The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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higher propensity toward criminal activity than someone arrested 11 months later” (p. 11). Survival (event history) analysis techniques overcome the problems <strong>of</strong> truncation and omitting the time element by incorporating special regression techniques that allow for the censoring <strong>of</strong> cases. If low-intensity supervision had no effect on participation or frequency compared to SAU, but survival analysis revealed that low-intensity participants were likely to fail more quickly than the control group, it may still be necessary to re-evaluate the policy to avoid turning the low-risk unit into a ‘revolving door’ that sends participants right back into the criminal justice system. We use the Kaplan-Meier (KM) method to compare the average risk <strong>of</strong> failure over time for the treatment and control groups. <strong>The</strong> KM survival estimator gives the estimated probability <strong>of</strong> the <strong>of</strong>fender surviving to the end <strong>of</strong> each time interval for which failure events are calculated. In the present analysis, we measure time to failure in days post-random assignment. We then construct a Cox proportional hazards regression model to explore the risk <strong>of</strong> failure depending on experimental status and other covariates. <strong>The</strong> Cox model also allows us to control for post-random assignment time at risk. <strong>The</strong> proportional hazards model is a semi-parametric model, meaning that it does not impose a specific shape for the hazard function, or probability <strong>of</strong> failure over time (although we assume that each individual’s hazard is proportional to those <strong>of</strong> others). This allows a greater degree <strong>of</strong> flexibility than parametric models for time, which require us to choose a particular distribution for the hazard function. Our choice <strong>of</strong> covariates and methods for accounting for time at risk are described in detail below. 77
Differential treatment take-up and subgroup effects Treatment take-up (who actually receives the treatment, regardless <strong>of</strong> random assignment) and subgroup effects are two different but related issues that could affect the impact <strong>of</strong> LIS on recidivism. Very few experiments conducted in ‘real world’ settings operate perfectly (Berk, 2005). Characteristics or circumstances <strong>of</strong> the <strong>of</strong>fender, probation <strong>of</strong>ficer, or agency (as well as errors and individual overrides or ethical concerns) could prevent the delivery <strong>of</strong> the treatment to those assigned to receive it, or lead to control group members receiving the treatment (‘crossover’). Both situations affect the conclusions we are able to draw about experimental outcomes. Similarly, it is conceivable that these characteristics may also interact with treatment, leading to differential outcomes that could masked by the average effect for the full sample. For example, a treatment could prove to be more effective for women than it is for men. As described above, the analyses <strong>of</strong> both the main first year results <strong>of</strong> this experiment and the other research questions presented here are based on the randomly assigned treatment condition (ITT) rather than the treatment actually delivered (TAD). ITT is the preferred method <strong>of</strong> analysis <strong>of</strong> the two, because it reduces the possibility <strong>of</strong> bias resulting from differences in treatment compliance. For example, we know that some treatment group members did not receive LIS because they were later found to be wanted for absconding. <strong>The</strong>ir noncompliance may place them at higher risk <strong>of</strong> re<strong>of</strong>fending than other LIS participants. Excluding them from the analysis <strong>of</strong> the treatment group outcomes could introduce an upward bias in the effectiveness <strong>of</strong> LIS. <strong>The</strong> ITT approach avoids this bias by retaining these <strong>of</strong>fenders in the treatment group. As such, ITT provides a better estimate <strong>of</strong> the policy <strong>of</strong> LIS, because in the real world 78
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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
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 and 90: 1. Does the effect of</stro
Page 91: all, resulting in a large number <s
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
Page 141 and 142: 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
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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