metafor (version 3.0-2)

dat.konstantopoulos2011: Studies on the Effects of Modified School Calendars on Student Achievement

Description

Results from 56 studies on the effects of modified school calendars on student achievement.

Usage

dat.konstantopoulos2011

Format

The data frame contains the following columns:

district	numeric	district id number
school	numeric	school id number (within district)
study	numeric	study id number
yi	numeric	standardized mean difference
vi	numeric	corresponding sampling variance

Details

Instead of following the more traditional school calendar with a long summer break (in addition to a short winter and spring break), some schools have switched to a modified school calendar comprising more frequent but shorter intermittent breaks (e.g., 9 weeks of school followed by 3 weeks off), while keeping the total number of days at school approximately the same. The effects of using such a modified calendar on student achievement have been examined in a number of studies and were meta-analyzed by Cooper et al. (2003).

The dataset (taken from Konstantopoulos, 2011) contains the results from 56 studies, each comparing the level of academic achievement in a group of students following a modified school calendar with that of a group of students following a more traditional school calendar. The difference between the two groups was quantified in terms of a standardized mean difference (with positive values indicating a higher mean level of achievement in the group following the modified school calendar).

The studies were conducted at various schools that were clustered within districts. The data therefore have a multilevel structure, with schools nested within districts. A multilevel meta-analysis of these data can be used to estimate and account for the amount of heterogeneity between districts and between schools within districts.

References

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. https://doi.org/10.3102/00346543073001001

Examples

# NOT RUN {
### copy data into 'dat' and examine data
dat <- dat.konstantopoulos2011
dat

### regular random-effects model
res <- rma(yi, vi, data=dat)
print(res, digits=3)

### regular random-effects model using rma.mv()
res <- rma.mv(yi, vi, random = ~ 1 | study, data=dat)
print(res, digits=3)

### multilevel random-effects model
res.ml <- rma.mv(yi, vi, random = ~ 1 | district/school, data=dat)
print(res.ml, digits=3)

### profile variance components
profile(res.ml, progbar=FALSE)

### multivariate parameterization of the model
res.mv <- rma.mv(yi, vi, random = ~ factor(school) | district, data=dat)
print(res.mv, digits=3)

### tau^2 from multivariate model = sum of the two variance components from the multilevel model
round(sum(res.ml$sigma2), 3)

### rho from multivariate model = intraclass correlation coefficient based on the multilevel model
round(res.ml$sigma2[1] / sum(res.ml$sigma2), 3)
# }