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the use <strong>of</strong> low-intensity supervision with the lowest-level <strong>of</strong>fenders. <strong>The</strong>se clients need no more than the minimum input <strong>of</strong> resources necessary to ensure that they are not ‘false positives’ and have the tools needed to rebuild their lives. <strong>The</strong> strength <strong>of</strong> the Philadelphia prediction model allows us to say with some confidence that we can identify a large proportion <strong>of</strong> <strong>of</strong>fenders who fall into this category. This leaves the agency much better equipped to deal with the ‘power few’ highest-risk <strong>of</strong>fenders. Of course, no assessment or validation <strong>of</strong> a statistical prediction model can bring complete peace <strong>of</strong> mind in terms <strong>of</strong> guaranteeing the <strong>of</strong>fender management approach that best assures public safety, just as the model itself cannot indicate exactly who will turn out to be low or high risk. We conclude that the Philadelphia model is successful in classifying <strong>of</strong>fenders by risk, but 30 per cent <strong>of</strong> the predicted low risk <strong>of</strong>fenders in our sample had committed at least one <strong>of</strong> the most serious <strong>of</strong>fenses at some point in their adult <strong>of</strong>fending careers. Some <strong>of</strong> that group do not react as well to low-intensity supervision as their counterparts with no history <strong>of</strong> serious <strong>of</strong>fending, and we do not yet know why, or if their performance will worsen as more data are collected. We return to consideration <strong>of</strong> Grove and Meehl’s (1996) comment: “<strong>The</strong> statistics furnish us with the probabilities so far as anything can” (p. 306). Any attempt to routinize criminal justice decision making will necessarily be concerned with averages, but it seems that Philadelphia’s probation agency has developed a successful model that can help to allocate resources where they are needed most, and can easily be adapted for use elsewhere based on the information available to the specific agency. 171
Notes 1 In this study, murder, attempted murder, aggravated assault, robbery, and sexual <strong>of</strong>fenses were deemed ‘serious’ <strong>of</strong>fenses. 2 Intake information includes the <strong>of</strong>fender’s personal and residential characteristics, and information about the instant <strong>of</strong>fense and prior criminal history. 3 Full details about the experimental design and how the sample was selected, assessed for eligibility, and randomly assigned may be found in Barnes et al. (forthcoming). 4 An alternative approach could be to use incarceration status as a proxy for cost. <strong>The</strong> disadvantage is that we must use either actual incarceration data for our sample, or assume the types <strong>of</strong> <strong>of</strong>fenses that might result in a sentence <strong>of</strong> imprisonment. Assumptions may be too subjective given the discretion involved in sentencing (although state sentencing guidelines could assist), and full incarceration data are not available for our sample. In particular, it is possible that some post-random assignment sentencing decisions are still pending given the relatively short period <strong>of</strong> time since the experiment ended. While assigning victim status to each <strong>of</strong>fense type also involves assumptions, the level <strong>of</strong> subjectivity is likely considerably lower than it would be for incarceration. 5 Most <strong>of</strong>fenses in the dataset are derived from the Pennsylvania Consolidated Statutes, Section 18 (Crimes and Offenses). 6 <strong>The</strong> risk ratio is simply pE=1 /p E=0 (where p = probability and E = dichotomous exposure status), whereas the odds ratio is (p/(1-p) E=1 )/(p/(1-p) E=0 ). <strong>The</strong> odds ratio tends to overstate our ‘natural’ interpretation <strong>of</strong> relative outcomes: if the exposed group has a 50% risk <strong>of</strong> disease and the unexposed group has a 25% risk, the risk ratio is clearly 2 (the exposed group is twice as likely to get the disease than the unexposed group), but the odds ratio is 3 (the odds <strong>of</strong> disease in the exposed group are three times those <strong>of</strong> disease in the unexposed group), which seems greater. <strong>The</strong> risk ratio also remains stable regardless <strong>of</strong> the size <strong>of</strong> the risk; the magnitude <strong>of</strong> the odds ratio is closer to the risk ratio when the probability <strong>of</strong> disease in each group is small, and further away when it is large. Following the example above, if the risks were reduced to 20% and 10% respectively, the risk ratio would still be 2 but the odds ratio would fall to 2.25. 7 In addition, an adjusted risk ratio that is substantially different from the unadjusted risk ratio (a 10-15% difference is a commonly-used rule <strong>of</strong> thumb) suggests that the stratifying variable is a confounder. 8 <strong>The</strong> race indicator variable was populated with data from two different sources, with one source selected as the default. However, serious discrepancies arose because the categories <strong>of</strong> race in the two original sources were substantially different. 9 On the other hand, it is also possible that non-low risk probationers are in fact more serious <strong>of</strong>fenders, and are more likely to be incarcerated as a result. 10 That past behavior is one <strong>of</strong> the strongest predictors <strong>of</strong> future behavior is one <strong>of</strong> the best documented findings in criminological research (e.g., Nagin & Paternoster, 1991; Nagin & Farrington, 1992). 172
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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
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: strategy allows probation o
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-<
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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
Page 265 and 266:
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
Page 271 and 272:
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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