The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

More documents

Recommendations

Info

above. <strong>The</strong> random assignment sequence was designed so that the West and Northeast LIS groups each contained 400 <strong>of</strong>fenders. <strong>The</strong> control group contained slightly more cases from the West than the Northeast (52.8 per cent <strong>of</strong> the control group was from the West regional unit). Table 2.1 shows basic demographic and <strong>of</strong>fending history characteristics for the two groups. <strong>The</strong>re are no statistically significant differences between the treatment and control groups on any measure, indicating successful random assignment. <strong>The</strong> sample is predominantly male (67.0%), nonwhite (60.1%), and on average 41 years old at the time <strong>of</strong> random assignment. <strong>The</strong> majority (91.8%) <strong>of</strong> <strong>of</strong>fenders lived in zip code areas with an average household income <strong>of</strong> $20,000 or more. As we might expect from a sample already predicted to be low-risk, prior <strong>of</strong>fending rates are low. Approximately 12 per cent (N = 193) members <strong>of</strong> the sample were incarcerated at any time post-random assignment, for an average <strong>of</strong> 1.1 months. Members <strong>of</strong> both groups committed 1.3 <strong>of</strong>fenses per month at risk on average in the year prior to random assignment, with much lower rates for violent and drug <strong>of</strong>fending. Post-RA, the marginal first year difference between the treatment and control groups reported above disappears by year two. Control group members tended to engage in violent recidivism more than the treatment group in the first year post-RA (4.1% vs. 2.9%), but by the second year the gap has begun to close (5.4% vs. 4.5%). Treatment and control group participants committed new drug <strong>of</strong>fenses in similar proportions in the first year post-RA (treatment: 6.4% vs. control: 6.5%), but slightly more <strong>of</strong> the control group had failed by the second year (control: 10.1% vs. treatment: 8.9%). In all, 21.5 per cent (N = 335) <strong>of</strong> the 1,559 <strong>of</strong>fenders in the sample committed a new <strong>of</strong>fense <strong>of</strong> any kind two years post-RA. Among the 335 87
ecidivists in the sample, 77 committed violent <strong>of</strong>fenses and 148 committed drug <strong>of</strong>fenses. <strong>The</strong> violent and drug <strong>of</strong>fender samples are not independent: some engaged in both types <strong>of</strong> <strong>of</strong>fending during the follow-up period. Results Table 2.2 shows the odds ratios (OR) from the logistic regression model for the effect <strong>of</strong> assigned treatment on <strong>of</strong>fending participation two years post-random assignment. <strong>The</strong> results indicate that when other <strong>of</strong>fender characteristics are controlled, there is no notable difference in the odds <strong>of</strong> recidivism between the LIS and SAU groups (OR = 1.05, p ≤ .707). Several other <strong>of</strong>fender characteristics appear to have a greater impact on the odds <strong>of</strong> a new <strong>of</strong>fense regardless <strong>of</strong> treatment status. Each additional year <strong>of</strong> age at random assignment is associated with a 2 per cent decline in the odds <strong>of</strong> recidivism (OR = .98, p ≤ .002). SES is consistently associated with significantly reduced odds <strong>of</strong> re<strong>of</strong>fending: between 40 and 75 per cent compared to the lowest SES group. Each additional month in jail post-RA is associated with a 29 per cent increase in the odds <strong>of</strong> a new <strong>of</strong>fense (OR = 1.29, p < .001). This could suggest that <strong>of</strong>fenders who spent more time in jail were likely to be at higher risk <strong>of</strong> re<strong>of</strong>fending while on the streets. 16 We tested an additional model that included a squared term for post-RA jail time, to account for nonlinearity, such that increased jail time could eventually lead to a decline in re<strong>of</strong>fending by curtailing time at risk. This term was dropped as it did not reach statistical significance, 17 perhaps because we did not have jail data for the second year. Probationers in the West probation region had 40 per cent lower odds <strong>of</strong> 88
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 and 48:
Which other components of</
Page 49 and 50:
.450; technical violations: Q B < .
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: offenders in our s
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
Page 113 and 114: 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
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: 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
Page 157 and 158:
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
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
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 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
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
show all

The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

Create successful ePaper yourself

Delete template?

Save as template?