metadat (version 1.4-0)

dat.bornmann2007: Studies on Gender Differences in Grant and Fellowship Awards

Description

Results from 21 studies on gender differences in grant and fellowship awards.

Usage

dat.bornmann2007

Format

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

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

sociology, odds ratios, multilevel models

Details

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.

References

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

Examples

### copy data into 'dat' and examine data
dat <- dat.bornmann2007
head(dat, 16)

if (FALSE) {
### 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)
}