong>of</strong> a new <strong>of</strong>fense regardless <strong>of</strong> treatment status. Each additional year <strong>of</strong> age at random assignment is associated with a 2 per cent decline in the odds <strong>of</strong> recidivism (OR = .98, p ≤ .002). SES is consistently associated with significantly reduced odds <strong>of</strong> re<strong>of</strong>fending: between 40 and 75 per cent compared to the lowest SES group. Each additional month in jail post-RA is associated with a 29 per cent increase in the odds <strong>of</strong> a new <strong>of</strong>fense (OR = 1.29, p < .001). This could suggest that <strong>of</strong>fenders who spent more time in jail were likely to be at higher risk <strong>of</strong> re<strong>of</strong>fending while on the streets. 16 We tested an additional model that included a squared term for post-RA jail time, to account for nonlinearity, such that increased jail time could eventually lead to a decline in re<strong>of</strong>fending by curtailing time at risk. This term was dropped as it did not reach statistical significance, 17 perhaps because we did not have jail data for the second year. Probationers in the West probation region had 40 per cent lower odds <strong>of</strong> 88
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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
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Methods Scale (SMS: Farrington et a
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Types of Outcomes
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Description of met
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actual behavior. We analyze technic
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Treatment of quali
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treatment group, and those to the r
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Discussion of Resu
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were evaluated in the same report (
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direction of effec
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To what extent does program philoso
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Which other components of</
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.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: offenders in our s
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
Page 129 and 130: Table 2.5: Instrumental Variables M
Page 131 and 132: Table 2.7: Prevalence of</s
Page 133 and 134: 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
Page 139 and 140: Table 2.16: Instrumental Variables
Page 141 and 142: Figures Figure 2.1: Risk-Based Allo
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: sensitivity of the
Page 149 and 150: Programs should be designed to adhe
Page 151 and 152: 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
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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
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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
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Marsh, K., & Fox, C., (2008). <stro
Page 279:
Sherman, L. W. (1993). Defiance, de
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