metaSEM (version 0.9.8)

Cooper03: Selected effect sizes from Cooper et al. (2003)

Description

Fifty-six effect sizes from 11 districts from Cooper et al. (2003) were reported by Konstantopoulos (2011).

Usage

data(Cooper03)

Source

Cooper, H., Valentine, J. C., Charlton, K., & Melson, A. (2003). The Effects of Modified School Calendars on Student Achievement and on School and Community Attitudes. Review of Educational Research, 73(1), 1-52. doi:10.3102/00346543073001001

Details

The variables are:

District

District ID

Study

Study ID

y

Effect size

v

Sampling variance

Year

Year of publication

References

Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2, 61-76. doi:10.1002/jrsm.35

Examples

## Not run: 
# data(Cooper03)
# 
# #### ML estimation method
# ## No predictor
# summary( model1 <- meta3(y=y, v=v, cluster=District, data=Cooper03) )
# 
# ## Show all heterogeneity indices and their 95% confidence intervals
# summary( meta3(y=y, v=v, cluster=District, data=Cooper03,
#                intervals.type="LB", I2=c("I2q", "I2hm", "I2am", "ICC")) )
# 
# ## Year as a predictor
# summary( meta3(y=y, v=v, cluster=District, x=scale(Year, scale=FALSE),
#                data=Cooper03, model.name="Year as a predictor") )
# 
# ## Equality of level-2 and level-3 heterogeneity
# summary( model2 <- meta3(y=y, v=v, cluster=District, data=Cooper03,
#                          RE2.constraints="0.2*EqTau2",
#                          RE3.constraints="0.2*EqTau2",
#                          model.name="Equal Tau2") )
# 
# ## Compare model2 vs. model1
# anova(model1, model2)
# 
# #### REML estimation method
# ## No predictor
# summary( reml3(y=y, v=v, cluster=District, data=Cooper03) )
# 
# ## Level-2 and level-3 variances are constrained equally 
# summary( reml3(y=y, v=v, cluster=District, data=Cooper03,
#                RE.equal=TRUE, model.name="Equal Tau2") )
# 
# ## Year as a predictor
# summary( reml3(y=y, v=v, cluster=District, x=scale(Year, scale=FALSE),
#                data=Cooper03, intervals.type="LB") )
# 
# ## Handling missing covariates with FIML
# ## Create 20/56 MCAR data in Year
# set.seed(10000)
# Year_MCAR <- Cooper03$Year
# Year_MCAR[sample(56, 20)] <- NA
# summary( meta3X(y=y, v=v, cluster=District, x2=scale(Year_MCAR, scale=FALSE),
#                 data=Cooper03, model.name="NA in Year_MCAR") )
# ## End(Not run)