\title{studies on gender differences in grant and fellowship awards}
\description{results from 21 studies on gender differences in grant and fellowship awards.}
\bold{study}      \tab \code{character} \tab study reference \cr
\bold{obs}        \tab \code{numeric}   \tab observation within study \cr
\bold{doctype}    \tab \code{character} \tab document type \cr
\bold{gender}     \tab \code{character} \tab gender of the study authors \cr
\bold{year}       \tab \code{numeric}   \tab (average) cohort year \cr
\bold{org}        \tab \code{character} \tab funding organization / program \cr
\bold{country}    \tab \code{character} \tab country of the funding organization / program \cr
\bold{type}       \tab \code{character} \tab fellowship or grant application \cr
\bold{discipline} \tab \code{character} \tab discipline / field \cr
\bold{waward}     \tab \code{numeric}   \tab number of women who received a grant/fellowship award \cr
\bold{wtotal}     \tab \code{numeric}   \tab number of women who applied for an award \cr
\bold{maward}     \tab \code{numeric}   \tab number of men who received a grant/fellowship award \cr
\bold{mtotal}     \tab \code{numeric}   \tab number of men who applied for an award
the studies in this dataset examine whether the chances of receiving a grant or fellowship award differs for men and women. note that many studies provide multiple comparisons (e.g., for different years / cohorts / disciplines). a multilevel meta-analysis model can be used to account for the multilevel structure in these data.
bornmann, l., mutz, r., & daniel, h. (2007). gender differences in grant peer review: a meta-analysis. \emph{journal of informetrics}, \bold{1}(3), 226--238. \verb{https://doi.org/10.1016/j.joi.2007.03.001}
marsh, h. w., bornmann, l., mutz, r., daniel, h.-d., & o'mara, a. (2009). gender effects in the peer reviews of grant proposals: a comprehensive meta-analysis comparing traditional and multilevel approaches. \emph{review of educational research}, \bold{79}(3), 1290--1326. \verb{https://doi.org/10.3102/0034654309334143}
### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="or", ai=waward, n1i=wtotal, ci=maward, n2i=mtotal, data=dat)
res <- rma.mv(yi, vi, random = ~ 1 | study/obs, data=dat)
### estimated average odds ratio (with 95\% ci/pi)
### test for a difference between fellowship and grant applications
res <- rma.mv(yi, vi, mods = ~ type, random = ~ 1 | study/obs, data=dat)
sociology, odds ratios, multilevel models