The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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(arrests, convictions, violations, etc.) are unfavorable and the intention <strong>of</strong> the change in supervision intensity is to reduce their prevalence. Thus, a smaller effect size implies fewer events, which is the goal <strong>of</strong> the programs being tested. 9 <strong>The</strong> synthesis <strong>of</strong> effect sizes in a meta-analysis also requires the calculation <strong>of</strong> a weight for each effect size. Without the inclusion <strong>of</strong> the weight, each study’s effect size is assumed to contribute equally to the overall (mean) effect size. This is unjustified because smaller studies have greater sampling error and should not contribute as much to the mean outcome as larger, relatively more reliable studies. Lipsey and Wilson (2001, p. 36) suggest that the optimal study weight is based on the inverse <strong>of</strong> the squared standard error <strong>of</strong> the effect size (called the ‘inverse variance weight’). Formulas for calculating the standard error and inverse variance weight for the OR are presented in Appendix C. Computations <strong>of</strong> effect sizes and inverse variance weights, and calculation <strong>of</strong> the mean effect sizes and corresponding confidence intervals and statistical tests, are performed using specialized meta-analysis macros written for STATA s<strong>of</strong>tware (Wilson, 2002). We use RevMan s<strong>of</strong>tware (Cochrane Collaboration, 2008) to construct forest plots for the graphical representation <strong>of</strong> meta-analysis results. <strong>The</strong> forest plot shows the weighted mean effect size and associated 95 per cent confidence interval for each study. A square represents the point estimate <strong>of</strong> the effect size, the size <strong>of</strong> the square represents the study weight, and the lines on either side <strong>of</strong> the square are the confidence intervals. <strong>The</strong> mean effect size across all studies is displayed as a diamond, whose far left and right points represent the lower and upper bounds <strong>of</strong> that estimate’s confidence interval. <strong>The</strong> plot is centered around 1, the point at which no difference is observed between treatment and control groups. Point estimates to the left <strong>of</strong> center represent outcomes favoring the 21
treatment group, and those to the right favor the control group. When the confidence intervals do not touch or cross the center line, the point estimate is statistically significant. We assume a random effects model, rather than fixed effects, for all analyses (see Appendix C for details). Fixed effects models in meta-analysis assume that the only random error in the distribution <strong>of</strong> effect sizes arises from within-study sampling error. We do not consider this to be theoretically justified in our analysis because <strong>of</strong> the considerable between-study differences (heterogeneity: see below) in program characteristics, settings, and populations. Further, because we know we have not been able to capture all the available research on intensive probation programs, we can consider our set <strong>of</strong> studies a sub-sample <strong>of</strong> a larger ‘population’ <strong>of</strong> studies, with its own sampling error. Both <strong>of</strong> these factors justify the use <strong>of</strong> the random effects model (Lipsey & Wilson, 2001, pp. 117-120). 10 Meta-analytic methods are also used to investigate whether the overall mean effect size is moderated by other factors. We are interested in the potential impact <strong>of</strong> certain program and <strong>of</strong>fender characteristics on the variation in effect sizes across studies. Because all our moderator variables are categorical and we have a small set <strong>of</strong> a priori hypotheses about potential moderators, such as risk/need level and supervision philosophy, we use the meta-analytic analog to the analysis <strong>of</strong> variance (ANOVA) to test whether these factors might account for any variability in the observed effect sizes from each study (Lipsey & Wilson, 2001, pp. 120-122). We assess each categorical variable separately using this strategy. Even though we include a substantial number <strong>of</strong> studies in this meta-analysis, cell frequencies became very small when they were broken out by 22
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: Treatment of quali
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
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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
Page 97 and 98:
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
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
Page 265 and 266:
Clear, T. R., & Hardyman, P. L. (19
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period266.pdf Lowenkamp, C. T., Lat
Page 269 and 270:
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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