metaSEM (version 1.0.0)

Nohe15: Correlation Matrices from Nohe et al. (2015)

Description

The data sets include two lists of correlation matrices of panel studies between work-family conflict and strain reported in Table A1 (Nohe15A1) and Table A2 (Nohe15A2) by Nohe et al. (2015).

Usage

data(Nohe15A1)
data(Nohe15A2)

Details

A list of data with the following structure:

data

A list of studies of correlation matrices. The variables are W1, S1, W2, and S2 in Nohe15A1 and F1, S1, F2, and S2 in Nohe15A2

n

A vector of sample sizes

RelXX

The reliabilities of W1, S1, W2 and S2 in Nohe15A1 and the reliabilities of F1 S1, F2 , and S2 in Nohe15A2

FemalePer

Percentage of female participants

Publication

Whether the studies were published (P) or unpublished (U)

Lag

Time lag between the coded measurement waves in months

Examples

# NOT RUN {
## Set seed for replicability    
set.seed(23891)
    
## Table A1
randA1a <- tssem1(Nohe15A1$data, Nohe15A1$n, method="REM", RE.type="Diag")
summary(randA1a)

model1 <- 'W2 ~ w2w*W1 + s2w*S1
           S2 ~ w2s*W1 + s2s*S1
           W1 ~~ w1WITHs1*S1
           W2 ~~ w2WITHs2*S2
           W1 ~~ 1*W1
           S1 ~~ 1*S1
           W2 ~~ Errw2*W2
           S2 ~~ Errs2*S2'

RAM1 <- lavaan2RAM(model1, obs.variables=c("W1", "S1", "W2", "S2"))
RAM1

randA1b <- tssem2(randA1a, Amatrix=RAM1$A, Smatrix=RAM1$S)
summary(randA1b)

my.plotA1 <- meta2semPlot(randA1b)
semPlot::semPaths(my.plotA1, whatLabels="est", sizeMan=8, edge.label.cex=1.5, color="yellow",
                  edge.color = "black", weighted=FALSE)

## Table A2
randA2a <- tssem1(Nohe15A2$data, Nohe15A2$n, method="REM", RE.type="Diag")
## Rerun to remove error code
randA2a <- rerun(randA2a)
summary(randA2a)

model2 <- 'F2 ~ f2f*F1 + s2F*S1
           S2 ~ f2s*F1 + s2s*S1
           F1 ~~ f1WITHs1*S1
           F2 ~~ f2WITHs2*S2
           F1 ~~ 1*F1
           S1 ~~ 1*S1
           F2 ~~ Errf2*F2
           S2 ~~ Errs2*S2'

RAM2 <- lavaan2RAM(model2, obs.variables=c("F1", "S1", "F2", "S2"))
RAM2

randA2b <- tssem2(randA2a, Amatrix=RAM2$A, Smatrix=RAM2$S)
summary(randA2b)

## Estimate the heterogeneity of the parameter estimates
tssemParaVar(randA1a, randA2b)    
    
my.plotA2 <- meta2semPlot(randA2b)
semPlot::semPaths(my.plotA2, whatLabels="est", sizeMan=8, edge.label.cex=1.5, color="yellow",
                  edge.color = "black", weighted=FALSE)

## Parametric bootstrap based on Yu et al. (2016)
## I assume that you know what you are doing!

## Set seed for reproducibility
set.seed(39128482)

## Average the correlation coefficients with the univariate-r approach
uni1 <- uniR1(Nohe15A1$data, Nohe15A1$n)
uni1

## Generate random correlation matrices
boot.cor <- bootuniR1(uni1, Rep=50)

## Display the quality of the generated correlation matrices
summary(boot.cor)

## Proposed saturated model
model1 <- 'W2 + S2 ~ W1 + S1'

## Use the harmonic mean of the sample sizes as n in SEM
n <- uni1$n.harmonic    
    
boot.fit1 <- bootuniR2(model=model1, data=boot.cor, n=n)
summary(boot.fit1)

## Proposed model with equal regression coefficients
model2 <- 'W2 ~ Same*W1 + Cross*S1
           S2 ~ Cross*W1 + Same*S1'

boot.fit2 <- bootuniR2(model=model2, data=boot.cor, n=n)
summary(boot.fit2)
    
# }