The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk
The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk
The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk
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(arrests, c<strong>on</strong>victi<strong>on</strong>s, violati<strong>on</strong>s, etc.) are unfavorable and the intenti<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> the change in<br />
supervisi<strong>on</strong> intensity is to reduce their prevalence. Thus, a smaller effect size implies<br />
fewer events, which is the goal <str<strong>on</strong>g>of</str<strong>on</strong>g> the programs being tested. 9<br />
<str<strong>on</strong>g>The</str<strong>on</strong>g> synthesis <str<strong>on</strong>g>of</str<strong>on</strong>g> effect sizes in a meta-analysis also requires the calculati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> a<br />
weight for each effect size. Without the inclusi<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> the weight, each study’s effect size<br />
is assumed to c<strong>on</strong>tribute equally to the overall (mean) effect size. This is unjustified<br />
because smaller studies have greater sampling error and should not c<strong>on</strong>tribute as much to<br />
the mean outcome as larger, relatively more reliable studies. Lipsey and Wils<strong>on</strong> (2001, p.<br />
36) suggest that the optimal study weight is based <strong>on</strong> the inverse <str<strong>on</strong>g>of</str<strong>on</strong>g> the squared standard<br />
error <str<strong>on</strong>g>of</str<strong>on</strong>g> the effect size (called the ‘inverse variance weight’). Formulas for calculating<br />
the standard error and inverse variance weight for the OR are presented in Appendix C.<br />
Computati<strong>on</strong>s <str<strong>on</strong>g>of</str<strong>on</strong>g> effect sizes and inverse variance weights, and calculati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> the<br />
mean effect sizes and corresp<strong>on</strong>ding c<strong>on</strong>fidence intervals and statistical tests, are<br />
performed using specialized meta-analysis macros written for STATA s<str<strong>on</strong>g>of</str<strong>on</strong>g>tware (Wils<strong>on</strong>,<br />
2002). We use RevMan s<str<strong>on</strong>g>of</str<strong>on</strong>g>tware (Cochrane Collaborati<strong>on</strong>, 2008) to c<strong>on</strong>struct forest<br />
plots for the graphical representati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> meta-analysis results. <str<strong>on</strong>g>The</str<strong>on</strong>g> forest plot shows the<br />
weighted mean effect size and associated 95 per cent c<strong>on</strong>fidence interval for each study.<br />
A square represents the point estimate <str<strong>on</strong>g>of</str<strong>on</strong>g> the effect size, the size <str<strong>on</strong>g>of</str<strong>on</strong>g> the square represents<br />
the study weight, and the lines <strong>on</strong> either side <str<strong>on</strong>g>of</str<strong>on</strong>g> the square are the c<strong>on</strong>fidence intervals.<br />
<str<strong>on</strong>g>The</str<strong>on</strong>g> mean effect size across all studies is displayed as a diam<strong>on</strong>d, whose far left and right<br />
points represent the lower and upper bounds <str<strong>on</strong>g>of</str<strong>on</strong>g> that estimate’s c<strong>on</strong>fidence interval. <str<strong>on</strong>g>The</str<strong>on</strong>g><br />
plot is centered around 1, the point at which no difference is observed between treatment<br />
and c<strong>on</strong>trol groups. Point estimates to the left <str<strong>on</strong>g>of</str<strong>on</strong>g> center represent outcomes favoring the<br />
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