dat.bornmann2007 | R Documentation |
Results from 21 studies on gender differences in grant and fellowship awards.
dat.bornmann2007
The data frame contains the following columns:
study | character | study reference |
obs | numeric | observation within study |
doctype | character | document type |
gender | character | gender of the study authors |
year | numeric | (average) cohort year |
org | character | funding organization / program |
country | character | country of the funding organization / program |
type | character | fellowship or grant application |
discipline | character | discipline / field |
waward | numeric | number of women who received a grant/fellowship award |
wtotal | numeric | number of women who applied for an award |
maward | numeric | number of men who received a grant/fellowship award |
mtotal | numeric | 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.
sociology, odds ratios, multilevel models
Wolfgang Viechtbauer, wvb@metafor-project.org, https://www.metafor-project.org
Bornmann, L., Mutz, R., & Daniel, H. (2007). Gender differences in grant peer review: A meta-analysis. Journal of Informetrics, 1(3), 226–238. 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. Review of Educational Research, 79(3), 1290–1326. https://doi.org/10.3102/0034654309334143
### copy data into 'dat' and examine data
dat <- dat.bornmann2007
head(dat, 16)
## Not run:
### load metafor package
library(metafor)
### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=waward, n1i=wtotal, ci=maward, n2i=mtotal, data=dat)
### fit multilevel meta-analysis model
res <- rma.mv(yi, vi, random = ~ 1 | study/obs, data=dat)
res
### estimated average odds ratio (with 95% CI/PI)
predict(res, transf=exp, digits=2)
### test for a difference between fellowship and grant applications
res <- rma.mv(yi, vi, mods = ~ type, random = ~ 1 | study/obs, data=dat)
res
predict(res, newmods=0:1, transf=exp, digits=2)
## End(Not run)
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