metadat (version 1.4-0)

dat.bangertdrowns2004: Studies on the Effectiveness of Writing-to-Learn Interventions

Description

Results from 48 studies on the effectiveness of school-based writing-to-learn interventions on academic achievement.

Usage

dat.bangertdrowns2004

Format

The data frame contains the following columns:

id	numeric	study number
author	character	study author(s)
year	numeric	publication year
grade	numeric	grade level (1 = elementary; 2 = middle; 3 = high-school; 4 = college)
length	numeric	treatment length (in weeks)
minutes	numeric	minutes per assignment
wic	numeric	writing tasks were completed in class (0 = no; 1 = yes)
feedback	numeric	feedback on writing was provided (0 = no; 1 = yes)
info	numeric	writing contained informational components (0 = no; 1 = yes)
pers	numeric	writing contained personal components (0 = no; 1 = yes)
imag	numeric	writing contained imaginative components (0 = no; 1 = yes)
meta	numeric	prompts for metacognitive reflection (0 = no; 1 = yes)
subject	character	subject matter
ni	numeric	total sample size of the study
yi	numeric	standardized mean difference
vi	numeric	corresponding sampling variance

Author

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

Concepts

education, standardized mean differences, meta-regression

Details

In each of the studies included in this meta-analysis, an experimental group (i.e., a group of students that received instruction with increased emphasis on writing tasks) was compared against a control group (i.e., a group of students that received conventional instruction) with respect to some content-related measure of academic achievement (e.g., final grade, an exam/quiz/test score). The outcome measure for this meta-analysis was the standardized mean difference (with positive values indicating a higher mean level of academic achievement in the intervention group).

The standardized mean differences given here are bias-corrected and therefore differ slightly from the values reported in the article. Also, since only the total sample size is given in the article, the sampling variances were computed under the assumption that n_i1 = n_i2 = n_i / 2n_i1 = n_i2 = n_i / 2.

Examples

### copy data into 'dat' and examine data
dat <- dat.bangertdrowns2004
dat[1:10,-13]

if (FALSE) {
### load metafor package
library(metafor)

### fit random-effects model
res <- rma(yi, vi, data=dat)
res

### some examples of mixed-effects meta-regression models
res <- rma(yi, vi, mods = ~ factor(grade), data=dat)
res
res <- rma(yi, vi, mods = ~ length, data=dat)
res
res <- rma(yi, vi, mods = ~ info + pers + imag + meta, data=dat)
res
}