The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Appendix G: Conditional Distributions <strong>of</strong> Selected Model Covariates and Outcome Age at RA and Probability <strong>of</strong> a New Charge Age Charged (%) Not Charged (%) Total (%) Age Charged (%) Not Charged (%) Total (%) 19 1 (50.0) 1 (50.0) 2 (100) 46 14 (24.1) 44 (75.9) 58 (100) 20 0 (0.0) 2 (100.0) 2 (100) 47 13 (17.3) 62 (82.7) 75 (100) 21 4 (23.5) 13 (76.5) 17 (100) 48 9 (17.0) 44 (83.0) 53 (100) 22 8 (42.1) 11 (57.9) 19 (100) 49 7 (14.9) 40 (85.1) 47 (100) 23 8 (33.3) 16 (66.7) 24 (100) 50 16 (25.8) 46 (74.2) 62 (100) 24 5 (20.8) 19 (79.2) 24 (100) 51 6 (17.6) 28 (82.4) 34 (100) 25 10 (23.3) 33 (76.7) 43 (100) 52 11 (26.2) 31 (73.8) 42 (100) 26 6 (19.4) 25 (80.6) 31 (100) 53 4 (12.5) 28 (87.5) 32 (100) 27 20 (35.7) 36 (64.3) 56 (100) 54 6 (15.0) 34 (85.0) 40 (100) 28 8 (20.0) 32 (80.0) 40 (100) 55 6 (22.2) 21 (77.8) 27 (100) 29 13 (28.3) 33 (71.7) 46 (100) 56 3 (20.0) 12 (80.0) 15 (100) 30 9 (25.7) 26 (74.3) 35 (100) 57 2 (12.5) 14 (87.5) 16 (100) 31 8 (21.1) 30 (78.9) 38 (100) 58 3 (21.4) 11 (78.6) 14 (100) 32 9 (27.3) 24 (72.7) 33 (100) 59 0 (0.0) 15 (100.0) 15 (100) 33 10 (24.4) 31 (75.6) 41 (100) 60 0 (0.0) 7 (100.0) 7 (100) 34 8 (26.7) 22 (73.3) 30 (100) 61 0 (0.0) 7 (100.0) 7 (100) 35 7 (14.3) 42 (85.7) 49 (100) 62 0 (0.0) 5 (100.0) 5 (100) 36 8 (21.6) 29 (78.4) 37 (100) 63 0 (0.0) 7 (100.0) 7 (100) 37 10 (21.7) 36 (78.3) 46 (100) 64 0 (0.0) 3 (100.0) 3 (100) 38 9 (19.1) 38 (80.9) 47 (100) 65 1 (25.0) 3 (75.0) 4 (100) 39 13 (29.5) 31 (70.5) 44 (100) 66 2 (50.0) 2 (50.0) 4 (100) 40 12 (27.3) 32 (72.7) 44 (100) 67 0 (0.0) 5 (100.0) 5 (100) 41 9 (18.4) 40 (81.6) 49 (100) 68 1 (50.0) 1 (50.0) 2 (100) 42 10 (20.4) 39 (79.6) 49 (100) 69 0 (0.0) 2 (100.0) 2 (100) 43 6 (12.8) 41 (87.2) 47 (100) 70 1 (50.0) 1 (50.0) 2 (100) 44 5 (13.5) 32 (86.5) 37 (100) 71 1 (100.0) 0 (0.0) 1 (100) 45 13 (26.0) 37 (74.0) 50 (100) Total 335 (21.5) 1,224 (78.5) 1,559 (100) 229
Post-RA Months in Jail and Probability <strong>of</strong> a New Charge Months in jail Charged N (%) Not Charged N (%) Total N (%) 0 210 (15.4) 1,156 (84.6) 1,366 (100) 1 4 (44.4) 5 (55.6) 9 (100) 2 2 (28.6) 5 (71.4) 7 (100) 3 1 (33.3) 2 (66.7) 3 (100) 4 5 (36.5) 8 (61.5) 13 (100) 5 1 (20.0) 4 (80.0) 5 (100) 6 4 (44.4) 5 (55.6) 9 (100) 7 12 (75.0) 4 (25.0) 16 (100) 8 7 (63.6) 4 (36.4) 11 (100) 9 7 (70.0) 3 (30.0) 10 (100) 10 14 (87.5) 2 (12.5) 16 (100) 11 12 (70.6) 5 (29.4) 17 (100) 12 56 (72.7) 21 (27.3) 77 (100) Total 335 (21.5) 1,224 (78.5) 1,559 (100) 230
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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
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 and 238: Prevalence of Reci
Page 239 and 240: Frequency of Recid
Page 241 and 242: Instrumental Variables Model: Preva
Page 243: Instrumental Variables Model: Preva
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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