The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Table 2.4: Time to Failure (All Offenses, 2-Year Follow-Up) Cox Regression Number <strong>of</strong> subjects = 1,559 (Breslow Method for Ties) Number <strong>of</strong> failures = 335 Time at risk (person-days) = 976,440 Likelihood Ratio χ 2 (21 d.f.) = 298.04 Log likelihood = -2275.151 Pr > χ 2 = .000 Term Hazard Ratio S. E. z p Treatment group 1.03 .116 .28 .777 West probation region .64 .087 -3.30 .001 Male 1.24 .152 1.76 .078 White 1.04 .146 .27 .790 Age at RA .98 .006 -3.03 .002 Income $20,000-$29,999 .60 .118 -2.59 .010 Income $30,000-$39,999 .49 .103 -3.42 .001 Income $40,000 or more .29 .071 -5.01 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .99 .119 -.09 .929 In jail Oct 2007 3.76 1.915 2.61 .009 In jail Nov 2007 .60 .522 -.59 .556 In jail Dec 2007 .77 .947 -.21 .833 In jail Jan 2008 1.76 1.938 .52 .605 In jail Feb 2008 .35 .390 -.94 .346 In jail Mar 2008 3.79 4.263 1.18 .236 In jail Apr 2008 2.37 1.381 1.49 .137 In jail May 2008 .83 .397 -.38 .701 In jail Jun 2008 1.04 .559 .07 .945 In jail Jul 2008 1.46 .686 .80 .422 In jail Aug 2008 .68 .273 -.95 .340 In jail Sep 2008 1.08 .348 .24 .812 113
Table 2.5: Instrumental Variables Model: Prevalence <strong>of</strong> Recidivism (All Offenses, 2- Year Follow-Up) First Stage Treatment Take-Up Reduced Form (ITT) Post-RA Any Off. Observations = 1,559 Observations = 1,559 R 2 = .663 R 2 = .193 Adjusted R 2 = .659 Adjusted R 2 = .183 Instruments b (S.E.) b (S.E.) Assigned LIS .812 (.088)*** -.047 (.113) Assigned LIS*West -.075 (.035)* -.007 (.045) Assigned LIS*Male .014 (.032) -.004 (.040) Assigned LIS*White -.024 (.037) .029 (.048) Assigned LIS*Age -.000 (.001) -.001 (.002) Assigned LIS*Income20 .026 (.057) .111 (.073) Assigned LIS*Income30 .086 (.060) .095 (.077) Assigned LIS*Income40 .028 (.066) .122 (.085) Assigned LIS*Prior <strong>of</strong>fending -.030 (.027)* .032 (.035) Exogenous Months in jail post-RA -.014 (.002)*** . 052 (.003)*** West probation region .055 (.025)* -.068 (.032)* Male .009 (.023) .031 (.029) White .009 (.027) -.017 (.034) Age at RA .001 (.001) -.002 (.001) Income $20,000-$29,999 -.002 (.039) -.126 (.050)* Income $30,000-$39,999 -.013 (.042) -.139 (.054)** Income $40,000 or more -.002 (.047) -.245 (.060)*** Monthly <strong>of</strong>fending rate 1 year pre-RA (any <strong>of</strong>fense) -.012 (.022) -.024 (.028) Constant -.025 (.064) .408 (.081)*** * p ≤ .05; ** p ≤ .01; *** p ≤ .001. Second-Stage Instrumental Number <strong>of</strong> observations = 1,559 Variables Regression Wald χ 2 (10 d.f.) = 364.12 Pr > χ 2 = .000 Any New Charged Offense R 2 = .189 Term b S. E. Z p Predicted Treatment Take-up .009 .023 .40 .686 Months in jail post-RA .052 .003 17.50 .000 Constant .383 .057 6.76 .000 Controlling for region, gender, race, age, SES and <strong>of</strong>fending history. 114
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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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to do with it. Furthermore, the mor
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to the New York City and Oregon stu
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Page 79 and 80: paradox.” He suggests that ‘not
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Page 85 and 86: Interventions and Follow-Up Probati
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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
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Page 115 and 116: an end. On the other hand, research
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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: Table 2.3: Frequency of</st
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
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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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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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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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