The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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<strong>of</strong>fenders and focus the most intensive supervision on them, rather than treating them in the same way as low-risk <strong>of</strong>fenders (Sherman, 2007). Berk et al. (2009) argue that both false positives (<strong>of</strong>fenders incorrectly predicted to be low-risk) and false negatives (<strong>of</strong>fenders incorrectly predicted to be high-risk) are detrimental to the effective operation <strong>of</strong> the criminal justice system, as well as public safety. False negatives drive up prison populations, leading to overcrowding and financial pressures as well as the social consequences for <strong>of</strong>fenders, their families and communities. False positives lead to tragic crimes (such as the fatal shootings <strong>of</strong> several Philadelphia police <strong>of</strong>ficers in 2008 by paroled felons that lead to a moratorium on parole releases in Pennsylvania: Pennsylvania Fraternal Order <strong>of</strong> Police, 2008) and undermine public and political confidence in the criminal justice system. Thus, reliable risk prediction is vital for ensuring that as many <strong>of</strong>fenders as possible are correctly classified and managed. <strong>The</strong>re is considerable evidence that statistical risk prediction is superior to clinical methods in criminal justice (see Grove & Meehl, 1996 for a detailed review; also Van Voorhis & Brown, 1997; Lowenkamp, Holsinger, & Latessa, 2001). For example, one study <strong>of</strong> violent recidivism among mentally ill <strong>of</strong>fenders indicated predictive correlation coefficients <strong>of</strong> .09 for clinical predictions and .30 for statistical predictions. Similarly, for sex <strong>of</strong>fender recidivism, the correlation coefficient was .10 for clinical predictions and .46 for statistical instruments (reported in Bonta, 2002). Gottfredson and Moriarty (2006) cite numerous studies supporting their contention that statistical predictions outperform clinical predictions in almost all situations involving human decision-making. <strong>The</strong>y argue that humans do not use information reliably: in particular, we are poor at considering base rates, easily influenced by spurious causation, and do not systematically 135
weight information in an appropriate manner. Furthermore, clinical prediction is not standardized. Bonta (1996) notes that clinical decision rules are not easily observable or replicable. This is not to say that clinical predictions are not useful, nor that statistical prediction methods are without flaws. Statistical predictions by definition provide average results, and the experience <strong>of</strong> criminal justice pr<strong>of</strong>essionals plays an important role in highlighting deviations from the mean. In fact, pr<strong>of</strong>essional override is the littlediscussed fourth ‘principle <strong>of</strong> effective intervention’ (Andrews, Bonta, & Hoge, 1990). Some critics <strong>of</strong> statistical models have also pointed out the ethical concerns about imposing such impersonal judgments on <strong>of</strong>fenders with individualized needs (see Gottfredson & Jarjoura, 1996, for a review <strong>of</strong> the criticisms <strong>of</strong> statistical models). One important and much-discussed ethical concern about risk prediction highlighted by Gottfredson and Jarjoura is that many risk factors <strong>of</strong> crime are highly correlated with race. This makes agency staff fearful <strong>of</strong> taking them into account in decision making. However, Gottfredson and Moriarty (2006) defend statistical risk assessment, noting its importance at the “nexus <strong>of</strong> research and practice,” and pointing out that: “Properly developed and implemented, risk assessment devices can impose criminal justice decision making, properly target and potentially save resources, and potentially increase the public safety” (p. 195). Gottfredson and Jarjoura (ibid.) also set out a solution for reducing the bias in risk assessment. Although the role <strong>of</strong> pr<strong>of</strong>essional override cannot be discounted, both papers argue that ignoring important predictive variables because <strong>of</strong> ethical concerns, rather than investigating how prediction instruments can empirically deal with these difficult issues, severely limits the utility <strong>of</strong> predictive devices and thus 136
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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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Page 101 and 102: offenders in our s
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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
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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: Programs should be designed to adhe
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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
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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
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Other: txcxloth C6d. Additional pro
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D. SAMPLE LEVEL CODING SHEET Instru
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E. DEPENDENT VARIABLE CODING SHEET
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F14. Comparison group sample size f
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weight w is then recalculated (6),
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Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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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
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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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