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Table 2.2: Prevalence <strong>of</strong> Recidivism (All Offenses, 2-Year Follow Up) Logistic Regression Number <strong>of</strong> observations = 1,559 Likelihood Ratio χ 2 (10 d.f.) = 252.43 Pr > χ 2 = .000 Log likelihood = -685.013 Pseudo R 2 = .156 Term Odds Ratio S.E. z p Treatment group 1.05 .145 .38 .707 West probation region .60 .098 -3.15 .002 Male 1.24 .185 1.44 .151 White 1.00 .171 -.03 .978 Age at RA .98 .007 -3.03 .002 Income $20,000-$29,999 .60 .147 -2.08 .037 Income $30,000-$39,999 .52 .135 -2.54 .011 Income $40,000 or more .25 .076 -4.56 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .98 .132 -.15 .882 Months in jail post-RA 1.29 .025 13.06 .000 Hosmer-Lemeshow χ 2 for goodness-<strong>of</strong>-fit (8 d.f., 10 groups) = 9.84, p ≤ .276 111
Table 2.3: Frequency <strong>of</strong> Recidivism (All Offenses, 2-Year Follow-Up) Zero-Inflated Negative Number <strong>of</strong> observations = 1,559 Binomial Regression Nonzero observations = 335 Zero observations = 1,224 Inflation model = logit Likelihood Ratio χ 2 (9 d.f.) = 29.77 Log likelihood= -1700.398 Pr > χ 2 = .001 Full Model Incidence Rate Ratio S.E. z p Treatment group .89 .148 -.69 .489 West probation region 1.41 .296 1.61 .107 Male 2.03 .365 3.93 .000 White 1.14 .227 .64 .521 Age at RA .98 .009 -2.62 .009 Income $20,000-$29,999 1.53 .464 1.41 .160 Income $30,000-$39,999 1.35 .464 .86 .388 Income $40,000 or more 1.11 .435 .26 .798 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .99 .196 -.06 .951 Log <strong>of</strong> post-RA months at risk [<strong>of</strong>fset] Inflated Model b S.E. z p Treatment group -.067 .152 -.44 .658 West probation region .494 .187 2.64 .008 Male -.104 .171 -.61 .542 White .003 .191 .01 .989 Age at RA .020 .008 2.62 .009 Income $20,000-$29,999 .569 .307 1.85 .064 Income $30,000-$39,999 .795 .331 2.38 .018 Income $40,000 or more 1.478 .371 3.98 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .199 .186 1.07 .287 Constant -4.027 .510 -7.90 .000 Log <strong>of</strong> post-RA months at risk [<strong>of</strong>fset] Ln(Alpha) .779 .162 4.82 .000 Alpha 2.179 .352 Vuong test <strong>of</strong> zero-inflated vs. standard negative binomial: z = 7.04, p < .001 112
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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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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 and 124: 7 LIS participants who were transfe
Page 125: Tables Table 2.1: Sample Characteri
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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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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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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