The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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probation violation rates because <strong>of</strong>fenders are at greater risk <strong>of</strong> detection. <strong>The</strong> literature on intensive probation also sheds very little light on the nature <strong>of</strong> effective practices. Background Intensive supervision probation (ISP) is one aspect <strong>of</strong> probation that has received considerable research attention. ISP programs usually consist <strong>of</strong> small caseloads and enhanced reporting requirements. However, interest in the practice has evolved from a need to find punitive alternatives to imprisonment rather than a general desire to understand more about supervision practices. As a result, there has been very little articulation <strong>of</strong> the theoretical basis for its hypothesized effectiveness beyond the assumption that ‘more is better.’ Indeed, Bennett (1988) described ISP as “a practice in search <strong>of</strong> a theory.” Skeem and Manchak (2008) propose that probation supervision may follow one <strong>of</strong> three broad guiding philosophies: control/surveillance, treatment, or a hybrid <strong>of</strong> both. ISP programs developed over the last fifty years have fallen into all three <strong>of</strong> these categories, but the ‘classic’ model has been a surveillance strategy designed to keep track <strong>of</strong> serious <strong>of</strong>fenders who would otherwise be incarcerated. As such, ISP appears rooted in traditional theories <strong>of</strong> formal social control and deterrence. Offenders are <strong>of</strong>fered the opportunity to remain in the community on the understanding that they are being constantly monitored, and the consequence <strong>of</strong> failure is the loss <strong>of</strong> liberty. Several qualitative studies have noted that most <strong>of</strong>fenders express a preference for incarceration over intermediate sanctions like ISP (e.g., Crouch, 1993; Petersilia & Deschenes, 1994), 3
which perhaps suggests that ISP is a more unpleasant prospect than prison for adjudicated <strong>of</strong>fenders and could therefore have a strong deterrent effect against future <strong>of</strong>fending. MacKenzie and Brame (2001) suggested an alternative mechanism by which social controls operate through ISP. <strong>The</strong>y proposed that increased supervision intensity could lead to increased involvement in conventional and therapeutic activities, and found some support for that hypothesis through empirical testing. Overall, ISP studies have usually focused on the field testing <strong>of</strong> programs and avoided any explication <strong>of</strong> the theoretical foundations <strong>of</strong> probation supervision. Clear and Hardyman (1990) describe two waves <strong>of</strong> interest in ISP research: the first in the 1960s, and another in the mid-1980s. More recently, a third wave <strong>of</strong> research has refined the application <strong>of</strong> increased supervision intensity, considering its relationship with carefully matched programming and treatment. <strong>The</strong> earliest set <strong>of</strong> field studies <strong>of</strong> what may be characterized as ISP programs focused on the impact <strong>of</strong> reducing probation <strong>of</strong>ficers’ caseload sizes, and followed the ‘treatment’ philosophy. At the time, the rehabilitative ideal prevailed in corrections, and it was believed that smaller caseloads allowed probation <strong>of</strong>ficers more time to help their clients (Petersilia & Turner, 1990). However, these initiatives appeared to make little impact on recidivism, and even increased probation failures and technical violations. Clear and Hardyman (1990) suggest that one important reason for the lack <strong>of</strong> effectiveness <strong>of</strong> these initiatives was a lack <strong>of</strong> insight into how probation supervision activity could best serve the treatment goal. Probation <strong>of</strong>ficers simply did not know how to use the additional time made available to them. 4
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: impact of probatio
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 and 38: treatment group, and those to the r
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
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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
Page 81 and 82:
fatal shootings in Philadelphia are
Page 83 and 84:
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
Page 131 and 132:
Table 2.7: Prevalence of</s
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Table 2.9: Time to Failure (Violent
Page 135 and 136:
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 (
Page 191 and 192:
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
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
Page 239 and 240:
Frequency of Recid
Page 241 and 242:
Instrumental Variables Model: Preva
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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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