The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Study Seattle WA 1992 (Petersilia, Turner, & Deschenes, 1992b) Waycross GA 1992 (Petersilia, Turner, & Deschenes, 1992b) Winchester VA 1992 (Petersilia, Turner, & Deschenes, 1992b) Philadelphia PA 1993 (Sontheimer & Goodstein, 1993) Research Design Randomized controlled trial Randomized controlled trial Randomized controlled trial Randomized controlled trial Population Felony drug <strong>of</strong>fenders likely to have high recidivism and drug dependent. Prisoners released to ISP or probationers due to be revoked and returned to prison. Drug involved, high risk/need. High risk probationers and parolees with drug <strong>of</strong>fenses and history <strong>of</strong> abuse. Serious juvenile <strong>of</strong>fenders Intervention 20:1 caseload. Focus on treatment participation, employment and drug testing. Initially 12 face-t<strong>of</strong>ace contacts and 8 drug tests per month, gradually decreasing. Augmented version <strong>of</strong> existing ISP with increased surveillance (increased drug testing). 40:3 caseload. Rehabilitation, employment, drug testing, monitoring <strong>of</strong> activities. 24:1 caseload. Initially 12 face-to-face contacts, 4 phone contacts, 4 monitoring checks per month. Discretionary drug testing. Counseling. Halfway house and detox. 12:1 caseload, weekly contact with collateral and out <strong>of</strong> hours contacts mandated. Employment assistance and education. Comparison Routine probation supervision. 85:1 caseload, 4 face-t<strong>of</strong>ace contacts per month, additional contacts/tests discretionary. Existing ISP. Contact level same in both groups: 12 face-to-face and 10 phone contacts per month, random drug tests, job verification weekly. Routine supervision: 80:1 caseload. 2 face-t<strong>of</strong>ace and 2 phone contacts per month. Other contact and testing discretionary. 70-100:1 caseload, monthly visits, limited access to services. Outcome Treatment group more likely to fail on arrests, technical violations, slightly less on convictions. Treatment group more likely to fail on technical violations, less likely on arrests. No convictions in either group. Treatment group more likely to fail on technical violations, arrests, and convictions. Treatment group less likely to fail on new arrests. 199
Study Minnesota ISR 1995 (Deschenes, Turner, & Petersilia, 1995) Midwest 1998 (Latessa et al., 1998) Northeast 1998 (Latessa et al., 1998) CA Drug Test 2002 (Haapanen & Britton, 2002) Research Design Randomized controlled trial Randomized controlled trial Randomized controlled trial Randomized controlled trial Population Parolees considered a “public risk monitoring case” (high risk). Rural population, mostly white males (youth and adult), higher risk and need levels. Urban population, mostly males, youth and adult, higher risk and need levels. Youthful parolees. Generally serious <strong>of</strong>fenders with substance abuse and other problems. Intervention 12-15:1 caseload, 4 contacts/month reduced over time, period <strong>of</strong> house arrest followed by curfew, mandatory 40 hours/week <strong>of</strong> work, drug treatment or education, random weekly drug tests. 20:1 caseload, about 10 contacts a month, drug testing, treatment referrals, probation <strong>of</strong>ficers trained in CBT. 25:1 caseload, 1-2 contacts per week, drug testing/treatment, home and <strong>of</strong>fice visits, brokerage for needs assessment and services. Parole with enhanced routine unscheduled drug testing requirements: 1 every week in first 90 days <strong>of</strong> parole then biweekly. Comparison Regular parole supervision. Larger caseloads, fewer contacts (approx. 3 per month). 100-200:1 caseload, less frequent contact (about half that in treatment group), no services. 200:1 caseload, less frequent contacts (about half the number in the treatment group, no services). Routine parole with drug testing only after arrest. Outcome Treatment group less likely to fail on technical violations and arrests; slightly more likely on convictions. Treatment group more likely to fail on technical violations but less likely to fail on arrests. Treatment group more likely to fail on technical violations but less likely to fail on arrests. Treatment group more likely to fail on new arrests. 200
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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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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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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: Study Dallas TX 1992 (Turner & Pete
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 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
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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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