The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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Appendix F: Logistic, Zero-Inflated Negative Binomial, and Two-Stage Least Squares Regression Models Without Jail Time Controls Prevalence <strong>of</strong> Recidivism (All Offenses, 2-Year Follow Up, Original Table 2.2) Logistic Regression Number <strong>of</strong> observations = 1,559 Likelihood Ratio χ 2 (9 d.f.) = 47.14 Pr > χ 2 = .000 Log likelihood = -787.658 Pseudo R 2 = .029 Term Odds Ratio S.E. z p Treatment group 1.03 .129 .20 .844 West probation region .73 .109 -2.12 .034 Male 1.35 .186 2.20 .028 White 1.06 .167 .34 .733 Age at RA .98 .006 -3.88 .000 Income $20,000-$29,999 .72 .164 -1.43 .153 Income $30,000-$39,999 .57 .138 -2.32 .020 Income $40,000 or more .29 .081 -4.41 .000 Monthly <strong>of</strong>fending rate 1 year pre-RA (any charged <strong>of</strong>fense) .79 .138 -1.36 .174 Hosmer-Lemeshow χ 2 for goodness-<strong>of</strong>-fit (8 d.f., 10 groups) = 5.53, p ≤ .699 Prevalence <strong>of</strong> Recidivism (Violent Offenses, 2-Year Follow Up, Original Table 2.7) Logistic Regression Number <strong>of</strong> observations = 1,559 Likelihood Ratio χ 2 (9 d.f.) = 37.48 Pr > χ 2 = .000 Log likelihood = -287.943 Pseudo R 2 = .061 Term Odds Ratio S.E. z p Treatment group .86 .205 -.63 .527 West probation region .68 .193 -1.36 .173 Male 3.82 1.323 3.87 .000 White 1.03 .305 .12 .905 Age at RA .97 .012 -2.89 .004 Income $20,000-$29,999 .63 .253 -1.14 .254 Income $30,000-$39,999 .41 .179 -2.04 .042 Income $40,000 or more .21 .113 -2.92 .004 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged violent <strong>of</strong>f.) 2.03 .921 1.55 .121 Hosmer-Lemeshow χ 2 for goodness-<strong>of</strong>-fit (8 d.f., 10 groups) = 9.63, p ≤ .292 221
Prevalence <strong>of</strong> Recidivism (Drug Offenses, 2-Year Follow Up, Original Table 2.13) Logistic Regression Number <strong>of</strong> observations = 1,559 Likelihood Ratio χ 2 (9 d.f.) = 34.79 Pr > χ 2 = .000 Log likelihood = -471.825 Pseudo R 2 = .036 Term Odds Ratio S.E. z p Treatment group .89 .157 -.65 .517 West probation region .98 .207 -.08 .939 Male 2.08 .437 3.48 .001 White 1.22 .270 .92 .359 Age at RA .97 .009 -3.85 .000 Income $20,000-$29,999 .62 .187 -1.59 .113 Income $30,000-$39,999 .49 .160 -2.19 .029 Income $40,000 or more .29 .111 -3.24 .001 Monthly <strong>of</strong>fending rate 1 year pre-RA (charged drug <strong>of</strong>f.) .96 .503 -.08 .937 Hosmer-Lemeshow χ 2 for goodness-<strong>of</strong>-fit (8 d.f., 10 groups) = 14.46, p ≤ .071 222
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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
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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
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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
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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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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 and 208: F14. Comparison group sample size f
Page 209 and 210: weight w is then recalculated (6),
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: Appendix E: Philadelphia APPD Low-<
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
Page 259 and 260: 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)
Page 273 and 274: 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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