The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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not the other. For example, the results <strong>of</strong> a set <strong>of</strong> experiments carried out in the United Kingdom to test the effect <strong>of</strong> face-to-face restorative justice on recidivism showed no change in the number <strong>of</strong> <strong>of</strong>fenders who participated in crime after the program compared to before. However, those <strong>of</strong>fenders committed 27 per cent fewer crimes on average than they did prior to the program (Shapland et al., 2008). Looking at participation alone, one might conclude that restorative justice was no more effective than regular court processing. However, society still benefits if fewer crimes are committed overall. Conversely, the success <strong>of</strong> the low-intensity supervision strategy might be doubtful if the lack <strong>of</strong> difference between groups in the proportion <strong>of</strong> <strong>of</strong>fenders participating in crime masked an increase in the frequency <strong>of</strong> <strong>of</strong>fending for the LIS group compared to those receiving SAU. Measures <strong>of</strong> participation and frequency also produce different policyrelevant estimates <strong>of</strong> the treatment effect. In the present experiment, the participation measure gives the more accurate effect <strong>of</strong> assignment to LIS versus SAU, since LIS <strong>of</strong>fenders are returned to SAU after their first new <strong>of</strong>fense. However, if we continue to follow experimental participants after they complete or fail LIS, the number <strong>of</strong> <strong>of</strong>fenses they commit in the longer term <strong>of</strong>fers an indication <strong>of</strong> whether spending any amount <strong>of</strong> time in LIS has a deterrent effect on subsequent criminal behavior. We assess the effect <strong>of</strong> the experimental treatment on participation and frequency using regression models designed for binary and count data. We construct a binary logit model for participation. Frequency <strong>of</strong> <strong>of</strong>fending is analyzed according to a Poisson regression model and several <strong>of</strong> its variants. When there is evidence <strong>of</strong> over-dispersion – excess variation not captured by the Poisson distribution – a negative binomial regression model is examined. Because a substantial proportion <strong>of</strong> our sample did not re<strong>of</strong>fend at 75
all, resulting in a large number <strong>of</strong> zero counts in our data, we explore whether zeroinflated Poisson or negative binomial models fit the data better. 10 Zero-inflated models correct for the large share <strong>of</strong> zero observations by allowing the zeros to be predicted by two different theoretical processes (e.g., Sarkisian, 2009): the “always zeros” (<strong>of</strong>fenders who will never re<strong>of</strong>fend, regardless <strong>of</strong> changes in other conditions); and the “possible zeros” (those who might have re<strong>of</strong>fended but did not do so during the follow-up period). We present the results <strong>of</strong> several diagnostic tests used to assess the appropriateness <strong>of</strong> each model. All the models control for baseline <strong>of</strong>fender characteristics, and account for time at risk according to the time during which <strong>of</strong>fenders were not in jail one year preand one year post-random assignment. <strong>The</strong>se features are described in detail below. Time to failure and survival analysis An alternative approach to assessing experimental outcomes is to look at the time to failure (time to first <strong>of</strong>fense) rather than simple proportions or counts <strong>of</strong> new <strong>of</strong>fenses. An experimental intervention may affect time to failure as well as, or even independently <strong>of</strong>, its effect on participation and frequency. In a probation agency, an understanding <strong>of</strong> how quickly probationers tend to recidivate after the probation term begins may be important for the allocation <strong>of</strong> supervision resources. Whether we analyze participation or frequency, we can only say that the treatment group was more or less likely to <strong>of</strong>fend than the control group. We lose important information about the timing <strong>of</strong> events, and cannot account for the participants who did not re<strong>of</strong>fend. As Allison (1984) notes: “One might suspect … that someone arrested immediately after release [from prison] had a 76
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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
Page 39 and 40: Discussion of Resu
Page 41 and 42: were evaluated in the same report (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47 and 48: Which other components of</
Page 49 and 50: .450; technical violations: Q B < .
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
Page 57 and 58: 2 One U.S. estimate indicates that
Page 59 and 60: Tables Table 1.1: Characteristics <
Page 61 and 62: Table 1.3: Moderator Variable <stro
Page 63 and 64: Table 1.4: Moderator Variable <stro
Page 65 and 66: Figures Figure 1.1: Effect
Page 67 and 68: Figure 1.5: Effect of</stro
Page 69 and 70: CHAPTER 2. ‘Low-Intensity
Page 71 and 72: What Works in Probation Supervision
Page 73 and 74: to do with it. Furthermore, the mor
Page 75 and 76: to the New York City and Oregon stu
Page 77 and 78: Barnes et al. (forthcoming) have su
Page 79 and 80: paradox.” He suggests that ‘not
Page 81 and 82: fatal shootings in Philadelphia are
Page 83 and 84: the proportions of
Page 85 and 86: Interventions and Follow-Up Probati
Page 87 and 88: officers more <str
Page 89: 1. Does the effect of</stro
Page 93 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97 and 98: effect of LIS on r
Page 99 and 100: incarcerated must also be removed f
Page 101 and 102: offenders in our s
Page 103 and 104: ecidivists in the sample, 77 commit
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
Page 113 and 114: 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
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Figures Figure 2.1: Risk-Based Allo
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Figure 2.5: Survival Time for LIS E
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CHAPTER 3. Risk Prediction for Effe
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sensitivity of the
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Programs should be designed to adhe
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weight information in an appropriat
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statistical model that would predic
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The Philadelphia m
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assignment pool. Backfill probation
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Little research has been done on th
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already predicted to be at low risk
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It assumes that if of</stro
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involved. Crimes that obviously met
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We examine the potential interactio
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age is based on this date regardles
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obberies. The only
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of low-intensity s
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classified as low risk. The
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All the risk ratios in Table 3.9 in
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0.55. Raising the threshold results
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are partly selected on the basis <s
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make up the majority of</st
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strategy allows probation o
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Notes 1 In this study, murder, atte
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Table 3.2: Sample Characteristics (
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Table 3.6: Predictive Ability at Al
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Table 3.9: Post-Random Assignment O
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APPENDICES Appendix A: Systematic R
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combination. The s
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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
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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