The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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serious <strong>of</strong>fenses in the low risk sample as a whole, this analysis focuses only on prevalence, not frequency. However, we do consider the two alternative definitions <strong>of</strong> severity along with model-defined severity. Table 3.9 presents a stratified analysis according to whether or not the <strong>of</strong>fender had committed a serious <strong>of</strong>fense pre-RA. Table 3.8 shows that control group members were slightly more likely to have committed a serious <strong>of</strong>fense post-RA, regardless <strong>of</strong> the definition, although none <strong>of</strong> the results reaches statistical significance. <strong>The</strong> treatment group was 39 per cent less likely than the control group to have committed a serious <strong>of</strong>fense as defined by the model (RR = .61, p ≤ .079); 17 per cent less likely to have committed a UCR Part I <strong>of</strong>fense (RR = .83, p ≤ .259); and 21 per cent less likely to have committed an <strong>of</strong>fense involving a victim or damage (RR = .79, p ≤ .179). It appears, then, that the low-intensity supervision model did not lead to any increase in the propensity for serious <strong>of</strong>fending. However, we cannot say this with certainty, nor suggest that LIS helps to reduce serious <strong>of</strong>fending, because the analysis does not account for past behavior. We examined whether prior <strong>of</strong>fending interacts with treatment assignment to affect future <strong>of</strong>fending by stratifying our analysis according to the prevalence <strong>of</strong> pre-RA serious <strong>of</strong>fending. Table 3.9 sets out the stratum-specific and Mantel-Haenszel adjusted risk ratios for each definition <strong>of</strong> severity. <strong>The</strong> stratum-specific risk ratios tell us if there is any difference in the effect <strong>of</strong> LIS on post-RA <strong>of</strong>fending depending on whether or not the <strong>of</strong>fender had previously committed a serious <strong>of</strong>fenses. We then compare the overall Mantel-Haenszel adjusted risk ratios to the unadjusted risk ratios from Table 3.8 to assess whether the evidence for an interaction effect is sufficient. 161
All the risk ratios in Table 3.9 indicate that treatment group participants were less likely to commit a serious <strong>of</strong>fense post-RA than the control group, although only one is statistically significant. <strong>The</strong>re is also a notable difference between the risk ratios <strong>of</strong> probationers who had and had not committed a serious <strong>of</strong>fense pre-RA. For modeldefined severity, treatment group participants who had not committed a prior <strong>of</strong>fense were 51 per cent less likely to have been charged post-RA than similarly-situated control group members (2.0% vs. 4.0%, stratum-specific RR = .49, p ≤ .042). However, there was little difference between treatment and control group participants who had committed a prior serious <strong>of</strong>fense (3.8% vs. 4.2%, stratum-specific RR = .90, p ≤ .816). We see the same pattern with UCR and victim/damage crimes. For UCR <strong>of</strong>fenses, treatment group participants without a prior <strong>of</strong>fense were 44 per cent less likely to be charged than control group participants without a prior <strong>of</strong>fense, but there was no difference between treatment and control group participants with a prior <strong>of</strong>fense (no prior: 3.7% vs. 6.5%, RR = .56, p ≤ .136; prior: 10.2% vs. 11.1%, RR = .92, p ≤ .660). For victim/damage crimes, the risk ratio was .58 for non-serious prior <strong>of</strong>fenders compared to .87 for serious prior <strong>of</strong>fenders (no prior: 3.9% vs. 6.8%, p ≤ ..134; prior: 8.4% vs. 9.7%, p ≤ .494). Despite the magnitude <strong>of</strong> some <strong>of</strong> these results, the Mantel-Haenszel adjusted risk ratios show no evidence <strong>of</strong> an interaction effect between prior serious <strong>of</strong>fending and treatment. For all outcome measures they are identical to the unadjusted risk ratios, and we were unable to reject the null hypothesis <strong>of</strong> homogeneity in any case (model defined: p ≤ .294; UCR: p ≤ .248; victim/damage: p ≤ .326), meaning that the stratum-specific risk ratios do not differ enough statistically to suggest a strong interaction. Nonetheless, 162
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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
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
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: sensitivity of the
Page 149 and 150: Programs should be designed to adhe
Page 151 and 152: weight information in an appropriat
Page 153 and 154: statistical model that would predic
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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
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Page 175: classified as low risk. The
Page 179 and 180: 0.55. Raising the threshold results
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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 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
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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
Page 263 and 264:
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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