metafor (version 1.9-8)

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 in class (0 = no; 1 = yes)	feedback
numeric	feedback (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	id

Source

Bangert-Drowns, R. L., Hurley, M. M., & Wilkinson, B. (2004). The effects of school-based writing-to-learn interventions on academic achievement: A meta-analysis. Review of Educational Research, 74, 29--58.

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 effect size measure for this meta-analysis was the standardized mean difference (with positive scores indicating a higher mean level of academic achievement in the intervention group).

The standardized mean differences given here are bias-corrected and therefore differ slighty from the values reported in the article. Also, since only the total sample size is given in the article, the estimated sampling variances were computed under the assumption that nᵢ₁ = nᵢ₁₂ = nᵢ / 2.

Examples

### load data
dat <- get(data(dat.bangertdrowns2004))

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