The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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(HR = .91, p ≤ .686: Table 2.9 and Figure 2.6 23 ). As we saw in the logistic regression model, males had a significantly higher risk <strong>of</strong> failure than females (HR = 3.24, p ≤ .001), and <strong>of</strong>fenders with prior violent <strong>of</strong>fenses in the year pre-RA were at greater risk <strong>of</strong> failure than those without (HR = 2.35, p ≤ .02). SES and age were also associated with declines in the risk <strong>of</strong> failure. Note that we only use one variable to control for time at risk post-RA in this model, compared with the twelve monthly indicator variables included in the full <strong>of</strong>fending model. <strong>The</strong> dummy indicators were dropped from this model because <strong>of</strong> problems that were likely caused by the very small proportion <strong>of</strong> failures in this sample. In the first few months <strong>of</strong> the first year follow-up period, the numbers <strong>of</strong> eventually failing <strong>of</strong>fenders who were in jail did not vary at all. <strong>The</strong> resulting multicollinearity between each <strong>of</strong> these monthly variables prevented us from being able to estimate their parameters. However, the single jail indicator violates the proportional hazards assumption for this model (see Appendix H for the results <strong>of</strong> the diagnostic tests). This is to be expected, because we are treating jail stays as time-constant in our model, when they clearly vary with time. We account for this nonproportionality by constructing a new model that allows jail to vary with time by including a jail*time interaction term. <strong>The</strong> results <strong>of</strong> this model are presented in Table 2.10. We present the unexponentiated coefficients for this model because the interaction term makes more sense on that scale. However, if we obtained the hazard ratios for the other covariates we would see that the addition <strong>of</strong> the interaction term barely makes a difference to our original estimates overall. For example, the coefficient <strong>of</strong> -.092 for treatment group assignment converts to a hazard ratio <strong>of</strong> .91 – identical to the estimate from the first model. <strong>The</strong> p-value for this 95
estimate is also practically identical: .691 compared to .686. Thus, our two alternative controls for time at risk do not appear to impact our estimates <strong>of</strong> the risk <strong>of</strong> committing a violent <strong>of</strong>fense over time. Table 2.11 shows the results <strong>of</strong> the IV regression for violent <strong>of</strong>fending. Consistent with earlier findings, the reduced form (ITT) model shows a slight, nonsignificant reduction in the probability <strong>of</strong> violent recidivism for <strong>of</strong>fenders assigned to LIS (b = -.073, p ≤ .239). Gender, income, and months in jail are also significantly associated with the outcome. However, as in the model for all <strong>of</strong>fense types, the small effect <strong>of</strong> treatment disappears completely for those who actually received it (b = - .007, p ≤ .586). <strong>The</strong> subgroup effects for violent <strong>of</strong>fending (Table 2.12) are similar to those for all <strong>of</strong>fenses. <strong>The</strong> overall probability <strong>of</strong> <strong>of</strong>fending is small across all the subgroups. Region, gender, age, and SES are all significantly associated with outcomes for LIS compliers, and race is not. <strong>The</strong> prior <strong>of</strong>fending rate (which is dichotomized into no <strong>of</strong>fending vs. any <strong>of</strong>fending for violent and drug <strong>of</strong>fending due to very low <strong>of</strong>fending rates) does not quite reach statistical significance, but the results are in the opposite direction from those observed in the all <strong>of</strong>fenses model. Those with no prior violent <strong>of</strong>fenses were less likely to re<strong>of</strong>fend compared to those with one or more charge (3.7% vs. 6.1%, p ≤ .073). <strong>The</strong> outcomes <strong>of</strong> the logistic regression model for prevalence <strong>of</strong> drug <strong>of</strong>fending are presented in Table 2.13. <strong>The</strong> results are very similar to the preceding analyses. Treatment group assignment has almost no effect on re<strong>of</strong>fending (OR = .92, p ≤ .644). West region, age, and increasing SES are again associated with decreased odds <strong>of</strong> re<strong>of</strong>fending, and males had twice the odds <strong>of</strong> females <strong>of</strong> committing a new drug <strong>of</strong>fense. On this occasion, we also found that an additional squared jail time term was significant 96
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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
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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 and 108: probability of fai
Page 109: The count model ou
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
Page 159 and 160: 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
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Federal Bureau of
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Marsh, K., & Fox, C., (2008). <stro
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Sherman, L. W. (1993). Defiance, de
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