The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Study Reference Otoyo (1983) A study <strong>of</strong> the relationship <strong>of</strong> increased supervisory contacts to recidivism. Ed.D. Diss., Pepperdine Univ. Petersilia (1989) Probation and felony <strong>of</strong>fenders. Fed. Prob., 49, 4-9. Petersilia & Turner (1990) Los Angeles Electronic Supervision (other programs, including Los Angeles non- EM, are eligible). See reference in Bibliography. Petersilia & Turner (1990) Diverting prisoners to intensive probation: results <strong>of</strong> an experiment in Oregon. RAND Corp. Petersilia & Turner (1993) Evaluating Intensive Supervision Probation/Parole: results <strong>of</strong> a national experiment. [Marion Co., OR & Milwaukee, WI only. Other studies eligible.] NIJ Research in Brief Petersilia, Turner, & Deschenes (1992b) Macon, GA program (others are eligible) See reference in Bibliography. Reichel & Sudbrack (1994) Differences among eligibles: who gets an ISP sentence? Fed. Prob., 58, 51-58. Rengifo & Scott-Hayward (2008) Assessing the effectiveness <strong>of</strong> intermediate sanctions: Multnomah County, Oregon. Vera Inst. <strong>of</strong> Justice Report Summary. Rhyne & Hamblin (2008) What works with the DV <strong>of</strong>fender? Services, sanctions and supervision. Multnomah Co. Dept <strong>of</strong> Community Justice, Portland, OR. Reason for Exclusion Not a test <strong>of</strong> probation supervision intensity (looked at type <strong>of</strong> contact rather than frequency). No comparison group. Not a test <strong>of</strong> probation supervision intensity. Electronic monitoring is the only difference between treatment and control programs. Incarcerated controls. Electronic monitoring is the only difference between treatment and control programs. Not a test <strong>of</strong> probation supervision intensity. Not a test <strong>of</strong> probation supervision intensity. No comparison group. 215
Study Reference Robertson (2000) Comparison <strong>of</strong> community-based models for youth <strong>of</strong>fenders. Part 1: Program effectiveness and cost-effectiveness. Soc. Sci. Res. Cen., MS State Univ./NIDA Robertson & Blackburn (1984) An assessment <strong>of</strong> treatment effectiveness by case classification. Fed. Prob., 48, 34-38. Romero & Williams (1983) Group psychotherapy and intensive probation supervision with sex <strong>of</strong>fenders. Fed. Prob., 47, 36-41. Rossman et al. (1999) Confronting relapse and recidivism: case management and aftercare services in the OPTS program. Urban Institute. Rubin & Dodge (2009) Probation in Maine: setting the baseline. Univ. <strong>of</strong> Southern ME Muskie School <strong>of</strong> Public Service/NIC Sawyer (1975) <strong>The</strong> effects <strong>of</strong> community probation unit services versus conventional probation services on recidivism by juvenile probationers. Ph.D. Diss., Brigham Young Univ. Seng et al. (2000) A comparison <strong>of</strong> evaluation findings on sex <strong>of</strong>fender probation projects in six Illinois counties. IL Criminal Justice Information Auth. Serin, Vuong, & Briggs (2003) Intensive supervision practices: a preliminary examination. Correctional Service <strong>of</strong> Canada. Simon (2008) Effectiveness <strong>of</strong> the Probation and Parole Service Delivery Model (PPSDM) in reducing recidivism. M.A. <strong>The</strong>sis, Univ. <strong>of</strong> Saskatchewan Reason for Exclusion Unmatched controls. Primarily a test <strong>of</strong> treatment conditions in probation sentences. Unmatched controls. Primarily a test <strong>of</strong> group psychotherapy. Insufficient data to calculate effect size. Not a test <strong>of</strong> probation supervision intensity. Not a test <strong>of</strong> probation supervision intensity. No systematic change in intensity. No regular probation comparison group. See also Stalans, Seng, & Yarnold (2002). Unmatched controls/control group consists <strong>of</strong> ISP exclusions. Not a test <strong>of</strong> probation supervision intensity. 216
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
Page 179 and 180: 0.55. Raising the threshold results
Page 181 and 182: are partly selected on the basis <s
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: Study Reference Lasater et al. (n.d
Page 233 and 234: Study Reference Turner & Greene (19
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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