\title{studies on the association between recidivism and mental health}
\description{results from 17 studies on the association between recidivism and mental health in delinquent juveniles.}
\bold{study}     \tab \code{numeric}   \tab study id number \cr
\bold{esid}      \tab \code{numeric}   \tab effect size within study id number \cr
\bold{id}        \tab \code{numeric}   \tab row id number \cr
\bold{yi}        \tab \code{numeric}   \tab standardized mean difference \cr
\bold{vi}        \tab \code{numeric}   \tab corresponding sampling variance \cr
\bold{pubstatus} \tab \code{numeric}   \tab published study (0 = no; 1 = yes) \cr
\bold{year}      \tab \code{numeric}   \tab publication year of the study (approximately mean centered) \cr
\bold{deltype}   \tab \code{character} \tab type of delinquent behavior in which juveniles could have recidivated (either general, overt, or covert)
the studies included in this dataset (which is a subset of the data used in assink et al., 2015) compared the difference in recidivism between delinquent juveniles with a mental health disorder and a comparison group of juveniles without a mental health disorder. since studies differed in the way recidivism was defined and assessed, results are given in terms of standardized mean differences, with positive values indicating a higher prevalence of recidivism in the group of juveniles with a mental health disorder.
multiple effect size estimates could be extracted from most studies (e.g., for different delinquent behaviors in which juveniles could have recidivated), necessitating the use of appropriate models/methods for the analysis. assink and wibbelink (2016) illustrate the use of multilevel meta-analysis models for this purpose.
the \code{year} variable is not constant within study 3, as this study refers to two different publications using the same data.
assink, m., & wibbelink, c. j. m. (2016). fitting three-level meta-analytic models in r: a step-by-step tutorial. \emph{the quantitative methods for psychology}, \bold{12}(3), 154--174. \verb{https://doi.org/10.20982/tqmp.12.3.p154}
assink, m., van der put, c. e., hoeve, m., de vries, s. l. a., stams, g. j. j. m., & oort, f. j. (2015). risk factors for persistent delinquent behavior among juveniles: a meta-analytic review. \emph{clinical psychology review}, \bold{42}, 47--61. \verb{https://doi.org/10.1016/j.cpr.2015.08.002}
res <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat)
### use cluster-robust inference methods
res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(0,na))
res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(na,0))
### examine some potential moderators via meta-regression
rma.mv(yi, vi, mods = ~ pubstatus, random = ~ 1 | study/esid, data=dat)
rma.mv(yi, vi, mods = ~ year, random = ~ 1 | study/esid, data=dat)
rma.mv(yi, vi, mods = ~ deltype, random = ~ 1 | study/esid, data=dat)
rma.mv(yi, vi, mods = ~ year + deltype, random = ~ 1 | study/esid, data=dat)
### assume that the effect sizes within studies are correlated with rho=0.6
v <- vcalc(vi, cluster=study, obs=esid, data=dat, rho=0.6)
round(v[dat$study \%in\% c(1,2), dat$study \%in\% c(1,2)], 4)
### fit multilevel model using this approximate v matrix
res <- rma.mv(yi, v, random = ~ 1 | study/esid, data=dat)
### use a correlation of 0.7 for effect sizes corresponding to the same type of
### delinquent behavior and a correlation of 0.5 for effect sizes corresponding
### to different types of delinquent behavior
v <- vcalc(vi, cluster=study, type=deltype, obs=esid, data=dat, rho=c(0.7, 0.5))
psychology, criminology, standardized mean differences, multilevel models, cluster-robust inference