\title{studies on the relationship between school motivation and criminal behavior}
\description{results from 17 studies on the correlation between school motivation/attitudes and subsequent delinquent/criminal behavior.}
\bold{studyid}  \tab \code{numeric}   \tab study identifier \cr
\bold{yi}       \tab \code{numeric}   \tab r-to-z transformed correlation coefficient \cr
\bold{sei}      \tab \code{numeric}   \tab corresponding standard error \cr
\bold{aget1}    \tab \code{numeric}   \tab age at which the school motivation/attitudes were assessed \cr
\bold{aget2}    \tab \code{numeric}   \tab age at which the delinquent/criminal behavior was assessed \cr
\bold{propmale} \tab \code{numeric}   \tab proportion of male participants in the sample \cr
\bold{sexmix}   \tab \code{character} \tab whether the sample consisted only of males, only of females, or a mix
the dataset includes 113 r-to-z transformed correlation coefficients from 17 prospective longitudinal studies that examined the relationship between school motivation/attitudes and subsequent delinquent/criminal behavior.
multiple coefficients could be extracted from the studies \dquote{given the numerous ways in which school motivation/attitudes variables could be operationalized (e.g., academic aspirations, academic self-efficacy) as well as the numerous ways in which crime/delinquency could be operationalized (e.g., property crime, violent crime)} (tanner-smith et al., 2016).
since information to compute the covariance between multiple coefficients within studies is not available, tanner-smith et al. (2016) illustrate the use of cluster-robust inference methods for the analysis of this dataset.
note that this dataset is only meant to be used for pedagogical and demonstration purposes and does not constitute a proper review or synthesis of the complete and current research evidence on the given topic.
tanner-smith, e. e., tipton, e. & polanin, j. r. (2016). handling complex meta-analytic data structures using robust variance estimates: a tutorial in r. \emph{journal of developmental and life-course criminology}, \bold{2}(1), 85--112. \verb{https://doi.org/10.1007/s40865-016-0026-5}
### compute mean age variables within studies
### construct an effect size identifier variable
### construct an approximate var-cov matrix assuming a correlation of 0.8
### for multiple coefficients arising from the same study
v <- vcalc(vi, cluster=studyid, obs=esid, rho=0.8, data=dat)
### fit a multivariate random-effects model using the approximate var-cov matrix v
res <- rma.mv(yi, v, random = ~ esid | studyid, data=dat)
### note: the results obtained above and below are slightly different compared
### to those given by tanner-smith et al. (2016) since the approach illustrated
### here makes use a multivariate random-effects model for the 'working model'
### before applying the cluster-robust inference methods, while the results given
### in the paper are based on a somewhat simpler working model
### examine the main effects of the age variables
res <- rma.mv(yi, v, mods = ~ aget1 + aget2,
res <- rma.mv(yi, v, mods = ~ aget1 * aget2,
res <- rma.mv(yi, v, mods = ~ aget1 * aget2 + sexmix,
psychology, criminology, correlation coefficients, multilevel models, cluster-robust inference, meta-regression