metaSEM (version 0.9.4)

Becker94: Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)

Description

This data set includes five studies of ten correlation matrices reported by Becker and Schram (1994).

Usage

data(Becker94)

source

Becker, B. J., & Schram, C. M. (1994). Examining explanatory models through research synthesis. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 357-381). New York: Russell Sage Foundation.

Details

A list of data with the following structure: [object Object],[object Object],[object Object]

Examples

data(Becker94)

#### Fixed-effects model
## First stage analysis
fixed1 <- tssem1(Becker94$data, Becker94$n, method="FEM")
summary(fixed1)

## Prepare a regression model using create.mxMatrix()
A1 <- create.mxMatrix(c(0,0,0,"0.2*Spatial2Aptitude",
                        0,0,"0.2*Verbal2Aptitude",0,0), type="Full",
                      ncol=3, nrow=3, name="A1")
S1 <-
create.mxMatrix(c("0.2*ErrorVarAptitude",0,0,1,"0.2*CorBetweenSpatialVerbal",1),
                type="Symm", name="S1")

## An alternative method to create a regression model using mxMatrix()
# A1 <- mxMatrix("Full", ncol=3, nrow=3, value=0, free=c(FALSE,FALSE,FALSE,TRUE,FALSE,
#                                                        FALSE,TRUE,FALSE,FALSE),
#                label=c(NA,NA,NA,"Spatial2Aptitude",NA,NA,"Verbal2Aptitude",NA,NA),
#                name="A1")
# S1 <- mxMatrix("Symm", ncol=3, nrow=3, value=c(0.5,0,0,1,0.2,1),
#                free=c(TRUE,FALSE,FALSE,FALSE,TRUE,FALSE),
#                label=c("ErrorVarAptitude",NA,NA,NA,"CorBetweenSpatialVerbal",NA),
#                        name="S1")

## Second stage analysis
fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, intervals.type="LB")
summary(fixed2)


#### Fixed-effects model: with gender as cluster
## First stage analysis
cluster1 <- tssem1(Becker94$data, Becker94$n, method="FEM", cluster=Becker94$gender)
summary(cluster1)

## Second stage analysis  
cluster2 <- tssem2(cluster1, Amatrix=A1, Smatrix=S1, intervals.type="LB")
summary(cluster2)


#### Conventional fixed-effects GLS approach
## First stage analysis
## No random effects
## Replicate Becker's (1992) analysis using 4 studies only
gls1 <- tssem1(Becker92$data[1:4], Becker92$n[1:4], method="REM", RE.type="Zero",
               model.name="Fixed effects GLS Stage 1")
summary(gls1)

## Fixed-effects GLS model: Second stage analysis
gls2 <- tssem2(gls1, Amatrix=A1, Smatrix=S1, intervals.type="LB",
               model.name="Fixed effects GLS Stage 2")
summary(gls2)