Factor Correlation Matrices of Big Five Model from Digman (1997)
The data set includes fourteen studies of the factor correlation matrices of the Five-Factor Model of personality reported by Digman (1997).
data(Digman97)
A list of data with the following structure:
A list of 14 studies of correlation matrices. The variables are Agreeableness (A), Conscientiousness (C), Emotional Stability (ES), Extraversion (E) and Intellect (I)
A vector of sample sizes
Types of participants of the studies
Digman, J.M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73, 1246-1256.
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.
## 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" ## Display the model plot(model) 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$F ################################################################################ fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, Fmatrix=F1, model.name="TSSEM2 Digman97") summary(fixed2) ## Display the model with the parameter estimates plot(fixed2) #### Fixed-effects TSSEM with several clusters #### Create a variable for different samples #### Younger participants: Children and Adolescents #### Older participants: others cluster <- ifelse(Digman97$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) ## Setup two plots layout(t(1:2)) ## Plot the first group plot(fixed2.cluster[[1]]) title("Younger participants") ## Plot the second group plot(fixed2.cluster[[2]]) title("Older participants") #### 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) ## Display the model with the parameter estimates plot(random2, color="green") ## End(Not run)
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