The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Appendix C: Meta-Analytic Procedures Odds ratio effect size <strong>The</strong> odds ratio (OR) is given by the following formula, based on a 2x2 table: Events Non-Events Treatment Group a b Control Group c d ES OR = ad bc (1) Analyses are performed on the natural log <strong>of</strong> the OR and converted back for presentation. Standard error <strong>of</strong> the ! logged OR: SE LOR = 1 + 1 + 1 + 1 (2) a b c d Inverse variance weight <strong>of</strong> the logged OR (fixed effects): ! w LOR = 1 2 (3) SE LOR Heterogeneity (Q-statistic) ! Q is distributed as a chi-square with k – 1 d.f. Q = "w i ( ES i # E S ) 2 (4) ES i = individual effect size for i = 1 to k (the number <strong>of</strong> effect sizes). E S = weighted mean effect size over the k effect sizes. ! w i = weight for ES i . ! Random effects model A second variance component " # is computed, reflecting between-study error. We use the DerSimonian and Laird method <strong>of</strong> moments estimator <strong>of</strong> " # (5). <strong>The</strong> inverse variance ! 193 !
weight w is then recalculated (6), with " # added to the within-study variance component 2 (" i = SE LOR ) from the denominator <strong>of</strong> (3) above. If Q is smaller than k – 1 in (5), " # is set ! ! to 0 and the random and fixed effects weights are the same. ! ( ) Q $ k $1 " # = %w i $ %w 2 i /%w i ( ) ! (5) ! w RANDOM = 1 " # +" i (6) Meta-analytic analog to the ANOVA ! <strong>The</strong> total variability Q is partitioned into two groups: variability within categories <strong>of</strong> the moderator variable ( Q W ); i.e., the variability <strong>of</strong> the effect sizes around the category mean, and variability between categories ( Q B ). ! ! ! Q W = "w ij ( ES ij # E S j ) 2 (7) $ p ' p &" E S j w j ) Q B = "(E S j w j ) 2 % j =1 ( # p j =1 " j =1 w j 2 (8) j = 1 to c for c categories <strong>of</strong> the independent (moderator) variable. E S j = weighted mean effect size for each group. ! w j = sum <strong>of</strong> the weights within each group. ! In the mixed effects analog to the ANOVA, the moderator variable is treated as fixed and the variability between groups is random. Thus, the estimator for the random effects component is based on Q W , not Q (formula not shown). Q W estimates from the model are not interpretable. Q B is the meta-analytic equivalent to the F-statistic. ! ! References: Lipsey & Wilson (2001), pp. 47-49, 54, 115-116, 121.; Wilson (2010), pp. 195-198. ! 194
Page 1 and 2:
University of Penn
Page 3 and 4:
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
Page 47 and 48:
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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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
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
Page 169 and 170: age is based on this date regardles
Page 171 and 172: obberies. The only
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
Page 201 and 202: Other: txcxloth C6d. Additional pro
Page 203 and 204: D. SAMPLE LEVEL CODING SHEET Instru
Page 205 and 206: E. DEPENDENT VARIABLE CODING SHEET
Page 207: F14. Comparison group sample size f
Page 211 and 212: Study Sheffield (Males) 1976 (Folka
Page 213 and 214: Study Dallas TX 1992 (Turner & Pete
Page 215 and 216: Study Minnesota ISR 1995 (Deschenes
Page 217 and 218: Study Clark WA 2003 (Barnoski, 2003
Page 219 and 220: Study Whatcom WA 2003 (Barnoski, 20
Page 221 and 222: Study Los Angeles CA 2005 (Zhang &
Page 223 and 224: Fed. Prob., 56, 12-17. Turner, Pete
Page 225 and 226: Study Reference Benekos & Sonnenber
Page 227 and 228: Study Reference Giblin (2002) Using
Page 229 and 230: Study Reference Lasater et al. (n.d
Page 231 and 232: Study Reference Robertson (2000) Co
Page 233 and 234: Study Reference Turner & Greene (19
Page 235 and 236: Appendix E: Philadelphia APPD Low-<
Page 237 and 238: Prevalence of Reci
Page 239 and 240: Frequency of Recid
Page 241 and 242: Instrumental Variables Model: Preva
Page 243 and 244: Instrumental Variables Model: Preva
Page 245 and 246: Post-RA Months in Jail and Probabil
Page 247 and 248: Scaled Schoenfeld Residuals Time =
Page 249 and 250: BIBLIOGRAPHY Chapter 1 ** Denotes t
Page 251 and 252: Cook, T. D., & Campbell, D. T. (197
Page 253 and 254: 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
Page 267 and 268:
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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