The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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17 OR = .98, p ≤ .08. A likelihood ratio test comparing the models with and without the squared terms also indicated that the inclusion <strong>of</strong> the squared term did not improve model fit: LR χ 2 (1 d.f.) = 3.16, p ≤ .08. 18 Probationers from the West were significantly older than those in the Northeast, and significantly more likely to be nonwhite and <strong>of</strong> lower SES. We examined several combinations <strong>of</strong> interaction terms between region and these <strong>of</strong>fender characteristics, and found that West region <strong>of</strong>fenders at the $20,000-$29,999 SES level were significantly less likely to re<strong>of</strong>fend (b = –1.84, p ≤ .023). When this interaction is controlled, the probability <strong>of</strong> recidivism is higher in the West than the Northeast, but the association is non-significant (OR = 2.91, p ≤ .172). 19 <strong>The</strong> likelihood ratio test comparing the negative binomial and Poisson models was highly significant, suggesting that the negative binomial model fits the data better (LR χ 2 (1 d.f.) = 6913.3, p < .001). <strong>The</strong> likelihood ratio test comparing zero-inflated negative binomial versus zero-inflated Poisson also supports the use <strong>of</strong> the former model (LR χ 2 (1 d.f.) = 1930.81, p < .0001). <strong>The</strong> Vuong test statistic comparing the zero-inflated and standard negative binomial models is positive and highly significant (z = 7.04, p < .0001), again suggesting that the zero-inflated negative binomial model is the most appropriate. 20 <strong>The</strong> key assumption <strong>of</strong> Cox regression is that the hazard function for each individual follows the same form, although we do not impose any shape for the form. We used scaled Schoenfeld residuals to examine proportionality. <strong>The</strong> detailed results <strong>of</strong> this test are presented in Appendix G. Non-significant coefficients indicate that the proportional hazards assumption is satisfied. For this model, our assumption appears to be justified. One <strong>of</strong> the control variables for jail time appeared to be nonproportional, but this is not a substantial cause for concern. 21 We again tested a squared jail time term in this model, which was not statistically significant and did not improve model fit (OR = .99, p ≤ .630; LR χ 2 (1 d.f.) = .23, p ≤ .634. 22 Likelihood ratio test for Poisson vs. negative binomial: LR χ 2 (1 d.f.) = 2946.78, p < .001. Likelihood ratio test for zero-inflated Poisson vs. zero-inflated negative binomial: LR χ 2 (1 d.f.) = 304.47, p < .0001. Vuong test for zero-inflated vs. standard negative binomial: z = 5.68, p < .0001. 23 Covariates in Figures 2.6 and 2.8 are held at the same values as they were for Figure 2.4. 24 Likelihood ratio test comparing models with and without squared term: LR χ 2 (1 d.f.) = 6.62, p ≤ .010. 25 Likelihood ratio test for Poisson vs. negative binomial: LR χ 2 (1 d.f.) = 1041.73, p < .001. Likelihood ratio test for zero-inflated Poisson vs. zero-inflated negative binomial: LR χ 2 (1 d.f.) = 90.93, p < .0001. Vuong test for zero-inflated vs. standard negative binomial: z = 5.62, p < .0001. 26 See Appendix G for test <strong>of</strong> the assumptions <strong>of</strong> proportional hazards. We proceeded with the proportional hazards model despite some evidence for nonproportionality in one <strong>of</strong> the monthly jail indicator variables. 109
Tables Table 2.1: Sample Characteristics Treatment (N=800) Control (N=759) % West region 50.0 52.8 % Male 66.5 67.6 % White 41.8 37.9 Mean age at RA date 40.78 40.58 Average household income in ZIP % less than $20,000 7.6 8.8 % $20,000 - $29,999 37.9 41.2 % $30,000 - $39,999 33.9 31.6 % $40,000 or more 20.6 18.3 Mean monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .13 .13 Mean monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>fenses) .02 .02 Mean monthly <strong>of</strong>fending rate 1 year pre-RA (charged drug <strong>of</strong>fenses) .03 .04 Mean number <strong>of</strong> months in jail 1 year post-RA 1.1 1.1 % with any charged <strong>of</strong>fense 1 year post-RA 16.0 15.0 % with any charged <strong>of</strong>fense 2 years post-RA 21.5 21.5 % with charged violent <strong>of</strong>fense 1 year post-RA 2.9 4.1 % with charged violent <strong>of</strong>fense 2 years post-RA 4.5 5.4 % with charged drug <strong>of</strong>fense 1 year post-RA 6.4 6.5 % with charged drug <strong>of</strong>fense 2 years post-RA 8.9 10.1 No significant differences between groups at p ≤ .05 (χ 2 for proportions & 2-tailed t for means). 110
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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 <
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Table 1.3: Moderator Variable <stro
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Table 1.4: Moderator Variable <stro
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Figures Figure 1.1: Effect
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Figure 1.5: Effect of</stro
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CHAPTER 2. ‘Low-Intensity
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What Works in Probation Supervision
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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
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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 and 112: estimate is also practically identi
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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: 7 LIS participants who were transfe
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
Page 163 and 164: It assumes that if of</stro
Page 165 and 166: involved. Crimes that obviously met
Page 167 and 168: We examine the potential interactio
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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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