The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Frequency <strong>of</strong> Recidivism (All Offenses, 2-Year Follow-Up, Original Table 2.3) 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.) = 27.43 Log likelihood= -1692.398 Pr > χ 2 = .001 Full Model Incidence Rate Ratio S.E. z p Treatment group .93 .142 -.44 .658 West probation region 1.17 .220 .83 .408 Male 1.98 .330 4.11 .000 White 1.13 .203 .68 .495 Age at RA .98 .008 -2.12 .034 Income $20,000-$29,999 1.53 .425 1.55 .122 Income $30,000-$39,999 1.38 .432 1.02 .310 Income $40,000 or more 1.10 .391 .27 .789 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) 1.24 .249 1.09 .277 Inflated Model b S.E. z p Treatment group -.060 .154 -.39 .696 West probation region .432 .189 2.28 .022 Male -.097 .176 -.55 .582 White -.018 .193 .09 .925 Age at RA .023 .008 2.91 .004 Income $20,000-$29,999 .612 .332 1.84 .065 Income $30,000-$39,999 .858 .356 2.41 .016 Income $40,000 or more 1.554 .397 3.91 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .340 .195 1.59 .112 Constant -1.080 .561 -1.93 .054 Ln(Alpha) .588 .207 2.84 .005 Alpha 1.800 .373 Vuong test <strong>of</strong> zero-inflated vs. standard negative binomial: z = 4.03, p < .001 223
Frequency <strong>of</strong> Recidivism (Violent Offenses, 2-Year Follow-Up, Original Table 2.8) 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.) = 5.20 Log likelihood = -509.488 Pr > χ 2 = .817 Full Model Incidence Rate Ratio S.E. z p Treatment group .93 .218 -.32 .748 West probation region .95 .288 -.18 .858 Male 1.05 .373 .13 .894 White 1.03 .326 .09 .928 Age at RA .99 .015 -.75 .452 Income $20,000-$29,999 1.64 .725 1.12 .263 Income $30,000-$39,999 1.18 .681 .28 .779 Income $40,000 or more 1.02 .642 .04 .969 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) .48 .263 -1.34 .181 Inflated Model b S.E. z p Treatment group .133 .243 .55 .584 West probation region .396 .292 1.36 .175 Male -1.340 .351 -3.82 .000 White -.034 .300 -.11 .909 Age at RA .034 .013 2.69 .007 Income $20,000-$29,999 .517 .418 1.24 .216 Income $30,000-$39,999 .918 .461 1.99 .047 Income $40,000 or more 1.533 .553 2.77 .006 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) -1.198 .871 -1.38 .169 Constant 1.577 .734 2.15 .032 Ln(Alpha) -.216 .283 -.76 .445 Alpha .806 .228 Vuong test <strong>of</strong> zero-inflated vs. standard negative binomial: z = 3.95, p < .001. 224
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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
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fatal shootings in Philadelphia are
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the proportions of
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Interventions and Follow-Up Probati
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officers more <str
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1. Does the effect of</stro
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all, resulting in a large number <s
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Differential treatment take-up and
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control group members who received
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effect of LIS on r
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incarcerated must also be removed f
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offenders in our s
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ecidivists in the sample, 77 commit
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(IRR = 1.41, p ≤ .107). SES did n
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probability of fai
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The count model ou
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estimate is also practically identi
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twice the risk of
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an end. On the other hand, research
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low-intensity supervision would imp
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Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
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Table 2.3: Frequency of</st
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Table 2.5: Instrumental Variables M
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Table 2.7: Prevalence of</s
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Table 2.9: Time to Failure (Violent
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Table 2.12: Treatment Effect by Sub
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Table 2.14: Frequency of</s
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Table 2.16: Instrumental Variables
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Figures Figure 2.1: Risk-Based Allo
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Figure 2.5: Survival Time for LIS E
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CHAPTER 3. Risk Prediction for Effe
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sensitivity of the
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Programs should be designed to adhe
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weight information in an appropriat
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statistical model that would predic
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The Philadelphia m
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assignment pool. Backfill probation
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Little research has been done on th
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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
Page 187 and 188: Notes 1 In this study, murder, atte
Page 189 and 190: Table 3.2: Sample Characteristics (
Page 191 and 192: Table 3.6: Predictive Ability at Al
Page 193 and 194: Table 3.9: Post-Random Assignment O
Page 195 and 196: APPENDICES Appendix A: Systematic R
Page 197 and 198: combination. The s
Page 199 and 200: B. ELIGIBILITY CHECKLIST B1. First
Page 201 and 202: Other: txcxloth C6d. Additional pro
Page 203 and 204: D. SAMPLE LEVEL CODING SHEET Instru
Page 205 and 206: E. DEPENDENT VARIABLE CODING SHEET
Page 207 and 208: F14. Comparison group sample size f
Page 209 and 210: weight w is then recalculated (6),
Page 211 and 212: Study Sheffield (Males) 1976 (Folka
Page 213 and 214: Study Dallas TX 1992 (Turner & Pete
Page 215 and 216: Study Minnesota ISR 1995 (Deschenes
Page 217 and 218: Study Clark WA 2003 (Barnoski, 2003
Page 219 and 220: Study Whatcom WA 2003 (Barnoski, 20
Page 221 and 222: Study Los Angeles CA 2005 (Zhang &
Page 223 and 224: Fed. Prob., 56, 12-17. Turner, Pete
Page 225 and 226: Study Reference Benekos & Sonnenber
Page 227 and 228: Study Reference Giblin (2002) Using
Page 229 and 230: Study Reference Lasater et al. (n.d
Page 231 and 232: Study Reference Robertson (2000) Co
Page 233 and 234: Study Reference Turner & Greene (19
Page 235 and 236: Appendix E: Philadelphia APPD Low-<
Page 237: Prevalence of Reci
Page 241 and 242: Instrumental Variables Model: Preva
Page 243 and 244: Instrumental Variables Model: Preva
Page 245 and 246: Post-RA Months in Jail and Probabil
Page 247 and 248: Scaled Schoenfeld Residuals Time =
Page 249 and 250: BIBLIOGRAPHY Chapter 1 ** Denotes t
Page 251 and 252: Cook, T. D., & Campbell, D. T. (197
Page 253 and 254: Glaze, L. E, & Bonczar, T. P. (2009
Page 255 and 256: MacKenzie, D. L. (2006a). What work
Page 257 and 258: Petersilia, J., Turner, S., & Desch
Page 259 and 260: safety first: 13 strategies for suc
Page 261 and 262: Wilson, D. B. (2010). Meta-analysis
Page 263 and 264: Angrist, J. D. (2006). Instrumental
Page 265 and 266: Clear, T. R., & Hardyman, P. L. (19
Page 267 and 268: period266.pdf Lowenkamp, C. T., Lat
Page 269 and 270: Rosch, J. (2006). Deviant peer cont
Page 271 and 272: Taxman, F. S., & Thanner, M. (2006)
Page 273 and 274: statistical learning. Journal <stro
Page 275 and 276: Federal Bureau of
Page 277 and 278: Marsh, K., & Fox, C., (2008). <stro
Page 279: Sherman, L. W. (1993). Defiance, de
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