The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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the risk ratio is bounded by zero at the lower end and has no upper bound. A risk ratio <strong>of</strong> 1 indicates no difference in risk between the groups. In the present study, a risk ratio <strong>of</strong> 2 would indicate that one group (‘exposure’: risk level or treatment status) is twice as likely to have a serious <strong>of</strong>fense (‘disease’) than the other. We use another epidemiological tool, sensitivity/specificity analysis, to assess the effect <strong>of</strong> changing the model’s cut-<strong>of</strong>f point for classifying risk. We examine how many <strong>of</strong>fenders were correctly classified as low-risk (having no serious <strong>of</strong>fense two years postrisk assessment date) at each cut point. Sensitivity is defined as the proportion <strong>of</strong> ‘true positives’ correctly identified by the model, or the proportion <strong>of</strong> <strong>of</strong>fenders without a future serious <strong>of</strong>fense who had been predicted low risk. Specificity is defined as the proportion <strong>of</strong> ‘true negatives’ identified: the proportion <strong>of</strong> serious recidivists who were classified as non-low risk. Sensitivity or specificity <strong>of</strong> 100 per cent indicate that the classification tool is able to identify all the true positives or all the true negatives, respectively. In practice, most classification models require a trade-<strong>of</strong>f between one or the other: no model will perfectly classify every case, so users must decide whether it is more important to identify mostly true positives, or mostly true negatives. We also present the positive and negative predictive values <strong>of</strong> the model. <strong>The</strong> positive predictive value is the proportion <strong>of</strong> <strong>of</strong>fenders predicted to be low risk who are actually low risk, and the negative predictive value is the proportion <strong>of</strong> <strong>of</strong>fenders predicted non-low risk who go on to commit a serious <strong>of</strong>fense. Formulas for calculating each <strong>of</strong> these measures are presented in Appendix J. We also define false positives as the proportion <strong>of</strong> predicted low-risk <strong>of</strong>fenders committing serious <strong>of</strong>fenses, and false negatives as the proportion <strong>of</strong> predicted non-low risk <strong>of</strong>fenders not committing serious <strong>of</strong>fenses. 151
We examine the potential interaction <strong>of</strong> low-intensity supervision and pre-random assignment (RA) serious <strong>of</strong>fending on post-RA serious <strong>of</strong>fending for participants enrolled in the Low Risk Experiment using Mantel-Haenszel methods for calculating an adjusted risk ratio across different levels <strong>of</strong> a covariate. This is also a commonly-used approach in epidemiological research. First, we calculate the unadjusted risk ratio for the prevalence <strong>of</strong> post-RA <strong>of</strong>fending by assigned treatment. We then stratify by presence or absence <strong>of</strong> pre-RA serious <strong>of</strong>fending, calculating two stratum-specific risk ratios. <strong>The</strong> Mantel- Haenszel method assigns a weight to each stratum and produces an adjusted overall risk ratio based on the weighted stratum-specific values. <strong>The</strong> accompanying Mantel-Haenszel chi-square test <strong>of</strong> homogeneity is used to consider whether an interaction effect may be present. If χ 2 is statistically significant, we reject the null hypothesis <strong>of</strong> homogeneity <strong>of</strong> the stratum-specific risk ratios. That is, we consider them sufficiently different to constitute evidence that the stratifying variable (serious <strong>of</strong>fending history) interacts with the independent variable (assigned treatment) to affect post-RA serious <strong>of</strong>fending outcomes. 7 All analyses are conducted using the epidemiological methods suite in STATA 10. Sample characteristics We assess each <strong>of</strong> our four research questions using one <strong>of</strong> two separate samples (‘full sample’ and ‘experimental sample’) drawn from the complete set <strong>of</strong> 3,207 probationers described above. That dataset comprised 1,559 predicted low-risk experimental participants (800 LIS treatment and 759 SAU control), 648 predicted low- 152
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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
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: involved. Crimes that obviously met
Page 169 and 170: age is based on this date regardles
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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
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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
Page 257 and 258:
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
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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