The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Results How successfully does the model categorize <strong>of</strong>fenders as low or non-low risk? <strong>The</strong> first three rows <strong>of</strong> Table 3.3 show the prevalence and frequency <strong>of</strong> post-risk assessment serious <strong>of</strong>fending (as defined in the Philadelphia APPD prediction model) for probationers predicted to be low or non-low risk. <strong>The</strong> differences between the two groups are substantial and highly statistically significant on both measures. Among <strong>of</strong>fenders predicted to be low risk, 3.4 per cent were charged with a serious <strong>of</strong>fense such as murder, aggravated assault, or a sexual <strong>of</strong>fense over the course <strong>of</strong> their available <strong>of</strong>fending histories. In the non-low risk group, 10.2 per cent were so charged. Thus, <strong>of</strong>fenders receiving a low risk prediction were 67 per cent less likely to have ever been charged with a serious <strong>of</strong>fense than their non-low risk counterparts (risk ratio RR = .33, p < .001). Similarly, the mean number <strong>of</strong> serious <strong>of</strong>fenses committed by the non-low risk group in the available records was almost three times greater than the mean for the low risk group (.38 vs. .13, p < .001). It appears, therefore, that the predictive model was successful in classifying <strong>of</strong>fenders into low and non-low risk groups at the 0.5 reliability score threshold, when applied to a new set <strong>of</strong> current probationers. Table 3.4 shows more detail about the types <strong>of</strong> serious <strong>of</strong>fenses committed by the two groups, and provides compelling evidence that the low risk group poses a substantially smaller threat to public safety. Offenders predicted to be non-low risk were nearly eight times more likely than those predicted low risk to be charged with homicide or attempted homicide after the risk assessment date (1.5% vs. .2%, p < .001). We see similar, highly significant differences for sexual <strong>of</strong>fenses, aggravated assaults, and 155
obberies. <strong>The</strong> only crime on which the two groups did not differ statistically is forcible rape (a subset <strong>of</strong> all sexual <strong>of</strong>fenses), but this is most likely due to the very small number <strong>of</strong> events. Fewer than 1 per cent <strong>of</strong> each group were charged with rape, but the probability is still more than three times greater in the non-low risk group (.39% vs. .12%, p ≤ .160). Does the model’s sensitivity change under different definitions <strong>of</strong> serious <strong>of</strong>fending? We conducted similar analyses with the low and non-low risk groups using two alternative definitions <strong>of</strong> <strong>of</strong>fending severity, based on UCR Part I <strong>of</strong>fenses and <strong>of</strong>fenses deemed to be more likely to involve a victim or serious damage (i.e., involving a greater economic cost). Of course, the Philadelphia model was not designed to predict such <strong>of</strong>fenses, so the purpose <strong>of</strong> this question is not to validate the model, but rather to examine the types <strong>of</strong> <strong>of</strong>fenses committed by probationers deemed to be at low risk <strong>of</strong> serious harm, and whether they could be considered serious under alternative definitions. <strong>The</strong> results <strong>of</strong> these analyses are presented in the remaining parts <strong>of</strong> Table 3.3. As we would expect from the preceding analysis, the low and non-low risk groups also differ substantially and significantly on these alternative measures <strong>of</strong> severity. However, our alternative definitions slightly inflate the proportion <strong>of</strong> both groups that would be identified as ‘serious’ <strong>of</strong>fenders. 9.1 per cent <strong>of</strong> low risk <strong>of</strong>fenders had been charged post-risk assessment with a UCR Part I <strong>of</strong>fense, which include murder and rape, and also burglary and motor vehicle theft. <strong>The</strong> proportion <strong>of</strong> non-low risk <strong>of</strong>fenders charged with a UCR Part I <strong>of</strong>fense is 15.7 per cent. Low risk <strong>of</strong>fenders are 42 per cent 156
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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
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: age is based on this date regardles
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
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Study Los Angeles CA 2005 (Zhang &
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Fed. Prob., 56, 12-17. Turner, Pete
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Study Reference Benekos & Sonnenber
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Study Reference Giblin (2002) Using
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Study Reference Lasater et al. (n.d
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Study Reference Robertson (2000) Co
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Study Reference Turner & Greene (19
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Appendix E: Philadelphia APPD Low-<
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Prevalence of Reci
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Frequency of Recid
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Instrumental Variables Model: Preva
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Instrumental Variables Model: Preva
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Post-RA Months in Jail and Probabil
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Scaled Schoenfeld Residuals Time =
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BIBLIOGRAPHY Chapter 1 ** Denotes t
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Cook, T. D., & Campbell, D. T. (197
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Glaze, L. E, & Bonczar, T. P. (2009
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MacKenzie, D. L. (2006a). What work
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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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
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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