metaSEM (version 0.9.8)

Digman97: Factor Correlation Matrices of Big Five Model from Digman (1997)

Description

The data set includes fourteen studies of the factor correlation matrices of the Five-Factor Model of personality reported by Digman (1997).

Usage

data(Digman97)

Source

Digman, J.M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73, 1246-1256.

Details

A list of data with the following structure:

data

A list of 14 studies of correlation matrices. The variables are Agreeableness (A), Conscientiousness (C), Emotional Stability (ES), Extraversion (E) and Intellect (I)

n

A vector of sample sizes

cluster

Types of participants of the studies

References

Cheung, M. W.-L., & Chan, W. (2005). Classifying correlation matrices into relatively homogeneous subgroups: A cluster analytic approach. Educational and Psychological Measurement, 65, 954-979.

Examples

## Not run: 
# Digman97
# 
# ##### Fixed-effects TSSEM
# fixed1 <- tssem1(Digman97$data, Digman97$n, method="FEM")
# summary(fixed1)
# 
# ## Factor covariance among latent factors
# Phi <- matrix(c(1,"0.3*cor","0.3*cor",1), ncol=2, nrow=2)
# 
# ## Error covariance matrix
# Psi <- Diag(c("0.2*e1","0.2*e2","0.2*e3","0.2*e4","0.2*e5"))
# 
# ## S matrix
# S1 <- bdiagMat(list(Psi, Phi))
# 
# ## This step is not necessary but it is useful for inspecting the model.
# dimnames(S1)[[1]] <- dimnames(S1)[[2]] <- c("A","C","ES","E","I","Alpha","Beta")
# 
# ## Display S1
# S1
# 
# ## A matrix
# Lambda <-
# matrix(c(".3*Alpha_A",".3*Alpha_C",".3*Alpha_ES",rep(0,5),".3*Beta_E",".3*Beta_I"),
#        ncol=2, nrow=5)
# A1 <- rbind( cbind(matrix(0,ncol=5,nrow=5), Lambda),
#              matrix(0, ncol=7, nrow=2) )
# 
# ## This step is not necessary but it is useful for inspecting the model.
# dimnames(A1)[[1]] <- dimnames(A1)[[2]] <- c("A","C","ES","E","I","Alpha","Beta")
# 
# ## Display A1
# A1
# 
# ## F matrix to select the observed variables
# F1 <- create.Fmatrix(c(1,1,1,1,1,0,0), as.mxMatrix=FALSE)
# 
# ## Display F1
# F1
# 
# ################################################################################
# ## Alternative model specification in lavaan model syntax
# model <- "## Factor loadings
#           Alpha=~A+C+ES
#           Beta=~E+I
#           ## Factor correlation
#           Alpha~~Beta"
# 
# RAM <- lavaan2RAM(model, obs.variables=c("A","C","ES","E","I"),
#                   A.notation="on", S.notation="with")
# RAM
# 
# A1 <- RAM$A
# S1 <- RAM$S
# F1 <- RAM$F
# ################################################################################
# 
# fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, Fmatrix=F1,
#                  model.name="TSSEM2 Digman97")
# summary(fixed2)
# 
# #### Fixed-effects TSSEM with several clusters
# #### Create a variable for different samples
# #### Younger participants: Children and Adolescents
# #### Older participants: others
# cluster <- ifelse(Digman97$cluster %in% c("Children","Adolescents"),
#                   yes="Younger participants", no="Older participants")
# 
# #### Show the cluster
# cluster
# 
# ## Example of Fixed-effects TSSEM with several clusters
# fixed1.cluster <- tssem1(Digman97$data, Digman97$n, method="FEM",
#                          cluster=cluster)
# summary(fixed1.cluster)
# 
# fixed2.cluster <- tssem2(fixed1.cluster, Amatrix=A1, Smatrix=S1, Fmatrix=F1)
# #### Please note that the estimates for the younger participants are problematic.
# summary(fixed2.cluster)
# 
# ## Load the library
# library("semPlot")
# 
# ## Convert the model to semPlotModel object with 2 plots
# my.plots <- lapply(X=fixed2.cluster, FUN=meta2semPlot, latNames=c("Alpha","Beta"))
# 
# ## Setup two plots
# layout(t(1:2))
# semPaths(my.plots[[1]], whatLabels="est", nCharNodes=10, color="green")
# semPaths(my.plots[[2]], whatLabels="est", nCharNodes=10, color="green")
# 
# #### Random-effects TSSEM with random effects on the diagonals
# random1 <- tssem1(Digman97$data, Digman97$n, method="REM",
#                   RE.type="Diag")
# summary(random1)
# 
# random2 <- tssem2(random1, Amatrix=A1, Smatrix=S1, Fmatrix=F1)
# summary(random2)
# 
# ## Convert the model to semPlotModel object
# my.plot <- meta2semPlot(random2, latNames=c("Alpha","Beta"))
# 
# ## Plot the model with labels
# semPaths(my.plot, whatLabels="path", nCharEdges=10, nCharNodes=10, color="red")
# 
# ## Plot the parameter estimates
# semPaths(my.plot, whatLabels="est", nCharNodes=10, color="green")
# ## End(Not run)