dat.kalaian1996: Studies on the Effectiveness of Coaching for the SAT

In metadat: Meta-Analysis Datasets

Studies on the Effectiveness of Coaching for the SAT

Description

Results from studies examining the effectiveness of coaching on the performance on the Scholastic Aptitude Test (SAT).

Usage

dat.kalaian1996

Format

The data frame contains the following columns:

id	numeric	row (effect) id
study	character	study identifier
year	numeric	publication year
n1i	numeric	number of participants in the coached group
n2i	numeric	number of participants in the uncoached group
outcome	character	subtest (verbal or math)
yi	numeric	standardized mean difference
vi	numeric	corresponding sampling variance
hrs	numeric	hours of coaching
ets	numeric	study conducted by the Educational Testing Service (ETS) (0 = no, 1 = yes)
homework	numeric	assignment of homework outside of the coaching course (0 = no, 1 = yes)
type	numeric	study type (1 = randomized study, 2 = matched study, 3 = nonequivalent comparison study)

Details

The effectiveness of coaching for the Scholastic Aptitude Test (SAT) has been examined in numerous studies. This dataset contains standardized mean differences comparing the performance of a coached versus uncoached group on the verbal and/or math subtest of the SAT. Studies may report a standardized mean difference for the verbal subtest, the math subtest, or both. In the latter case, the two standardized mean differences are not independent (since they were measured in the same group of subjects). The number of hours of coaching (variable hrs), whether the study was conducted by the Educational Testing Service (variable ets), whether homework was assigned outside of the coaching course (variable homework), and the study type (variable type) may be potential moderators of the treatment effect.

Concepts

education, standardized mean differences, multivariate models, meta-regression

Note

The dataset was obtained from Table 1 in Kalaian and Raudenbush (1996). However, there appear to be some inconsistencies between the data in the table and those that were actually used for the analyses (see ‘Examples’).

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

Source

Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227–235. https://doi.org/10.1037/1082-989X.1.3.227

Examples

### copy data into 'dat' and examine data
dat <- dat.kalaian1996
head(dat, 12)

## Not run: 

### load metafor package
library(metafor)

### check ranges
range(dat$yi[dat$outcome == "verbal"]) # -0.35 to 0.74 according to page 230
range(dat$yi[dat$outcome == "math"])   # -0.53 to 0.60 according to page 231

### comparing this with Figure 1 in the paper reveals some discrepancies
par(mfrow=c(1,2), mar=c(5,4,1,1))
plot(log(dat$hrs[dat$outcome == "verbal"]), dat$yi[dat$outcome == "verbal"],
     pch=19, xlab="Log(Coaching Hours)", ylab="Effect Size (verbal)",
     xlim=c(1,6), ylim=c(-0.5,1), xaxs="i", yaxs="i")
abline(h=c(-0.5,0,0.5), lty="dotted")
abline(v=log(c(5,18)),  lty="dotted")
plot(log(dat$hrs[dat$outcome == "math"]), dat$yi[dat$outcome == "math"],
     pch=19, xlab="Log(Coaching Hours)", ylab="Effect Size (math)",
     xlim=c(1,6), ylim=c(-1.0,1), xaxs="i", yaxs="i")
abline(h=c(-0.5,0,0.5), lty="dotted")
abline(v=log(c(5,18)),  lty="dotted")

### construct variance-covariance matrix assuming rho = 0.66 for effect sizes
### corresponding to the 'verbal' and 'math' outcome types
V <- vcalc(vi, cluster=study, type=outcome, data=dat, rho=0.66)

### fit multivariate random-effects model
res <- rma.mv(yi, V, mods = ~ outcome - 1,
              random = ~ outcome | study, struct="UN",
              data=dat, digits=3)
res

### test whether the effect differs for the math and verbal subtest
anova(res, X=c(1,-1))

### log-transform and mean center the hours of coaching variable
dat$loghrs <- log(dat$hrs) - mean(log(dat$hrs), na.rm=TRUE)

### fit multivariate model with log(hrs) as moderator
res <- rma.mv(yi, V, mods = ~ outcome + outcome:loghrs - 1,
              random = ~ outcome | study, struct="UN",
              data=dat, digits=3)
res

### fit model with tau2 = 0 for outcome verbal (which also constrains rho = 0)
res <- rma.mv(yi, V, mods = ~ outcome + outcome:loghrs - 1,
              random = ~ outcome | study, struct="UN", tau2=c(NA,0),
              data=dat, digits=3)
res


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.