Results from 48 studies on the effectiveness of school-based writing-to-learn interventions on academic achievement.
dat.bangertdrowns2004
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 |
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.
# NOT RUN {
### copy data into 'dat' and examine data
dat <- dat.bangertdrowns2004
dat
### fit random-effects model
res <- rma(yi, vi, data=dat)
res
# }
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