The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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isk backfill cases who were not randomly assigned but received LIS, and 1,000 predicted non-low risk <strong>of</strong>fenders selected as part <strong>of</strong> a separate study. Our ‘full sample’ is a subset <strong>of</strong> all the groups that make up the 3,207-<strong>of</strong>fender dataset, divided into low and non-low risk cases. Most <strong>of</strong> the backfill cases (N = 588) did not have a recorded reliability score from the prediction model. Although we could assume that they were low risk because they were in the backfill group, they could not be used to examine the questions relating to the cut-<strong>of</strong>f point for a low risk prediction, and we decided to exclude them from the analysis completely. In addition, one treatment group case had no recorded reliability score and was excluded from the full sample, but is included in the experimental sample. Finally, fifteen backfill cases had a reliability score between 0.49 and 0.5. We strictly followed the requirement for classifying cases with a reliability score <strong>of</strong> over 0.5 as low risk in this analysis, so those fifteen cases are classified as non-low risk in the full sample. Thus, the full sample comprises 2,618 <strong>of</strong>fenders in total: 1,603 predicted by the model to be low risk, and 1,015 predicted to be non-low risk. Our ‘experimental sample’ consists <strong>of</strong> the 1,559 experimental participants, analyzed on an intention-to-treat basis (i.e., they remain in their assigned groups regardless <strong>of</strong> whether they actually received the assigned treatment). Tables 3.1 and 3.2 show basic demographic and <strong>of</strong>fending history characteristics for the two samples. Race is presented only as the proportion <strong>of</strong> white <strong>of</strong>fenders because <strong>of</strong> problems in the recording <strong>of</strong> race in the original dataset, 8 which meant that it was only possible to reliably say whether the <strong>of</strong>fender was white or nonwhite. <strong>The</strong> mean age is calculated according to the <strong>of</strong>fender’s age (based on recorded date <strong>of</strong> birth) on October 1, 2007. This was the date on which experimental participants were randomly assigned, and 153
age is based on this date regardless <strong>of</strong> whether or not the <strong>of</strong>fender participated in the experiment. <strong>The</strong> proportions and means <strong>of</strong> charged <strong>of</strong>fenses are based on the full range <strong>of</strong> <strong>of</strong>fending data from the <strong>of</strong>fender’s first recorded charge until September 30, 2009. For the full sample, data were available on a total <strong>of</strong> 81,643 charged <strong>of</strong>fenses committed between 1967 and 2009. Our analyses are based only on those <strong>of</strong>fenses committed after the date <strong>of</strong> the risk assessment (July 27, 2007; N = 6,808), because prior <strong>of</strong>fending history variables were used in the predictive model. For the experimental sample we had data on 34,777 charged <strong>of</strong>fenses over the same timeframe. Of this number, around 6 per cent (N = 2,147) were committed post-random assignment. As we might expect, the low risk and non-low risk groups in the full sample look very different (Table 3.1). <strong>The</strong> non-low risk group is much more likely to be male (87.4% vs. 66.9%, p < .001), nonwhite (75.6% vs. 60.4%, p < .001), and younger (31 years old vs. 40.7, p < .001). <strong>The</strong> non-low risk group has also been charged with more than twice as many <strong>of</strong>fenses overall as the low risk group (45 vs. 22.4, p < .001). <strong>The</strong> characteristics <strong>of</strong> the treatment and control groups in the experimental sample are very similar, indicating successful random assignment. 66.5 per cent <strong>of</strong> the treatment group and 67.6 per cent <strong>of</strong> the control group are male. Slightly more treatment group members than control group members are white (41.8% vs. 38.0%, p ≤ .125). Participants in both groups were, on average, just under 41 years old on the date <strong>of</strong> random assignment, and members <strong>of</strong> both groups have been charged with an <strong>of</strong>fense on average 22.3 times as adults up to two years post-random assignment. 154
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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
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: We examine the potential interactio
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
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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
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Petersilia, J., Turner, S., & Desch
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safety first: 13 strategies for suc
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Wilson, D. B. (2010). Meta-analysis
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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
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Federal Bureau of
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Marsh, K., & Fox, C., (2008). <stro
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Sherman, L. W. (1993). Defiance, de
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