The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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** Taxman, F. S., Yancey, C., & Bilanin, J. E. (2006). Proactive community supervision in Maryland: Changing <strong>of</strong>fender outcomes. Towson, MD: Maryland Department <strong>of</strong> Public Safety and Correctional Services. Retrieved from http://www.dpscs. state.md.us/publicinfo/publications/pdfs/PCS_Evaluation_Feb06.pdf ** Turner, S., & Petersilia, J. (1992). Focusing on high-risk parolees: An experiment to reduce commitments to the Texas Department <strong>of</strong> Corrections. Journal <strong>of</strong> Research in Crime & Delinquency, 29, 34-61. doi:10.1177/ 0022427892029001003 Weisburd, D., Sherman, L., & Petrosino, A. J. (1990). Registry <strong>of</strong> randomized criminal justice experiments in sanctions. NCJ 129725. Washington, DC: United States Department <strong>of</strong> Justice, National Institute <strong>of</strong> Justice. Retrieved from http://www .icpsr.umich.edu/NACJD/nij_pubs.html ** Wiebush, R. G., Wagner, D., McNulty, B., Wang, Y., & Le, T. N. (2005). Implementation and outcome evaluation <strong>of</strong> the Intensive Aftercare Program: Final report. NCJ 206177. Washington, DC: United States Department <strong>of</strong> Justice, Office <strong>of</strong> Juvenile Justice and Delinquency Prevention. Retrieved from http://www.ncjrs.org/pdffiles1/ojjdp/206177.pdf Wilson, D. B. (2002). Meta-analysis macros for SAS, SPSS, and Stata. Retrieved December 7, 2009, from http://mason.gmu.edu/~dwilsonb/ma.html 245
Wilson, D. B. (2010). Meta-analysis. In A. R. Piquero & D. Weisburd (Eds.), Handbook <strong>of</strong> quantitative criminology (pp. 181-208). New York, NY: Springer. Wilson, J. A., Naro, W., & Austin, J. F. (2007). Innovations in probation: Assessing New York City’s automated reporting system. Washington, D.C.: JFA Associates. Retrieved from http://www.nyc.gov/html/prob/downloads/pdf/kiosk_report_ 2007.pdf Worrall, J.L., Schram, P., Hays, E., & Newman, M. (2004). An analysis <strong>of</strong> the relationship between probation caseloads and property crime rates in California counties. Journal <strong>of</strong> Criminal Justice, 32, 231-241. doi:10.1016/j.jcrimjus.2004. 02.003 ** Zhang, S. X., & Zhang, L. (2005). An experimental study <strong>of</strong> the Los Angeles County Repeat Offender Prevention Program: Its implementation and evaluation. Criminology & Public Policy, 4, 205-236. doi:10.1111/j.1745-9133.2005.00017.x Chapter 2 Agnew, R. (1991). <strong>The</strong> interactive effects <strong>of</strong> peer variables on delinquency. Criminology, 29, 41-72. doi:10.1111/j.1745-9125.1991.tb01058.x 246
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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
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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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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 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: safety first: 13 strategies for suc
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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