Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)
This data set includes five studies of ten correlation matrices reported by Becker and Schram (1994).
data(Becker94)
A list of data with the following structure:
A list of 10 correlation matrices. The variables are Math (math aptitude), Spatial (spatial ability), and Verbal (verbal ability)
A vector of sample sizes
Females or Males samples
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.
## Not run:
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*Spatial2Math",
## 0,0,"0.2*Verbal2Math",0,0), type="Full",
## ncol=3, nrow=3, name="A1")
## S1 <- create.mxMatrix(c("0.2*ErrorVarMath",0,0,1,
## "0.2*CorBetweenSpatialVerbal",1),
## type="Symm", name="S1")
## An alternative method to create a regression model with the lavaan syntax
model <- "## Regression model
Math ~ Spatial2Math*Spatial + Verbal2Math*Verbal
## Error variance of Math
Math ~~ ErrorVarMath*Math
## Variances of Spatial and Verbal fixed at 1.0
Spatial ~~ 1*Spatial
Verbal ~~ 1*Verbal
## Correlation between Spatial and Verbal
Spatial ~~ CorBetweenSpatialVerbal*Verbal"
## Display the model
plot(model)
RAM <- lavaan2RAM(model, obs.variables=c("Math", "Spatial", "Verbal"))
RAM
## Second stage analysis
## A1 <- RAM$A
## S1 <- RAM$S
## fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, intervals.type="LB")
fixed2 <- tssem2(fixed1, RAM=RAM, intervals.type="LB")
summary(fixed2)
## Display the model with the parameter estimates
plot(fixed2)
#### Fixed-effects model: with gender as cluster
## First stage analysis
cluster1 <- tssem1(Becker94$data, Becker94$n, method="FEM", cluster=Becker94$gender)
summary(cluster1)
## Second stage analysis
cluster2 <- tssem2(cluster1, RAM=RAM, intervals.type="LB")
summary(cluster2)
#### Conventional fixed-effects GLS approach
## First stage analysis
## No random effects
## Replicate Becker's (1992) analysis using 4 studies only
gls1 <- tssem1(Becker92$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, RAM=RAM, intervals.type="LB",
model.name="Fixed effects GLS Stage 2")
summary(gls2)
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