issp89: A Dataset from Cheung and Chan (2005; 2009)

In metaSEM: Meta-Analysis using Structural Equation Modeling

A Dataset from Cheung and Chan (2005; 2009)

Description

Eleven covariance matrices on work-related attitudes were extracted from the Inter-University Consortium for Political and Social Research (1989). Nine variables were selected by Cheung and Chan (2005; 2009) for demonstration purposes. They were grouped into three constructs: Job Prospects measured by job security (JP1), income (JP2), and advancement opportunity (JP3); Job Nature measured by interesting job (JN1), independent work (JN2), help other people (JN3), and useful to society (JN4); and Time Demand measured by flexible working hours (TD1) and lots of leisure time (TD2).

Usage

data(issp89)

Details

A list of data with the following structure:

data

A list of 11 studies of covariance matrices

n

A vector of sample sizes

Author(s)

Mike W.-L. Cheung <[e-mail]>

Source

Inter-University Consortium for Political and Social Research. (1989). International Social Survey Program: Work orientation. Ann Arbor, MI: Author.

References

Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.

Cheung, M. W.-L., & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 16, 28-53.

See Also

issp05

Examples

data(issp89)

#### Analysis of correlation structure in Cheung and Chan (2005)
#### Fixed-effects model: Stage 1 analysis
cor1 <- tssem1(issp89$data, issp89$n, method="FEM", cor.analysis=TRUE)
summary(cor1)
  
## Prepare a model implied matrix
## Factor correlation matrix
Phi <- create.mxMatrix( c("0.3*corf2f1","0.3*corf3f1","0.3*corf3f2"),
                        type="Stand", as.mxMatrix=FALSE )
## Error variances
Psi <- create.mxMatrix( paste("0.2*e", 1:9, sep=""), type="Diag",
                        as.mxMatrix=FALSE )

## Create Smatrix
S1 <- bdiagMat(list(Psi, Phi))
## dimnames(S1)[[1]] <- dimnames(S1)[[2]] <- c(paste("x",1:9,sep=""),
##                                             paste("f",1:3,sep=""))
## S1
S1 <- as.mxMatrix(S1)

## Factor loadings
Lambda <- create.mxMatrix( c(".3*f1x1",".3*f1x2",".3*f1x3",rep(0,9),
                             ".3*f2x4",".3*f2x5",".3*f2x6",".3*f2x7",
                             rep(0,9),".3*f3x8",".3*f3x9"), type="Full",
                             ncol=3, nrow=9, as.mxMatrix=FALSE )
Zero1 <- matrix(0, nrow=9, ncol=9)
Zero2 <- matrix(0, nrow=3, ncol=12)

## Create Amatrix
A1 <- rbind( cbind(Zero1, Lambda),
             Zero2 )
## dimnames(A1)[[1]] <- dimnames(A1)[[2]] <- c(paste("x",1:9,sep=""),
##                                             paste("f",1:3,sep=""))
## A1
A1 <- as.mxMatrix(A1)

## Create Fmatrix
F1 <- create.Fmatrix(c(rep(1,9), rep(0,3)))
  
#### Fixed-effects model: Stage 2 analysis
cor2 <- tssem2(cor1, Amatrix=A1, Smatrix=S1, Fmatrix=F1, intervals.type="LB")
summary(cor2)

## Display the model with the parameter estimates
plot(cor2, nDigits=1)

#### Analysis of covariance structure in Cheung and Chan (2009)
#### Fixed-effects model: Stage 1 analysis
cov1 <- tssem1(issp89$data, issp89$n, method="FEM", cor.analysis=FALSE)
summary(cov1)
  
#### Fixed-effects model: Stage 2 analysis
cov2 <- tssem2(cov1, Amatrix=A1, Smatrix=S1, Fmatrix=F1)              
summary(cov2)

## Display the model with the parameter estimates
plot(cov2, nDigits=1)

metaSEM documentation built on Sept. 30, 2024, 9:21 a.m.