The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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(p ≤ .008), 24 and it was retained in this model. It would appear that for drug <strong>of</strong>fending, more time in jail may lead to a reduced likelihood <strong>of</strong> re<strong>of</strong>fending to some degree. Since we only have one year <strong>of</strong> jail data, it could be the case that the odds <strong>of</strong> drug <strong>of</strong>fending decline as jail time increases, but increase again when all <strong>of</strong>fenders are ‘returned to the risk set’ in the second year. However, because our outcome data extend beyond the range <strong>of</strong> the jail data, these coefficients should be interpreted with caution. We used a zero-inflated negative binomial model to assess the frequency <strong>of</strong> drug <strong>of</strong>fending (Table 2.14). 25 Although it does not reach statistical significance, there is a notable 22 per cent decline in the drug re<strong>of</strong>fending rate for the treatment group compared to the control group (IRR = .78, p ≤ .169). Increased age was also associated with a decline in the rate <strong>of</strong> drug <strong>of</strong>fending <strong>of</strong> 2 per cent per additional year (IRR = .98, p ≤ .045). As before, males re<strong>of</strong>fended at a higher rate than females and increased SES was associated with reduced re<strong>of</strong>fending, but these relationships were not as strong as in previous models. <strong>The</strong> Kaplan-Meier survival estimates for drug <strong>of</strong>fending are shown in Figure 2.7. <strong>The</strong> pattern <strong>of</strong> survival probability is very similar to the patterns for overall and violent <strong>of</strong>fending, with the control group at slightly greater risk <strong>of</strong> failure by the end <strong>of</strong> the twoyear follow-up period (log-rank test for equality: χ 2 (1 d.f.) = .72, p ≤ .395). Table 2.15 and Figure 2.8 present the hazard ratios and graphical representation <strong>of</strong> the estimated survivor functions from the Cox regression model. 26 Again, no difference is evident in the risk <strong>of</strong> failure over time between the treatment and control groups (HR = .93, p ≤ .673). Gender is again associated with a significantly higher risk <strong>of</strong> failure, with males at 97
twice the risk <strong>of</strong> females (HR = 1.96, p ≤ .001), and age and increasing SES is associated with a lower risk over time. <strong>The</strong> IV regression results for drug <strong>of</strong>fending are presented in Table 2.16. Again, there is a slight reduction in the probability <strong>of</strong> drug <strong>of</strong>fending associated with LIS assignment and take-up, but its impact is extremely small and becomes even smaller among those who actually receive the treatment, controlling for time at risk (ITT: 3.5% reduction, p ≤ .680; second stage: 1% reduction, p ≤ .550). As before, age and increased SES were associated with a reduced risk <strong>of</strong> re<strong>of</strong>fending in the ITT model. Those in the highest SES category were 10 per cent less likely to commit a new drugs <strong>of</strong>fense than those in the lowest category, regardless <strong>of</strong> treatment assignment. Contrary to other models, we see no effect <strong>of</strong> probation region or prior <strong>of</strong>fending history on recidivism for drug <strong>of</strong>fenses among LIS compliers (Table 2.17). <strong>The</strong> only significant differences we observe are by gender, age, and SES. Males had a 9.5 per cent probability <strong>of</strong> re<strong>of</strong>fending compared to 4.5 per cent for females (p < .001) when actually supervised in the LIS caseload. <strong>The</strong>re is a linear relationship between increased age and SES and a reduced probability <strong>of</strong> recidivism. Discussion Our first research question assessed whether the impact <strong>of</strong> LIS changes if we consider <strong>of</strong>fending participation (the proportion <strong>of</strong> <strong>of</strong>fenders with a new <strong>of</strong>fense) compared to frequency (the number <strong>of</strong> <strong>of</strong>fenses committed by the <strong>of</strong>fenders who fail). Overall, there was no difference at all in the prevalence <strong>of</strong> new charges between the 98
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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
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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 and 110: The count model ou
Page 111: estimate is also practically identi
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
Page 161 and 162: 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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