Becker94 {metaSEM}

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)

Details

A list of data with the following structure:

data

A list of 10 correlation matrices. The variables are Math (math aptitude), Spatial (spatial ability), and Verbal (verbal ability)

n

A vector of sample sizes

gender

Females or Males samples

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.

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*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)