What is lavaan?
lavaan is a free, open source R package for latent variable analysis. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.
What is “Confirmatory Factor Analysis for Applied Research”?
It is a very comprehensive book by Timothy A. Brown emphasizing practical and theoretical aspects of Confirmatory Factor Analysis (CFA), a well-established multivariate statistics method. CFA/SEM is mostly performed via commercial software, such as Mplus, EQS, Lisrel and Amos. The R package lavaan offers a very promising open source alternative. The companion website of Brown’s book on CFA currently contains the syntax code for running the book examples in most (if not all) of the above commercial packages. So what’s missing? A lavaan implementation. Here it is, following the chapters of the book.
Chapter 4 - Specification and Interpretation of CFA Models
Contains two examples a) Two-Factor CFA (Neuroticism, Extraversion) and b) CFA with Single Indicators: Health Status
#############################################################
# Run this code at your own risk!
#############################################################
# Chapter 4: Specification and Interpretation of CFA Models
install.packages(“lavaan”)
library(lavaan)
# Two Factor CFA Model of Neuroticism and Extraversion (Tables 4.2, 4.3, 4.4)
# input: the sample variance-covariance matrix of page 116
covmat <- ‘32.49
24.4826 31.36
26.6669 25.4106 40.96
25.2772 23.557 27.7978 32.49
-12.0042 -10.1472 -13.6704 -10.8756 36
-11.1674 -9.7216 -11.904 -9.4358 25.11 38.44
-9.617 -9.2249 -10.8346 -9.617 21.6828 23.0063 32.49
-9.0014 -7.9654 -10.4653 -7.8204 17.9424 20.589 18.0667 31.36’
neodat.cov <- getCov(covmat, names=c(“N1”,“N2”,“N3”,“N4”,“E1”,“E2”,“E3”,“E4”))
#set up model
new.model <- ’ neuro =~ N1 + N2 + N3 + N4
extrav =~ E1 + E2 + E3 + E4’
# fit model
fit <- cfa(new.model, sample.cov=neodat.cov, sample.nobs=250, mimic=“Mplus”)
#summary statistics
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
parameterEstimates(fit) #coefficients with se, 95%CI and z values etc.
fitted(fit) #the model implied variance-covariance matrix, slightly different values from Table 4.2 p. 117
resid(fit) # unstandardized residuals matrix
resid(fit,type=“standardized”) #standardized residuals matrix, there are differences with the values found in Table 4.2 p. 117
#############################################################
#CFA with Single Indicators: Health Status
#############################################################
# read Health Status data from the book’s companion website
health <- read.table(“http://people.bu.edu/tabrown/Ch4/fig4.3.dat”)
# remove id variable
health <- health[,-1]
# assign variable names
names(health) <- c(“ACTIV”,“SOMA”,“PAIN”,“MENTH”,“SOCF”,“VITAL”,“GENHLTH”,“AGE”)
head(health)
#set up model
model <- ’
# measurement model
psysfun =~ ACTIV + SOMA + PAIN
mentfun =~ MENTH + SOCF + VITAL
AGEF =~ AGE
GWB =~ GENHLTH
#fix variances of single indicators
AGE ~~ 0AGE
GENHLTH ~~ 7.88GENHLTH
# residual correlations
ACTIV ~~ SOMA
’
# fit model
fit <- cfa(model, data=health,mimic=“Mplus”)
# summary statistics
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
Chapter 5 - CFA Model Revision and Comparison
Contains two examples a) a Three-Factor CFA Model (Alcohol Motives) and b) E/CFA (EFA in the CFA framework)
################################################
# Chapter 5: CFA Model Revision and Comparison #
################################################
library(lavaan)
####################################################################
# Three-Factor CFA (Alcohol Motives QN) model (Figure 1, page 168) #
####################################################################
#input: the lower part of a correlation matrix, see p.169, Table 5.2 (the lisrel syntax)
cormat <- ’
1.000
0.300 1.000
0.229 0.261 1.000
0.411 0.406 0.429 1.000
0.172 0.252 0.218 0.481 1.000
0.214 0.268 0.267 0.579 0.484 1.000
0.200 0.214 0.241 0.543 0.426 0.492 1.000
0.185 0.230 0.185 0.545 0.463 0.548 0.522 1.000
0.134 0.146 0.108 0.186 0.122 0.131 0.108 0.151 1.000
0.134 0.099 0.061 0.223 0.133 0.188 0.105 0.170 0.448 1.000
0.160 0.131 0.158 0.161 0.044 0.124 0.066 0.061 0.370 0.350 1.000
0.087 0.088 0.101 0.198 0.077 0.177 0.128 0.112 0.356 0.359 0.507 1.000’
#convert it to a square matrix
Cmat <- getCov(cormat)
# the standard deviations of the variables (Table 5.2)
neodat.sdev = c(2.06, 1.52, 1.92, 1.41, 1.73, 1.77, 2.49, 2.27, 2.68, 1.75, 2.57, 2.66)
# convert the correlation matrix to a covariance matrix (lavaan needs a covariance matrix as input)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“X1”,“X2”,“X3”,“X4”,“X5”,“X6”,“X7”,“X8”,“X9”,“X10”,“X11”,“X12”)
colnames(covmat)=rownames(covmat)
#model set up - this is the model presented in Figure 1, p.168
# you can play around with this model following the text of Ch.5
new.model <- ‘#X4 loads on both coping and social factors
coping =~ X1 + X2 + X3 + X4
social =~ X5 + X6 + X7 + X8 + X4
enhance =~ X9 + X10 + X11 + X12
# residual correlation
X11 ~~ X12’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=500)
#results are almost identical due to rounding errors in the matrix conversion step
fit
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
########################################################
#E/CFA (EFA in the CFA framework) page 193 - Table 5.8 #
########################################################
# read Alcohol Motives data from the book’s companion website
alcohol <- read.table(“http://people.bu.edu/tabrown/Ch5/efa.dat”)
#remove last column
alcohol <- alcohol[,-13]
# assign variable names
names(alcohol) <- c(“X1”,“X2”,“X3”,“X4”,“X5”,“X6”,“X7”,“X8”,“X9”,“X10”,“X11”,“X12”)
head(alcohol)
#set up model
new.model <- ‘#Force the loadings of the first indicators to be free
coping =~ NAX1 + X2+ X3 + X4 + X5 + X6 + X7 + X9 + X10 + X11
social =~ NAX8 + X2 + X3 + X4 + X5 + X6 + X7 + X9 + X10 + X11
enhance =~ NAX12 + X2 + X3 + X4 + X5 + X6 + X7 + X9 + X10 + X11
#factor variances fixed to 1.0
coping ~~ 1coping
social ~~ 1social
enhance ~~ 1enhance’
#fit model
fit <- sem(new.model, data=alcohol)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
Chapter 6 - CFA of Multitrait-Multimethod Matrices
Contains two examples a) a Correlated Methods Model of a Multitrait-Multimethod Matrix (MTMM) and b) a Correlated Uniqueness Model of a MTMM matrix
#####################################################
# Chapter 6: CFA of Multitrait-Multimethod Matrices #
#####################################################
library(lavaan)
######################################################
# Correlated Methods Model of the MTMM Matrix, p.218 #
######################################################
#Input matrix: the correlation matrix of Table 6.2 (Lisrel syntax)
cormat <- ’
1.000
0.290 1.000
0.372 0.478 1.000
0.587 0.238 0.209 1.000
0.201 0.586 0.126 0.213 1.000
0.218 0.281 0.681 0.195 0.096 1.000
0.557 0.228 0.195 0.664 0.242 0.232 1.000
0.196 0.644 0.146 0.261 0.641 0.248 0.383 1.000
0.219 0.241 0.676 0.290 0.168 0.749 0.361 0.342 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(3.61, 3.66, 3.59, 2.94, 3.03, 2.85, 2.22, 2.42, 2.04)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“PARI”,“SZTI”,“SZDI”,“PARC”,“SZTC”,“SZDC”,“PARO”,“SZTO”,“SZDO”)
colnames(covmat)= rownames(covmat)
#Correlated Methods Model p.218
#model set up - this is the model presented in Figure 6.1, p.218
new.model <- ‘paranoid =~ PARI + PARC + PARO
schizotypal =~ SZTI + SZTC + SZTO
schizoid =~ SZDI + SZDC + SZDO
inventory =~ SZTI + PARI + SZDI
clininter =~ PARC + SZTC + SZDC
obsrating =~ PARO + SZTO + SZDO’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=500)
#The model has NOT converged! And this is exactly what we expected.
# Brown does not present the results of this model for the same reason.
fit
#########################################################
# Correlated Uniqueness Model of the MTMM Matrix, p.220 #
#########################################################
#Input matrix: the correlation matrix of Table 6.3 (Lisrel syntax)
cormat <- ’
1.000
0.290 1.000
0.372 0.478 1.000
0.587 0.238 0.209 1.000
0.201 0.586 0.126 0.213 1.000
0.218 0.281 0.681 0.195 0.096 1.000
0.557 0.228 0.195 0.664 0.242 0.232 1.000
0.196 0.644 0.146 0.261 0.641 0.248 0.383 1.000
0.219 0.241 0.676 0.290 0.168 0.749 0.361 0.342 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(3.61, 3.66, 3.59, 2.94, 3.03, 2.85, 2.22, 2.42, 2.04)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“PARI”,“SZTI”,“SZDI”,“PARC”,“SZTC”,“SZDC”,“PARO”,“SZTO”,“SZDO”)
colnames(covmat)= rownames(covmat)
#Correlated Uniqueness Model of the MTMM Matrix, p.228
#model set up - this is the model presented in Figure 6.2, p.220
new.model <- ‘paranoid =~ PARI + PARC + PARO
schizotypal =~ SZTI + SZTC + SZTO
schizoid =~ SZDI + SZDC + SZDO
# residual correlation
PARI ~~ SZTI
SZTI ~~ SZDI
PARI ~~ SZDI
PARC ~~ SZTC
SZTC ~~ SZDC
PARC ~~ SZDC
PARO ~~ SZTO
SZTO ~~ SZDO
PARO ~~ SZDO’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=500)
#results same as in Table 6.4, page 226
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
Chapter 7 - CFA with Equality Constraints, Multiple Groups, and Mean Structures
Contains examples for a) Tau Equivalent and Parallel Indicators b) Longitudinal Measurement Invariance c) Multiple Groups CFA (Depression example) d) MIMIC Models (Phobia example)
#####################################################
# Chapter 7: CFA with Equality Constraints, #
# Multiple Groups, and Mean Structures #
#####################################################
library(lavaan)
#####################################
# Equality Constraints p.236 - 252 #
#####################################
#Input matrix: the correlation matrix of Figure 7.1
cormat <- ’
1.000
0.661 1.000
0.630 0.643 1.000
0.270 0.300 0.268 1.000
0.297 0.265 0.225 0.805 1.000
0.290 0.287 0.248 0.796 0.779 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(2.610, 2.660, 2.590, 1.940, 2.030, 2.050)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“x1”,“x2”,“x3”,“x4”,“x5”,“x6”)
colnames(covmat)= rownames(covmat)
############ Congeneric Model #############
#model set up - this is the model presented in Figure 7.1, p.238
new.model <- ‘auditory =~ x1 + x2 + x3
visual =~ x4 + x5 + x6’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=200)
#results same as in Table 7.1, page 241
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
############# Tau-equivalent Model - Auditory Memory only ############
#model set up - this is the model presented in Figure 7.1, p.238
new.model <- ‘auditory =~ v1x1 + v1x2 + v1*x3
visual =~ x4 + x5 + x6’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=200)
#results same as in Table 7.3, page 246 (only model fit)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
############# Tau-equivalent Model - Auditory & Visual memory ############
#model set up - this is the model presented in Figure 7.1, p.238
new.model <- ‘auditory =~ v1x1 + v1x2 + v1x3
visual =~ v2x4 + v2x5 + v2x6’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=200)
#results same as in Table 7.3, page 246 (only model fit)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
############# Parallel Model - Auditory Memory only ############
#model set up - this is the model presented in Figure 7.1, p.238
new.model <- ‘auditory =~ v1x1 + v1x2 + v1x3
visual =~ v2x4 + v2x5 + v2x6
#equality constraints of error variances (Auditory)
f1x1 ~~ f1x1
f1x2 ~~ f1x2
f1x3 ~~ f1x3’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=200)
#results same as in Table 7.3, page 246 (only model fit)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
############# Parallel Model - Auditory & Visual memory ############
#model set up - this is the model presented in Figure 7.1, p.238
new.model <- ‘auditory =~ v1x1 + v1x2 + v1x3
visual =~ v2x4 + v2x5 + v2x6
#equality constraints of error variances (Auditory & Visual)
f1x1 ~~ f1x1
f1x2 ~~ f1x2
f1x3 ~~ f1x3
f2x4 ~~ f2x4
f2x5 ~~ f2x5
f2x6 ~~ f2x6’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=200)
#results same as in Table 7.4, page 248
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#to see the internal representation of the model (this helps understand the model definition)
inspect(fit,what=“list”)
########################################
# Longitudinal Invariance p.252 - 268 #
########################################
#Input matrix: the correlation matrix of Figure 7.2
cormat <- ’
1.000
0.736 1.000
0.731 0.648 1.000
0.771 0.694 0.700 1.000
0.685 0.512 0.496 0.508 1.000
0.481 0.638 0.431 0.449 0.726 1.000
0.485 0.442 0.635 0.456 0.743 0.672 1.000
0.508 0.469 0.453 0.627 0.759 0.689 0.695 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(1.940, 2.030, 2.050, 1.990, 2.610, 2.660, 2.590, 2.550)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“A1”,“B1”,“C1”,“D1”,“A2”,“B2”,“C2”,“D2”)
colnames(covmat)= rownames(covmat)
#Means: 1.500 1.320 1.450 1.410 6.600 6.420 6.560 6.310
#I couldn’t find a way to bring in the indicator means,
#so I used the meanstructure = TRUE argument in cfa call (which is not the same thing).
##################################################################
#Results of the Equal Form Longitudinal Model of Job Satisfaction
##################################################################
#model set up - this is the model presented in Figure 7.2, p.254
new.model <- ‘SATIS1 =~ A1 + B1 + C1 + D1
SATIS2 =~ A2 + B2 + C2 + D2
A1 ~~ A2
B1 ~~ B2
C1 ~~ C2
D1 ~~ D2’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=250, meanstructure = TRUE)
#results same as in Table 7.6, page 260 and fit same as in Table 7.7
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
##############################################################################
#Results of the Equal Factor Loadings Longitudinal Model of Job Satisfaction #
##############################################################################
#model set up - this is the model presented in Figure 7.2, p.254
# loadings constrained to be equal using custom labels
new.model <- ‘SATIS1 =~ v1A1 + v2B1 + v3C1 + v4D1
SATIS2 =~ v1A2 + v2B2 + v3C2 + v4D2
A1 ~~ A2
B1 ~~ B2
C1 ~~ C2
D1 ~~ D2’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=250,meanstructure = TRUE)
#Fit same as in Table 7.7
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
##############################################################################
#Equal indicator intercepts Longitudinal Model of Job Satisfaction #
##############################################################################
#model set up - this is the model presented in Figure 7.2, p.254
# loadings constrained to be equal using custom labels
new.model <- ‘SATIS1 =~ v1A1 + v2B1 + v3C1 + v4D1
SATIS2 =~ v1A2 + v2B2 + v3C2 + v4D2
A1 ~~ A2
B1 ~~ B2
C1 ~~ C2
D1 ~~ D2
# intercepts constrained to be equal
# using custom labels
A1 ~ int2 * 1
A2 ~ int2 * 1
B1 ~ int3 * 1
B2 ~ int3 * 1
C1 ~ int4 * 1
C2 ~ int4 * 1
D1 ~ int5 * 1
D2 ~ int5 * 1’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=250,meanstructure=TRUE)
#results are not the same as in Table 7.7 - no way to bring in means in lavaan (?)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
##############################################################################
#Equal indicator error variances Longitudinal Model of Job Satisfaction #
##############################################################################
#model set up - this is the model presented in Figure 7.2, p.254
new.model <- ‘SATIS1 =~ v1A1 + v2B1 + v3C1 + v4D1
SATIS2 =~ v1A2 + v2B2 + v3C2 + v4D2
A1 ~~ A2
B1 ~~ B2
C1 ~~ C2
D1 ~~ D2
# intercepts constrained to be equal
# using custom labels
A1 ~ int2 * 1
A2 ~ int2 * 1
B1 ~ int3 * 1
B2 ~ int3 * 1
C1 ~ int4 * 1
C2 ~ int4 * 1
D1 ~ int5 * 1
D2 ~ int5 * 1
#error variances constrained to be equal
#using custom labels
f1A1 ~~ f1A1
f1A2 ~~ f1A2
f2B1 ~~ f2B1
f2B2 ~~ f2B2
f3C1 ~~ f3C1
f3C2 ~~ f3C2
f4D1 ~~ f4D1
f4D2 ~~ f4D2’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=250,meanstructure=TRUE)
#results are not the same as in Table 7.7 - no way to bring in means in lavaan (?)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#to see the internal representation of the model (this helps understand the model definition)
inspect(fit,what=“list”)
#############################################
#### Multiple Group Analysis pp. 268-299 ####
#############################################
# read Depression example from the book’s companion website
depres <- read.table(“http://people.bu.edu/tabrown/Ch7/MDDALL.dat”)
# assign variable names
names(depres) <- c(“Sex”,“M1”,“M2”,“M3”,“M4”,“M5”,“M6”,“M7”,“M8”,“M9”)
head(depres)
#model set up - this is the model presented in Figure 7.2, p.238
new.model <- ‘majdep =~ M1 + M2 + M3 + M4 + M5 + M6 + M7 + M8 + M9
M1 ~~ M2’
### Separate analysis of males and females to inspect model fit
#model fit for Female
fit <- cfa(new.model, data=depres[Sex==0,-1])
#results are not the same as in Table xxxx
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#model fit for Male
fit2 <- cfa(new.model, data=depres[Sex==1,-1])
#results are not the same as in Table xxxx
summary(fit2,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#Tests of Measurement Invariance and Population Heterogeneity
#Measurement Invariance - same model fit as in Table 7.9
# model 1: configural invariance
fit1 <- cfa(new.model, data=depres, group=“Sex”)
# model 2: weak invariance
fit2 <- cfa(new.model, data=depres, group=“Sex”,
group.equal=“loadings”)
# model 3: strong invariance
fit3 <- cfa(new.model, data=depres, group=“Sex”,
group.equal=c(“loadings”, “intercepts”))
# model 4: equal loadings + intercepts + residuals
fit4 <- cfa(new.model, data=depres, group=“Sex”,
group.equal=c(“loadings”, “intercepts”, “residuals”))
#Population heterogeneity- # same model fit as in Table 7.9
# model 5: equal loadings + intercepts + residuals + factor variances
fit5 <- cfa(new.model, data=depres, group=“Sex”,
group.equal=c(“loadings”, “intercepts”, “residuals”,“lv.variances”))
# model 6: equal loadings + intercepts + residuals + factor variances + factor means
fit6 <- cfa(new.model, data=depres, group=“Sex”,
group.equal=c(“loadings”, “intercepts”, “residuals”,“lv.variances”,“means”))
############## Alternatively for Models 1-3 and equal loadings + intercepts + means ##########
############## with chi-square tests of differences ##########
install.packages(“semTools”)
library(semTools)
measurementInvariance(new.model, data=depres, group=“Sex”, strict=FALSE)
###########################################
###################################
##### MIMIC Models pp.304-316 #####
###################################
cormat <- ’
1.000
0.705 1.000
0.724 0.646 1.000
0.213 0.195 0.190 1.000
0.149 0.142 0.128 0.521 1.000
0.155 0.162 0.135 0.557 0.479 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(2.26, 2.73, 2.11, 2.32, 2.61, 2.44)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“S1”,“S2”,“S3”,“A1”,“A2”,“A3”)
colnames(covmat)= rownames(covmat)
# Step 1. Ensure that the two-factor model of Social Phobia
# and Agoraphobia is reasonable and good fitting in the full sample (N = 730).
#model set up - this is the CFA model of the correlation matrix presented in Figure 7.5, p.308
new.model <- ‘socialphobia =~ S1 + S2 + S3
agoraphobia =~ A1 + A2 + A3
socialphobia ~~ agoraphobia’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=730)
#results same as p. 308
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
# Step 2. MIMIC Model of Fig. 7.5, p. 308.
#Introduce sex
cormat <- ’
1.000
0.705 1.000
0.724 0.646 1.000
0.213 0.195 0.190 1.000
0.149 0.142 0.128 0.521 1.000
0.155 0.162 0.135 0.557 0.479 1.000
-0.019 -0.024 -0.029 -0.110 -0.074 -0.291 1.000’
Cmat <- getCov(cormat)
neodat.sdev = c(2.26, 2.73, 2.11, 2.32, 2.61, 2.44, 0.50)
Dmat = diag(neodat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“S1”,“S2”,“S3”,“A1”,“A2”,“A3”,“sex”)
colnames(covmat)= rownames(covmat)
new.model <- ‘socialphobia =~ S1 + S2 + S3
agoraphobia =~ A1 + A2 + A3
socialphobia ~~ agoraphobia
#regressions
socialphobia ~ sex
agoraphobia ~ sex’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=730)
#results same as p. 315 - TABLE 7.15. Results of MIMIC Model of Social Phobia and Agoraphobia
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
### Extra: Evaluation of measurement invariance by fixing all direct effects
### between the covariate and the indicators to zero and then inspecting modification indices.
### pp.314-316
new.model <- ‘socialphobia =~ S1 + S2 + S3
agoraphobia =~ A1 + A2 + A3
socialphobia ~~ agoraphobia
#regressions
socialphobia ~ sex
agoraphobia ~ sex
#fix direct effects between sex and the indicators to zero
A3 ~ 0sex
A2 ~ 0sex
A1 ~ 0*sex’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=730)
#results same as p. 315 - TABLE 7.15. Results of MIMIC Model of Social Phobia and Agoraphobia
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
Chapter 8 - Higher-Order CFA, Scale Reliability Evaluation, Formative Indicators
Contains examples for a) a Higher Order model CFA b) estimating a scale’s reliability using the semtools package c) a Formative Indicators model
################################################
# Chapter 8: Other Types of CFA Models, #
# Higher-Order Factor Analysis #
# Scale Reliability and #
# Formative Indicators #
################################################
library(lavaan)
#############################################
# Higher-Order Factor Analysis p.320 - 337 #
#############################################
#Input matrix: the correlation matrix of Figure 8.1
cormat <- ’
1.00
0.78 1.00
0.80 0.77 1.00
0.56 0.51 0.48 1.00
0.52 0.51 0.46 0.78 1.00
0.59 0.51 0.51 0.80 0.79 1.00
0.16 0.15 0.17 0.14 0.18 0.16 1.00
0.19 0.13 0.18 0.14 0.16 0.16 0.81 1.00
0.12 0.17 0.17 0.17 0.20 0.16 0.75 0.80 1.00
0.16 0.13 0.17 0.15 0.16 0.18 0.56 0.52 0.50 1.00
0.16 0.14 0.18 0.15 0.16 0.18 0.51 0.58 0.51 0.81 1.00
0.16 0.15 0.14 0.16 0.16 0.14 0.52 0.57 0.52 0.80 0.79 1.00’
Cmat <- getCov(cormat)
solvedat.sdev = c(1.40, 2.10, 1.30, 2.30, 2.40, 1.90, 2.00, 2.90, 2.30, 3.10, 2.20, 1.20)
Dmat = diag(solvedat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“P1”,“P2”,“P3”,“C1”,“C2”,“C3”,“E1”,“E2”,“E3”,“S1”,“S2”,“S3”)
colnames(covmat)= rownames(covmat)
#model set up - this is the model presented in Figure 8.1, p.322
new.model <- ‘PROBSLV =~ P1 + P2 + P3
COGRES =~ C1 + C2 + C3
EXPREMOT =~ E1 + E2 + E3
SOCSPT =~ S1 + S2 + S3
PROBFOC =~ PROBSLV + COGRES
EMOTFOC =~ EXPREMOT + SOCSPT’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=275)
#results very close to Table 8.3, page 333
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
###########################################
# Scale Reliability Estimation p. 337-351 #
###########################################
#Input matrix: the correlation matrix of Figure 8.1
cormat <- ’
1.000
0.594 1.000
0.607 0.613 1.000
0.736 0.765 0.717 1.000
0.378 0.321 0.360 0.414 1.000
0.314 0.301 0.345 0.363 0.732 1.000
0.310 0.262 0.323 0.337 0.665 0.583 1.000
0.317 0.235 0.276 0.302 0.632 0.557 0.796 1.000’
Cmat <- getCov(cormat)
solvedat.sdev = c(1.150, 1.200, 1.570, 2.820, 1.310, 1.240, 1.330, 1.290)
Dmat = diag(solvedat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“Y1”,“Y2”,“Y3”,“Y4”,“Y5”,“Y6”,“Y7”,“Y8”)
colnames(covmat)= rownames(covmat)
#model set up - this is the model presented in Figure 8.2, p.340
new.model <- ‘INTRUS =~ Y1 + Y2 + Y3 + Y4
AVOID =~ Y5 + Y6 + Y7 + Y8
Y7 ~~ Y8’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=500)
#model fit is close but not identical to p.339
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#EXTRA: For scale reliability estimation you can use the reliability function
#of the semTools package
install.packages(“semTools”)
library(semTools)
reliability(fit)
#OUTPUT
# INTRUS AVOID
#alpha 0.8399990 0.8865353
#omega 0.9193497 0.8232744
#omega2 0.9193497 0.8232744
#omega3 0.9195232 0.8234095
# the omega values equal the point estimates of scale reliability
# shown in p.344, but standard errors or confidence intervals are not calculated.
#For handmade calculations of omega and omega_h you can visit
#https://bearspace.baylor.edu/Alex_Beaujean/R/Omega.pdf
#for an example
#######################################################
##### Models with Formative Indicators pp.351-362 #####
#######################################################
#Input matrix: the correlation matrix of Figure 8.5
cormat <- ’
1.000
0.700 1.000
0.713 0.636 1.000
0.079 0.066 0.076 1.000
0.088 0.058 0.070 0.681 1.000
0.084 0.056 0.074 0.712 0.633 1.000
0.279 0.248 0.240 0.177 0.155 0.170 1.000
0.250 0.214 0.222 0.157 0.143 0.152 0.373 1.000
0.280 0.236 0.251 0.173 0.178 0.171 0.448 0.344 1.000’
Cmat <- getCov(cormat)
solvedat.sdev = c(2.5, 2.1, 3.0, 4.1, 3.9, 4.4, 1.2, 1.0, 1.2)
Dmat = diag(solvedat.sdev)
covmat = Dmat%%Cmat%%Dmat
# assign row and column names to the covariance matrix
rownames(covmat) = c(“Y1”,“Y2”,“Y3”,“Y4”,“Y5”,“Y6”,“X1”,“X2”,“X3”)
colnames(covmat)= rownames(covmat)
#Life Stress example
#model set up - this is the model presented in Figure 8.5, p.359
new.model <- ‘SATIS =~ Y1 + Y2 + Y3
OPTIM =~ Y4 + Y5 + Y6
STRESS ~ 1X1 + X2 +X3
STRESS =~ NASATIS + OPTIM
X1 ~~ X2
X1 ~~ X3
X2 ~~ X3’
#model fit
fit <- cfa(new.model, sample.cov=covmat, sample.nobs=500)
#results same as in Table 8.9, page 360-361 (only model fit)
summary(fit,standardized=TRUE, fit.measures=TRUE, rsq=TRUE, modindices=TRUE)
#### EXTRA: Visualize this model with semPlot ####
install.packages(“semPlot”)
library(semPlot)
semPaths(fit, “std”, title = FALSE, curvePivot = TRUE)
Chapter 9 - Data Issues in CFA: Missing, Non-normal, and Categorical Data (coming soon)
Chapter 10 - Statistical Power and Sample Size (coming soon)
Useful links:
- A discussion group for the R package lavaan
- Y. Rosseel’s paper on lavaan in the Journal of Statistical Software