The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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24.1%; p ≤ .001). Offenders in the lowest SES category were most likely to re<strong>of</strong>fend, and those in the highest category least likely; there was little difference between the central groups (probability range: 10.8% - 25.1%; overall p < .001). <strong>The</strong> <strong>of</strong>fender’s rate <strong>of</strong> <strong>of</strong>fending in the year before random assignment also predicts treatment failure. Interestingly, those who had not <strong>of</strong>fended in the preceding year were more likely to fail than those with up to or more than one <strong>of</strong>fense per month at risk (20.9% vs. 16.8% and 17.3% respectively; p ≤ .026). We explore this finding in the discussion section. We now examine the <strong>of</strong>fense-specific models for violent and drug recidivism. Table 2.7 shows the logistic regression model for the prevalence <strong>of</strong> new charged violent <strong>of</strong>fenses in the two-year follow-up period. Assignment to the treatment group was associated with a non-significant reduction in the odds <strong>of</strong> violent re<strong>of</strong>fending compared with the treatment group (OR = .89, p ≤ .644). As was the case in the full re<strong>of</strong>fending model, West probation region, increased age, and increased SES were associated with significant reductions in the odds <strong>of</strong> recidivism, while post-RA jail time was associated with increased re<strong>of</strong>fending. 21 In addition, we see substantial effects <strong>of</strong> gender and prior <strong>of</strong>fending history on the prevalence <strong>of</strong> violent recidivism, which was not apparent in the full <strong>of</strong>fending model. <strong>The</strong> odds <strong>of</strong> a new violent <strong>of</strong>fense were 3.5 times higher for males than females (OR = 3.53, p < .001), and <strong>of</strong>fenders who had committed at least one violent <strong>of</strong>fense during the months they were at risk one year pre-RA had 2.5 times the odds <strong>of</strong> a violent <strong>of</strong>fense as those who had not. Recall that the sample probability <strong>of</strong> committing a violent <strong>of</strong>fense was very small (less than 5%; N = 77), which may affect the size <strong>of</strong> the test statistics for these estimates. 93
<strong>The</strong> count model outcomes for violent re<strong>of</strong>fending are presented in Table 2.8. Again, a zero-inflated negative binomial model proved to be the best fit for our data. 22 Assignment to LIS supervision, controlling for other factors, is associated with a small, non-significant decline in <strong>of</strong>fending over the two-year follow up period than assignment to the control group (IRR = .83, p ≤ .453). <strong>The</strong> only significant predictor in the full model is the pre-RA violent <strong>of</strong>fending rate, which is associated with a large decline in the rate <strong>of</strong> violent re<strong>of</strong>fending (IRR = .34, p ≤ .047). This is surprising because violent <strong>of</strong>fending history was strongly associated with an increase in the prevalence <strong>of</strong> violent recidivism. Similarly, gender, which was also associated with increased prevalence <strong>of</strong> re<strong>of</strong>fending, is associated with a decline in <strong>of</strong>fending frequency. Income and probation region, which were associated with reductions in overall <strong>of</strong>fending, are associated with increased frequency <strong>of</strong> violent <strong>of</strong>fending However, it is likely that these results are skewed by the very small number <strong>of</strong> <strong>of</strong>fenders charged with violent <strong>of</strong>fenses post-RA, and the wide variation in the number <strong>of</strong> charged <strong>of</strong>fenses. Most <strong>of</strong> the 77 violent <strong>of</strong>fenders were charged with between one and three crimes, but the count ranges up to 52. Figure 2.5 shows the comparative Kaplan-Meier survival estimates for violent <strong>of</strong>fenses in the treatment and control groups. <strong>The</strong> graph supports the estimates from the prevalence and count models. It appears that the probability <strong>of</strong> failure is slightly higher in the control group, and that they fail more quickly than the treatment group (although note that the scale on the y-axis magnifies the size <strong>of</strong> the gap between the two lines). <strong>The</strong> log-rank test indicates no significant difference between the time to failure across the two groups (χ 2 (1 d.f.) = .69, p ≤ .405). Similarly, when controlling for other covariates in a Cox regression model, we see no difference between the treatment and control groups 94
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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 < .
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it should be noted that those progr
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up in unpublished reports than thos
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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
Page 103 and 104: ecidivists in the sample, 77 commit
Page 105 and 106: (IRR = 1.41, p ≤ .107). SES did n
Page 107: probability of fai
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
Page 153 and 154: statistical model that would predic
Page 155 and 156: The Philadelphia m
Page 157 and 158: 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
Page 263 and 264:
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
Page 277 and 278:
Marsh, K., & Fox, C., (2008). <stro
Page 279:
Sherman, L. W. (1993). Defiance, de
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