The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

2 One U.S. estimate indicates that over 40 per cent <strong>of</strong> probationers and more than half <strong>of</strong> parolees do not
complete their supervision terms successfully, and that parole violators account for nearly 35 per cent <strong>of</strong>
admissions to state prisons (Solomon et al., 2008).
3 Worrall et al. conducted a cross-sectional study that indicated an increase in property crime rates across
the state <strong>of</strong> California as that state’s average probation caseloads increased.
4 <strong>The</strong>re are multiple ways in which supervision can be intensified, particularly in the light <strong>of</strong> advances in
information technology. Electronic monitoring, satellite tracking (GPS), and voice verification systems are
popular methods for ‘passively’ managing <strong>of</strong>fender caseloads. Because such a wide range <strong>of</strong> automated
systems are available, some <strong>of</strong> which have been the focus <strong>of</strong> systematic reviews in their own right (e.g.,
Renzema & Mayo-Wilson, 2005, on electronic monitoring), we do not include evaluations that focus solely
on passive monitoring technology. However, many intensive supervision programs use technology as part
<strong>of</strong> a range <strong>of</strong> surveillance measures implemented alongside direct contact with probation <strong>of</strong>ficers, and these
studies will be considered if the monitoring technology is not the only difference in intensity between
treatment and comparison cases.
5 Grey literature refers to studies that are not commercially published or available through traditional
sources, such as technical reports and dissertations. Failure to allow for the identification <strong>of</strong> grey literature
in systematic searches can lead to publication bias, which occurs when the published or otherwise readily
available literature is not representative <strong>of</strong> all studies. This is a real possibility: for example, some authors
and journal editors may be more inclined to submit or accept statistically significant findings, whereas
studies that show no discernible effect may be written up for funding agencies but never published in peerreviewed
academic journals (Rothstein & Hopewell, 2009).
6 Important journals include: British Journal <strong>of</strong> Criminology; Crime & Delinquency; Crime & Justice;
Criminology; Criminology & Public Policy; Federal Probation; Journal <strong>of</strong> Criminal Justice; Journal <strong>of</strong>
Experimental Criminology; Journal <strong>of</strong> Offender Rehabilitation; Journal <strong>of</strong> Quantitative Criminology;
Journal <strong>of</strong> Research in Crime & Delinquency; Justice Quarterly; Probation Journal.
7 http://www.zotero.org.
8 <strong>The</strong> odds <strong>of</strong> the event occurring are given by p/(1-p) (the probability <strong>of</strong> the event occurring divided by the
probability <strong>of</strong> the event not occurring).
9 Note that although we present our results as odds ratios, analyses are actually performed on the natural
log <strong>of</strong> the OR, which is centered around 0 rather than 1 and has a standard error that is easier to calculate
(Lipsey & Wilson, 2001, p. 54).
10 Current thinking in meta-analytic methods states that the random effects model should always be used.
Previously, fixed effects models were considered acceptable when the Q-statistic from the main effects
analysis was non-significant, indicating homogeneity between effect sizes. However, the assumptions <strong>of</strong>
the random effects model are probably more defensible for many criminological applications. <strong>The</strong> random
effects model also converges on the fixed effects as the distribution becomes homogeneous (see Appendix
C) (Lipsey & Wilson, 2001, p. 120; David B. Wilson, personal communication, December 2009). Most <strong>of</strong>
our analyses displayed substantial heterogeneity. We obtained both fixed and random effects estimates for
the mean effect sizes and did not observe much difference between the two. <strong>The</strong> random effects estimates
were generally more conservative (results not shown).
11 <strong>The</strong> mixed effects analog to the ANOVA has a lower risk <strong>of</strong> Type I error than the fixed effects, which
assumes that differences are systematic and thus does not perform well when the distribution is very
heterogeneous. We employ a method <strong>of</strong> moments estimator <strong>of</strong> the random effects variance component.
We also use this estimator for the main effects model (Appendix C; Lipsey & Wilson, 2001, pp. 124-5;
Wilson, 2010, pp. 195-8). This is the least biased estimator available in the current version <strong>of</strong> the STATA
macro, but it is less efficient than the alternative maximum likelihood approach. However, it is well-suited
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