The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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esearch design, outcomes, and different levels <strong>of</strong> each moderator variable. <strong>The</strong>refore, we focus only on bivariate comparisons on each moderator, and do not attempt to model outcomes any further. This is a limitation <strong>of</strong> our moderator analysis: the results we present do not control for the presence <strong>of</strong> any additional moderators. <strong>The</strong> analog to the ANOVA is based on the Q-statistic calculated as part <strong>of</strong> the main random effects model. Q is the weighted sum-<strong>of</strong>-squares <strong>of</strong> each effect size around the grand mean. It represents the extent to which differences between the effect sizes are statistically related to differences in moderators (a statistically significant Q-statistic indicates evidence <strong>of</strong> between-study heterogeneity). We use the random effects analog to the ANOVA (also called a ‘mixed effects’ model), which assumes there is still unmeasured variability after moderators are modeled. We justify this on the basis <strong>of</strong> our limited set <strong>of</strong> moderators, which are unlikely to explain all the variability between studies. 11 <strong>The</strong> relevant formulas are presented in Appendix C. <strong>The</strong>se analyses are also performed using the STATA macros. Due to the greater risk <strong>of</strong> bias in non-randomized studies, experimental and quasiexperimental results are treated separately in all analyses. Randomized experiments that indicate large baseline differences between participants on characteristics likely to be related to outcomes (such as prior <strong>of</strong>fending history), or which experienced substantial attrition <strong>of</strong> participants or other implementation problems are analyzed with the quasiexperiments. <strong>The</strong> concern with such experiments is that the attrition may be caused by reasons related to the treatment and/or outcome; for example, higher-risk <strong>of</strong>fenders may be more likely to abscond from probation and be subsequently lost to follow-up, thus <strong>of</strong>fending outcomes for the remaining lower-risk <strong>of</strong>fenders are biased. 23
Discussion <strong>of</strong> Results Systematic search results We present detailed search results in Appendices A and D. Appendix A includes a flowchart describing how the number <strong>of</strong> database ‘hits’ obtained through the systematic search translated into the 47 studies included in the final analysis. Our search terms initially produced 30,591 hits. Recall that we deliberately left the search terms broad to pick up background information as well as evaluations, so this number by no means reflects the total number <strong>of</strong> studies available. It also includes a substantial number <strong>of</strong> duplicate hits both across and within databases. We identified 528 references to potential evaluations <strong>of</strong> changes in probation intensity, <strong>of</strong> which 410 were put forward for more detailed title and abstract screening. We identified 239 references requiring full coding, and obtained 81 per cent (n=194) <strong>of</strong> these. Most <strong>of</strong> the studies we could not obtain were the tests <strong>of</strong> caseload size variation conducted by local government agencies (mostly the state <strong>of</strong> California) in the 1950s and 1960s. We do not feel that our results are greatly biased by these missing studies, because literature reviews <strong>of</strong> ISP have indicated that they found similar results to the research <strong>of</strong> the 1980s and early 1990s – ISP had no effect on recidivism and increased technical violations. Of the 194 reports we coded in full, 21 were eligible for inclusion in the metaanalysis, 102 were evaluations that did not meet our eligibility criteria (this number reflects some multiple reports <strong>of</strong> the same study), and the remainder were either relevant background literature or additional reports <strong>of</strong> eligible studies that were used as 24
Page 1 and 2: University of Penn
Page 3 and 4: In memory of Edwar
Page 5 and 6: Janel McCaffrey and Knakiya Hagans,
Page 7 and 8: traditional practice, we find no ev
Page 9 and 10: Methodology .......................
Page 11 and 12: LIST OF FIGURES Figure 1.1: Effect
Page 13 and 14: intensity makes no difference to re
Page 15 and 16: isk of committing
Page 17 and 18: impact of probatio
Page 19 and 20: which perhaps suggests that ISP is
Page 21 and 22: program intended to reduce the stra
Page 23 and 24: supervision with probation
Page 25 and 26: 3. To what extent do the risk/need
Page 27 and 28: Methods Scale (SMS: Farrington et a
Page 29 and 30: Types of Outcomes
Page 31 and 32: Description of met
Page 33 and 34: actual behavior. We analyze technic
Page 35 and 36: Treatment of quali
Page 37: treatment group, and those to the r
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
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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
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low-intensity supervision would imp
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Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
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Table 2.3: Frequency of</st
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Table 2.5: Instrumental Variables M
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Table 2.7: Prevalence of</s
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Table 2.9: Time to Failure (Violent
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Table 2.12: Treatment Effect by Sub
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Table 2.14: Frequency of</s
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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
Page 255 and 256:
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
Page 261 and 262:
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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