metafor (version 1.7-0)

dat.bonett2010: Studies on the Reliability of the CES-D Scale

Description

Results from 9 studies on the reliability of the Center for Epidemiologic Studies Depression (CES-D) Scale administered to children providing care to an elderly parent.

Usage

dat.bonett2010

format

The data frame contains the following columns: lll{ study numeric study number source character source of data ni numeric sample size mi numeric number of items in the scale ai numeric observed value of Cronbach's alpha caregivers character gender of the children in the sample }

source

Bonett, D. G. (2010). Varying coefficient meta-analytic methods for alpha reliability. Psychological Methods, 15, 368--385.

Details

The Center for Epidemiologic Studies Depression (CES-D) Scale is a 20-item questionnaire assessing various symptoms of depression, with each item scored on a 4-point scale. The scale has been used in several studies to examine depressive symptoms in children providing care to an elderly parent. The dataset includes information on the reliability of the scale as measured with Cronbach's alpha in 9 such studies. Also, the gender composition of the children in each sample is indicated.

References

Hakstian, A. R., & Whalen, T. E. (1976). A k-sample significance test for independent alpha coefficients. Psychometrika, 41, 219--231.

Examples

### load data
data(dat.bonett2010)

### meta-analysis using the raw alpha values
res <- rma(measure="ARAW", ai=ai, mi=mi, ni=ni, data=dat.bonett2010)
res

### meta-analysis using transformed alpha values (using the
### transformation suggested by Hakstian & Whalen, 1976)
res <- rma(measure="AHW", ai=ai, mi=mi, ni=ni, data=dat.bonett2010)
res
predict(res, transf=transf.iahw)

### meta-analysis using transformed alpha values (using the
### transformation suggested by Bonett, 2002)
res <- rma(measure="ABT", ai=ai, mi=mi, ni=ni, data=dat.bonett2010)
res
predict(res, transf=transf.iabt)

### examine whether women and mixed samples yield different alphas (using raw alpha values)
res <- rma(measure="ARAW", mods = ~ caregivers, ai=ai, mi=mi, ni=ni, data=dat.bonett2010)
res
predict(res, newmods=c(0,1), digits=2)