metaSEM (version 0.9.8)

Bornmann07: A Dataset from Bornmann et al. (2007)

Description

A dataset from Bornmann et al. (2007) for three-level meta-analysis.

Usage

data(Bornmann07)

Source

Bornmann, L., Mutz, R., & Daniel, H.-D. (2007). Gender differences in grant peer review: A meta-analysis. Journal of Informetrics, 1(3), 226-238. doi:10.1016/j.joi.2007.03.001

Details

The variables are:

ID

ID of the study

Study

Study name

Cluster

Cluster for effect sizes

logOR

Effect size: log odds ratio

v

Sampling variance of logOR

Year

Year of publication

Type

Type of proposal: either Grant or Fellowship

Discipline

Discipline of proposal: either Physical sciences, Life sciences/biology, Social sciences/humanities or Multidisciplinary)

Country

Country of proposal: either United States, Canada, Australia, United Kingdom or Europe

References

Cheung, M. W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19, 211-229.

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. doi:10.3102/0034654309334143

Examples

## Not run: 
# data(Bornmann07)
# 
# #### ML estimation method
# ## No predictor
# summary( meta3(y=logOR, v=v, cluster=Cluster, data=Bornmann07) )
# 
# ## Type as a predictor
# ## Grant: 0
# ## Fellowship: 1
# summary( meta3(y=logOR, v=v, x=(as.numeric(Type)-1),
#                cluster=Cluster, data=Bornmann07) )
# 
# ## Centered Year as a predictor
# summary( meta3(y=logOR, v=v, x=scale(Year, scale=FALSE),
#                cluster=Cluster, data=Bornmann07) )
# 
# #### REML estimation method
# ## No predictor
# summary( reml3(y=logOR, v=v, cluster=Cluster, data=Bornmann07) )
# 
# ## Type as a predictor
# ## Grants: 0
# ## Fellowship: 1
# summary( reml3(y=logOR, v=v, x=(as.numeric(Type)-1),
#                cluster=Cluster, data=Bornmann07) )
# 
# ## Centered Year as a predictor
# summary( reml3(y=logOR, v=v, x=scale(Year, scale=FALSE),
#                cluster=Cluster, data=Bornmann07) )
# 
# ## Handling missing covariates with FIML
# ## MCAR
# ## Set seed for replication
# set.seed(1000000)
# 
# ## Copy Bornmann07 to my.df
# my.df <- Bornmann07
# ## "Fellowship": 1; "Grant": 0
# my.df$Type_MCAR <- ifelse(Bornmann07$Type=="Fellowship", yes=1, no=0)
# 
# ## Create 17 out of 66 missingness with MCAR
# my.df$Type_MCAR[sample(1:66, 17)] <- NA
# summary(meta3X(y=logOR, v=v, cluster=Cluster, x2=Type_MCAR, data=my.df))
# 
# ## MAR
# Type_MAR <- ifelse(Bornmann07$Type=="Fellowship", yes=1, no=0)
# 
# ## Create 27 out of 66 missingness with MAR for cases Year<1996
# index_MAR <- ifelse(Bornmann07$Year<1996, yes=TRUE, no=FALSE)
# Type_MAR[index_MAR] <- NA
# 
# ## Include auxiliary variable
# summary(meta3X(y=logOR, v=v, cluster=Cluster, x2=Type_MAR, av2=Year, data=my.df))
# ## End(Not run)