The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Table 2.8: Frequency <strong>of</strong> Recidivism (Violent Offenses, 2-Year Follow-Up) Zero-Inflated Negative Number <strong>of</strong> observations = 1,559 Binomial Regression Nonzero observations = 77 Zero observations = 1,482 Inflation model = logit Likelihood Ratio χ 2 (9 d.f.) = 7.08 Log likelihood = -499.228 Pr > χ 2 = .629 Full Model Incidence Rate Ratio S.E. z p Treatment group .83 .205 -.75 .453 West probation region 1.18 .389 .49 .624 Male .87 .336 -.35 .724 White .99 .331 -.04 .966 Age at RA .98 .016 -1.08 .280 Income $20,000-$29,999 1.53 .717 .90 .366 Income $30,000-$39,999 1.24 .767 .34 .731 Income $40,000 or more 1.13 .767 .18 .857 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) .34 .185 -1.98 .047 Log <strong>of</strong> post-RA months at risk [<strong>of</strong>fset] Inflated Model b S.E. z p Treatment group .109 .243 .45 .654 West probation region .444 .293 1.52 .130 Male -1.335 .350 -3.81 .000 White -.031 .301 -.10 .917 Age at RA .032 .013 2.53 .011 Income $20,000-$29,999 .522 .415 1.26 .209 Income $30,000-$39,999 .923 .459 2.01 .044 Income $40,000 or more 1.530 .547 2.80 .005 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) -1.469 .814 -1.80 .071 Constant -1.441 .732 -1.97 .049 Log <strong>of</strong> post-RA months at risk [<strong>of</strong>fset] Ln(Alpha) -.089 .268 -.33 .740 Alpha .915 .245 Vuong test <strong>of</strong> zero-inflated vs. standard negative binomial: z = 5.68, p < .001. 117
Table 2.9: Time to Failure (Violent Offenses, 2-Year Follow-Up) Cox Regression Number <strong>of</strong> subjects = 1,559 (Breslow Method for Ties) Number <strong>of</strong> failures = 77 Time at risk (person-days) = 1,103,248 Likelihood Ratio χ 2 (10 d.f.) = 105.11 Log likelihood = -511.625 Pr > χ 2 = .000 Term Hazard Ratio S. E. z p Treatment group .91 .211 -.40 .686 West probation region .65 .180 -1.55 .122 Male 3.24 1.108 3.44 .001 White .97 .279 -.09 .927 Age at RA .97 .012 -2.25 .024 Income $20,000-$29,999 .45 .175 -2.05 .040 Income $30,000-$39,999 .36 .153 -2.41 .016 Income $40,000 or more .22 .113 -2.94 .003 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) 2.35 .864 2.33 .020 In jail 1 year post-RA 7.81 1.839 8.73 .000 Table 2.10: Time to Failure with Jail-Time Interaction (Violent Offenses, 2-Year Follow- Up) Cox Regression Number <strong>of</strong> subjects = 1,559 (Breslow Method for Ties) Number <strong>of</strong> failures = 77 Time at risk (person-days) = 1,103,248 Likelihood Ratio χ 2 (11 d.f.) = 112.10 Log likelihood = -508.130 Pr > χ 2 = .000 Term b S. E. z p Treatment group -.092 .231 -.40 .691 West probation region -.425 .277 -1.53 .126 Male 1.173 .342 3.43 .001 White -.025 .287 -.09 .932 Age at RA -.027 .012 -2.23 .025 Income $20,000-$29,999 -.791 .391 -2.02 .043 Income $30,000-$39,999 -1.013 .425 -2.38 .017 Income $40,000 or more -1.513 .518 -2.92 .003 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) .859 .366 2.35 .019 In jail 1 year post-RA 3.017 .455 6.64 .000 Jail*time -.004 .001 -2.51 .012 118
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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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Barnes et al. (forthcoming) have su
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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 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: Table 2.7: Prevalence of</s
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
Page 169 and 170: age is based on this date regardles
Page 171 and 172: obberies. The only
Page 173 and 174: of low-intensity s
Page 175 and 176: classified as low risk. The
Page 177 and 178: All the risk ratios in Table 3.9 in
Page 179 and 180: 0.55. Raising the threshold results
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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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