The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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e<strong>of</strong>fending than probationers in the Northeast (OR = .60, p ≤ .002). This is most likely due to some significant demographic differences between the two regional samples, rather than any effect <strong>of</strong> the treatment. 18 Table 2.3 shows the count model outcomes for frequency <strong>of</strong> re<strong>of</strong>fending. We used a zero-inflated negative binomial model to produce the coefficients. We display the incidence rate ratios (IRR) for the number <strong>of</strong> new <strong>of</strong>fenses in the sample across the total time at risk. Following the strategy explained above, the zero-inflated negative binomial model was selected following tests <strong>of</strong> its fit against the actual observed criminal <strong>of</strong>fending frequencies versus those fitted from the Poisson and negative binomial regression models. 19 <strong>The</strong> full model estimates presented in Table 2.3 indicate that assignment to LIS supervision, controlling for other factors, is associated with a small, non-significant reduction in the number <strong>of</strong> <strong>of</strong>fenses committed post-random assignment (IRR = .89, p ≤ .489). Other <strong>of</strong>fender characteristics had a greater effect on <strong>of</strong>fending frequency, regardless <strong>of</strong> treatment assignment. Gender, which had no effect in the participation model, appeared to be an important factor in explaining <strong>of</strong>fending frequency. <strong>The</strong> rate <strong>of</strong> <strong>of</strong>fending for men was twice that <strong>of</strong> women (IRR = 2.03, p < .001). Increased age was again associated with declining <strong>of</strong>fending rates (IRR = .98, p ≤ .009), and was also an important predictor <strong>of</strong> non-<strong>of</strong>fending in the inflated model. Interestingly, although membership <strong>of</strong> the West region group was associated with a lower odds <strong>of</strong> committing any new <strong>of</strong>fense in the logistic model, and also predicts non-<strong>of</strong>fending in the inflated model, those probationers in the West who did <strong>of</strong>fend committed considerably more <strong>of</strong>fenses than recidivists in the Northeast, although this was not statistically significant 89
(IRR = 1.41, p ≤ .107). SES did not predict frequency <strong>of</strong> recidivism, but increasing status was significantly associated with non-<strong>of</strong>fending. Figure 2.3 shows the Kaplan-Meier survival estimates for the treatment and control groups. <strong>The</strong> Kaplan-Meier survival estimate is the probability <strong>of</strong> not <strong>of</strong>fending on a daily basis. <strong>The</strong> most striking feature <strong>of</strong> the graph is that the probability <strong>of</strong> failure is very low for all low-risk <strong>of</strong>fenders. Figure 2.3 is a visual representation <strong>of</strong> the figures reported above: approximately 21 per cent <strong>of</strong> the sample had failed after two years. <strong>The</strong> survivor functions for the treatment and control groups look extremely similar. This is confirmed by the log-rank test for equality <strong>of</strong> survivor functions, which indicates that the probability <strong>of</strong> survival in the two groups is identical (χ 2 (1 d.f.) = .00, p ≤ .996). We extend this basic comparison by modeling time to first failure with controls for additional covariates using Cox proportional hazards regression. 20 Table 2.4 presents the results <strong>of</strong> the regression model in terms <strong>of</strong> hazard ratios (HR). <strong>The</strong> hazard is the risk <strong>of</strong> failure over time, and thus is equivalent to the incidence rate ratio. <strong>The</strong> model confirms that there is no difference in hazards between the treatment and control groups (HR = 1.03, p ≤ .777). As we saw in the results for prevalence and frequency outcomes, other factors appear to have a greater influence on the risk <strong>of</strong> failure regardless <strong>of</strong> treatment assignment. Being in the West probation region is associated with a 36 per cent lower risk <strong>of</strong> failure over time than being in the Northeast, as we would expect from the result <strong>of</strong> our logistic model (HR = .64, p ≤ .001). Older and higher SES <strong>of</strong>fenders were also at a significantly lower risk <strong>of</strong> failure over time. As we have seen, Cox regression does not require us to make assumptions about the shape <strong>of</strong> the hazard or survivor functions. However, it is possible to graph the 90
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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
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
Page 103: ecidivists in the sample, 77 commit
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
Page 153 and 154: 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
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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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