The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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were not were all conducted in the U.K. at the same time by the same evaluators, and reported in the same paper. We see similar results for technical violations, although the smaller number <strong>of</strong> studies in this category affected the analyses we could run (Table 1.4). We combined government and other unpublished reports to compare them with published academic articles. Although the between-group difference was non-significant (Q B = 2.31, p ≤ .129), there was a large, statistically significant increase in technical violations for ISP participants in published studies, compared to a slight increase in unpublished studies (published: N = 8, OR = 2.08, p ≤ .007; unpublished: N = 8, OR = 1.19, p ≤ .472). We only had technical violation data for studies conducted in the 1980s and 1990s, and although studies from both decades had increased violations among ISP participants, the increase was larger and significant in the 1980s (N = 11, OR = 1.81, p ≤ .023; 1990s: N = 4, OR = 1.22, p ≤ .631). <strong>The</strong> between-group difference is non-significant (Q B = .63, p ≤ .429). Although the number <strong>of</strong> studies in the 1990s is very small, limiting the conclusions we can draw from these results, it may be the case that 1980s studies showed more technical violations because control/surveillance was the prevailing supervision philosophy in that era. We examined the moderating effects <strong>of</strong> a limited set <strong>of</strong> sample characteristics on ISP outcomes. We found no effect <strong>of</strong> age on the relationship between supervision intensity and recidivism (Q B = .07, p ≤ .794; juveniles: N = 16, mean OR = .97, p ≤ .778; youth and adults: N = 20, OR = .93, p ≤ .465). We could not assess the effects <strong>of</strong> age on technical violations because too few studies reported this outcome for juveniles. We also found no effect for gender on either recidivism or violations (recidivism: Q B = 1.59, p ≤ 33
.450; technical violations: Q B < .01, p ≤ .987). <strong>The</strong> odds <strong>of</strong> recidivism were 50 per cent greater among ISP participants in the all-female studies, but this was based on only two studies and was non-significant (mean OR = 1.50, p ≤ .299). <strong>The</strong> additional program characteristics we examined were program type (enhanced probation/parole or prison diversion); the population (probationers, parolees, or both) and <strong>of</strong>fense types (any <strong>of</strong>fenses, or a specialized caseload such as drugs) targeted by the program, and some more specific effects <strong>of</strong> changes in intensity. Again, small cell frequencies limit our ability to draw any firm conclusions from these analyses. We found that the odds <strong>of</strong> recidivism were lower in prison diversion programs than probation enhancement programs (there were no technical violation data for prison diversion programs), but with only two studies aiming to divert <strong>of</strong>fenders from prison it is not possible to say that this reduction was due to intensive supervision. Not all <strong>of</strong> the studies we included accounted for time at risk in their reporting <strong>of</strong> outcomes, so it is possible that prison diversion programs appear more successful because participants had a higher likelihood <strong>of</strong> being reincarcerated and thus incapacitated (Q B = 1.44, p ≤ .231; enhancement: N = 36, OR = .99, p ≤ .952; diversion: N = 2, OR = .69, p ≤ .207). Few substantial differences were observed between target populations and <strong>of</strong>fense types either, beyond what might be expected given the nature <strong>of</strong> the categories. <strong>The</strong> odds <strong>of</strong> recidivism and violations were slightly higher among parolees compared to probationers and mixed caseloads, and among specialized caseloads compared to mixed <strong>of</strong>fense types. Parolees may be more likely to re<strong>of</strong>fend than <strong>of</strong>fenders who were sentenced to probation; and <strong>of</strong>fenders who have been singled out for <strong>of</strong>fense-specific caseloads (e.g., specialized supervision for drug <strong>of</strong>fenders) have already been designated as posing a greater risk <strong>of</strong> 34
Page 1 and 2: University of Penn
Page 3 and 4: In memory of Edwar
Page 5 and 6: Janel McCaffrey and Knakiya Hagans,
Page 7 and 8: traditional practice, we find no ev
Page 9 and 10: Methodology .......................
Page 11 and 12: LIST OF FIGURES Figure 1.1: Effect
Page 13 and 14: intensity makes no difference to re
Page 15 and 16: isk of committing
Page 17 and 18: impact of probatio
Page 19 and 20: which perhaps suggests that ISP is
Page 21 and 22: program intended to reduce the stra
Page 23 and 24: supervision with probation
Page 25 and 26: 3. To what extent do the risk/need
Page 27 and 28: Methods Scale (SMS: Farrington et a
Page 29 and 30: Types of Outcomes
Page 31 and 32: Description of met
Page 33 and 34: actual behavior. We analyze technic
Page 35 and 36: Treatment of quali
Page 37 and 38: treatment group, and those to the r
Page 39 and 40: Discussion of Resu
Page 41 and 42: were evaluated in the same report (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47: Which other components of</
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
Page 57 and 58: 2 One U.S. estimate indicates that
Page 59 and 60: Tables Table 1.1: Characteristics <
Page 61 and 62: Table 1.3: Moderator Variable <stro
Page 63 and 64: Table 1.4: Moderator Variable <stro
Page 65 and 66: Figures Figure 1.1: Effect
Page 67 and 68: Figure 1.5: Effect of</stro
Page 69 and 70: CHAPTER 2. ‘Low-Intensity
Page 71 and 72: What Works in Probation Supervision
Page 73 and 74: to do with it. Furthermore, the mor
Page 75 and 76: to the New York City and Oregon stu
Page 77 and 78: Barnes et al. (forthcoming) have su
Page 79 and 80: paradox.” He suggests that ‘not
Page 81 and 82: fatal shootings in Philadelphia are
Page 83 and 84: the proportions of
Page 85 and 86: Interventions and Follow-Up Probati
Page 87 and 88: officers more <str
Page 89 and 90: 1. Does the effect of</stro
Page 91 and 92: all, resulting in a large number <s
Page 93 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97 and 98: effect of LIS on r
Page 99 and 100:
incarcerated must also be removed f
Page 101 and 102:
offenders in our s
Page 103 and 104:
ecidivists in the sample, 77 commit
Page 105 and 106:
(IRR = 1.41, p ≤ .107). SES did n
Page 107 and 108:
probability of fai
Page 109 and 110:
The count model ou
Page 111 and 112:
estimate is also practically identi
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twice the risk of
Page 115 and 116:
an end. On the other hand, research
Page 117 and 118:
low-intensity supervision would imp
Page 119 and 120:
Conclusion The aim
Page 121 and 122:
We found that low-risk of</
Page 123 and 124:
7 LIS participants who were transfe
Page 125 and 126:
Tables Table 2.1: Sample Characteri
Page 127 and 128:
Table 2.3: Frequency of</st
Page 129 and 130:
Table 2.5: Instrumental Variables M
Page 131 and 132:
Table 2.7: Prevalence of</s
Page 133 and 134:
Table 2.9: Time to Failure (Violent
Page 135 and 136:
Table 2.12: Treatment Effect by Sub
Page 137 and 138:
Table 2.14: Frequency of</s
Page 139 and 140:
Table 2.16: Instrumental Variables
Page 141 and 142:
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
Page 147 and 148:
sensitivity of the
Page 149 and 150:
Programs should be designed to adhe
Page 151 and 152:
weight information in an appropriat
Page 153 and 154:
statistical model that would predic
Page 155 and 156:
The Philadelphia m
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assignment pool. Backfill probation
Page 159 and 160:
Little research has been done on th
Page 161 and 162:
already predicted to be at low risk
Page 163 and 164:
It assumes that if of</stro
Page 165 and 166:
involved. Crimes that obviously met
Page 167 and 168:
We examine the potential interactio
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age is based on this date regardles
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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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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
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
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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
Page 203 and 204:
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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weight w is then recalculated (6),
Page 211 and 212:
Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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Study Minnesota ISR 1995 (Deschenes
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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
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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
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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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