metaSEM (version 1.2.4)

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Whether the studies were published (P) or unpublished (U)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 {
#### TSSEM
    
## 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'

## Display the model
plot(model1, layout="spring")    
    
RAM1 <- lavaan2RAM(model1, obs.variables=c("W1", "S1", "W2", "S2"))
RAM1

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

## Display the model with the parameter estimates
plot(randA1b, layout="spring")    

## 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'

## Display the model
plot(model2, layout="spring")
    
RAM2 <- lavaan2RAM(model2, obs.variables=c("F1", "S1", "F2", "S2"))
RAM2

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

## Display the model with the parameter estimates
plot(randA2b, layout="spring")  
    
## Estimate the heterogeneity of the parameter estimates
tssemParaVar(randA1a, randA2b)    

## 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)

#### OSMASEM    

## Calculate the sampling variance-covariance matrix of the correlation matrices.    
my.df <- Cor2DataFrame(Nohe15A1$data, Nohe15A1$n)

## Add the centered Lag as a moderator
my.df$data <- data.frame(my.df$data, Lag=scale(Nohe15A1$Lag),
                         check.names=FALSE)
head(my.df$data)

## Proposed model
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'
plot(model1)     

## Convert it into RAM specification    
RAM1 <- lavaan2RAM(model1, obs.variables=c("W1", "S1", "W2", "S2"))
RAM1

## Create vechs of the model implied correlation matrix
## with implicit diagonal constraints
## M0 <- create.vechsR(A0=RAM1$A, S0=RAM1$S)

## Create heterogeneity variances
## RE.type= either "Diag" or "Symm"
##
## Transform= either "expLog" or "sqSD" for better estimation on variances
## T0 <- create.Tau2(RAM=RAM1, RE.type="Diag")
##
## Fit the model    
## fit0 <- osmasem(model.name="No moderator", Mmatrix=M0, Tmatrix=T0, data=my.df)

## Fit the model
fit0 <- osmasem(model.name="No moderator", RAM=RAM1, data=my.df)
summary(fit0)

## Get the SRMR
osmasemSRMR(fit0)

## Get the transformed variance component of the random effects    
VarCorr(fit0)
    
## "lag" as a moderator on A matrix
A1 <- matrix(c(0,0,0,0,
               0,0,0,0,
               "0*data.Lag","0*data.Lag",0,0,
               "0*data.Lag","0*data.Lag",0,0),
             nrow=4, ncol=4, byrow=TRUE)
              
## M1 <- create.vechsR(A0=RAM1$A, S0=RAM1$S, Ax=A1)
##
## Fit the nodel
## fit1 <- osmasem(model.name="Lag as a moderator for Amatrix", Mmatrix=M1,
##                 Tmatrix=T0, data= my.df)
    
fit1 <- osmasem(model.name="Lag as a moderator for Amatrix",
                RAM=RAM1, Ax=A1, data= my.df)
summary(fit1)
VarCorr(fit1)

## Compare the models with and without the moderator "lag"
anova(fit1, fit0)

## Calculate the R2    
osmasemR2(fit0, fit1)
# }