The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Study Reference Smith (1984) Alabama prison option: supervised intensive restitution program. Fed. Prob., 48, 32-35. Solomon, Kachnowski, & Bhati (2005) Does parole work? Analyzing the impact <strong>of</strong> postprison supervision on rearrest outcomes. Urban Institute. Stalans, Seng, & Yarnold (2002) Long-term impact evaluation <strong>of</strong> specialized sex <strong>of</strong>fender probation programs in Lake, DuPage, & Winnebago Counties. IL Criminal Justice Information Auth. Stalans et al. (n.d.) Process and initial impact evaluation <strong>of</strong> the Cook County Adult Probation Department’s sex <strong>of</strong>fender program. No publication details. Taxman & Thanner (2006) Risk, need, and responsivity (RNR): it all depends. Crime & Delinquency, 52, 28-51. Texas Adult Probation Commission (1988) Recidivism study on intensive supervision, specialized caseloads, and restitution centers for 1985-1987. Travis County (n.d.) <strong>The</strong> probation experiment. No publication details. Trotter (1993 & 1995) <strong>The</strong> supervision <strong>of</strong> <strong>of</strong>fenders – what works? Australian Criminol. Res. Council. Trusty & Arrigona (2001) Project Spotlight: first year implementation overview and recommendations for improvement. TX Criminal Justice Policy Council. Trusty & Arrigona (2001) Project Spotlight: program overview, early implementation issues and outcome measures. TX Criminal Justice Policy Council. Reason for Exclusion No comparison group. No crime outcomes. Control group did not receive regular probation supervision (compares parolees to prisoners released without parole). Unmatched controls. See also Seng et al. (2000) Not a test <strong>of</strong> probation supervision intensity: experiment tests seamless treatment delivery (sometimes via probation, but not always). Not an evaluation. Outcome comparisons for different types <strong>of</strong> disposals. Limited information on experiment, but does not appear to be a test <strong>of</strong> probation supervision intensity. No comparison group. Not a test <strong>of</strong> probation supervision intensity (evaluates a probation <strong>of</strong>ficer training program). No comparison group. 217
Study Reference Turner & Greene (1999) <strong>The</strong> FARE probation experiment: implementation and outcomes <strong>of</strong> day fines for felony <strong>of</strong>fenders in Maricopa County. Justice Sys. J., 21, 1-21. Turner & Jannetta (2007) Implementation and early outcomes for the San Diego High Risk Sex Offender (HRSO) GPS pilot program Cen. for Evidence-Based Corrections, UC Irvine. Turner, Petersilia, Deschenes (1992) Evaluating Intensive Supervision Probation/Parole (ISP) for drug <strong>of</strong>fenders. Crime & Delinquency, 38, 539-556. Turner et al. (2002) Evaluation <strong>of</strong> the South Oxnard Challenge Project 1997-2001. RAND Corporation. Ulmer & van Asten (2004) Restrictive Intermediate Punishments and recidivism in Pennsylvania. Fed. Sent. Rep., 16, 182-187. Van Vleet et al. (2002) Evaluation <strong>of</strong> Utah’s Early Intervention Mandate: the juvenile sentencing guidelines and intermediate sanctions. National Institute <strong>of</strong> Justice. Virginia Department <strong>of</strong> Corrections (1988) Intensive Supervision Program (ISP) final evaluation report: client characteristics and supervision outcomes: a caseload comparison. Vito (1986) Felony probation and recidivism: replication and response. Fed. Prob., 50, 17-25. Wagner & Baird (1993) Evaluation <strong>of</strong> the Florida Community Control Program. National Institute <strong>of</strong> Justice. Reason for Exclusion Not a test <strong>of</strong> probation supervision intensity (tests fine payment condition with no probation supervision). Unmatched controls (subjects who score highly on an instrument are assigned to treatment). Insufficient data to calculate effect sizes. See also Petersilia & Turner (1993). Supervision for the treatment group not always provided by probation <strong>of</strong>ficers. Unmatched controls. Not a test <strong>of</strong> probation supervision intensity. Unmatched incarcerated or probation violator controls. Not a test <strong>of</strong> probation supervision intensity. Incarcerated controls. 218
Page 1 and 2:
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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Page 183 and 184: make up the majority of</st
Page 185 and 186: 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: Study Reference Robertson (2000) Co
Page 235 and 236: Appendix E: Philadelphia APPD Low-<
Page 237 and 238: Prevalence of Reci
Page 239 and 240: Frequency of Recid
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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