CES-D
Leonardo Martins
17 de janeiro de 2016
Preparing new analysis to CES-D manuscript review
Loading required packages
require(foreign) # Read data stored SPSS## Loading required package: foreignrequire(car) #Recode Variables## Loading required package: carrequire(psych) #Psychometrics## Loading required package: psych
##
## Attaching package: 'psych'
##
## The following object is masked from 'package:car':
##
## logitrequire(lavaan) #Confirmatory and SEM## Loading required package: lavaan
## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.require(semPlot) # Plots for SEM## Loading required package: semPlot#Setting Directory
setwd("~/CESD")
#Importing SPSS file .sav
base.dat <- read.spss("PD10.sav", to.data.frame = T)## Warning in read.spss("PD10.sav", to.data.frame = T): PD10.sav: Unrecognized
## record type 7, subtype 18 encountered in system file## re-encoding from latin1#Sum CESD itens in order to find NA
base.dat$scaleSum <- rowSums(base.dat[,267:286])
#Creating a subset for analysis without NA
base.CESD <- subset(base.dat, subset=!is.na(base.dat$scaleSum))
#Creating a subset only with CESD
fullScale <- base.CESD[ , 267:286]
#Recoding reversed itens
fullScale$F4r<- recode(fullScale$F4r, "0=3; 1=2; 2=1; 3=0")
fullScale$F8r<- recode(fullScale$F8r, "0=3; 1=2; 2=1; 3=0")
fullScale$F12r<- recode(fullScale$F12r, "0=3; 1=2; 2=1; 3=0")
fullScale$F16r<- recode(fullScale$F16r, "0=3; 1=2; 2=1; 3=0")
#Aninha needs to check if are these ones the reversed itens
#Creating a Correlation Matrix
correl <- cor(fullScale)
#Creating a polychoric correlation
fullScaleT<-polychoric(fullScale)
#Cloning fullScale
fullScale2 <- fullScale
#Creating a categorical ordered subset for CFA analisys
orderedScale <-fullScale2[,c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r")] <-
lapply(fullScale2[,c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r")], ordered)
orderedScale<-as.data.frame(orderedScale)# Bartlett Test
cortest.bartlett(fullScaleT$rho, n=nrow(fullScale))## $chisq
## [1] 4462.717
##
## $p.value
## [1] 0
##
## $df
## [1] 190# KMO
KMO(fullScaleT$rho)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = fullScaleT$rho)
## Overall MSA = 0.9
## MSA for each item =
## F1r F2r F3r F4r F5r F6r F7r F8r F9r F10r F11r F12r F13r F14r F15r
## 0.92 0.89 0.93 0.75 0.92 0.92 0.95 0.79 0.95 0.91 0.91 0.83 0.88 0.94 0.84
## F16r F17r F18r F19r F20r
## 0.84 0.85 0.89 0.87 0.93# Parallel Analysis
fa.parallel(fullScaleT$rho, fm="minres", fa="both", n.obs=513)
## Parallel analysis suggests that the number of factors = 6 and the number of components = 2#Very Simple Structure
VSS(fullScaleT$rho, n.obs=513)
##
## Very Simple Structure
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
## VSS complexity 1 achieves a maximimum of 0.83 with 1 factors
## VSS complexity 2 achieves a maximimum of 0.88 with 2 factors
##
## The Velicer MAP achieves a minimum of 0.02 with 2 factors
## BIC achieves a minimum of -324.51 with 4 factors
## Sample Size adjusted BIC achieves a minimum of 5.4 with 8 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex
## 1 0.83 0.00 0.016 170 1105 7.9e-137 11.2 0.83 0.105 44 584.1 1.0
## 2 0.77 0.88 0.015 151 761 3.5e-82 8.4 0.88 0.090 -181 298.5 1.2
## 3 0.59 0.82 0.018 133 556 3.6e-53 7.6 0.89 0.080 -273 148.7 1.6
## 4 0.47 0.76 0.021 116 399 8.6e-33 6.8 0.90 0.070 -325 43.7 1.9
## 5 0.43 0.70 0.027 100 339 9.8e-28 6.1 0.91 0.069 -285 32.3 2.2
## 6 0.29 0.54 0.032 85 276 4.8e-22 5.6 0.92 0.067 -254 15.4 2.5
## 7 0.29 0.53 0.041 71 231 7.9e-19 5.3 0.92 0.068 -212 13.4 2.7
## 8 0.26 0.50 0.048 58 183 6.6e-15 4.9 0.93 0.066 -179 5.4 2.8
## eChisq SRMR eCRMS eBIC
## 1 1064 0.074 0.078 3.3
## 2 472 0.049 0.055 -469.8
## 3 350 0.042 0.051 -480.2
## 4 235 0.035 0.044 -488.8
## 5 172 0.030 0.041 -452.1
## 6 130 0.026 0.039 -400.9
## 7 109 0.024 0.039 -333.9
## 8 81 0.020 0.037 -280.8Principal Components Analysis
#PCA - 2 components unrotated
PCA2u <- principal(fullScaleT$rho, nfactors = 2)
print.psych(PCA2u, digits=2, cut= .4)## Principal Components Analysis
## Call: principal(r = fullScaleT$rho, nfactors = 2)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 h2 u2 com
## F1r 0.61 0.41 0.59 1.2
## F2r 0.54 0.30 0.70 1.0
## F3r 0.59 0.39 0.61 1.2
## F4r 0.71 0.51 0.49 1.0
## F5r 0.63 0.40 0.60 1.0
## F6r 0.74 0.61 0.39 1.2
## F7r 0.63 0.41 0.59 1.0
## F8r 0.62 0.38 0.62 1.0
## F9r 0.69 0.50 0.50 1.1
## F10r 0.69 0.51 0.49 1.1
## F11r 0.62 0.42 0.58 1.2
## F12r 0.75 0.67 0.33 1.4
## F13r 0.41 0.21 0.79 1.4
## F14r 0.69 0.59 0.41 1.4
## F15r 0.63 0.42 0.58 1.1
## F16r 0.66 0.52 0.48 1.4
## F17r 0.62 0.39 0.61 1.0
## F18r 0.77 0.67 0.33 1.3
## F19r 0.71 0.54 0.46 1.1
## F20r 0.66 0.50 0.50 1.3
##
## PC1 PC2
## SS loadings 6.89 2.46
## Proportion Var 0.34 0.12
## Cumulative Var 0.34 0.47
## Proportion Explained 0.74 0.26
## Cumulative Proportion 0.74 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
##
## Fit based upon off diagonal values = 0.97plot.psych(PCA2u)
#PCA - 2 components oblique rotated (assuming the components are correlated)
PCA2 <- principal(fullScaleT$rho, nfactors = 2, rotate="oblimin")## Loading required namespace: GPArotationprint.psych(PCA2, digits=2, cut= .4)## Principal Components Analysis
## Call: principal(r = fullScaleT$rho, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 h2 u2 com
## F1r 0.66 0.41 0.59 1.5
## F2r 0.56 0.30 0.70 1.0
## F3r 0.60 0.39 0.61 1.0
## F4r 0.74 0.51 0.49 1.1
## F5r 0.66 0.40 0.60 1.0
## F6r 0.75 0.61 0.39 1.0
## F7r 0.66 0.41 0.59 1.0
## F8r 0.63 0.38 0.62 1.0
## F9r 0.70 0.50 0.50 1.0
## F10r 0.70 0.51 0.49 1.0
## F11r 0.63 0.42 0.58 1.0
## F12r 0.70 0.67 0.33 1.3
## F13r 0.45 0.21 0.79 1.7
## F14r 0.69 0.59 0.41 1.1
## F15r 0.65 0.42 0.58 1.0
## F16r 0.62 0.52 0.48 1.3
## F17r 0.65 0.39 0.61 1.1
## F18r 0.78 0.67 0.33 1.0
## F19r 0.72 0.54 0.46 1.0
## F20r 0.66 0.50 0.50 1.1
##
## PC1 PC2
## SS loadings 7.20 2.15
## Proportion Var 0.36 0.11
## Cumulative Var 0.36 0.47
## Proportion Explained 0.77 0.23
## Cumulative Proportion 0.77 1.00
##
## With component correlations of
## PC1 PC2
## PC1 1.00 0.31
## PC2 0.31 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
##
## Fit based upon off diagonal values = 0.97plot.psych(PCA2)
### Alfa de Cronbach (FA 2 Components)
alpha(fullScale, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = fullScale, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.87 0.87 0.89 0.26 6.9 0.011 1.6 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.87 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F1r 0.87 0.87 0.88 0.26 6.7 0.012
## F2r 0.87 0.87 0.88 0.26 6.6 0.012
## F3r 0.87 0.87 0.88 0.25 6.5 0.012
## F4r 0.88 0.88 0.89 0.28 7.2 0.011
## F5r 0.87 0.87 0.88 0.25 6.5 0.012
## F6r 0.86 0.86 0.88 0.25 6.2 0.012
## F7r 0.87 0.87 0.88 0.25 6.5 0.012
## F8r 0.88 0.88 0.89 0.27 7.2 0.011
## F9r 0.86 0.86 0.88 0.25 6.4 0.012
## F10r 0.86 0.86 0.88 0.25 6.3 0.012
## F11r 0.87 0.87 0.88 0.25 6.5 0.012
## F12r 0.87 0.87 0.88 0.26 6.5 0.012
## F13r 0.88 0.88 0.89 0.27 7.0 0.011
## F14r 0.86 0.86 0.88 0.25 6.3 0.012
## F15r 0.87 0.87 0.88 0.25 6.5 0.012
## F16r 0.87 0.87 0.88 0.26 6.7 0.012
## F17r 0.87 0.87 0.88 0.26 6.6 0.012
## F18r 0.86 0.86 0.87 0.24 6.2 0.012
## F19r 0.86 0.86 0.88 0.25 6.3 0.012
## F20r 0.86 0.86 0.88 0.25 6.4 0.012
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F1r 513 0.47 0.47 0.43 0.40 1.5 1.1
## F2r 513 0.50 0.50 0.46 0.43 1.4 1.2
## F3r 513 0.57 0.57 0.54 0.51 1.7 1.2
## F4r 513 0.24 0.24 0.17 0.15 1.4 1.2
## F5r 513 0.56 0.56 0.53 0.49 1.5 1.2
## F6r 513 0.69 0.69 0.68 0.64 2.0 1.1
## F7r 513 0.56 0.56 0.53 0.49 1.7 1.2
## F8r 513 0.26 0.27 0.20 0.18 1.1 1.2
## F9r 513 0.63 0.62 0.60 0.56 1.8 1.2
## F10r 513 0.64 0.64 0.62 0.58 1.5 1.2
## F11r 513 0.58 0.58 0.55 0.51 1.9 1.2
## F12r 513 0.56 0.55 0.53 0.49 1.6 1.2
## F13r 513 0.32 0.32 0.25 0.23 1.4 1.2
## F14r 513 0.68 0.68 0.66 0.62 1.8 1.2
## F15r 513 0.58 0.58 0.55 0.51 1.3 1.2
## F16r 513 0.48 0.48 0.44 0.40 1.5 1.3
## F17r 513 0.52 0.52 0.49 0.45 1.2 1.2
## F18r 513 0.72 0.72 0.73 0.68 2.0 1.1
## F19r 513 0.65 0.65 0.64 0.59 1.4 1.3
## F20r 513 0.63 0.62 0.60 0.56 1.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F1r 0.27 0.26 0.22 0.26 0
## F2r 0.34 0.21 0.21 0.24 0
## F3r 0.21 0.22 0.19 0.38 0
## F4r 0.33 0.21 0.21 0.24 0
## F5r 0.27 0.23 0.21 0.29 0
## F6r 0.15 0.16 0.20 0.50 0
## F7r 0.25 0.17 0.21 0.37 0
## F8r 0.44 0.18 0.19 0.18 0
## F9r 0.24 0.18 0.15 0.43 0
## F10r 0.30 0.19 0.18 0.32 0
## F11r 0.19 0.15 0.18 0.47 0
## F12r 0.27 0.17 0.21 0.34 0
## F13r 0.30 0.25 0.17 0.28 0
## F14r 0.23 0.16 0.16 0.45 0
## F15r 0.36 0.22 0.16 0.26 0
## F16r 0.36 0.12 0.18 0.34 0
## F17r 0.41 0.19 0.14 0.26 0
## F18r 0.15 0.17 0.17 0.51 0
## F19r 0.36 0.18 0.15 0.31 0
## F20r 0.24 0.18 0.16 0.41 0#Pay attention to negative correlated itens in component one
#Component 1
C1_PCA2 <- fullScale[, c("F1r","F2r","F3r","F5r","F6r","F7r","F9r","F10r","F11r","F13r","F14r","F15r","F17r","F18r","F19r","F20r")]
alpha(C1_PCA2, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = C1_PCA2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.88 0.88 0.88 0.31 7.3 0.012 1.6 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.88 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F1r 0.88 0.88 0.88 0.32 7.1 0.012
## F2r 0.88 0.88 0.88 0.32 7.1 0.012
## F3r 0.87 0.87 0.88 0.32 6.9 0.012
## F5r 0.87 0.87 0.88 0.32 6.9 0.012
## F6r 0.87 0.87 0.87 0.31 6.6 0.013
## F7r 0.87 0.87 0.88 0.32 6.9 0.012
## F9r 0.87 0.87 0.88 0.31 6.8 0.013
## F10r 0.87 0.87 0.87 0.31 6.7 0.013
## F11r 0.87 0.87 0.88 0.32 6.9 0.012
## F13r 0.88 0.88 0.89 0.34 7.6 0.012
## F14r 0.87 0.87 0.87 0.31 6.7 0.013
## F15r 0.87 0.87 0.88 0.31 6.9 0.012
## F17r 0.88 0.88 0.88 0.32 7.0 0.012
## F18r 0.87 0.87 0.87 0.30 6.5 0.013
## F19r 0.87 0.87 0.87 0.31 6.7 0.013
## F20r 0.87 0.87 0.88 0.31 6.8 0.013
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F1r 513 0.52 0.53 0.47 0.44 1.5 1.1
## F2r 513 0.52 0.52 0.47 0.44 1.4 1.2
## F3r 513 0.58 0.58 0.54 0.50 1.7 1.2
## F5r 513 0.58 0.58 0.54 0.51 1.5 1.2
## F6r 513 0.69 0.69 0.68 0.63 2.0 1.1
## F7r 513 0.58 0.58 0.54 0.51 1.7 1.2
## F9r 513 0.64 0.64 0.61 0.57 1.8 1.2
## F10r 513 0.65 0.65 0.62 0.58 1.5 1.2
## F11r 513 0.59 0.59 0.55 0.51 1.9 1.2
## F13r 513 0.36 0.36 0.29 0.27 1.4 1.2
## F14r 513 0.67 0.67 0.65 0.60 1.8 1.2
## F15r 513 0.60 0.60 0.57 0.52 1.3 1.2
## F17r 513 0.56 0.55 0.51 0.48 1.2 1.2
## F18r 513 0.72 0.73 0.72 0.67 2.0 1.1
## F19r 513 0.67 0.66 0.64 0.60 1.4 1.3
## F20r 513 0.63 0.63 0.59 0.56 1.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F1r 0.27 0.26 0.22 0.26 0
## F2r 0.34 0.21 0.21 0.24 0
## F3r 0.21 0.22 0.19 0.38 0
## F5r 0.27 0.23 0.21 0.29 0
## F6r 0.15 0.16 0.20 0.50 0
## F7r 0.25 0.17 0.21 0.37 0
## F9r 0.24 0.18 0.15 0.43 0
## F10r 0.30 0.19 0.18 0.32 0
## F11r 0.19 0.15 0.18 0.47 0
## F13r 0.30 0.25 0.17 0.28 0
## F14r 0.23 0.16 0.16 0.45 0
## F15r 0.36 0.22 0.16 0.26 0
## F17r 0.41 0.19 0.14 0.26 0
## F18r 0.15 0.17 0.17 0.51 0
## F19r 0.36 0.18 0.15 0.31 0
## F20r 0.24 0.18 0.16 0.41 0#Component 2
C2_PCA2 <- fullScale[, c("F4r","F8r","F12r","F16r")]
alpha(C2_PCA2, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = C2_PCA2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.62 0.61 0.57 0.28 1.6 0.043 1.4 0.82
##
## lower alpha upper 95% confidence boundaries
## 0.53 0.62 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F4r 0.59 0.58 0.51 0.32 1.39 0.052
## F8r 0.61 0.60 0.53 0.34 1.53 0.051
## F12r 0.45 0.45 0.35 0.21 0.82 0.060
## F16r 0.52 0.52 0.42 0.27 1.09 0.056
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F4r 513 0.63 0.64 0.43 0.34 1.4 1.2
## F8r 513 0.61 0.62 0.39 0.31 1.1 1.2
## F12r 513 0.76 0.76 0.67 0.52 1.6 1.2
## F16r 513 0.72 0.70 0.57 0.42 1.5 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F4r 0.33 0.21 0.21 0.24 0
## F8r 0.44 0.18 0.19 0.18 0
## F12r 0.27 0.17 0.21 0.34 0
## F16r 0.36 0.12 0.18 0.34 0Fatorial Analysis
## FA - 4 factors unrotated
fa4u <- fa(fullScaleT$rho, nfactors = 4, fm="minres")
print.psych(fa4u, digits=2, cut= .4)## Factor Analysis using method = minres
## Call: fa(r = fullScaleT$rho, nfactors = 4, fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR3 MR1 MR4 h2 u2 com
## F1r 0.65 0.35 0.6456 1.1
## F2r 0.59 0.31 0.6898 1.0
## F3r 0.63 0.42 0.5815 1.2
## F4r 0.51 0.24 0.7553 1.7
## F5r 0.57 0.38 0.6163 1.0
## F6r 0.54 0.62 0.3786 1.6
## F7r 0.54 0.39 0.6143 1.1
## F8r 0.41 0.16 0.8404 1.1
## F9r 0.47 0.47 0.5331 1.4
## F10r 0.56 0.49 0.5138 1.1
## F11r 0.46 0.39 0.6076 1.3
## F12r 0.84 0.78 0.2194 1.0
## F13r 0.14 0.8631 3.0
## F14r 0.56 0.4401 3.5
## F15r 0.83 0.66 0.3411 1.0
## F16r 0.63 0.47 0.5282 1.1
## F17r 0.66 0.49 0.5137 1.2
## F18r 0.94 0.99 0.0066 1.0
## F19r 0.71 0.68 0.3151 1.1
## F20r 0.48 0.5220 2.7
##
## MR2 MR3 MR1 MR4
## SS loadings 3.63 1.90 2.06 1.89
## Proportion Var 0.18 0.09 0.10 0.09
## Cumulative Var 0.18 0.28 0.38 0.47
## Proportion Explained 0.38 0.20 0.22 0.20
## Cumulative Proportion 0.38 0.58 0.80 1.00
##
## With factor correlations of
## MR2 MR3 MR1 MR4
## MR2 1.00 0.38 0.65 0.63
## MR3 0.38 1.00 0.42 0.34
## MR1 0.65 0.42 1.00 0.58
## MR4 0.63 0.34 0.58 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 8.85
## The degrees of freedom for the model are 116 and the objective function was 0.8
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR3 MR1 MR4
## Correlation of scores with factors 0.93 0.92 1.00 0.91
## Multiple R square of scores with factors 0.87 0.84 0.99 0.83
## Minimum correlation of possible factor scores 0.73 0.67 0.98 0.66fa.diagram(fa4u)
## FA - 4 factors oblique rotated (assuming the components are correlated)
fa4 <- fa(fullScaleT$rho, nfactors = 4, rotate = "oblimin", fm="minres")
print.psych(fa4, digits=2, cut= .4)## Factor Analysis using method = minres
## Call: fa(r = fullScaleT$rho, nfactors = 4, rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR3 MR1 MR4 h2 u2 com
## F1r 0.65 0.35 0.6456 1.1
## F2r 0.59 0.31 0.6898 1.0
## F3r 0.63 0.42 0.5815 1.2
## F4r 0.51 0.24 0.7553 1.7
## F5r 0.57 0.38 0.6163 1.0
## F6r 0.54 0.62 0.3786 1.6
## F7r 0.54 0.39 0.6143 1.1
## F8r 0.41 0.16 0.8404 1.1
## F9r 0.47 0.47 0.5331 1.4
## F10r 0.56 0.49 0.5138 1.1
## F11r 0.46 0.39 0.6076 1.3
## F12r 0.84 0.78 0.2194 1.0
## F13r 0.14 0.8631 3.0
## F14r 0.56 0.4401 3.5
## F15r 0.83 0.66 0.3411 1.0
## F16r 0.63 0.47 0.5282 1.1
## F17r 0.66 0.49 0.5137 1.2
## F18r 0.94 0.99 0.0066 1.0
## F19r 0.71 0.68 0.3151 1.1
## F20r 0.48 0.5220 2.7
##
## MR2 MR3 MR1 MR4
## SS loadings 3.63 1.90 2.06 1.89
## Proportion Var 0.18 0.09 0.10 0.09
## Cumulative Var 0.18 0.28 0.38 0.47
## Proportion Explained 0.38 0.20 0.22 0.20
## Cumulative Proportion 0.38 0.58 0.80 1.00
##
## With factor correlations of
## MR2 MR3 MR1 MR4
## MR2 1.00 0.38 0.65 0.63
## MR3 0.38 1.00 0.42 0.34
## MR1 0.65 0.42 1.00 0.58
## MR4 0.63 0.34 0.58 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 8.85
## The degrees of freedom for the model are 116 and the objective function was 0.8
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR2 MR3 MR1 MR4
## Correlation of scores with factors 0.93 0.92 1.00 0.91
## Multiple R square of scores with factors 0.87 0.84 0.99 0.83
## Minimum correlation of possible factor scores 0.73 0.67 0.98 0.66fa.diagram(fa4)
## FA - 4 factors oblique rotated (assuming the components are correlated) - without three unloaded itens (weight < 0.4)
fa4r <- fa(fullScaleT$rho[-c(13,14,20),-c(13,14,20) ], nfactors = 4, rotate = "oblimin", fm="minres")
print.psych(fa4r, digits=2, cut= 0.4)## Factor Analysis using method = minres
## Call: fa(r = fullScaleT$rho[-c(13, 14, 20), -c(13, 14, 20)], nfactors = 4,
## rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR4 MR3 MR1 MR2 h2 u2 com
## F1r 0.64 0.35 0.654 1.2
## F2r 0.59 0.31 0.685 1.0
## F3r 0.64 0.41 0.588 1.2
## F4r 0.52 0.24 0.761 1.3
## F5r 0.57 0.38 0.623 1.0
## F6r 0.56 0.63 0.372 1.6
## F7r 0.56 0.39 0.613 1.1
## F8r 0.42 0.17 0.833 1.2
## F9r 0.52 0.46 0.538 1.2
## F10r 0.64 0.50 0.500 1.0
## F11r 0.45 0.40 0.604 1.3
## F12r 0.81 0.74 0.264 1.0
## F15r 0.99 1.00 0.005 1.0
## F16r 0.65 0.50 0.504 1.0
## F17r 0.66 0.49 0.506 1.2
## F18r 0.94 1.00 0.005 1.0
## F19r 0.44 0.57 0.433 2.0
##
## MR4 MR3 MR1 MR2
## SS loadings 3.55 1.74 1.77 1.45
## Proportion Var 0.21 0.10 0.10 0.09
## Cumulative Var 0.21 0.31 0.42 0.50
## Proportion Explained 0.42 0.20 0.21 0.17
## Cumulative Proportion 0.42 0.62 0.83 1.00
##
## With factor correlations of
## MR4 MR3 MR1 MR2
## MR4 1.00 0.39 0.65 0.50
## MR3 0.39 1.00 0.41 0.26
## MR1 0.65 0.41 1.00 0.44
## MR2 0.50 0.26 0.44 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 136 and the objective function was 7.1
## The degrees of freedom for the model are 74 and the objective function was 0.51
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.05
##
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR4 MR3 MR1 MR2
## Correlation of scores with factors 0.93 0.90 1.00 1.00
## Multiple R square of scores with factors 0.87 0.81 0.99 0.99
## Minimum correlation of possible factor scores 0.74 0.63 0.99 0.99fa.diagram(fa4r)
#Pay attention to negative correlated itens in component one
#Factor 1
F1_FA4 <- fullScale[, c("F1r","F2r","F3r","F5r","F6r","F7r","F9r","F10r","F11r")]
alpha(F1_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F1_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.82 0.82 0.8 0.33 4.4 0.019 1.7 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F1r 0.81 0.81 0.79 0.34 4.1 0.021
## F2r 0.80 0.80 0.79 0.34 4.1 0.021
## F3r 0.80 0.80 0.78 0.33 3.9 0.022
## F5r 0.80 0.80 0.78 0.33 3.9 0.021
## F6r 0.79 0.79 0.77 0.32 3.7 0.022
## F7r 0.80 0.80 0.78 0.33 4.0 0.021
## F9r 0.79 0.79 0.78 0.32 3.8 0.022
## F10r 0.79 0.79 0.77 0.32 3.8 0.022
## F11r 0.80 0.80 0.78 0.33 4.0 0.021
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F1r 513 0.57 0.58 0.49 0.44 1.5 1.1
## F2r 513 0.59 0.59 0.51 0.46 1.4 1.2
## F3r 513 0.64 0.64 0.57 0.52 1.7 1.2
## F5r 513 0.63 0.63 0.56 0.51 1.5 1.2
## F6r 513 0.70 0.70 0.66 0.59 2.0 1.1
## F7r 513 0.63 0.63 0.56 0.50 1.7 1.2
## F9r 513 0.66 0.66 0.60 0.54 1.8 1.2
## F10r 513 0.68 0.68 0.63 0.57 1.5 1.2
## F11r 513 0.62 0.62 0.55 0.49 1.9 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F1r 0.27 0.26 0.22 0.26 0
## F2r 0.34 0.21 0.21 0.24 0
## F3r 0.21 0.22 0.19 0.38 0
## F5r 0.27 0.23 0.21 0.29 0
## F6r 0.15 0.16 0.20 0.50 0
## F7r 0.25 0.17 0.21 0.37 0
## F9r 0.24 0.18 0.15 0.43 0
## F10r 0.30 0.19 0.18 0.32 0
## F11r 0.19 0.15 0.18 0.47 0#Factor 2
F2_FA4 <- fullScale[, c("F4r","F8r","F12r","F16r")]
alpha(F2_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F2_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.62 0.61 0.57 0.28 1.6 0.043 1.4 0.82
##
## lower alpha upper 95% confidence boundaries
## 0.53 0.62 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F4r 0.59 0.58 0.51 0.32 1.39 0.052
## F8r 0.61 0.60 0.53 0.34 1.53 0.051
## F12r 0.45 0.45 0.35 0.21 0.82 0.060
## F16r 0.52 0.52 0.42 0.27 1.09 0.056
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F4r 513 0.63 0.64 0.43 0.34 1.4 1.2
## F8r 513 0.61 0.62 0.39 0.31 1.1 1.2
## F12r 513 0.76 0.76 0.67 0.52 1.6 1.2
## F16r 513 0.72 0.70 0.57 0.42 1.5 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F4r 0.33 0.21 0.21 0.24 0
## F8r 0.44 0.18 0.19 0.18 0
## F12r 0.27 0.17 0.21 0.34 0
## F16r 0.36 0.12 0.18 0.34 0#Factor 3
F3_FA4 <- fullScale[, c("F17r","F18r")]
alpha(F3_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F3_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.68 0.68 0.52 0.52 2.1 0.067 1.6 1
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.68 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F17r 0.52 0.52 0.27 0.52 NA NA
## F18r 0.52 0.52 0.27 0.52 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F17r 513 0.88 0.87 0.63 0.52 1.2 1.2
## F18r 513 0.86 0.87 0.63 0.52 2.0 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F17r 0.41 0.19 0.14 0.26 0
## F18r 0.15 0.17 0.17 0.51 0#Factor 4
F4_FA4 <- fullScale[, c("F15r","F19r")]
alpha(F4_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F4_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.71 0.71 0.55 0.55 2.5 0.065 1.4 1.1
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.71 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F15r 0.55 0.55 0.3 0.55 NA NA
## F19r 0.55 0.55 0.3 0.55 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F15r 513 0.87 0.88 0.65 0.55 1.3 1.2
## F19r 513 0.89 0.88 0.65 0.55 1.4 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F15r 0.36 0.22 0.16 0.26 0
## F19r 0.36 0.18 0.15 0.31 0Confirmatory Models
#Batistoni CFA Model
#Model Identification
Batistoni <- '
# latent variable definitions
f1 =~ F18r + F14r + F6r + F13r + F19r + F17r + F9r + F10r + F20r
f2 =~ F3r + F1r + F7r + F5r + F2r
f3 =~F12r + F8r + F16r + F4r
# variances and covariances
f1 ~~ f2
f2 ~~ f3
f1 ~~ f3
'
#CFA Fit
fitBatistoni <- cfa(Batistoni, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))## Found more than one class "Model" in cache; using the first, from namespace 'MatrixModels'#Model Summary
summary(fitBatistoni, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 32 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 224.932 319.826
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.773
## Shift parameter 28.752
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 10633.966 5116.052
## Degrees of freedom 153 153
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.991 0.962
## Tucker-Lewis Index (TLI) 0.990 0.956
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.053
## 90 Percent Confidence Interval 0.029 0.045 0.045 0.060
## P-value RMSEA <= 0.05 0.996 0.263
##
## Weighted Root Mean Square Residual:
##
## WRMR 1.042 1.042
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F18r 1.000 0.840 0.840
## F14r 0.902 0.037 24.670 0.000 0.757 0.757
## F6r 0.935 0.036 25.951 0.000 0.785 0.785
## F13r 0.356 0.053 6.699 0.000 0.299 0.299
## F19r 0.839 0.038 22.110 0.000 0.704 0.704
## F17r 0.704 0.041 17.136 0.000 0.591 0.591
## F9r 0.818 0.040 20.635 0.000 0.687 0.687
## F10r 0.817 0.040 20.352 0.000 0.686 0.686
## F20r 0.823 0.041 19.972 0.000 0.691 0.691
## f2 =~
## F3r 1.000 0.654 0.654
## F1r 0.839 0.075 11.225 0.000 0.549 0.549
## F7r 0.995 0.077 12.893 0.000 0.650 0.650
## F5r 0.996 0.076 13.073 0.000 0.651 0.651
## F2r 0.823 0.068 12.068 0.000 0.538 0.538
## f3 =~
## F12r 1.000 0.897 0.897
## F8r 0.406 0.067 6.086 0.000 0.364 0.364
## F16r 0.810 0.068 11.844 0.000 0.726 0.726
## F4r 0.368 0.067 5.503 0.000 0.330 0.330
##
## Covariances:
## f1 ~~
## f2 0.474 0.033 14.239 0.000 0.864 0.864
## f2 ~~
## f3 0.248 0.035 7.049 0.000 0.424 0.424
## f1 ~~
## f3 0.435 0.036 12.081 0.000 0.578 0.578
##
## Intercepts:
## F18r 0.000 0.000 0.000
## F14r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F13r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F20r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F1r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F12r 0.000 0.000 0.000
## F8r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F4r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
## f3 0.000 0.000 0.000
##
## Thresholds:
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
## F14r|t1 -0.752 0.061 -12.229 0.000 -0.752 -0.752
## F14r|t2 -0.290 0.056 -5.154 0.000 -0.290 -0.290
## F14r|t3 0.115 0.056 2.073 0.038 0.115 0.115
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F13r|t1 -0.529 0.058 -9.081 0.000 -0.529 -0.529
## F13r|t2 0.115 0.056 2.073 0.038 0.115 0.115
## F13r|t3 0.575 0.059 9.771 0.000 0.575 0.575
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F20r|t1 -0.695 0.061 -11.475 0.000 -0.695 -0.695
## F20r|t2 -0.189 0.056 -3.394 0.001 -0.189 -0.189
## F20r|t3 0.224 0.056 4.011 0.000 0.224 0.224
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F8r|t1 -0.140 0.056 -2.513 0.012 -0.140 -0.140
## F8r|t2 0.321 0.056 5.680 0.000 0.321 0.321
## F8r|t3 0.918 0.065 14.173 0.000 0.918 0.918
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F4r|t1 -0.431 0.057 -7.517 0.000 -0.431 -0.431
## F4r|t2 0.110 0.056 1.984 0.047 0.110 0.110
## F4r|t3 0.701 0.061 11.559 0.000 0.701 0.701
##
## Variances:
## F18r 0.295 0.295 0.295
## F14r 0.426 0.426 0.426
## F6r 0.384 0.384 0.384
## F13r 0.911 0.911 0.911
## F19r 0.504 0.504 0.504
## F17r 0.651 0.651 0.651
## F9r 0.528 0.528 0.528
## F10r 0.530 0.530 0.530
## F20r 0.523 0.523 0.523
## F3r 0.573 0.573 0.573
## F1r 0.699 0.699 0.699
## F7r 0.577 0.577 0.577
## F5r 0.576 0.576 0.576
## F2r 0.710 0.710 0.710
## F12r 0.196 0.196 0.196
## F8r 0.867 0.867 0.867
## F16r 0.473 0.473 0.473
## F4r 0.891 0.891 0.891
## f1 0.705 0.039 1.000 1.000
## f2 0.427 0.048 1.000 1.000
## f3 0.804 0.072 1.000 1.000
##
## R-Square:
##
## F18r 0.705
## F14r 0.574
## F6r 0.616
## F13r 0.089
## F19r 0.496
## F17r 0.349
## F9r 0.472
## F10r 0.470
## F20r 0.477
## F3r 0.427
## F1r 0.301
## F7r 0.423
## F5r 0.424
## F2r 0.290
## F12r 0.804
## F8r 0.133
## F16r 0.527
## F4r 0.109#Model Fit Measures
fitMeasures(fitBatistoni)## npar fmin
## 75.000 0.219
## chisq df
## 224.932 132.000
## pvalue chisq.scaled
## 0.000 319.826
## df.scaled pvalue.scaled
## 132.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.773 10633.966
## baseline.df baseline.pvalue
## 153.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 5116.052 153.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.112
## cfi tli
## 0.991 0.990
## nnfi rfi
## 0.990 0.975
## nfi pnfi
## 0.979 0.844
## ifi rni
## 0.991 0.991
## cfi.scaled tli.scaled
## 0.962 0.956
## nnfi.scaled rfi.scaled
## 0.956 0.928
## nfi.scaled ifi.scaled
## 0.937 0.937
## rni.scaled rmsea
## 0.982 0.037
## rmsea.ci.lower rmsea.ci.upper
## 0.029 0.045
## rmsea.pvalue rmsea.scaled
## 0.996 0.053
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.045 0.060
## rmsea.pvalue.scaled wrmr
## 0.263 1.042
## cn_05 cn_01
## 364.775 394.132
## gfi agfi
## 0.984 0.975
## pgfi mfi
## 0.627 0.913#Parameters Estimates
EstBatistoni <- parameterEstimates(fitBatistoni, standardized=T, ci=F)
subset(EstBatistoni, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F18r 1.000 0.000 NA NA 0.840 0.840 0.840
## 2 f1 =~ F14r 0.902 0.037 24.670 0 0.757 0.757 0.757
## 3 f1 =~ F6r 0.935 0.036 25.951 0 0.785 0.785 0.785
## 4 f1 =~ F13r 0.356 0.053 6.699 0 0.299 0.299 0.299
## 5 f1 =~ F19r 0.839 0.038 22.110 0 0.704 0.704 0.704
## 6 f1 =~ F17r 0.704 0.041 17.136 0 0.591 0.591 0.591
## 7 f1 =~ F9r 0.818 0.040 20.635 0 0.687 0.687 0.687
## 8 f1 =~ F10r 0.817 0.040 20.352 0 0.686 0.686 0.686
## 9 f1 =~ F20r 0.823 0.041 19.972 0 0.691 0.691 0.691
## 10 f2 =~ F3r 1.000 0.000 NA NA 0.654 0.654 0.654
## 11 f2 =~ F1r 0.839 0.075 11.225 0 0.549 0.549 0.549
## 12 f2 =~ F7r 0.995 0.077 12.893 0 0.650 0.650 0.650
## 13 f2 =~ F5r 0.996 0.076 13.073 0 0.651 0.651 0.651
## 14 f2 =~ F2r 0.823 0.068 12.068 0 0.538 0.538 0.538
## 15 f3 =~ F12r 1.000 0.000 NA NA 0.897 0.897 0.897
## 16 f3 =~ F8r 0.406 0.067 6.086 0 0.364 0.364 0.364
## 17 f3 =~ F16r 0.810 0.068 11.844 0 0.726 0.726 0.726
## 18 f3 =~ F4r 0.368 0.067 5.503 0 0.330 0.330 0.330#Parameters Table
parTable(fitBatistoni)## id lhs op rhs user group free ustart exo label eq.id unco plabel
## 1 1 f1 =~ F18r 1 1 0 1 0 0 0 .p1.
## 2 2 f1 =~ F14r 1 1 1 NA 0 0 1 .p2.
## 3 3 f1 =~ F6r 1 1 2 NA 0 0 2 .p3.
## 4 4 f1 =~ F13r 1 1 3 NA 0 0 3 .p4.
## 5 5 f1 =~ F19r 1 1 4 NA 0 0 4 .p5.
## 6 6 f1 =~ F17r 1 1 5 NA 0 0 5 .p6.
## 7 7 f1 =~ F9r 1 1 6 NA 0 0 6 .p7.
## 8 8 f1 =~ F10r 1 1 7 NA 0 0 7 .p8.
## 9 9 f1 =~ F20r 1 1 8 NA 0 0 8 .p9.
## 10 10 f2 =~ F3r 1 1 0 1 0 0 0 .p10.
## 11 11 f2 =~ F1r 1 1 9 NA 0 0 9 .p11.
## 12 12 f2 =~ F7r 1 1 10 NA 0 0 10 .p12.
## 13 13 f2 =~ F5r 1 1 11 NA 0 0 11 .p13.
## 14 14 f2 =~ F2r 1 1 12 NA 0 0 12 .p14.
## 15 15 f3 =~ F12r 1 1 0 1 0 0 0 .p15.
## 16 16 f3 =~ F8r 1 1 13 NA 0 0 13 .p16.
## 17 17 f3 =~ F16r 1 1 14 NA 0 0 14 .p17.
## 18 18 f3 =~ F4r 1 1 15 NA 0 0 15 .p18.
## 19 19 f1 ~~ f2 1 1 16 NA 0 0 16 .p19.
## 20 20 f2 ~~ f3 1 1 17 NA 0 0 17 .p20.
## 21 21 f1 ~~ f3 1 1 18 NA 0 0 18 .p21.
## 22 22 F18r | t1 0 1 19 NA 0 0 19 .p22.
## 23 23 F18r | t2 0 1 20 NA 0 0 20 .p23.
## 24 24 F18r | t3 0 1 21 NA 0 0 21 .p24.
## 25 25 F14r | t1 0 1 22 NA 0 0 22 .p25.
## 26 26 F14r | t2 0 1 23 NA 0 0 23 .p26.
## 27 27 F14r | t3 0 1 24 NA 0 0 24 .p27.
## 28 28 F6r | t1 0 1 25 NA 0 0 25 .p28.
## 29 29 F6r | t2 0 1 26 NA 0 0 26 .p29.
## 30 30 F6r | t3 0 1 27 NA 0 0 27 .p30.
## 31 31 F13r | t1 0 1 28 NA 0 0 28 .p31.
## 32 32 F13r | t2 0 1 29 NA 0 0 29 .p32.
## 33 33 F13r | t3 0 1 30 NA 0 0 30 .p33.
## 34 34 F19r | t1 0 1 31 NA 0 0 31 .p34.
## 35 35 F19r | t2 0 1 32 NA 0 0 32 .p35.
## 36 36 F19r | t3 0 1 33 NA 0 0 33 .p36.
## 37 37 F17r | t1 0 1 34 NA 0 0 34 .p37.
## 38 38 F17r | t2 0 1 35 NA 0 0 35 .p38.
## 39 39 F17r | t3 0 1 36 NA 0 0 36 .p39.
## 40 40 F9r | t1 0 1 37 NA 0 0 37 .p40.
## 41 41 F9r | t2 0 1 38 NA 0 0 38 .p41.
## 42 42 F9r | t3 0 1 39 NA 0 0 39 .p42.
## 43 43 F10r | t1 0 1 40 NA 0 0 40 .p43.
## 44 44 F10r | t2 0 1 41 NA 0 0 41 .p44.
## 45 45 F10r | t3 0 1 42 NA 0 0 42 .p45.
## 46 46 F20r | t1 0 1 43 NA 0 0 43 .p46.
## 47 47 F20r | t2 0 1 44 NA 0 0 44 .p47.
## 48 48 F20r | t3 0 1 45 NA 0 0 45 .p48.
## 49 49 F3r | t1 0 1 46 NA 0 0 46 .p49.
## 50 50 F3r | t2 0 1 47 NA 0 0 47 .p50.
## 51 51 F3r | t3 0 1 48 NA 0 0 48 .p51.
## 52 52 F1r | t1 0 1 49 NA 0 0 49 .p52.
## 53 53 F1r | t2 0 1 50 NA 0 0 50 .p53.
## 54 54 F1r | t3 0 1 51 NA 0 0 51 .p54.
## 55 55 F7r | t1 0 1 52 NA 0 0 52 .p55.
## 56 56 F7r | t2 0 1 53 NA 0 0 53 .p56.
## 57 57 F7r | t3 0 1 54 NA 0 0 54 .p57.
## 58 58 F5r | t1 0 1 55 NA 0 0 55 .p58.
## 59 59 F5r | t2 0 1 56 NA 0 0 56 .p59.
## 60 60 F5r | t3 0 1 57 NA 0 0 57 .p60.
## 61 61 F2r | t1 0 1 58 NA 0 0 58 .p61.
## 62 62 F2r | t2 0 1 59 NA 0 0 59 .p62.
## 63 63 F2r | t3 0 1 60 NA 0 0 60 .p63.
## 64 64 F12r | t1 0 1 61 NA 0 0 61 .p64.
## 65 65 F12r | t2 0 1 62 NA 0 0 62 .p65.
## 66 66 F12r | t3 0 1 63 NA 0 0 63 .p66.
## 67 67 F8r | t1 0 1 64 NA 0 0 64 .p67.
## 68 68 F8r | t2 0 1 65 NA 0 0 65 .p68.
## 69 69 F8r | t3 0 1 66 NA 0 0 66 .p69.
## 70 70 F16r | t1 0 1 67 NA 0 0 67 .p70.
## 71 71 F16r | t2 0 1 68 NA 0 0 68 .p71.
## 72 72 F16r | t3 0 1 69 NA 0 0 69 .p72.
## 73 73 F4r | t1 0 1 70 NA 0 0 70 .p73.
## 74 74 F4r | t2 0 1 71 NA 0 0 71 .p74.
## 75 75 F4r | t3 0 1 72 NA 0 0 72 .p75.
## 76 76 F18r ~~ F18r 0 1 0 1 0 0 0 .p76.
## 77 77 F14r ~~ F14r 0 1 0 1 0 0 0 .p77.
## 78 78 F6r ~~ F6r 0 1 0 1 0 0 0 .p78.
## 79 79 F13r ~~ F13r 0 1 0 1 0 0 0 .p79.
## 80 80 F19r ~~ F19r 0 1 0 1 0 0 0 .p80.
## 81 81 F17r ~~ F17r 0 1 0 1 0 0 0 .p81.
## 82 82 F9r ~~ F9r 0 1 0 1 0 0 0 .p82.
## 83 83 F10r ~~ F10r 0 1 0 1 0 0 0 .p83.
## 84 84 F20r ~~ F20r 0 1 0 1 0 0 0 .p84.
## 85 85 F3r ~~ F3r 0 1 0 1 0 0 0 .p85.
## 86 86 F1r ~~ F1r 0 1 0 1 0 0 0 .p86.
## 87 87 F7r ~~ F7r 0 1 0 1 0 0 0 .p87.
## 88 88 F5r ~~ F5r 0 1 0 1 0 0 0 .p88.
## 89 89 F2r ~~ F2r 0 1 0 1 0 0 0 .p89.
## 90 90 F12r ~~ F12r 0 1 0 1 0 0 0 .p90.
## 91 91 F8r ~~ F8r 0 1 0 1 0 0 0 .p91.
## 92 92 F16r ~~ F16r 0 1 0 1 0 0 0 .p92.
## 93 93 F4r ~~ F4r 0 1 0 1 0 0 0 .p93.
## 94 94 f1 ~~ f1 0 1 73 NA 0 0 73 .p94.
## 95 95 f2 ~~ f2 0 1 74 NA 0 0 74 .p95.
## 96 96 f3 ~~ f3 0 1 75 NA 0 0 75 .p96.
## 97 97 F18r ~1 0 1 0 0 0 0 0 .p97.
## 98 98 F14r ~1 0 1 0 0 0 0 0 .p98.
## 99 99 F6r ~1 0 1 0 0 0 0 0 .p99.
## 100 100 F13r ~1 0 1 0 0 0 0 0 .p100.
## 101 101 F19r ~1 0 1 0 0 0 0 0 .p101.
## 102 102 F17r ~1 0 1 0 0 0 0 0 .p102.
## 103 103 F9r ~1 0 1 0 0 0 0 0 .p103.
## 104 104 F10r ~1 0 1 0 0 0 0 0 .p104.
## 105 105 F20r ~1 0 1 0 0 0 0 0 .p105.
## 106 106 F3r ~1 0 1 0 0 0 0 0 .p106.
## 107 107 F1r ~1 0 1 0 0 0 0 0 .p107.
## 108 108 F7r ~1 0 1 0 0 0 0 0 .p108.
## 109 109 F5r ~1 0 1 0 0 0 0 0 .p109.
## 110 110 F2r ~1 0 1 0 0 0 0 0 .p110.
## 111 111 F12r ~1 0 1 0 0 0 0 0 .p111.
## 112 112 F8r ~1 0 1 0 0 0 0 0 .p112.
## 113 113 F16r ~1 0 1 0 0 0 0 0 .p113.
## 114 114 F4r ~1 0 1 0 0 0 0 0 .p114.
## 115 115 f1 ~1 0 1 0 0 0 0 0 .p115.
## 116 116 f2 ~1 0 1 0 0 0 0 0 .p116.
## 117 117 f3 ~1 0 1 0 0 0 0 0 .p117.
## start
## 1 1.000
## 2 0.819
## 3 0.802
## 4 0.345
## 5 0.771
## 6 0.661
## 7 0.677
## 8 0.742
## 9 0.693
## 10 1.000
## 11 0.803
## 12 0.884
## 13 0.898
## 14 0.832
## 15 1.000
## 16 0.478
## 17 0.746
## 18 0.522
## 19 0.000
## 20 0.000
## 21 0.000
## 22 -1.019
## 23 -0.452
## 24 -0.017
## 25 -0.752
## 26 -0.290
## 27 0.115
## 28 -1.028
## 29 -0.502
## 30 0.012
## 31 -0.529
## 32 0.115
## 33 0.575
## 34 -0.346
## 35 0.100
## 36 0.490
## 37 -0.224
## 38 0.264
## 39 0.658
## 40 -0.707
## 41 -0.204
## 42 0.179
## 43 -0.518
## 44 -0.007
## 45 0.469
## 46 -0.695
## 47 -0.189
## 48 0.224
## 49 -0.818
## 50 -0.184
## 51 0.310
## 52 -0.616
## 53 0.071
## 54 0.658
## 55 -0.676
## 56 -0.199
## 57 0.326
## 58 -0.622
## 59 0.002
## 60 0.546
## 61 -0.409
## 62 0.120
## 63 0.707
## 64 -0.604
## 65 -0.145
## 66 0.404
## 67 -0.140
## 68 0.321
## 69 0.918
## 70 -0.362
## 71 -0.046
## 72 0.415
## 73 -0.431
## 74 0.110
## 75 0.701
## 76 1.000
## 77 1.000
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 1.000
## 90 1.000
## 91 1.000
## 92 1.000
## 93 1.000
## 94 0.050
## 95 0.050
## 96 0.050
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
## 111 0.000
## 112 0.000
## 113 0.000
## 114 0.000
## 115 0.000
## 116 0.000
## 117 0.000#Model Coefficients
coef(fitBatistoni)## f1=~F14r f1=~F6r f1=~F13r f1=~F19r f1=~F17r f1=~F9r f1=~F10r f1=~F20r
## 0.902 0.935 0.356 0.839 0.704 0.818 0.817 0.823
## f2=~F1r f2=~F7r f2=~F5r f2=~F2r f3=~F8r f3=~F16r f3=~F4r f1~~f2
## 0.839 0.995 0.996 0.823 0.406 0.810 0.368 0.474
## f2~~f3 f1~~f3 F18r|t1 F18r|t2 F18r|t3 F14r|t1 F14r|t2 F14r|t3
## 0.248 0.435 -1.019 -0.452 -0.017 -0.752 -0.290 0.115
## F6r|t1 F6r|t2 F6r|t3 F13r|t1 F13r|t2 F13r|t3 F19r|t1 F19r|t2
## -1.028 -0.502 0.012 -0.529 0.115 0.575 -0.346 0.100
## F19r|t3 F17r|t1 F17r|t2 F17r|t3 F9r|t1 F9r|t2 F9r|t3 F10r|t1
## 0.490 -0.224 0.264 0.658 -0.707 -0.204 0.179 -0.518
## F10r|t2 F10r|t3 F20r|t1 F20r|t2 F20r|t3 F3r|t1 F3r|t2 F3r|t3
## -0.007 0.469 -0.695 -0.189 0.224 -0.818 -0.184 0.310
## F1r|t1 F1r|t2 F1r|t3 F7r|t1 F7r|t2 F7r|t3 F5r|t1 F5r|t2
## -0.616 0.071 0.658 -0.676 -0.199 0.326 -0.622 0.002
## F5r|t3 F2r|t1 F2r|t2 F2r|t3 F12r|t1 F12r|t2 F12r|t3 F8r|t1
## 0.546 -0.409 0.120 0.707 -0.604 -0.145 0.404 -0.140
## F8r|t2 F8r|t3 F16r|t1 F16r|t2 F16r|t3 F4r|t1 F4r|t2 F4r|t3
## 0.321 0.918 -0.362 -0.046 0.415 -0.431 0.110 0.701
## f1~~f1 f2~~f2 f3~~f3
## 0.705 0.427 0.804#Modification Index
MIBatistoni<-modindices(fitBatistoni)
MIIBatistoni<- MIBatistoni[which(MIBatistoni$mi>30),]
print(MIIBatistoni)## lhs op rhs mi mi.scaled epc sepc.lv sepc.all sepc.nox
## 1 F18r ~~ F17r 31.979 41.382 0.232 0.232 0.232 0.232#Model Plot
semPaths(fitBatistoni,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Silveira CFA Model
#Model Identification
Silveira <- '
# latent variable definitions
f1 =~ F18r + F14r + F6r + F3r + F13r
f2 =~ F19r + F15r + F17r + F1r + F9r + F10r
f3 =~ F20r + F7r + F5r + F11r
f4 =~ F12r + F8r + F16r
# variances and covariances
f1 ~~ f2
f2 ~~ f3
f3 ~~ f4
f1 ~~ f4
f1 ~~ f3
'
#Model Fit
fitSilveira <- cfa(Silveira, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitSilveira, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 35 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 218.990 325.302
## Degrees of freedom 129 129
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.734
## Shift parameter 27.010
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 12355.368 5666.919
## Degrees of freedom 153 153
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.993 0.964
## Tucker-Lewis Index (TLI) 0.991 0.958
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.055
## 90 Percent Confidence Interval 0.028 0.045 0.047 0.062
## P-value RMSEA <= 0.05 0.996 0.152
##
## Weighted Root Mean Square Residual:
##
## WRMR 1.029 1.029
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F18r 1.000 0.847 0.847
## F14r 0.905 0.036 25.393 0.000 0.767 0.767
## F6r 0.922 0.035 26.048 0.000 0.781 0.781
## F3r 0.689 0.045 15.330 0.000 0.584 0.584
## F13r 0.358 0.053 6.785 0.000 0.303 0.303
## f2 =~
## F19r 1.000 0.759 0.759
## F15r 0.870 0.043 20.162 0.000 0.661 0.661
## F17r 0.796 0.051 15.743 0.000 0.605 0.605
## F1r 0.658 0.052 12.693 0.000 0.500 0.500
## F9r 0.913 0.049 18.565 0.000 0.693 0.693
## F10r 0.927 0.047 19.826 0.000 0.704 0.704
## f3 =~
## F20r 1.000 0.711 0.711
## F7r 0.864 0.059 14.601 0.000 0.614 0.614
## F5r 0.858 0.057 15.082 0.000 0.609 0.609
## F11r 0.885 0.062 14.167 0.000 0.629 0.629
## f4 =~
## F12r 1.000 0.866 0.866
## F8r 0.377 0.070 5.384 0.000 0.327 0.327
## F16r 0.830 0.072 11.456 0.000 0.719 0.719
##
## Covariances:
## f1 ~~
## f2 0.605 0.031 19.248 0.000 0.941 0.941
## f2 ~~
## f3 0.511 0.032 15.818 0.000 0.948 0.948
## f3 ~~
## f4 0.357 0.037 9.559 0.000 0.581 0.581
## f1 ~~
## f4 0.474 0.037 12.716 0.000 0.646 0.646
## f3 0.550 0.033 16.612 0.000 0.914 0.914
## f2 ~~
## f4 0.308 0.036 8.499 0.000 0.469 0.469
##
## Intercepts:
## F18r 0.000 0.000 0.000
## F14r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F13r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F1r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F20r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F12r 0.000 0.000 0.000
## F8r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
## f3 0.000 0.000 0.000
## f4 0.000 0.000 0.000
##
## Thresholds:
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
## F14r|t1 -0.752 0.061 -12.229 0.000 -0.752 -0.752
## F14r|t2 -0.290 0.056 -5.154 0.000 -0.290 -0.290
## F14r|t3 0.115 0.056 2.073 0.038 0.115 0.115
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F13r|t1 -0.529 0.058 -9.081 0.000 -0.529 -0.529
## F13r|t2 0.115 0.056 2.073 0.038 0.115 0.115
## F13r|t3 0.575 0.059 9.771 0.000 0.575 0.575
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F20r|t1 -0.695 0.061 -11.475 0.000 -0.695 -0.695
## F20r|t2 -0.189 0.056 -3.394 0.001 -0.189 -0.189
## F20r|t3 0.224 0.056 4.011 0.000 0.224 0.224
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F8r|t1 -0.140 0.056 -2.513 0.012 -0.140 -0.140
## F8r|t2 0.321 0.056 5.680 0.000 0.321 0.321
## F8r|t3 0.918 0.065 14.173 0.000 0.918 0.918
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
##
## Variances:
## F18r 0.282 0.282 0.282
## F14r 0.412 0.412 0.412
## F6r 0.390 0.390 0.390
## F3r 0.659 0.659 0.659
## F13r 0.908 0.908 0.908
## F19r 0.424 0.424 0.424
## F15r 0.564 0.564 0.564
## F17r 0.635 0.635 0.635
## F1r 0.750 0.750 0.750
## F9r 0.520 0.520 0.520
## F10r 0.505 0.505 0.505
## F20r 0.495 0.495 0.495
## F7r 0.623 0.623 0.623
## F5r 0.629 0.629 0.629
## F11r 0.604 0.604 0.604
## F12r 0.250 0.250 0.250
## F8r 0.893 0.893 0.893
## F16r 0.483 0.483 0.483
## f1 0.718 0.038 1.000 1.000
## f2 0.576 0.039 1.000 1.000
## f3 0.505 0.046 1.000 1.000
## f4 0.750 0.071 1.000 1.000
##
## R-Square:
##
## F18r 0.718
## F14r 0.588
## F6r 0.610
## F3r 0.341
## F13r 0.092
## F19r 0.576
## F15r 0.436
## F17r 0.365
## F1r 0.250
## F9r 0.480
## F10r 0.495
## F20r 0.505
## F7r 0.377
## F5r 0.371
## F11r 0.396
## F12r 0.750
## F8r 0.107
## F16r 0.517#Model Fit Measures
fitMeasures(fitSilveira)## npar fmin
## 78.000 0.213
## chisq df
## 218.990 129.000
## pvalue chisq.scaled
## 0.000 325.302
## df.scaled pvalue.scaled
## 129.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.734 12355.368
## baseline.df baseline.pvalue
## 153.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 5666.919 153.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.213
## cfi tli
## 0.993 0.991
## nnfi rfi
## 0.991 0.979
## nfi pnfi
## 0.982 0.828
## ifi rni
## 0.993 0.993
## cfi.scaled tli.scaled
## 0.964 0.958
## nnfi.scaled rfi.scaled
## 0.958 0.932
## nfi.scaled ifi.scaled
## 0.943 0.943
## rni.scaled rmsea
## 0.984 0.037
## rmsea.ci.lower rmsea.ci.upper
## 0.028 0.045
## rmsea.pvalue rmsea.scaled
## 0.996 0.055
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.047 0.062
## rmsea.pvalue.scaled wrmr
## 0.152 1.029
## cn_05 cn_01
## 366.916 396.775
## gfi agfi
## 0.986 0.978
## pgfi mfi
## 0.615 0.916#Parameters Estimates
EstSilveira <- parameterEstimates(fitSilveira, standardized=T, ci=F)
subset(EstSilveira, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F18r 1.000 0.000 NA NA 0.847 0.847 0.847
## 2 f1 =~ F14r 0.905 0.036 25.393 0 0.767 0.767 0.767
## 3 f1 =~ F6r 0.922 0.035 26.048 0 0.781 0.781 0.781
## 4 f1 =~ F3r 0.689 0.045 15.330 0 0.584 0.584 0.584
## 5 f1 =~ F13r 0.358 0.053 6.785 0 0.303 0.303 0.303
## 6 f2 =~ F19r 1.000 0.000 NA NA 0.759 0.759 0.759
## 7 f2 =~ F15r 0.870 0.043 20.162 0 0.661 0.661 0.661
## 8 f2 =~ F17r 0.796 0.051 15.743 0 0.605 0.605 0.605
## 9 f2 =~ F1r 0.658 0.052 12.693 0 0.500 0.500 0.500
## 10 f2 =~ F9r 0.913 0.049 18.565 0 0.693 0.693 0.693
## 11 f2 =~ F10r 0.927 0.047 19.826 0 0.704 0.704 0.704
## 12 f3 =~ F20r 1.000 0.000 NA NA 0.711 0.711 0.711
## 13 f3 =~ F7r 0.864 0.059 14.601 0 0.614 0.614 0.614
## 14 f3 =~ F5r 0.858 0.057 15.082 0 0.609 0.609 0.609
## 15 f3 =~ F11r 0.885 0.062 14.167 0 0.629 0.629 0.629
## 16 f4 =~ F12r 1.000 0.000 NA NA 0.866 0.866 0.866
## 17 f4 =~ F8r 0.377 0.070 5.384 0 0.327 0.327 0.327
## 18 f4 =~ F16r 0.830 0.072 11.456 0 0.719 0.719 0.719#Parameters Table
parTable(fitSilveira)## id lhs op rhs user group free ustart exo label eq.id unco plabel
## 1 1 f1 =~ F18r 1 1 0 1 0 0 0 .p1.
## 2 2 f1 =~ F14r 1 1 1 NA 0 0 1 .p2.
## 3 3 f1 =~ F6r 1 1 2 NA 0 0 2 .p3.
## 4 4 f1 =~ F3r 1 1 3 NA 0 0 3 .p4.
## 5 5 f1 =~ F13r 1 1 4 NA 0 0 4 .p5.
## 6 6 f2 =~ F19r 1 1 0 1 0 0 0 .p6.
## 7 7 f2 =~ F15r 1 1 5 NA 0 0 5 .p7.
## 8 8 f2 =~ F17r 1 1 6 NA 0 0 6 .p8.
## 9 9 f2 =~ F1r 1 1 7 NA 0 0 7 .p9.
## 10 10 f2 =~ F9r 1 1 8 NA 0 0 8 .p10.
## 11 11 f2 =~ F10r 1 1 9 NA 0 0 9 .p11.
## 12 12 f3 =~ F20r 1 1 0 1 0 0 0 .p12.
## 13 13 f3 =~ F7r 1 1 10 NA 0 0 10 .p13.
## 14 14 f3 =~ F5r 1 1 11 NA 0 0 11 .p14.
## 15 15 f3 =~ F11r 1 1 12 NA 0 0 12 .p15.
## 16 16 f4 =~ F12r 1 1 0 1 0 0 0 .p16.
## 17 17 f4 =~ F8r 1 1 13 NA 0 0 13 .p17.
## 18 18 f4 =~ F16r 1 1 14 NA 0 0 14 .p18.
## 19 19 f1 ~~ f2 1 1 15 NA 0 0 15 .p19.
## 20 20 f2 ~~ f3 1 1 16 NA 0 0 16 .p20.
## 21 21 f3 ~~ f4 1 1 17 NA 0 0 17 .p21.
## 22 22 f1 ~~ f4 1 1 18 NA 0 0 18 .p22.
## 23 23 f1 ~~ f3 1 1 19 NA 0 0 19 .p23.
## 24 24 F18r | t1 0 1 20 NA 0 0 20 .p24.
## 25 25 F18r | t2 0 1 21 NA 0 0 21 .p25.
## 26 26 F18r | t3 0 1 22 NA 0 0 22 .p26.
## 27 27 F14r | t1 0 1 23 NA 0 0 23 .p27.
## 28 28 F14r | t2 0 1 24 NA 0 0 24 .p28.
## 29 29 F14r | t3 0 1 25 NA 0 0 25 .p29.
## 30 30 F6r | t1 0 1 26 NA 0 0 26 .p30.
## 31 31 F6r | t2 0 1 27 NA 0 0 27 .p31.
## 32 32 F6r | t3 0 1 28 NA 0 0 28 .p32.
## 33 33 F3r | t1 0 1 29 NA 0 0 29 .p33.
## 34 34 F3r | t2 0 1 30 NA 0 0 30 .p34.
## 35 35 F3r | t3 0 1 31 NA 0 0 31 .p35.
## 36 36 F13r | t1 0 1 32 NA 0 0 32 .p36.
## 37 37 F13r | t2 0 1 33 NA 0 0 33 .p37.
## 38 38 F13r | t3 0 1 34 NA 0 0 34 .p38.
## 39 39 F19r | t1 0 1 35 NA 0 0 35 .p39.
## 40 40 F19r | t2 0 1 36 NA 0 0 36 .p40.
## 41 41 F19r | t3 0 1 37 NA 0 0 37 .p41.
## 42 42 F15r | t1 0 1 38 NA 0 0 38 .p42.
## 43 43 F15r | t2 0 1 39 NA 0 0 39 .p43.
## 44 44 F15r | t3 0 1 40 NA 0 0 40 .p44.
## 45 45 F17r | t1 0 1 41 NA 0 0 41 .p45.
## 46 46 F17r | t2 0 1 42 NA 0 0 42 .p46.
## 47 47 F17r | t3 0 1 43 NA 0 0 43 .p47.
## 48 48 F1r | t1 0 1 44 NA 0 0 44 .p48.
## 49 49 F1r | t2 0 1 45 NA 0 0 45 .p49.
## 50 50 F1r | t3 0 1 46 NA 0 0 46 .p50.
## 51 51 F9r | t1 0 1 47 NA 0 0 47 .p51.
## 52 52 F9r | t2 0 1 48 NA 0 0 48 .p52.
## 53 53 F9r | t3 0 1 49 NA 0 0 49 .p53.
## 54 54 F10r | t1 0 1 50 NA 0 0 50 .p54.
## 55 55 F10r | t2 0 1 51 NA 0 0 51 .p55.
## 56 56 F10r | t3 0 1 52 NA 0 0 52 .p56.
## 57 57 F20r | t1 0 1 53 NA 0 0 53 .p57.
## 58 58 F20r | t2 0 1 54 NA 0 0 54 .p58.
## 59 59 F20r | t3 0 1 55 NA 0 0 55 .p59.
## 60 60 F7r | t1 0 1 56 NA 0 0 56 .p60.
## 61 61 F7r | t2 0 1 57 NA 0 0 57 .p61.
## 62 62 F7r | t3 0 1 58 NA 0 0 58 .p62.
## 63 63 F5r | t1 0 1 59 NA 0 0 59 .p63.
## 64 64 F5r | t2 0 1 60 NA 0 0 60 .p64.
## 65 65 F5r | t3 0 1 61 NA 0 0 61 .p65.
## 66 66 F11r | t1 0 1 62 NA 0 0 62 .p66.
## 67 67 F11r | t2 0 1 63 NA 0 0 63 .p67.
## 68 68 F11r | t3 0 1 64 NA 0 0 64 .p68.
## 69 69 F12r | t1 0 1 65 NA 0 0 65 .p69.
## 70 70 F12r | t2 0 1 66 NA 0 0 66 .p70.
## 71 71 F12r | t3 0 1 67 NA 0 0 67 .p71.
## 72 72 F8r | t1 0 1 68 NA 0 0 68 .p72.
## 73 73 F8r | t2 0 1 69 NA 0 0 69 .p73.
## 74 74 F8r | t3 0 1 70 NA 0 0 70 .p74.
## 75 75 F16r | t1 0 1 71 NA 0 0 71 .p75.
## 76 76 F16r | t2 0 1 72 NA 0 0 72 .p76.
## 77 77 F16r | t3 0 1 73 NA 0 0 73 .p77.
## 78 78 F18r ~~ F18r 0 1 0 1 0 0 0 .p78.
## 79 79 F14r ~~ F14r 0 1 0 1 0 0 0 .p79.
## 80 80 F6r ~~ F6r 0 1 0 1 0 0 0 .p80.
## 81 81 F3r ~~ F3r 0 1 0 1 0 0 0 .p81.
## 82 82 F13r ~~ F13r 0 1 0 1 0 0 0 .p82.
## 83 83 F19r ~~ F19r 0 1 0 1 0 0 0 .p83.
## 84 84 F15r ~~ F15r 0 1 0 1 0 0 0 .p84.
## 85 85 F17r ~~ F17r 0 1 0 1 0 0 0 .p85.
## 86 86 F1r ~~ F1r 0 1 0 1 0 0 0 .p86.
## 87 87 F9r ~~ F9r 0 1 0 1 0 0 0 .p87.
## 88 88 F10r ~~ F10r 0 1 0 1 0 0 0 .p88.
## 89 89 F20r ~~ F20r 0 1 0 1 0 0 0 .p89.
## 90 90 F7r ~~ F7r 0 1 0 1 0 0 0 .p90.
## 91 91 F5r ~~ F5r 0 1 0 1 0 0 0 .p91.
## 92 92 F11r ~~ F11r 0 1 0 1 0 0 0 .p92.
## 93 93 F12r ~~ F12r 0 1 0 1 0 0 0 .p93.
## 94 94 F8r ~~ F8r 0 1 0 1 0 0 0 .p94.
## 95 95 F16r ~~ F16r 0 1 0 1 0 0 0 .p95.
## 96 96 f1 ~~ f1 0 1 74 NA 0 0 74 .p96.
## 97 97 f2 ~~ f2 0 1 75 NA 0 0 75 .p97.
## 98 98 f3 ~~ f3 0 1 76 NA 0 0 76 .p98.
## 99 99 f4 ~~ f4 0 1 77 NA 0 0 77 .p99.
## 100 100 f2 ~~ f4 0 1 78 NA 0 0 78 .p100.
## 101 101 F18r ~1 0 1 0 0 0 0 0 .p101.
## 102 102 F14r ~1 0 1 0 0 0 0 0 .p102.
## 103 103 F6r ~1 0 1 0 0 0 0 0 .p103.
## 104 104 F3r ~1 0 1 0 0 0 0 0 .p104.
## 105 105 F13r ~1 0 1 0 0 0 0 0 .p105.
## 106 106 F19r ~1 0 1 0 0 0 0 0 .p106.
## 107 107 F15r ~1 0 1 0 0 0 0 0 .p107.
## 108 108 F17r ~1 0 1 0 0 0 0 0 .p108.
## 109 109 F1r ~1 0 1 0 0 0 0 0 .p109.
## 110 110 F9r ~1 0 1 0 0 0 0 0 .p110.
## 111 111 F10r ~1 0 1 0 0 0 0 0 .p111.
## 112 112 F20r ~1 0 1 0 0 0 0 0 .p112.
## 113 113 F7r ~1 0 1 0 0 0 0 0 .p113.
## 114 114 F5r ~1 0 1 0 0 0 0 0 .p114.
## 115 115 F11r ~1 0 1 0 0 0 0 0 .p115.
## 116 116 F12r ~1 0 1 0 0 0 0 0 .p116.
## 117 117 F8r ~1 0 1 0 0 0 0 0 .p117.
## 118 118 F16r ~1 0 1 0 0 0 0 0 .p118.
## 119 119 f1 ~1 0 1 0 0 0 0 0 .p119.
## 120 120 f2 ~1 0 1 0 0 0 0 0 .p120.
## 121 121 f3 ~1 0 1 0 0 0 0 0 .p121.
## 122 122 f4 ~1 0 1 0 0 0 0 0 .p122.
## start
## 1 1.000
## 2 0.952
## 3 0.940
## 4 0.651
## 5 0.419
## 6 1.000
## 7 0.775
## 8 0.603
## 9 0.511
## 10 0.744
## 11 0.739
## 12 1.000
## 13 1.004
## 14 1.060
## 15 0.779
## 16 1.000
## 17 0.396
## 18 0.731
## 19 0.000
## 20 0.000
## 21 0.000
## 22 0.000
## 23 0.000
## 24 -1.019
## 25 -0.452
## 26 -0.017
## 27 -0.752
## 28 -0.290
## 29 0.115
## 30 -1.028
## 31 -0.502
## 32 0.012
## 33 -0.818
## 34 -0.184
## 35 0.310
## 36 -0.529
## 37 0.115
## 38 0.575
## 39 -0.346
## 40 0.100
## 41 0.490
## 42 -0.357
## 43 0.209
## 44 0.658
## 45 -0.224
## 46 0.264
## 47 0.658
## 48 -0.616
## 49 0.071
## 50 0.658
## 51 -0.707
## 52 -0.204
## 53 0.179
## 54 -0.518
## 55 -0.007
## 56 0.469
## 57 -0.695
## 58 -0.189
## 59 0.224
## 60 -0.676
## 61 -0.199
## 62 0.326
## 63 -0.622
## 64 0.002
## 65 0.546
## 66 -0.867
## 67 -0.393
## 68 0.071
## 69 -0.604
## 70 -0.145
## 71 0.404
## 72 -0.140
## 73 0.321
## 74 0.918
## 75 -0.362
## 76 -0.046
## 77 0.415
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 1.000
## 90 1.000
## 91 1.000
## 92 1.000
## 93 1.000
## 94 1.000
## 95 1.000
## 96 0.050
## 97 0.050
## 98 0.050
## 99 0.050
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
## 111 0.000
## 112 0.000
## 113 0.000
## 114 0.000
## 115 0.000
## 116 0.000
## 117 0.000
## 118 0.000
## 119 0.000
## 120 0.000
## 121 0.000
## 122 0.000#Model Coefficients
coef(fitSilveira)## f1=~F14r f1=~F6r f1=~F3r f1=~F13r f2=~F15r f2=~F17r f2=~F1r f2=~F9r
## 0.905 0.922 0.689 0.358 0.870 0.796 0.658 0.913
## f2=~F10r f3=~F7r f3=~F5r f3=~F11r f4=~F8r f4=~F16r f1~~f2 f2~~f3
## 0.927 0.864 0.858 0.885 0.377 0.830 0.605 0.511
## f3~~f4 f1~~f4 f1~~f3 F18r|t1 F18r|t2 F18r|t3 F14r|t1 F14r|t2
## 0.357 0.474 0.550 -1.019 -0.452 -0.017 -0.752 -0.290
## F14r|t3 F6r|t1 F6r|t2 F6r|t3 F3r|t1 F3r|t2 F3r|t3 F13r|t1
## 0.115 -1.028 -0.502 0.012 -0.818 -0.184 0.310 -0.529
## F13r|t2 F13r|t3 F19r|t1 F19r|t2 F19r|t3 F15r|t1 F15r|t2 F15r|t3
## 0.115 0.575 -0.346 0.100 0.490 -0.357 0.209 0.658
## F17r|t1 F17r|t2 F17r|t3 F1r|t1 F1r|t2 F1r|t3 F9r|t1 F9r|t2
## -0.224 0.264 0.658 -0.616 0.071 0.658 -0.707 -0.204
## F9r|t3 F10r|t1 F10r|t2 F10r|t3 F20r|t1 F20r|t2 F20r|t3 F7r|t1
## 0.179 -0.518 -0.007 0.469 -0.695 -0.189 0.224 -0.676
## F7r|t2 F7r|t3 F5r|t1 F5r|t2 F5r|t3 F11r|t1 F11r|t2 F11r|t3
## -0.199 0.326 -0.622 0.002 0.546 -0.867 -0.393 0.071
## F12r|t1 F12r|t2 F12r|t3 F8r|t1 F8r|t2 F8r|t3 F16r|t1 F16r|t2
## -0.604 -0.145 0.404 -0.140 0.321 0.918 -0.362 -0.046
## F16r|t3 f1~~f1 f2~~f2 f3~~f3 f4~~f4 f2~~f4
## 0.415 0.718 0.576 0.505 0.750 0.308#Modification Index
MISilveira<-modindices(fitSilveira)
MIISilveira<- MISilveira[which(MISilveira$mi>30),]
print(MIISilveira)## lhs op rhs mi mi.scaled epc sepc.lv sepc.all sepc.nox
## 1 F18r ~~ F17r 37.259 50.752 0.250 0.250 0.250 0.250
## 2 F19r ~~ F15r 45.897 62.518 0.254 0.254 0.254 0.254#Model Plot
semPaths(fitSilveira,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Marliere - Principal Components Analysis - Two Components Solution - CFA Model
PCA2_CFA <- '
# latent variable definitions
f1 =~ F4r + F8r + F12r + F16r
f2 =~ F1r + F2r + F3r + F5r + F6r + F7r + F9r + F10r + F11r + F13r + F14r + F15r + F17r + F18r + F19r + F20r
#factor covariances
f1~~f2
'
fitPCA2 <- cfa(PCA2_CFA, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitPCA2, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 33 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 354.536 479.936
## Degrees of freedom 169 169
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.809
## Shift parameter 41.803
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 13400.870 6035.299
## Degrees of freedom 190 190
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.986 0.947
## Tucker-Lewis Index (TLI) 0.984 0.940
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046 0.060
## 90 Percent Confidence Interval 0.040 0.053 0.054 0.066
## P-value RMSEA <= 0.05 0.811 0.005
##
## Weighted Root Mean Square Residual:
##
## WRMR 1.191 1.191
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F4r 1.000 0.327 0.327
## F8r 1.086 0.237 4.591 0.000 0.355 0.355
## F12r 2.745 0.512 5.362 0.000 0.898 0.898
## F16r 2.225 0.410 5.422 0.000 0.728 0.728
## f2 =~
## F1r 1.000 0.490 0.490
## F2r 1.028 0.099 10.420 0.000 0.504 0.504
## F3r 1.210 0.110 11.018 0.000 0.593 0.593
## F5r 1.202 0.110 10.967 0.000 0.589 0.589
## F6r 1.575 0.123 12.800 0.000 0.772 0.772
## F7r 1.210 0.110 10.966 0.000 0.593 0.593
## F9r 1.377 0.114 12.070 0.000 0.675 0.675
## F10r 1.403 0.120 11.726 0.000 0.688 0.688
## F11r 1.266 0.115 10.988 0.000 0.621 0.621
## F13r 0.610 0.098 6.211 0.000 0.299 0.299
## F14r 1.534 0.124 12.358 0.000 0.752 0.752
## F15r 1.315 0.117 11.195 0.000 0.645 0.645
## F17r 1.195 0.113 10.588 0.000 0.586 0.586
## F18r 1.693 0.138 12.295 0.000 0.830 0.830
## F19r 1.507 0.119 12.694 0.000 0.739 0.739
## F20r 1.391 0.122 11.443 0.000 0.682 0.682
##
## Covariances:
## f1 ~~
## f2 0.088 0.019 4.704 0.000 0.548 0.548
##
## Intercepts:
## F4r 0.000 0.000 0.000
## F8r 0.000 0.000 0.000
## F12r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F1r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F13r 0.000 0.000 0.000
## F14r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F18r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F20r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
##
## Thresholds:
## F4r|t1 -0.431 0.057 -7.517 0.000 -0.431 -0.431
## F4r|t2 0.110 0.056 1.984 0.047 0.110 0.110
## F4r|t3 0.701 0.061 11.559 0.000 0.701 0.701
## F8r|t1 -0.140 0.056 -2.513 0.012 -0.140 -0.140
## F8r|t2 0.321 0.056 5.680 0.000 0.321 0.321
## F8r|t3 0.918 0.065 14.173 0.000 0.918 0.918
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F13r|t1 -0.529 0.058 -9.081 0.000 -0.529 -0.529
## F13r|t2 0.115 0.056 2.073 0.038 0.115 0.115
## F13r|t3 0.575 0.059 9.771 0.000 0.575 0.575
## F14r|t1 -0.752 0.061 -12.229 0.000 -0.752 -0.752
## F14r|t2 -0.290 0.056 -5.154 0.000 -0.290 -0.290
## F14r|t3 0.115 0.056 2.073 0.038 0.115 0.115
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F20r|t1 -0.695 0.061 -11.475 0.000 -0.695 -0.695
## F20r|t2 -0.189 0.056 -3.394 0.001 -0.189 -0.189
## F20r|t3 0.224 0.056 4.011 0.000 0.224 0.224
##
## Variances:
## F4r 0.893 0.893 0.893
## F8r 0.874 0.874 0.874
## F12r 0.193 0.193 0.193
## F16r 0.469 0.469 0.469
## F1r 0.760 0.760 0.760
## F2r 0.746 0.746 0.746
## F3r 0.648 0.648 0.648
## F5r 0.653 0.653 0.653
## F6r 0.404 0.404 0.404
## F7r 0.648 0.648 0.648
## F9r 0.544 0.544 0.544
## F10r 0.527 0.527 0.527
## F11r 0.615 0.615 0.615
## F13r 0.910 0.910 0.910
## F14r 0.434 0.434 0.434
## F15r 0.584 0.584 0.584
## F17r 0.657 0.657 0.657
## F18r 0.311 0.311 0.311
## F19r 0.454 0.454 0.454
## F20r 0.535 0.535 0.535
## f1 0.107 0.038 1.000 1.000
## f2 0.240 0.037 1.000 1.000
##
## R-Square:
##
## F4r 0.107
## F8r 0.126
## F12r 0.807
## F16r 0.531
## F1r 0.240
## F2r 0.254
## F3r 0.352
## F5r 0.347
## F6r 0.596
## F7r 0.352
## F9r 0.456
## F10r 0.473
## F11r 0.385
## F13r 0.090
## F14r 0.566
## F15r 0.416
## F17r 0.343
## F18r 0.689
## F19r 0.546
## F20r 0.465#Model Fit Measures
fitMeasures(fitPCA2)## npar fmin
## 81.000 0.346
## chisq df
## 354.536 169.000
## pvalue chisq.scaled
## 0.000 479.936
## df.scaled pvalue.scaled
## 169.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.809 13400.870
## baseline.df baseline.pvalue
## 190.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 6035.299 190.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.260
## cfi tli
## 0.986 0.984
## nnfi rfi
## 0.984 0.970
## nfi pnfi
## 0.974 0.866
## ifi rni
## 0.986 0.986
## cfi.scaled tli.scaled
## 0.947 0.940
## nnfi.scaled rfi.scaled
## 0.940 0.911
## nfi.scaled ifi.scaled
## 0.920 0.920
## rni.scaled rmsea
## 0.976 0.046
## rmsea.ci.lower rmsea.ci.upper
## 0.040 0.053
## rmsea.pvalue rmsea.scaled
## 0.811 0.060
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.054 0.066
## rmsea.pvalue.scaled wrmr
## 0.005 1.191
## cn_05 cn_01
## 290.310 311.036
## gfi agfi
## 0.979 0.970
## pgfi mfi
## 0.662 0.834#Parameters Estimates
EstPCA2 <- parameterEstimates(fitPCA2, standardized=T, ci=F)
subset(EstPCA2, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F4r 1.000 0.000 NA NA 0.327 0.327 0.327
## 2 f1 =~ F8r 1.086 0.237 4.591 0 0.355 0.355 0.355
## 3 f1 =~ F12r 2.745 0.512 5.362 0 0.898 0.898 0.898
## 4 f1 =~ F16r 2.225 0.410 5.422 0 0.728 0.728 0.728
## 5 f2 =~ F1r 1.000 0.000 NA NA 0.490 0.490 0.490
## 6 f2 =~ F2r 1.028 0.099 10.420 0 0.504 0.504 0.504
## 7 f2 =~ F3r 1.210 0.110 11.018 0 0.593 0.593 0.593
## 8 f2 =~ F5r 1.202 0.110 10.967 0 0.589 0.589 0.589
## 9 f2 =~ F6r 1.575 0.123 12.800 0 0.772 0.772 0.772
## 10 f2 =~ F7r 1.210 0.110 10.966 0 0.593 0.593 0.593
## 11 f2 =~ F9r 1.377 0.114 12.070 0 0.675 0.675 0.675
## 12 f2 =~ F10r 1.403 0.120 11.726 0 0.688 0.688 0.688
## 13 f2 =~ F11r 1.266 0.115 10.988 0 0.621 0.621 0.621
## 14 f2 =~ F13r 0.610 0.098 6.211 0 0.299 0.299 0.299
## 15 f2 =~ F14r 1.534 0.124 12.358 0 0.752 0.752 0.752
## 16 f2 =~ F15r 1.315 0.117 11.195 0 0.645 0.645 0.645
## 17 f2 =~ F17r 1.195 0.113 10.588 0 0.586 0.586 0.586
## 18 f2 =~ F18r 1.693 0.138 12.295 0 0.830 0.830 0.830
## 19 f2 =~ F19r 1.507 0.119 12.694 0 0.739 0.739 0.739
## 20 f2 =~ F20r 1.391 0.122 11.443 0 0.682 0.682 0.682#Model Coefficients
coef(fitPCA2)## f1=~F8r f1=~F12r f1=~F16r f2=~F2r f2=~F3r f2=~F5r f2=~F6r f2=~F7r
## 1.086 2.745 2.225 1.028 1.210 1.202 1.575 1.210
## f2=~F9r f2=~F10r f2=~F11r f2=~F13r f2=~F14r f2=~F15r f2=~F17r f2=~F18r
## 1.377 1.403 1.266 0.610 1.534 1.315 1.195 1.693
## f2=~F19r f2=~F20r f1~~f2 F4r|t1 F4r|t2 F4r|t3 F8r|t1 F8r|t2
## 1.507 1.391 0.088 -0.431 0.110 0.701 -0.140 0.321
## F8r|t3 F12r|t1 F12r|t2 F12r|t3 F16r|t1 F16r|t2 F16r|t3 F1r|t1
## 0.918 -0.604 -0.145 0.404 -0.362 -0.046 0.415 -0.616
## F1r|t2 F1r|t3 F2r|t1 F2r|t2 F2r|t3 F3r|t1 F3r|t2 F3r|t3
## 0.071 0.658 -0.409 0.120 0.707 -0.818 -0.184 0.310
## F5r|t1 F5r|t2 F5r|t3 F6r|t1 F6r|t2 F6r|t3 F7r|t1 F7r|t2
## -0.622 0.002 0.546 -1.028 -0.502 0.012 -0.676 -0.199
## F7r|t3 F9r|t1 F9r|t2 F9r|t3 F10r|t1 F10r|t2 F10r|t3 F11r|t1
## 0.326 -0.707 -0.204 0.179 -0.518 -0.007 0.469 -0.867
## F11r|t2 F11r|t3 F13r|t1 F13r|t2 F13r|t3 F14r|t1 F14r|t2 F14r|t3
## -0.393 0.071 -0.529 0.115 0.575 -0.752 -0.290 0.115
## F15r|t1 F15r|t2 F15r|t3 F17r|t1 F17r|t2 F17r|t3 F18r|t1 F18r|t2
## -0.357 0.209 0.658 -0.224 0.264 0.658 -1.019 -0.452
## F18r|t3 F19r|t1 F19r|t2 F19r|t3 F20r|t1 F20r|t2 F20r|t3 f1~~f1
## -0.017 -0.346 0.100 0.490 -0.695 -0.189 0.224 0.107
## f2~~f2
## 0.240#Modification Index
MIPCA2<-modindices(fitPCA2)
MIIPCA2<- MIPCA2[which(MIPCA2$mi>30),]
print(MIIPCA2)## lhs op rhs mi mi.scaled epc sepc.lv sepc.all sepc.nox
## 1 F15r ~~ F19r 53.772 66.451 0.258 0.258 0.258 0.258
## 2 F17r ~~ F18r 33.973 41.984 0.233 0.233 0.233 0.233#Model Plot
semPaths(fitPCA2,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Marliere - Principal Components Analysis - Two Components Solution Reviewed - CFA Model (removed itens - 4, 8, 13)
PCA2_CFAr <- '
# latent variable definitions
f1 =~ F12r + F16r
f2 =~ F1r + F2r + F3r + F5r + F6r + F7r + F9r + F10r + F11r + F14r + F15r + F17r + F18r + F19r + F20r
#factor covariances
f1~~f2
'
fitPCA2r <- cfa(PCA2_CFAr, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitPCA2r, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 26 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 263.246 393.304
## Degrees of freedom 118 118
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.712
## Shift parameter 23.644
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 12752.383 5676.220
## Degrees of freedom 136 136
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.988 0.950
## Tucker-Lewis Index (TLI) 0.987 0.943
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049 0.068
## 90 Percent Confidence Interval 0.041 0.057 0.060 0.075
## P-value RMSEA <= 0.05 0.567 0.000
##
## Weighted Root Mean Square Residual:
##
## WRMR 1.186 1.186
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F12r 1.000 0.856 0.856
## F16r 0.832 0.077 10.795 0.000 0.712 0.712
## f2 =~
## F1r 1.000 0.494 0.494
## F2r 1.020 0.097 10.491 0.000 0.504 0.504
## F3r 1.197 0.108 11.056 0.000 0.591 0.591
## F5r 1.195 0.108 11.072 0.000 0.590 0.590
## F6r 1.561 0.121 12.938 0.000 0.771 0.771
## F7r 1.201 0.109 11.022 0.000 0.593 0.593
## F9r 1.368 0.112 12.173 0.000 0.675 0.675
## F10r 1.394 0.118 11.808 0.000 0.688 0.688
## F11r 1.258 0.114 11.049 0.000 0.621 0.621
## F14r 1.513 0.122 12.411 0.000 0.747 0.747
## F15r 1.304 0.116 11.240 0.000 0.644 0.644
## F17r 1.191 0.112 10.643 0.000 0.588 0.588
## F18r 1.679 0.136 12.357 0.000 0.829 0.829
## F19r 1.497 0.117 12.796 0.000 0.739 0.739
## F20r 1.380 0.120 11.525 0.000 0.681 0.681
##
## Covariances:
## f1 ~~
## f2 0.250 0.028 8.885 0.000 0.591 0.591
##
## Intercepts:
## F12r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F1r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F14r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F18r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F20r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
##
## Thresholds:
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F14r|t1 -0.752 0.061 -12.229 0.000 -0.752 -0.752
## F14r|t2 -0.290 0.056 -5.154 0.000 -0.290 -0.290
## F14r|t3 0.115 0.056 2.073 0.038 0.115 0.115
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F20r|t1 -0.695 0.061 -11.475 0.000 -0.695 -0.695
## F20r|t2 -0.189 0.056 -3.394 0.001 -0.189 -0.189
## F20r|t3 0.224 0.056 4.011 0.000 0.224 0.224
##
## Variances:
## F12r 0.267 0.267 0.267
## F16r 0.493 0.493 0.493
## F1r 0.756 0.756 0.756
## F2r 0.746 0.746 0.746
## F3r 0.650 0.650 0.650
## F5r 0.652 0.652 0.652
## F6r 0.406 0.406 0.406
## F7r 0.648 0.648 0.648
## F9r 0.544 0.544 0.544
## F10r 0.526 0.526 0.526
## F11r 0.614 0.614 0.614
## F14r 0.442 0.442 0.442
## F15r 0.585 0.585 0.585
## F17r 0.654 0.654 0.654
## F18r 0.313 0.313 0.313
## F19r 0.454 0.454 0.454
## F20r 0.536 0.536 0.536
## f1 0.733 0.075 1.000 1.000
## f2 0.244 0.038 1.000 1.000
##
## R-Square:
##
## F12r 0.733
## F16r 0.507
## F1r 0.244
## F2r 0.254
## F3r 0.350
## F5r 0.348
## F6r 0.594
## F7r 0.352
## F9r 0.456
## F10r 0.474
## F11r 0.386
## F14r 0.558
## F15r 0.415
## F17r 0.346
## F18r 0.687
## F19r 0.546
## F20r 0.464#Model Fit Measures
fitMeasures(fitPCA2r)## npar fmin
## 69.000 0.257
## chisq df
## 263.246 118.000
## pvalue chisq.scaled
## 0.000 393.304
## df.scaled pvalue.scaled
## 118.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.712 12752.383
## baseline.df baseline.pvalue
## 136.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 5676.220 136.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.277
## cfi tli
## 0.988 0.987
## nnfi rfi
## 0.987 0.976
## nfi pnfi
## 0.979 0.850
## ifi rni
## 0.989 0.988
## cfi.scaled tli.scaled
## 0.950 0.943
## nnfi.scaled rfi.scaled
## 0.943 0.920
## nfi.scaled ifi.scaled
## 0.931 0.931
## rni.scaled rmsea
## 0.978 0.049
## rmsea.ci.lower rmsea.ci.upper
## 0.041 0.057
## rmsea.pvalue rmsea.scaled
## 0.567 0.068
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.060 0.075
## rmsea.pvalue.scaled wrmr
## 0.000 1.186
## cn_05 cn_01
## 281.760 305.673
## gfi agfi
## 0.983 0.974
## pgfi mfi
## 0.621 0.868#Parameters Estimates
EstPCA2r <- parameterEstimates(fitPCA2r, standardized=T, ci=F)
subset(EstPCA2r, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F12r 1.000 0.000 NA NA 0.856 0.856 0.856
## 2 f1 =~ F16r 0.832 0.077 10.795 0 0.712 0.712 0.712
## 3 f2 =~ F1r 1.000 0.000 NA NA 0.494 0.494 0.494
## 4 f2 =~ F2r 1.020 0.097 10.491 0 0.504 0.504 0.504
## 5 f2 =~ F3r 1.197 0.108 11.056 0 0.591 0.591 0.591
## 6 f2 =~ F5r 1.195 0.108 11.072 0 0.590 0.590 0.590
## 7 f2 =~ F6r 1.561 0.121 12.938 0 0.771 0.771 0.771
## 8 f2 =~ F7r 1.201 0.109 11.022 0 0.593 0.593 0.593
## 9 f2 =~ F9r 1.368 0.112 12.173 0 0.675 0.675 0.675
## 10 f2 =~ F10r 1.394 0.118 11.808 0 0.688 0.688 0.688
## 11 f2 =~ F11r 1.258 0.114 11.049 0 0.621 0.621 0.621
## 12 f2 =~ F14r 1.513 0.122 12.411 0 0.747 0.747 0.747
## 13 f2 =~ F15r 1.304 0.116 11.240 0 0.644 0.644 0.644
## 14 f2 =~ F17r 1.191 0.112 10.643 0 0.588 0.588 0.588
## 15 f2 =~ F18r 1.679 0.136 12.357 0 0.829 0.829 0.829
## 16 f2 =~ F19r 1.497 0.117 12.796 0 0.739 0.739 0.739
## 17 f2 =~ F20r 1.380 0.120 11.525 0 0.681 0.681 0.681#Model Coefficients
coef(fitPCA2r)## f1=~F16r f2=~F2r f2=~F3r f2=~F5r f2=~F6r f2=~F7r f2=~F9r f2=~F10r
## 0.832 1.020 1.197 1.195 1.561 1.201 1.368 1.394
## f2=~F11r f2=~F14r f2=~F15r f2=~F17r f2=~F18r f2=~F19r f2=~F20r f1~~f2
## 1.258 1.513 1.304 1.191 1.679 1.497 1.380 0.250
## F12r|t1 F12r|t2 F12r|t3 F16r|t1 F16r|t2 F16r|t3 F1r|t1 F1r|t2
## -0.604 -0.145 0.404 -0.362 -0.046 0.415 -0.616 0.071
## F1r|t3 F2r|t1 F2r|t2 F2r|t3 F3r|t1 F3r|t2 F3r|t3 F5r|t1
## 0.658 -0.409 0.120 0.707 -0.818 -0.184 0.310 -0.622
## F5r|t2 F5r|t3 F6r|t1 F6r|t2 F6r|t3 F7r|t1 F7r|t2 F7r|t3
## 0.002 0.546 -1.028 -0.502 0.012 -0.676 -0.199 0.326
## F9r|t1 F9r|t2 F9r|t3 F10r|t1 F10r|t2 F10r|t3 F11r|t1 F11r|t2
## -0.707 -0.204 0.179 -0.518 -0.007 0.469 -0.867 -0.393
## F11r|t3 F14r|t1 F14r|t2 F14r|t3 F15r|t1 F15r|t2 F15r|t3 F17r|t1
## 0.071 -0.752 -0.290 0.115 -0.357 0.209 0.658 -0.224
## F17r|t2 F17r|t3 F18r|t1 F18r|t2 F18r|t3 F19r|t1 F19r|t2 F19r|t3
## 0.264 0.658 -1.019 -0.452 -0.017 -0.346 0.100 0.490
## F20r|t1 F20r|t2 F20r|t3 f1~~f1 f2~~f2
## -0.695 -0.189 0.224 0.733 0.244#Modification Index
MIPCA2r<-modindices(fitPCA2r)
MIIPCA2r<- MIPCA2r[which(MIPCA2r$mi>30),]
print(MIIPCA2r)## lhs op rhs mi mi.scaled epc sepc.lv sepc.all sepc.nox
## 1 F15r ~~ F19r 54.117 75.993 0.259 0.259 0.259 0.259
## 2 F17r ~~ F18r 33.720 47.351 0.233 0.233 0.233 0.233#Model Plot
semPaths(fitPCA2r,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Marliere - Final Solution - Principal Components Analysis - Two Components Solution Reviewed - CFA Model (removed itens - F4r, F8r, F13r) - and included error covariance
PCA2_CFAr <- '
# latent variable definitions
f1 =~ F12r + F16r
f2 =~ F1r + F2r + F3r + F5r + F6r + F7r + F9r + F10r + F11r + F14r + F15r + F17r + F18r + F19r + F20r
#factor covariances
f1~~f2
#error covariance
F15r ~~ F19r
F17r ~~ F18r
'
fitPCA2r <- cfa(PCA2_CFAr, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitPCA2r, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 28 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 176.935 274.014
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.705
## Shift parameter 23.039
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 12752.383 5676.220
## Degrees of freedom 136 136
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.995 0.971
## Tucker-Lewis Index (TLI) 0.994 0.967
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.032 0.052
## 90 Percent Confidence Interval 0.022 0.041 0.044 0.060
## P-value RMSEA <= 0.05 1.000 0.360
##
## Weighted Root Mean Square Residual:
##
## WRMR 0.973 0.973
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F12r 1.000 0.857 0.857
## F16r 0.831 0.077 10.830 0.000 0.712 0.712
## f2 =~
## F1r 1.000 0.499 0.499
## F2r 1.020 0.097 10.528 0.000 0.509 0.509
## F3r 1.198 0.108 11.086 0.000 0.597 0.597
## F5r 1.195 0.108 11.105 0.000 0.596 0.596
## F6r 1.563 0.120 12.973 0.000 0.779 0.779
## F7r 1.201 0.109 11.045 0.000 0.599 0.599
## F9r 1.368 0.112 12.193 0.000 0.682 0.682
## F10r 1.394 0.118 11.832 0.000 0.695 0.695
## F11r 1.258 0.114 11.072 0.000 0.627 0.627
## F14r 1.514 0.122 12.425 0.000 0.755 0.755
## F15r 1.184 0.110 10.733 0.000 0.591 0.591
## F17r 1.089 0.110 9.897 0.000 0.543 0.543
## F18r 1.619 0.132 12.298 0.000 0.807 0.807
## F19r 1.396 0.111 12.576 0.000 0.696 0.696
## F20r 1.380 0.120 11.527 0.000 0.688 0.688
##
## Covariances:
## f1 ~~
## f2 0.255 0.028 8.948 0.000 0.597 0.597
## F15r ~~
## F19r 0.253 0.033 7.793 0.000 0.253 0.437
## F17r ~~
## F18r 0.227 0.036 6.297 0.000 0.227 0.458
##
## Intercepts:
## F12r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F1r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F14r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F18r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F20r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
##
## Thresholds:
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F14r|t1 -0.752 0.061 -12.229 0.000 -0.752 -0.752
## F14r|t2 -0.290 0.056 -5.154 0.000 -0.290 -0.290
## F14r|t3 0.115 0.056 2.073 0.038 0.115 0.115
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F20r|t1 -0.695 0.061 -11.475 0.000 -0.695 -0.695
## F20r|t2 -0.189 0.056 -3.394 0.001 -0.189 -0.189
## F20r|t3 0.224 0.056 4.011 0.000 0.224 0.224
##
## Variances:
## F12r 0.266 0.266 0.266
## F16r 0.493 0.493 0.493
## F1r 0.751 0.751 0.751
## F2r 0.741 0.741 0.741
## F3r 0.643 0.643 0.643
## F5r 0.645 0.645 0.645
## F6r 0.393 0.393 0.393
## F7r 0.642 0.642 0.642
## F9r 0.535 0.535 0.535
## F10r 0.517 0.517 0.517
## F11r 0.607 0.607 0.607
## F14r 0.430 0.430 0.430
## F15r 0.651 0.651 0.651
## F17r 0.705 0.705 0.705
## F18r 0.348 0.348 0.348
## F19r 0.515 0.515 0.515
## F20r 0.527 0.527 0.527
## f1 0.734 0.075 1.000 1.000
## f2 0.249 0.038 1.000 1.000
##
## R-Square:
##
## F12r 0.734
## F16r 0.507
## F1r 0.249
## F2r 0.259
## F3r 0.357
## F5r 0.355
## F6r 0.607
## F7r 0.358
## F9r 0.465
## F10r 0.483
## F11r 0.393
## F14r 0.570
## F15r 0.349
## F17r 0.295
## F18r 0.652
## F19r 0.485
## F20r 0.473#Model Fit Measures
fitMeasures(fitPCA2r)## npar fmin
## 71.000 0.172
## chisq df
## 176.935 116.000
## pvalue chisq.scaled
## 0.000 274.014
## df.scaled pvalue.scaled
## 116.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.705 12752.383
## baseline.df baseline.pvalue
## 136.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 5676.220 136.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.277
## cfi tli
## 0.995 0.994
## nnfi rfi
## 0.994 0.984
## nfi pnfi
## 0.986 0.841
## ifi rni
## 0.995 0.995
## cfi.scaled tli.scaled
## 0.971 0.967
## nnfi.scaled rfi.scaled
## 0.967 0.943
## nfi.scaled ifi.scaled
## 0.952 0.952
## rni.scaled rmsea
## 0.987 0.032
## rmsea.ci.lower rmsea.ci.upper
## 0.022 0.041
## rmsea.pvalue rmsea.scaled
## 1.000 0.052
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.044 0.060
## rmsea.pvalue.scaled wrmr
## 0.360 0.973
## cn_05 cn_01
## 412.307 447.627
## gfi agfi
## 0.989 0.982
## pgfi mfi
## 0.613 0.942#Parameters Estimates
EstPCA2r <- parameterEstimates(fitPCA2r, standardized=T, ci=F)
subset(EstPCA2r, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F12r 1.000 0.000 NA NA 0.857 0.857 0.857
## 2 f1 =~ F16r 0.831 0.077 10.830 0 0.712 0.712 0.712
## 3 f2 =~ F1r 1.000 0.000 NA NA 0.499 0.499 0.499
## 4 f2 =~ F2r 1.020 0.097 10.528 0 0.509 0.509 0.509
## 5 f2 =~ F3r 1.198 0.108 11.086 0 0.597 0.597 0.597
## 6 f2 =~ F5r 1.195 0.108 11.105 0 0.596 0.596 0.596
## 7 f2 =~ F6r 1.563 0.120 12.973 0 0.779 0.779 0.779
## 8 f2 =~ F7r 1.201 0.109 11.045 0 0.599 0.599 0.599
## 9 f2 =~ F9r 1.368 0.112 12.193 0 0.682 0.682 0.682
## 10 f2 =~ F10r 1.394 0.118 11.832 0 0.695 0.695 0.695
## 11 f2 =~ F11r 1.258 0.114 11.072 0 0.627 0.627 0.627
## 12 f2 =~ F14r 1.514 0.122 12.425 0 0.755 0.755 0.755
## 13 f2 =~ F15r 1.184 0.110 10.733 0 0.591 0.591 0.591
## 14 f2 =~ F17r 1.089 0.110 9.897 0 0.543 0.543 0.543
## 15 f2 =~ F18r 1.619 0.132 12.298 0 0.807 0.807 0.807
## 16 f2 =~ F19r 1.396 0.111 12.576 0 0.696 0.696 0.696
## 17 f2 =~ F20r 1.380 0.120 11.527 0 0.688 0.688 0.688#Model Coefficients
coef(fitPCA2r)## f1=~F16r f2=~F2r f2=~F3r f2=~F5r f2=~F6r f2=~F7r
## 0.831 1.020 1.198 1.195 1.563 1.201
## f2=~F9r f2=~F10r f2=~F11r f2=~F14r f2=~F15r f2=~F17r
## 1.368 1.394 1.258 1.514 1.184 1.089
## f2=~F18r f2=~F19r f2=~F20r f1~~f2 F15r~~F19r F17r~~F18r
## 1.619 1.396 1.380 0.255 0.253 0.227
## F12r|t1 F12r|t2 F12r|t3 F16r|t1 F16r|t2 F16r|t3
## -0.604 -0.145 0.404 -0.362 -0.046 0.415
## F1r|t1 F1r|t2 F1r|t3 F2r|t1 F2r|t2 F2r|t3
## -0.616 0.071 0.658 -0.409 0.120 0.707
## F3r|t1 F3r|t2 F3r|t3 F5r|t1 F5r|t2 F5r|t3
## -0.818 -0.184 0.310 -0.622 0.002 0.546
## F6r|t1 F6r|t2 F6r|t3 F7r|t1 F7r|t2 F7r|t3
## -1.028 -0.502 0.012 -0.676 -0.199 0.326
## F9r|t1 F9r|t2 F9r|t3 F10r|t1 F10r|t2 F10r|t3
## -0.707 -0.204 0.179 -0.518 -0.007 0.469
## F11r|t1 F11r|t2 F11r|t3 F14r|t1 F14r|t2 F14r|t3
## -0.867 -0.393 0.071 -0.752 -0.290 0.115
## F15r|t1 F15r|t2 F15r|t3 F17r|t1 F17r|t2 F17r|t3
## -0.357 0.209 0.658 -0.224 0.264 0.658
## F18r|t1 F18r|t2 F18r|t3 F19r|t1 F19r|t2 F19r|t3
## -1.019 -0.452 -0.017 -0.346 0.100 0.490
## F20r|t1 F20r|t2 F20r|t3 f1~~f1 f2~~f2
## -0.695 -0.189 0.224 0.734 0.249#Modification Index
MIPCA2r<-modindices(fitPCA2r)
MIIPCA2r<- MIPCA2r[which(MIPCA2r$mi>30),]
print(MIIPCA2r)## [1] lhs op rhs mi mi.scaled epc sepc.lv
## [8] sepc.all sepc.nox
## <0 rows> (or 0-length row.names)#Reability Alpha
#Component 1
C1_PCA2final <- fullScale[, c("F1r","F2r","F3r","F5r","F6r","F7r","F9r","F10r","F11r","F14r","F15r","F17r","F18r","F19r","F20r")]
alpha(C1_PCA2final, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = C1_PCA2final, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.88 0.88 0.89 0.34 7.6 0.012 1.6 0.74
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.88 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F1r 0.88 0.88 0.88 0.34 7.4 0.012
## F2r 0.88 0.88 0.88 0.34 7.4 0.012
## F3r 0.88 0.88 0.88 0.34 7.2 0.012
## F5r 0.88 0.88 0.88 0.34 7.2 0.012
## F6r 0.87 0.87 0.87 0.33 6.8 0.013
## F7r 0.88 0.88 0.88 0.34 7.2 0.012
## F9r 0.87 0.87 0.88 0.33 7.0 0.013
## F10r 0.87 0.87 0.88 0.33 6.9 0.013
## F11r 0.88 0.88 0.88 0.34 7.1 0.013
## F14r 0.87 0.87 0.88 0.33 6.9 0.013
## F15r 0.88 0.88 0.88 0.34 7.1 0.013
## F17r 0.88 0.88 0.88 0.34 7.3 0.012
## F18r 0.87 0.87 0.87 0.32 6.7 0.013
## F19r 0.87 0.87 0.87 0.33 6.9 0.013
## F20r 0.88 0.88 0.88 0.33 7.0 0.013
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F1r 513 0.52 0.52 0.47 0.44 1.5 1.1
## F2r 513 0.52 0.53 0.47 0.44 1.4 1.2
## F3r 513 0.58 0.59 0.54 0.51 1.7 1.2
## F5r 513 0.58 0.59 0.54 0.51 1.5 1.2
## F6r 513 0.69 0.69 0.68 0.63 2.0 1.1
## F7r 513 0.59 0.59 0.54 0.51 1.7 1.2
## F9r 513 0.65 0.64 0.61 0.57 1.8 1.2
## F10r 513 0.66 0.66 0.63 0.59 1.5 1.2
## F11r 513 0.59 0.59 0.55 0.52 1.9 1.2
## F14r 513 0.67 0.66 0.64 0.60 1.8 1.2
## F15r 513 0.60 0.60 0.56 0.52 1.3 1.2
## F17r 513 0.56 0.56 0.52 0.48 1.2 1.2
## F18r 513 0.72 0.73 0.72 0.67 2.0 1.1
## F19r 513 0.67 0.67 0.65 0.60 1.4 1.3
## F20r 513 0.63 0.63 0.59 0.56 1.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F1r 0.27 0.26 0.22 0.26 0
## F2r 0.34 0.21 0.21 0.24 0
## F3r 0.21 0.22 0.19 0.38 0
## F5r 0.27 0.23 0.21 0.29 0
## F6r 0.15 0.16 0.20 0.50 0
## F7r 0.25 0.17 0.21 0.37 0
## F9r 0.24 0.18 0.15 0.43 0
## F10r 0.30 0.19 0.18 0.32 0
## F11r 0.19 0.15 0.18 0.47 0
## F14r 0.23 0.16 0.16 0.45 0
## F15r 0.36 0.22 0.16 0.26 0
## F17r 0.41 0.19 0.14 0.26 0
## F18r 0.15 0.17 0.17 0.51 0
## F19r 0.36 0.18 0.15 0.31 0
## F20r 0.24 0.18 0.16 0.41 0#Component 2
C2_PCA2final <- fullScale[, c("F12r","F16r")]
alpha(C2_PCA2final, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = C2_PCA2final, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.67 0.67 0.5 0.5 2 0.068 1.6 1.1
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.67 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F12r 0.5 0.5 0.25 0.5 NA NA
## F16r 0.5 0.5 0.25 0.5 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F12r 513 0.86 0.87 0.62 0.5 1.6 1.2
## F16r 513 0.88 0.87 0.62 0.5 1.5 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F12r 0.27 0.17 0.21 0.34 0
## F16r 0.36 0.12 0.18 0.34 0#Model Plot
semPaths(fitPCA2r,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Marliere - Factorial Analysis -4 Factos Solution - CFA Model
FA4_CFA <- '
# latent variable definitions
f1 =~ F1r + F2r + F3r + F5r + F6r + F7r + F9r + F10r + F11r
f2 =~ F4r + F8r + F12r + F16r
f3 =~ F15r + F19r
f4 =~ F17r + F18r
# variances and covariances
f1 ~~ f2
f1 ~~ f3
f1 ~~ f4
f2 ~~ f3
f2 ~~ f4
f3 ~~ f4
'
fitFA4 <- cfa(FA4_CFA, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitFA4, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 46 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 168.095 246.715
## Degrees of freedom 113 113
## P-value (Chi-square) 0.001 0.000
## Scaling correction factor 0.751
## Shift parameter 22.948
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 9293.924 4655.600
## Degrees of freedom 136 136
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.994 0.970
## Tucker-Lewis Index (TLI) 0.993 0.964
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.031 0.048
## 90 Percent Confidence Interval 0.020 0.040 0.040 0.056
## P-value RMSEA <= 0.05 1.000 0.640
##
## Weighted Root Mean Square Residual:
##
## WRMR 0.948 0.948
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F1r 1.000 0.502 0.502
## F2r 1.070 0.102 10.489 0.000 0.537 0.537
## F3r 1.221 0.113 10.759 0.000 0.612 0.612
## F5r 1.205 0.111 10.829 0.000 0.604 0.604
## F6r 1.587 0.126 12.631 0.000 0.796 0.796
## F7r 1.226 0.114 10.783 0.000 0.615 0.615
## F9r 1.376 0.117 11.793 0.000 0.690 0.690
## F10r 1.433 0.122 11.716 0.000 0.719 0.719
## F11r 1.288 0.116 11.078 0.000 0.646 0.646
## f2 =~
## F4r 1.000 0.337 0.337
## F8r 1.090 0.226 4.834 0.000 0.367 0.367
## F12r 2.677 0.482 5.550 0.000 0.901 0.901
## F16r 2.136 0.375 5.695 0.000 0.719 0.719
## f3 =~
## F15r 1.000 0.750 0.750
## F19r 1.182 0.069 17.122 0.000 0.886 0.886
## f4 =~
## F17r 1.000 0.672 0.672
## F18r 1.475 0.103 14.339 0.000 0.990 0.990
##
## Covariances:
## f1 ~~
## f2 0.087 0.018 4.734 0.000 0.513 0.513
## f3 0.277 0.029 9.611 0.000 0.737 0.737
## f4 0.257 0.029 8.856 0.000 0.763 0.763
## f2 ~~
## f3 0.101 0.024 4.255 0.000 0.401 0.401
## f4 0.107 0.022 4.785 0.000 0.473 0.473
## f3 ~~
## f4 0.336 0.034 9.805 0.000 0.668 0.668
##
## Intercepts:
## F1r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F4r 0.000 0.000 0.000
## F8r 0.000 0.000 0.000
## F12r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F18r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
## f3 0.000 0.000 0.000
## f4 0.000 0.000 0.000
##
## Thresholds:
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F4r|t1 -0.431 0.057 -7.517 0.000 -0.431 -0.431
## F4r|t2 0.110 0.056 1.984 0.047 0.110 0.110
## F4r|t3 0.701 0.061 11.559 0.000 0.701 0.701
## F8r|t1 -0.140 0.056 -2.513 0.012 -0.140 -0.140
## F8r|t2 0.321 0.056 5.680 0.000 0.321 0.321
## F8r|t3 0.918 0.065 14.173 0.000 0.918 0.918
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
##
## Variances:
## F1r 0.748 0.748 0.748
## F2r 0.712 0.712 0.712
## F3r 0.625 0.625 0.625
## F5r 0.635 0.635 0.635
## F6r 0.366 0.366 0.366
## F7r 0.622 0.622 0.622
## F9r 0.524 0.524 0.524
## F10r 0.483 0.483 0.483
## F11r 0.582 0.582 0.582
## F4r 0.887 0.887 0.887
## F8r 0.865 0.865 0.865
## F12r 0.188 0.188 0.188
## F16r 0.483 0.483 0.483
## F15r 0.438 0.438 0.438
## F19r 0.215 0.215 0.215
## F17r 0.549 0.549 0.549
## F18r 0.019 0.019 0.019
## f1 0.252 0.039 1.000 1.000
## f2 0.113 0.038 1.000 1.000
## f3 0.562 0.046 1.000 1.000
## f4 0.451 0.045 1.000 1.000
##
## R-Square:
##
## F1r 0.252
## F2r 0.288
## F3r 0.375
## F5r 0.365
## F6r 0.634
## F7r 0.378
## F9r 0.476
## F10r 0.517
## F11r 0.418
## F4r 0.113
## F8r 0.135
## F12r 0.812
## F16r 0.517
## F15r 0.562
## F19r 0.785
## F17r 0.451
## F18r 0.981#Model Fit Measures
fitMeasures(fitFA4)## npar fmin
## 74.000 0.164
## chisq df
## 168.095 113.000
## pvalue chisq.scaled
## 0.001 246.715
## df.scaled pvalue.scaled
## 113.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.751 9293.924
## baseline.df baseline.pvalue
## 136.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 4655.600 136.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.026
## cfi tli
## 0.994 0.993
## nnfi rfi
## 0.993 0.978
## nfi pnfi
## 0.982 0.816
## ifi rni
## 0.994 0.994
## cfi.scaled tli.scaled
## 0.970 0.964
## nnfi.scaled rfi.scaled
## 0.964 0.936
## nfi.scaled ifi.scaled
## 0.947 0.947
## rni.scaled rmsea
## 0.985 0.031
## rmsea.ci.lower rmsea.ci.upper
## 0.020 0.040
## rmsea.pvalue rmsea.scaled
## 1.000 0.048
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.040 0.056
## rmsea.pvalue.scaled wrmr
## 0.640 0.948
## cn_05 cn_01
## 423.804 460.570
## gfi agfi
## 0.987 0.978
## pgfi mfi
## 0.596 0.948#Parameters Estimates
EstFA4 <- parameterEstimates(fitFA4, standardized=T, ci=F)
subset(EstFA4, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F1r 1.000 0.000 NA NA 0.502 0.502 0.502
## 2 f1 =~ F2r 1.070 0.102 10.489 0 0.537 0.537 0.537
## 3 f1 =~ F3r 1.221 0.113 10.759 0 0.612 0.612 0.612
## 4 f1 =~ F5r 1.205 0.111 10.829 0 0.604 0.604 0.604
## 5 f1 =~ F6r 1.587 0.126 12.631 0 0.796 0.796 0.796
## 6 f1 =~ F7r 1.226 0.114 10.783 0 0.615 0.615 0.615
## 7 f1 =~ F9r 1.376 0.117 11.793 0 0.690 0.690 0.690
## 8 f1 =~ F10r 1.433 0.122 11.716 0 0.719 0.719 0.719
## 9 f1 =~ F11r 1.288 0.116 11.078 0 0.646 0.646 0.646
## 10 f2 =~ F4r 1.000 0.000 NA NA 0.337 0.337 0.337
## 11 f2 =~ F8r 1.090 0.226 4.834 0 0.367 0.367 0.367
## 12 f2 =~ F12r 2.677 0.482 5.550 0 0.901 0.901 0.901
## 13 f2 =~ F16r 2.136 0.375 5.695 0 0.719 0.719 0.719
## 14 f3 =~ F15r 1.000 0.000 NA NA 0.750 0.750 0.750
## 15 f3 =~ F19r 1.182 0.069 17.122 0 0.886 0.886 0.886
## 16 f4 =~ F17r 1.000 0.000 NA NA 0.672 0.672 0.672
## 17 f4 =~ F18r 1.475 0.103 14.339 0 0.990 0.990 0.990#Parameters Table
parTable(fitFA4)## id lhs op rhs user group free ustart exo label eq.id unco plabel
## 1 1 f1 =~ F1r 1 1 0 1 0 0 0 .p1.
## 2 2 f1 =~ F2r 1 1 1 NA 0 0 1 .p2.
## 3 3 f1 =~ F3r 1 1 2 NA 0 0 2 .p3.
## 4 4 f1 =~ F5r 1 1 3 NA 0 0 3 .p4.
## 5 5 f1 =~ F6r 1 1 4 NA 0 0 4 .p5.
## 6 6 f1 =~ F7r 1 1 5 NA 0 0 5 .p6.
## 7 7 f1 =~ F9r 1 1 6 NA 0 0 6 .p7.
## 8 8 f1 =~ F10r 1 1 7 NA 0 0 7 .p8.
## 9 9 f1 =~ F11r 1 1 8 NA 0 0 8 .p9.
## 10 10 f2 =~ F4r 1 1 0 1 0 0 0 .p10.
## 11 11 f2 =~ F8r 1 1 9 NA 0 0 9 .p11.
## 12 12 f2 =~ F12r 1 1 10 NA 0 0 10 .p12.
## 13 13 f2 =~ F16r 1 1 11 NA 0 0 11 .p13.
## 14 14 f3 =~ F15r 1 1 0 1 0 0 0 .p14.
## 15 15 f3 =~ F19r 1 1 12 NA 0 0 12 .p15.
## 16 16 f4 =~ F17r 1 1 0 1 0 0 0 .p16.
## 17 17 f4 =~ F18r 1 1 13 NA 0 0 13 .p17.
## 18 18 f1 ~~ f2 1 1 14 NA 0 0 14 .p18.
## 19 19 f1 ~~ f3 1 1 15 NA 0 0 15 .p19.
## 20 20 f1 ~~ f4 1 1 16 NA 0 0 16 .p20.
## 21 21 f2 ~~ f3 1 1 17 NA 0 0 17 .p21.
## 22 22 f2 ~~ f4 1 1 18 NA 0 0 18 .p22.
## 23 23 f3 ~~ f4 1 1 19 NA 0 0 19 .p23.
## 24 24 F1r | t1 0 1 20 NA 0 0 20 .p24.
## 25 25 F1r | t2 0 1 21 NA 0 0 21 .p25.
## 26 26 F1r | t3 0 1 22 NA 0 0 22 .p26.
## 27 27 F2r | t1 0 1 23 NA 0 0 23 .p27.
## 28 28 F2r | t2 0 1 24 NA 0 0 24 .p28.
## 29 29 F2r | t3 0 1 25 NA 0 0 25 .p29.
## 30 30 F3r | t1 0 1 26 NA 0 0 26 .p30.
## 31 31 F3r | t2 0 1 27 NA 0 0 27 .p31.
## 32 32 F3r | t3 0 1 28 NA 0 0 28 .p32.
## 33 33 F5r | t1 0 1 29 NA 0 0 29 .p33.
## 34 34 F5r | t2 0 1 30 NA 0 0 30 .p34.
## 35 35 F5r | t3 0 1 31 NA 0 0 31 .p35.
## 36 36 F6r | t1 0 1 32 NA 0 0 32 .p36.
## 37 37 F6r | t2 0 1 33 NA 0 0 33 .p37.
## 38 38 F6r | t3 0 1 34 NA 0 0 34 .p38.
## 39 39 F7r | t1 0 1 35 NA 0 0 35 .p39.
## 40 40 F7r | t2 0 1 36 NA 0 0 36 .p40.
## 41 41 F7r | t3 0 1 37 NA 0 0 37 .p41.
## 42 42 F9r | t1 0 1 38 NA 0 0 38 .p42.
## 43 43 F9r | t2 0 1 39 NA 0 0 39 .p43.
## 44 44 F9r | t3 0 1 40 NA 0 0 40 .p44.
## 45 45 F10r | t1 0 1 41 NA 0 0 41 .p45.
## 46 46 F10r | t2 0 1 42 NA 0 0 42 .p46.
## 47 47 F10r | t3 0 1 43 NA 0 0 43 .p47.
## 48 48 F11r | t1 0 1 44 NA 0 0 44 .p48.
## 49 49 F11r | t2 0 1 45 NA 0 0 45 .p49.
## 50 50 F11r | t3 0 1 46 NA 0 0 46 .p50.
## 51 51 F4r | t1 0 1 47 NA 0 0 47 .p51.
## 52 52 F4r | t2 0 1 48 NA 0 0 48 .p52.
## 53 53 F4r | t3 0 1 49 NA 0 0 49 .p53.
## 54 54 F8r | t1 0 1 50 NA 0 0 50 .p54.
## 55 55 F8r | t2 0 1 51 NA 0 0 51 .p55.
## 56 56 F8r | t3 0 1 52 NA 0 0 52 .p56.
## 57 57 F12r | t1 0 1 53 NA 0 0 53 .p57.
## 58 58 F12r | t2 0 1 54 NA 0 0 54 .p58.
## 59 59 F12r | t3 0 1 55 NA 0 0 55 .p59.
## 60 60 F16r | t1 0 1 56 NA 0 0 56 .p60.
## 61 61 F16r | t2 0 1 57 NA 0 0 57 .p61.
## 62 62 F16r | t3 0 1 58 NA 0 0 58 .p62.
## 63 63 F15r | t1 0 1 59 NA 0 0 59 .p63.
## 64 64 F15r | t2 0 1 60 NA 0 0 60 .p64.
## 65 65 F15r | t3 0 1 61 NA 0 0 61 .p65.
## 66 66 F19r | t1 0 1 62 NA 0 0 62 .p66.
## 67 67 F19r | t2 0 1 63 NA 0 0 63 .p67.
## 68 68 F19r | t3 0 1 64 NA 0 0 64 .p68.
## 69 69 F17r | t1 0 1 65 NA 0 0 65 .p69.
## 70 70 F17r | t2 0 1 66 NA 0 0 66 .p70.
## 71 71 F17r | t3 0 1 67 NA 0 0 67 .p71.
## 72 72 F18r | t1 0 1 68 NA 0 0 68 .p72.
## 73 73 F18r | t2 0 1 69 NA 0 0 69 .p73.
## 74 74 F18r | t3 0 1 70 NA 0 0 70 .p74.
## 75 75 F1r ~~ F1r 0 1 0 1 0 0 0 .p75.
## 76 76 F2r ~~ F2r 0 1 0 1 0 0 0 .p76.
## 77 77 F3r ~~ F3r 0 1 0 1 0 0 0 .p77.
## 78 78 F5r ~~ F5r 0 1 0 1 0 0 0 .p78.
## 79 79 F6r ~~ F6r 0 1 0 1 0 0 0 .p79.
## 80 80 F7r ~~ F7r 0 1 0 1 0 0 0 .p80.
## 81 81 F9r ~~ F9r 0 1 0 1 0 0 0 .p81.
## 82 82 F10r ~~ F10r 0 1 0 1 0 0 0 .p82.
## 83 83 F11r ~~ F11r 0 1 0 1 0 0 0 .p83.
## 84 84 F4r ~~ F4r 0 1 0 1 0 0 0 .p84.
## 85 85 F8r ~~ F8r 0 1 0 1 0 0 0 .p85.
## 86 86 F12r ~~ F12r 0 1 0 1 0 0 0 .p86.
## 87 87 F16r ~~ F16r 0 1 0 1 0 0 0 .p87.
## 88 88 F15r ~~ F15r 0 1 0 1 0 0 0 .p88.
## 89 89 F19r ~~ F19r 0 1 0 1 0 0 0 .p89.
## 90 90 F17r ~~ F17r 0 1 0 1 0 0 0 .p90.
## 91 91 F18r ~~ F18r 0 1 0 1 0 0 0 .p91.
## 92 92 f1 ~~ f1 0 1 71 NA 0 0 71 .p92.
## 93 93 f2 ~~ f2 0 1 72 NA 0 0 72 .p93.
## 94 94 f3 ~~ f3 0 1 73 NA 0 0 73 .p94.
## 95 95 f4 ~~ f4 0 1 74 NA 0 0 74 .p95.
## 96 96 F1r ~1 0 1 0 0 0 0 0 .p96.
## 97 97 F2r ~1 0 1 0 0 0 0 0 .p97.
## 98 98 F3r ~1 0 1 0 0 0 0 0 .p98.
## 99 99 F5r ~1 0 1 0 0 0 0 0 .p99.
## 100 100 F6r ~1 0 1 0 0 0 0 0 .p100.
## 101 101 F7r ~1 0 1 0 0 0 0 0 .p101.
## 102 102 F9r ~1 0 1 0 0 0 0 0 .p102.
## 103 103 F10r ~1 0 1 0 0 0 0 0 .p103.
## 104 104 F11r ~1 0 1 0 0 0 0 0 .p104.
## 105 105 F4r ~1 0 1 0 0 0 0 0 .p105.
## 106 106 F8r ~1 0 1 0 0 0 0 0 .p106.
## 107 107 F12r ~1 0 1 0 0 0 0 0 .p107.
## 108 108 F16r ~1 0 1 0 0 0 0 0 .p108.
## 109 109 F15r ~1 0 1 0 0 0 0 0 .p109.
## 110 110 F19r ~1 0 1 0 0 0 0 0 .p110.
## 111 111 F17r ~1 0 1 0 0 0 0 0 .p111.
## 112 112 F18r ~1 0 1 0 0 0 0 0 .p112.
## 113 113 f1 ~1 0 1 0 0 0 0 0 .p113.
## 114 114 f2 ~1 0 1 0 0 0 0 0 .p114.
## 115 115 f3 ~1 0 1 0 0 0 0 0 .p115.
## 116 116 f4 ~1 0 1 0 0 0 0 0 .p116.
## start
## 1 1.000
## 2 0.917
## 3 1.118
## 4 1.060
## 5 1.266
## 6 1.069
## 7 1.156
## 8 1.191
## 9 1.072
## 10 1.000
## 11 0.912
## 12 1.574
## 13 1.352
## 14 1.000
## 15 0.664
## 16 1.000
## 17 0.665
## 18 0.000
## 19 0.000
## 20 0.000
## 21 0.000
## 22 0.000
## 23 0.000
## 24 -0.616
## 25 0.071
## 26 0.658
## 27 -0.409
## 28 0.120
## 29 0.707
## 30 -0.818
## 31 -0.184
## 32 0.310
## 33 -0.622
## 34 0.002
## 35 0.546
## 36 -1.028
## 37 -0.502
## 38 0.012
## 39 -0.676
## 40 -0.199
## 41 0.326
## 42 -0.707
## 43 -0.204
## 44 0.179
## 45 -0.518
## 46 -0.007
## 47 0.469
## 48 -0.867
## 49 -0.393
## 50 0.071
## 51 -0.431
## 52 0.110
## 53 0.701
## 54 -0.140
## 55 0.321
## 56 0.918
## 57 -0.604
## 58 -0.145
## 59 0.404
## 60 -0.362
## 61 -0.046
## 62 0.415
## 63 -0.357
## 64 0.209
## 65 0.658
## 66 -0.346
## 67 0.100
## 68 0.490
## 69 -0.224
## 70 0.264
## 71 0.658
## 72 -1.019
## 73 -0.452
## 74 -0.017
## 75 1.000
## 76 1.000
## 77 1.000
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 1.000
## 83 1.000
## 84 1.000
## 85 1.000
## 86 1.000
## 87 1.000
## 88 1.000
## 89 1.000
## 90 1.000
## 91 1.000
## 92 0.050
## 93 0.050
## 94 0.050
## 95 0.050
## 96 0.000
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000
## 105 0.000
## 106 0.000
## 107 0.000
## 108 0.000
## 109 0.000
## 110 0.000
## 111 0.000
## 112 0.000
## 113 0.000
## 114 0.000
## 115 0.000
## 116 0.000#Model Coefficients
coef(fitFA4)## f1=~F2r f1=~F3r f1=~F5r f1=~F6r f1=~F7r f1=~F9r f1=~F10r f1=~F11r
## 1.070 1.221 1.205 1.587 1.226 1.376 1.433 1.288
## f2=~F8r f2=~F12r f2=~F16r f3=~F19r f4=~F18r f1~~f2 f1~~f3 f1~~f4
## 1.090 2.677 2.136 1.182 1.475 0.087 0.277 0.257
## f2~~f3 f2~~f4 f3~~f4 F1r|t1 F1r|t2 F1r|t3 F2r|t1 F2r|t2
## 0.101 0.107 0.336 -0.616 0.071 0.658 -0.409 0.120
## F2r|t3 F3r|t1 F3r|t2 F3r|t3 F5r|t1 F5r|t2 F5r|t3 F6r|t1
## 0.707 -0.818 -0.184 0.310 -0.622 0.002 0.546 -1.028
## F6r|t2 F6r|t3 F7r|t1 F7r|t2 F7r|t3 F9r|t1 F9r|t2 F9r|t3
## -0.502 0.012 -0.676 -0.199 0.326 -0.707 -0.204 0.179
## F10r|t1 F10r|t2 F10r|t3 F11r|t1 F11r|t2 F11r|t3 F4r|t1 F4r|t2
## -0.518 -0.007 0.469 -0.867 -0.393 0.071 -0.431 0.110
## F4r|t3 F8r|t1 F8r|t2 F8r|t3 F12r|t1 F12r|t2 F12r|t3 F16r|t1
## 0.701 -0.140 0.321 0.918 -0.604 -0.145 0.404 -0.362
## F16r|t2 F16r|t3 F15r|t1 F15r|t2 F15r|t3 F19r|t1 F19r|t2 F19r|t3
## -0.046 0.415 -0.357 0.209 0.658 -0.346 0.100 0.490
## F17r|t1 F17r|t2 F17r|t3 F18r|t1 F18r|t2 F18r|t3 f1~~f1 f2~~f2
## -0.224 0.264 0.658 -1.019 -0.452 -0.017 0.252 0.113
## f3~~f3 f4~~f4
## 0.562 0.451#Modification Index
MIFA4<-modindices(fitFA4)
MIIFA4<- MIFA4[which(MIFA4$mi>30),]
print(MIIFA4)## [1] lhs op rhs mi mi.scaled epc sepc.lv
## [8] sepc.all sepc.nox
## <0 rows> (or 0-length row.names)#Model Plot
semPaths(fitFA4,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Marliere - Final Factorial Analysis -4 Factos Solution - CFA Model - Item Removed (F4r e F8r)
FA4_CFA <- '
# latent variable definitions
f1 =~ F1r + F2r + F3r + F5r + F6r + F7r + F9r + F10r + F11r
f2 =~ F12r + F16r
f3 =~ F15r + F19r
f4 =~ F17r + F18r
# variances and covariances
f1 ~~ f2
f1 ~~ f3
f1 ~~ f4
f2 ~~ f3
f2 ~~ f4
f3 ~~ f4
'
fitFA4 <- cfa(FA4_CFA, data = orderedScale,
ordered=c("F1r",
"F2r",
"F3r",
"F4r",
"F5r",
"F6r",
"F7r",
"F8r",
"F9r",
"F10r",
"F11r",
"F12r",
"F13r",
"F14r",
"F15r",
"F16r",
"F17r",
"F18r",
"F19r",
"F20r"))
#Model Summary
summary(fitFA4, standardized=T, fit.measures=T, rsquare=T)## lavaan (0.5-18) converged normally after 32 iterations
##
## Number of observations 513
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 107.252 175.351
## Degrees of freedom 84 84
## P-value (Chi-square) 0.044 0.000
## Scaling correction factor 0.661
## Shift parameter 13.151
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 8961.132 4472.271
## Degrees of freedom 105 105
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.997 0.979
## Tucker-Lewis Index (TLI) 0.997 0.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.023 0.046
## 90 Percent Confidence Interval 0.004 0.035 0.036 0.056
## P-value RMSEA <= 0.05 1.000 0.739
##
## Weighted Root Mean Square Residual:
##
## WRMR 0.846 0.846
##
## Parameter estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## f1 =~
## F1r 1.000 0.509 0.509
## F2r 1.055 0.099 10.670 0.000 0.537 0.537
## F3r 1.195 0.110 10.885 0.000 0.608 0.608
## F5r 1.190 0.108 11.033 0.000 0.606 0.606
## F6r 1.562 0.121 12.929 0.000 0.795 0.795
## F7r 1.209 0.110 11.001 0.000 0.616 0.616
## F9r 1.354 0.113 12.013 0.000 0.689 0.689
## F10r 1.410 0.118 11.923 0.000 0.718 0.718
## F11r 1.269 0.112 11.288 0.000 0.646 0.646
## f2 =~
## F12r 1.000 0.857 0.857
## F16r 0.831 0.084 9.914 0.000 0.712 0.712
## f3 =~
## F15r 1.000 0.751 0.751
## F19r 1.179 0.069 17.173 0.000 0.885 0.885
## f4 =~
## F17r 1.000 0.673 0.673
## F18r 1.470 0.102 14.420 0.000 0.989 0.989
##
## Covariances:
## f1 ~~
## f2 0.240 0.029 8.285 0.000 0.550 0.550
## f3 0.282 0.029 9.774 0.000 0.737 0.737
## f4 0.261 0.029 8.994 0.000 0.763 0.763
## f2 ~~
## f3 0.270 0.039 6.927 0.000 0.419 0.419
## f4 0.296 0.035 8.525 0.000 0.514 0.514
## f3 ~~
## f4 0.337 0.034 9.832 0.000 0.668 0.668
##
## Intercepts:
## F1r 0.000 0.000 0.000
## F2r 0.000 0.000 0.000
## F3r 0.000 0.000 0.000
## F5r 0.000 0.000 0.000
## F6r 0.000 0.000 0.000
## F7r 0.000 0.000 0.000
## F9r 0.000 0.000 0.000
## F10r 0.000 0.000 0.000
## F11r 0.000 0.000 0.000
## F12r 0.000 0.000 0.000
## F16r 0.000 0.000 0.000
## F15r 0.000 0.000 0.000
## F19r 0.000 0.000 0.000
## F17r 0.000 0.000 0.000
## F18r 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
## f3 0.000 0.000 0.000
## f4 0.000 0.000 0.000
##
## Thresholds:
## F1r|t1 -0.616 0.059 -10.371 0.000 -0.616 -0.616
## F1r|t2 0.071 0.055 1.279 0.201 0.071 0.071
## F1r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F2r|t1 -0.409 0.057 -7.168 0.000 -0.409 -0.409
## F2r|t2 0.120 0.056 2.161 0.031 0.120 0.120
## F2r|t3 0.707 0.061 11.643 0.000 0.707 0.707
## F3r|t1 -0.818 0.063 -13.053 0.000 -0.818 -0.818
## F3r|t2 -0.184 0.056 -3.306 0.001 -0.184 -0.184
## F3r|t3 0.310 0.056 5.505 0.000 0.310 0.310
## F5r|t1 -0.622 0.059 -10.456 0.000 -0.622 -0.622
## F5r|t2 0.002 0.055 0.044 0.965 0.002 0.002
## F5r|t3 0.546 0.059 9.340 0.000 0.546 0.546
## F6r|t1 -1.028 0.067 -15.237 0.000 -1.028 -1.028
## F6r|t2 -0.502 0.058 -8.648 0.000 -0.502 -0.502
## F6r|t3 0.012 0.055 0.221 0.825 0.012 0.012
## F7r|t1 -0.676 0.060 -11.222 0.000 -0.676 -0.676
## F7r|t2 -0.199 0.056 -3.571 0.000 -0.199 -0.199
## F7r|t3 0.326 0.056 5.768 0.000 0.326 0.326
## F9r|t1 -0.707 0.061 -11.643 0.000 -0.707 -0.707
## F9r|t2 -0.204 0.056 -3.659 0.000 -0.204 -0.204
## F9r|t3 0.179 0.056 3.218 0.001 0.179 0.179
## F10r|t1 -0.518 0.058 -8.908 0.000 -0.518 -0.518
## F10r|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
## F10r|t3 0.469 0.058 8.127 0.000 0.469 0.469
## F11r|t1 -0.867 0.064 -13.619 0.000 -0.867 -0.867
## F11r|t2 -0.393 0.057 -6.906 0.000 -0.393 -0.393
## F11r|t3 0.071 0.055 1.279 0.201 0.071 0.071
## F12r|t1 -0.604 0.059 -10.200 0.000 -0.604 -0.604
## F12r|t2 -0.145 0.056 -2.602 0.009 -0.145 -0.145
## F12r|t3 0.404 0.057 7.081 0.000 0.404 0.404
## F16r|t1 -0.362 0.057 -6.381 0.000 -0.362 -0.362
## F16r|t2 -0.046 0.055 -0.838 0.402 -0.046 -0.046
## F16r|t3 0.415 0.057 7.256 0.000 0.415 0.415
## F15r|t1 -0.357 0.057 -6.294 0.000 -0.357 -0.357
## F15r|t2 0.209 0.056 3.747 0.000 0.209 0.209
## F15r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F19r|t1 -0.346 0.057 -6.119 0.000 -0.346 -0.346
## F19r|t2 0.100 0.055 1.808 0.071 0.100 0.100
## F19r|t3 0.490 0.058 8.474 0.000 0.490 0.490
## F17r|t1 -0.224 0.056 -4.011 0.000 -0.224 -0.224
## F17r|t2 0.264 0.056 4.714 0.000 0.264 0.264
## F17r|t3 0.658 0.060 10.967 0.000 0.658 0.658
## F18r|t1 -1.019 0.067 -15.163 0.000 -1.019 -1.019
## F18r|t2 -0.452 0.057 -7.866 0.000 -0.452 -0.452
## F18r|t3 -0.017 0.055 -0.309 0.758 -0.017 -0.017
##
## Variances:
## F1r 0.741 0.741 0.741
## F2r 0.712 0.712 0.712
## F3r 0.630 0.630 0.630
## F5r 0.633 0.633 0.633
## F6r 0.368 0.368 0.368
## F7r 0.621 0.621 0.621
## F9r 0.525 0.525 0.525
## F10r 0.485 0.485 0.485
## F11r 0.583 0.583 0.583
## F12r 0.266 0.266 0.266
## F16r 0.493 0.493 0.493
## F15r 0.437 0.437 0.437
## F19r 0.216 0.216 0.216
## F17r 0.548 0.548 0.548
## F18r 0.022 0.022 0.022
## f1 0.259 0.040 1.000 1.000
## f2 0.734 0.081 1.000 1.000
## f3 0.563 0.046 1.000 1.000
## f4 0.452 0.044 1.000 1.000
##
## R-Square:
##
## F1r 0.259
## F2r 0.288
## F3r 0.370
## F5r 0.367
## F6r 0.632
## F7r 0.379
## F9r 0.475
## F10r 0.515
## F11r 0.417
## F12r 0.734
## F16r 0.507
## F15r 0.563
## F19r 0.784
## F17r 0.452
## F18r 0.978#Model Fit Measures
fitMeasures(fitFA4)## npar fmin
## 66.000 0.105
## chisq df
## 107.252 84.000
## pvalue chisq.scaled
## 0.044 175.351
## df.scaled pvalue.scaled
## 84.000 0.000
## chisq.scaling.factor baseline.chisq
## 0.661 8961.132
## baseline.df baseline.pvalue
## 105.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 4472.271 105.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 2.028
## cfi tli
## 0.997 0.997
## nnfi rfi
## 0.997 0.985
## nfi pnfi
## 0.988 0.790
## ifi rni
## 0.997 0.997
## cfi.scaled tli.scaled
## 0.979 0.974
## nnfi.scaled rfi.scaled
## 0.974 0.951
## nfi.scaled ifi.scaled
## 0.961 0.961
## rni.scaled rmsea
## 0.990 0.023
## rmsea.ci.lower rmsea.ci.upper
## 0.004 0.035
## rmsea.pvalue rmsea.scaled
## 1.000 0.046
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.036 0.056
## rmsea.pvalue.scaled wrmr
## 0.739 0.846
## cn_05 cn_01
## 508.906 559.803
## gfi agfi
## 0.991 0.984
## pgfi mfi
## 0.555 0.978#Parameters Estimates
EstFA4 <- parameterEstimates(fitFA4, standardized=T, ci=F)
subset(EstFA4, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f1 =~ F1r 1.000 0.000 NA NA 0.509 0.509 0.509
## 2 f1 =~ F2r 1.055 0.099 10.670 0 0.537 0.537 0.537
## 3 f1 =~ F3r 1.195 0.110 10.885 0 0.608 0.608 0.608
## 4 f1 =~ F5r 1.190 0.108 11.033 0 0.606 0.606 0.606
## 5 f1 =~ F6r 1.562 0.121 12.929 0 0.795 0.795 0.795
## 6 f1 =~ F7r 1.209 0.110 11.001 0 0.616 0.616 0.616
## 7 f1 =~ F9r 1.354 0.113 12.013 0 0.689 0.689 0.689
## 8 f1 =~ F10r 1.410 0.118 11.923 0 0.718 0.718 0.718
## 9 f1 =~ F11r 1.269 0.112 11.288 0 0.646 0.646 0.646
## 10 f2 =~ F12r 1.000 0.000 NA NA 0.857 0.857 0.857
## 11 f2 =~ F16r 0.831 0.084 9.914 0 0.712 0.712 0.712
## 12 f3 =~ F15r 1.000 0.000 NA NA 0.751 0.751 0.751
## 13 f3 =~ F19r 1.179 0.069 17.173 0 0.885 0.885 0.885
## 14 f4 =~ F17r 1.000 0.000 NA NA 0.673 0.673 0.673
## 15 f4 =~ F18r 1.470 0.102 14.420 0 0.989 0.989 0.989#Parameters Table
parTable(fitFA4)## id lhs op rhs user group free ustart exo label eq.id unco plabel
## 1 1 f1 =~ F1r 1 1 0 1 0 0 0 .p1.
## 2 2 f1 =~ F2r 1 1 1 NA 0 0 1 .p2.
## 3 3 f1 =~ F3r 1 1 2 NA 0 0 2 .p3.
## 4 4 f1 =~ F5r 1 1 3 NA 0 0 3 .p4.
## 5 5 f1 =~ F6r 1 1 4 NA 0 0 4 .p5.
## 6 6 f1 =~ F7r 1 1 5 NA 0 0 5 .p6.
## 7 7 f1 =~ F9r 1 1 6 NA 0 0 6 .p7.
## 8 8 f1 =~ F10r 1 1 7 NA 0 0 7 .p8.
## 9 9 f1 =~ F11r 1 1 8 NA 0 0 8 .p9.
## 10 10 f2 =~ F12r 1 1 0 1 0 0 0 .p10.
## 11 11 f2 =~ F16r 1 1 9 NA 0 0 9 .p11.
## 12 12 f3 =~ F15r 1 1 0 1 0 0 0 .p12.
## 13 13 f3 =~ F19r 1 1 10 NA 0 0 10 .p13.
## 14 14 f4 =~ F17r 1 1 0 1 0 0 0 .p14.
## 15 15 f4 =~ F18r 1 1 11 NA 0 0 11 .p15.
## 16 16 f1 ~~ f2 1 1 12 NA 0 0 12 .p16.
## 17 17 f1 ~~ f3 1 1 13 NA 0 0 13 .p17.
## 18 18 f1 ~~ f4 1 1 14 NA 0 0 14 .p18.
## 19 19 f2 ~~ f3 1 1 15 NA 0 0 15 .p19.
## 20 20 f2 ~~ f4 1 1 16 NA 0 0 16 .p20.
## 21 21 f3 ~~ f4 1 1 17 NA 0 0 17 .p21.
## 22 22 F1r | t1 0 1 18 NA 0 0 18 .p22.
## 23 23 F1r | t2 0 1 19 NA 0 0 19 .p23.
## 24 24 F1r | t3 0 1 20 NA 0 0 20 .p24.
## 25 25 F2r | t1 0 1 21 NA 0 0 21 .p25.
## 26 26 F2r | t2 0 1 22 NA 0 0 22 .p26.
## 27 27 F2r | t3 0 1 23 NA 0 0 23 .p27.
## 28 28 F3r | t1 0 1 24 NA 0 0 24 .p28.
## 29 29 F3r | t2 0 1 25 NA 0 0 25 .p29.
## 30 30 F3r | t3 0 1 26 NA 0 0 26 .p30.
## 31 31 F5r | t1 0 1 27 NA 0 0 27 .p31.
## 32 32 F5r | t2 0 1 28 NA 0 0 28 .p32.
## 33 33 F5r | t3 0 1 29 NA 0 0 29 .p33.
## 34 34 F6r | t1 0 1 30 NA 0 0 30 .p34.
## 35 35 F6r | t2 0 1 31 NA 0 0 31 .p35.
## 36 36 F6r | t3 0 1 32 NA 0 0 32 .p36.
## 37 37 F7r | t1 0 1 33 NA 0 0 33 .p37.
## 38 38 F7r | t2 0 1 34 NA 0 0 34 .p38.
## 39 39 F7r | t3 0 1 35 NA 0 0 35 .p39.
## 40 40 F9r | t1 0 1 36 NA 0 0 36 .p40.
## 41 41 F9r | t2 0 1 37 NA 0 0 37 .p41.
## 42 42 F9r | t3 0 1 38 NA 0 0 38 .p42.
## 43 43 F10r | t1 0 1 39 NA 0 0 39 .p43.
## 44 44 F10r | t2 0 1 40 NA 0 0 40 .p44.
## 45 45 F10r | t3 0 1 41 NA 0 0 41 .p45.
## 46 46 F11r | t1 0 1 42 NA 0 0 42 .p46.
## 47 47 F11r | t2 0 1 43 NA 0 0 43 .p47.
## 48 48 F11r | t3 0 1 44 NA 0 0 44 .p48.
## 49 49 F12r | t1 0 1 45 NA 0 0 45 .p49.
## 50 50 F12r | t2 0 1 46 NA 0 0 46 .p50.
## 51 51 F12r | t3 0 1 47 NA 0 0 47 .p51.
## 52 52 F16r | t1 0 1 48 NA 0 0 48 .p52.
## 53 53 F16r | t2 0 1 49 NA 0 0 49 .p53.
## 54 54 F16r | t3 0 1 50 NA 0 0 50 .p54.
## 55 55 F15r | t1 0 1 51 NA 0 0 51 .p55.
## 56 56 F15r | t2 0 1 52 NA 0 0 52 .p56.
## 57 57 F15r | t3 0 1 53 NA 0 0 53 .p57.
## 58 58 F19r | t1 0 1 54 NA 0 0 54 .p58.
## 59 59 F19r | t2 0 1 55 NA 0 0 55 .p59.
## 60 60 F19r | t3 0 1 56 NA 0 0 56 .p60.
## 61 61 F17r | t1 0 1 57 NA 0 0 57 .p61.
## 62 62 F17r | t2 0 1 58 NA 0 0 58 .p62.
## 63 63 F17r | t3 0 1 59 NA 0 0 59 .p63.
## 64 64 F18r | t1 0 1 60 NA 0 0 60 .p64.
## 65 65 F18r | t2 0 1 61 NA 0 0 61 .p65.
## 66 66 F18r | t3 0 1 62 NA 0 0 62 .p66.
## 67 67 F1r ~~ F1r 0 1 0 1 0 0 0 .p67.
## 68 68 F2r ~~ F2r 0 1 0 1 0 0 0 .p68.
## 69 69 F3r ~~ F3r 0 1 0 1 0 0 0 .p69.
## 70 70 F5r ~~ F5r 0 1 0 1 0 0 0 .p70.
## 71 71 F6r ~~ F6r 0 1 0 1 0 0 0 .p71.
## 72 72 F7r ~~ F7r 0 1 0 1 0 0 0 .p72.
## 73 73 F9r ~~ F9r 0 1 0 1 0 0 0 .p73.
## 74 74 F10r ~~ F10r 0 1 0 1 0 0 0 .p74.
## 75 75 F11r ~~ F11r 0 1 0 1 0 0 0 .p75.
## 76 76 F12r ~~ F12r 0 1 0 1 0 0 0 .p76.
## 77 77 F16r ~~ F16r 0 1 0 1 0 0 0 .p77.
## 78 78 F15r ~~ F15r 0 1 0 1 0 0 0 .p78.
## 79 79 F19r ~~ F19r 0 1 0 1 0 0 0 .p79.
## 80 80 F17r ~~ F17r 0 1 0 1 0 0 0 .p80.
## 81 81 F18r ~~ F18r 0 1 0 1 0 0 0 .p81.
## 82 82 f1 ~~ f1 0 1 63 NA 0 0 63 .p82.
## 83 83 f2 ~~ f2 0 1 64 NA 0 0 64 .p83.
## 84 84 f3 ~~ f3 0 1 65 NA 0 0 65 .p84.
## 85 85 f4 ~~ f4 0 1 66 NA 0 0 66 .p85.
## 86 86 F1r ~1 0 1 0 0 0 0 0 .p86.
## 87 87 F2r ~1 0 1 0 0 0 0 0 .p87.
## 88 88 F3r ~1 0 1 0 0 0 0 0 .p88.
## 89 89 F5r ~1 0 1 0 0 0 0 0 .p89.
## 90 90 F6r ~1 0 1 0 0 0 0 0 .p90.
## 91 91 F7r ~1 0 1 0 0 0 0 0 .p91.
## 92 92 F9r ~1 0 1 0 0 0 0 0 .p92.
## 93 93 F10r ~1 0 1 0 0 0 0 0 .p93.
## 94 94 F11r ~1 0 1 0 0 0 0 0 .p94.
## 95 95 F12r ~1 0 1 0 0 0 0 0 .p95.
## 96 96 F16r ~1 0 1 0 0 0 0 0 .p96.
## 97 97 F15r ~1 0 1 0 0 0 0 0 .p97.
## 98 98 F19r ~1 0 1 0 0 0 0 0 .p98.
## 99 99 F17r ~1 0 1 0 0 0 0 0 .p99.
## 100 100 F18r ~1 0 1 0 0 0 0 0 .p100.
## 101 101 f1 ~1 0 1 0 0 0 0 0 .p101.
## 102 102 f2 ~1 0 1 0 0 0 0 0 .p102.
## 103 103 f3 ~1 0 1 0 0 0 0 0 .p103.
## 104 104 f4 ~1 0 1 0 0 0 0 0 .p104.
## start
## 1 1.000
## 2 0.917
## 3 1.118
## 4 1.060
## 5 1.266
## 6 1.069
## 7 1.156
## 8 1.191
## 9 1.072
## 10 1.000
## 11 0.610
## 12 1.000
## 13 0.664
## 14 1.000
## 15 0.665
## 16 0.000
## 17 0.000
## 18 0.000
## 19 0.000
## 20 0.000
## 21 0.000
## 22 -0.616
## 23 0.071
## 24 0.658
## 25 -0.409
## 26 0.120
## 27 0.707
## 28 -0.818
## 29 -0.184
## 30 0.310
## 31 -0.622
## 32 0.002
## 33 0.546
## 34 -1.028
## 35 -0.502
## 36 0.012
## 37 -0.676
## 38 -0.199
## 39 0.326
## 40 -0.707
## 41 -0.204
## 42 0.179
## 43 -0.518
## 44 -0.007
## 45 0.469
## 46 -0.867
## 47 -0.393
## 48 0.071
## 49 -0.604
## 50 -0.145
## 51 0.404
## 52 -0.362
## 53 -0.046
## 54 0.415
## 55 -0.357
## 56 0.209
## 57 0.658
## 58 -0.346
## 59 0.100
## 60 0.490
## 61 -0.224
## 62 0.264
## 63 0.658
## 64 -1.019
## 65 -0.452
## 66 -0.017
## 67 1.000
## 68 1.000
## 69 1.000
## 70 1.000
## 71 1.000
## 72 1.000
## 73 1.000
## 74 1.000
## 75 1.000
## 76 1.000
## 77 1.000
## 78 1.000
## 79 1.000
## 80 1.000
## 81 1.000
## 82 0.050
## 83 0.050
## 84 0.050
## 85 0.050
## 86 0.000
## 87 0.000
## 88 0.000
## 89 0.000
## 90 0.000
## 91 0.000
## 92 0.000
## 93 0.000
## 94 0.000
## 95 0.000
## 96 0.000
## 97 0.000
## 98 0.000
## 99 0.000
## 100 0.000
## 101 0.000
## 102 0.000
## 103 0.000
## 104 0.000#Model Coefficients
coef(fitFA4)## f1=~F2r f1=~F3r f1=~F5r f1=~F6r f1=~F7r f1=~F9r f1=~F10r f1=~F11r
## 1.055 1.195 1.190 1.562 1.209 1.354 1.410 1.269
## f2=~F16r f3=~F19r f4=~F18r f1~~f2 f1~~f3 f1~~f4 f2~~f3 f2~~f4
## 0.831 1.179 1.470 0.240 0.282 0.261 0.270 0.296
## f3~~f4 F1r|t1 F1r|t2 F1r|t3 F2r|t1 F2r|t2 F2r|t3 F3r|t1
## 0.337 -0.616 0.071 0.658 -0.409 0.120 0.707 -0.818
## F3r|t2 F3r|t3 F5r|t1 F5r|t2 F5r|t3 F6r|t1 F6r|t2 F6r|t3
## -0.184 0.310 -0.622 0.002 0.546 -1.028 -0.502 0.012
## F7r|t1 F7r|t2 F7r|t3 F9r|t1 F9r|t2 F9r|t3 F10r|t1 F10r|t2
## -0.676 -0.199 0.326 -0.707 -0.204 0.179 -0.518 -0.007
## F10r|t3 F11r|t1 F11r|t2 F11r|t3 F12r|t1 F12r|t2 F12r|t3 F16r|t1
## 0.469 -0.867 -0.393 0.071 -0.604 -0.145 0.404 -0.362
## F16r|t2 F16r|t3 F15r|t1 F15r|t2 F15r|t3 F19r|t1 F19r|t2 F19r|t3
## -0.046 0.415 -0.357 0.209 0.658 -0.346 0.100 0.490
## F17r|t1 F17r|t2 F17r|t3 F18r|t1 F18r|t2 F18r|t3 f1~~f1 f2~~f2
## -0.224 0.264 0.658 -1.019 -0.452 -0.017 0.259 0.734
## f3~~f3 f4~~f4
## 0.563 0.452#Modification Index
MIFA4<-modindices(fitFA4)
MIIFA4<- MIFA4[which(MIFA4$mi>30),]
print(MIIFA4)## [1] lhs op rhs mi mi.scaled epc sepc.lv
## [8] sepc.all sepc.nox
## <0 rows> (or 0-length row.names)#Model Plot
semPaths(fitFA4,"std", edge.label.cex = 0.5, exoVar = T, exoCov = T, layout = "tree2", optimizeLatRes=F, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 2, sizeMan2 = 2, title = F, ThreshAtSide=F)
#Factor 1
F1_FA4 <- fullScale[, c("F1r","F2r","F3r","F5r","F6r","F7r","F9r","F10r","F11r")]
alpha(F1_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F1_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.82 0.82 0.8 0.33 4.4 0.019 1.7 0.75
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F1r 0.81 0.81 0.79 0.34 4.1 0.021
## F2r 0.80 0.80 0.79 0.34 4.1 0.021
## F3r 0.80 0.80 0.78 0.33 3.9 0.022
## F5r 0.80 0.80 0.78 0.33 3.9 0.021
## F6r 0.79 0.79 0.77 0.32 3.7 0.022
## F7r 0.80 0.80 0.78 0.33 4.0 0.021
## F9r 0.79 0.79 0.78 0.32 3.8 0.022
## F10r 0.79 0.79 0.77 0.32 3.8 0.022
## F11r 0.80 0.80 0.78 0.33 4.0 0.021
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F1r 513 0.57 0.58 0.49 0.44 1.5 1.1
## F2r 513 0.59 0.59 0.51 0.46 1.4 1.2
## F3r 513 0.64 0.64 0.57 0.52 1.7 1.2
## F5r 513 0.63 0.63 0.56 0.51 1.5 1.2
## F6r 513 0.70 0.70 0.66 0.59 2.0 1.1
## F7r 513 0.63 0.63 0.56 0.50 1.7 1.2
## F9r 513 0.66 0.66 0.60 0.54 1.8 1.2
## F10r 513 0.68 0.68 0.63 0.57 1.5 1.2
## F11r 513 0.62 0.62 0.55 0.49 1.9 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F1r 0.27 0.26 0.22 0.26 0
## F2r 0.34 0.21 0.21 0.24 0
## F3r 0.21 0.22 0.19 0.38 0
## F5r 0.27 0.23 0.21 0.29 0
## F6r 0.15 0.16 0.20 0.50 0
## F7r 0.25 0.17 0.21 0.37 0
## F9r 0.24 0.18 0.15 0.43 0
## F10r 0.30 0.19 0.18 0.32 0
## F11r 0.19 0.15 0.18 0.47 0#Factor 2
F2_FA4 <- fullScale[, c("F12r","F16r")]
alpha(F2_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F2_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.67 0.67 0.5 0.5 2 0.068 1.6 1.1
##
## lower alpha upper 95% confidence boundaries
## 0.54 0.67 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F12r 0.5 0.5 0.25 0.5 NA NA
## F16r 0.5 0.5 0.25 0.5 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F12r 513 0.86 0.87 0.62 0.5 1.6 1.2
## F16r 513 0.88 0.87 0.62 0.5 1.5 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F12r 0.27 0.17 0.21 0.34 0
## F16r 0.36 0.12 0.18 0.34 0#Factor 3
F4_FA3 <- fullScale[, c("F15r","F19r")]
alpha(F4_FA3, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F4_FA3, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.71 0.71 0.55 0.55 2.5 0.065 1.4 1.1
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.71 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F15r 0.55 0.55 0.3 0.55 NA NA
## F19r 0.55 0.55 0.3 0.55 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F15r 513 0.87 0.88 0.65 0.55 1.3 1.2
## F19r 513 0.89 0.88 0.65 0.55 1.4 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F15r 0.36 0.22 0.16 0.26 0
## F19r 0.36 0.18 0.15 0.31 0#Factor 4
F3_FA4 <- fullScale[, c("F17r","F18r")]
alpha(F3_FA4, check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F3_FA4, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.68 0.68 0.52 0.52 2.1 0.067 1.6 1
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.68 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## F17r 0.52 0.52 0.27 0.52 NA NA
## F18r 0.52 0.52 0.27 0.52 NA NA
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## F17r 513 0.88 0.87 0.63 0.52 1.2 1.2
## F18r 513 0.86 0.87 0.63 0.52 2.0 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## F17r 0.41 0.19 0.14 0.26 0
## F18r 0.15 0.17 0.17 0.51 0Final Solutions
#PCA2
#Sum CESD itens PCA2
#Component 1
base.dat$PCA2C1 <- base.dat$F1r+ base.dat$F2r+ base.dat$F3r+ base.dat$F5r+ base.dat$F6r+ base.dat$F7r+ base.dat$F9r+ base.dat$F10r+ base.dat$F11r+ base.dat$F14r+ base.dat$F15r+ base.dat$F17r+ base.dat$F18r+ base.dat$F19r+ base.dat$F20r
#Component 1
base.dat$PCA2C2 <- base.dat$F12r+ base.dat$F16r
#FA4
#Sum CESD itens FA4
#Factor 1
base.dat$FA4F1 <- base.dat$F1r + base.dat$F2r + base.dat$F3r + base.dat$F5r + base.dat$F6r + base.dat$F7r + base.dat$F9r + base.dat$F10r + base.dat$F11r
#Factor 2
base.dat$FA4F2 <- base.dat$F12r + base.dat$F16r
#Factor 3
base.dat$FA4F3 <- base.dat$F15r + base.dat$F19r
#Factor 4
base.dat$FA4F4 <- base.dat$F17r + base.dat$F18r#Correlation - ISMI and CES-D
MatrixcorrPCA2<- base.dat[,c(230:236,288:289)]
corr.test(MatrixcorrPCA2)## Call:corr.test(x = MatrixcorrPCA2)
## Correlation matrix
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 PCA2C1 PCA2C2
## ISMIG1 1.00 0.97 0.84 0.77 0.77 0.83 0.12 0.45 -0.23
## ISMIG2 0.97 1.00 0.83 0.74 0.74 0.81 0.35 0.44 -0.24
## ISMIF1 0.84 0.83 1.00 0.47 0.56 0.67 0.15 0.39 -0.21
## ISMIF2 0.77 0.74 0.47 1.00 0.51 0.49 0.05 0.30 -0.13
## ISMIF3 0.77 0.74 0.56 0.51 1.00 0.49 0.06 0.35 -0.16
## ISMIF4 0.83 0.81 0.67 0.49 0.49 1.00 0.11 0.39 -0.23
## ISMIF5 0.12 0.35 0.15 0.05 0.06 0.11 1.00 0.09 -0.11
## PCA2C1 0.45 0.44 0.39 0.30 0.35 0.39 0.09 1.00 -0.44
## PCA2C2 -0.23 -0.24 -0.21 -0.13 -0.16 -0.23 -0.11 -0.44 1.00
## Sample Size
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 PCA2C1 PCA2C2
## ISMIG1 525 525 525 525 525 525 525 516 520
## ISMIG2 525 525 525 525 525 525 525 516 520
## ISMIF1 525 525 525 525 525 525 525 516 520
## ISMIF2 525 525 525 525 525 525 525 516 520
## ISMIF3 525 525 525 525 525 525 525 516 520
## ISMIF4 525 525 525 525 525 525 525 516 520
## ISMIF5 525 525 525 525 525 525 525 516 520
## PCA2C1 516 516 516 516 516 516 516 516 515
## PCA2C2 520 520 520 520 520 520 520 515 520
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 PCA2C1 PCA2C2
## ISMIG1 0.00 0 0 0.00 0.00 0.00 0.04 0.00 0.00
## ISMIG2 0.00 0 0 0.00 0.00 0.00 0.00 0.00 0.00
## ISMIF1 0.00 0 0 0.00 0.00 0.00 0.01 0.00 0.00
## ISMIF2 0.00 0 0 0.00 0.00 0.00 0.30 0.00 0.03
## ISMIF3 0.00 0 0 0.00 0.00 0.00 0.30 0.00 0.00
## ISMIF4 0.00 0 0 0.00 0.00 0.00 0.05 0.00 0.00
## ISMIF5 0.01 0 0 0.23 0.15 0.01 0.00 0.15 0.06
## PCA2C1 0.00 0 0 0.00 0.00 0.00 0.05 0.00 0.00
## PCA2C2 0.00 0 0 0.00 0.00 0.00 0.01 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionMatrixcorrFA4<- base.dat[,c(230:236,290:293)]
corr.test(MatrixcorrFA4)## Call:corr.test(x = MatrixcorrFA4)
## Correlation matrix
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 FA4F1 FA4F2 FA4F3
## ISMIG1 1.00 0.97 0.84 0.77 0.77 0.83 0.12 0.41 -0.23 0.36
## ISMIG2 0.97 1.00 0.83 0.74 0.74 0.81 0.35 0.40 -0.24 0.36
## ISMIF1 0.84 0.83 1.00 0.47 0.56 0.67 0.15 0.36 -0.21 0.34
## ISMIF2 0.77 0.74 0.47 1.00 0.51 0.49 0.05 0.29 -0.13 0.21
## ISMIF3 0.77 0.74 0.56 0.51 1.00 0.49 0.06 0.32 -0.16 0.31
## ISMIF4 0.83 0.81 0.67 0.49 0.49 1.00 0.11 0.36 -0.23 0.33
## ISMIF5 0.12 0.35 0.15 0.05 0.06 0.11 1.00 0.06 -0.11 0.06
## FA4F1 0.41 0.40 0.36 0.29 0.32 0.36 0.06 1.00 -0.40 0.55
## FA4F2 -0.23 -0.24 -0.21 -0.13 -0.16 -0.23 -0.11 -0.40 1.00 -0.30
## FA4F3 0.36 0.36 0.34 0.21 0.31 0.33 0.06 0.55 -0.30 1.00
## FA4F4 0.33 0.33 0.30 0.23 0.27 0.25 0.10 0.58 -0.33 0.47
## FA4F4
## ISMIG1 0.33
## ISMIG2 0.33
## ISMIF1 0.30
## ISMIF2 0.23
## ISMIF3 0.27
## ISMIF4 0.25
## ISMIF5 0.10
## FA4F1 0.58
## FA4F2 -0.33
## FA4F3 0.47
## FA4F4 1.00
## Sample Size
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 FA4F1 FA4F2 FA4F3
## ISMIG1 525 525 525 525 525 525 525 520 520 521
## ISMIG2 525 525 525 525 525 525 525 520 520 521
## ISMIF1 525 525 525 525 525 525 525 520 520 521
## ISMIF2 525 525 525 525 525 525 525 520 520 521
## ISMIF3 525 525 525 525 525 525 525 520 520 521
## ISMIF4 525 525 525 525 525 525 525 520 520 521
## ISMIF5 525 525 525 525 525 525 525 520 520 521
## FA4F1 520 520 520 520 520 520 520 520 518 519
## FA4F2 520 520 520 520 520 520 520 518 520 519
## FA4F3 521 521 521 521 521 521 521 519 519 521
## FA4F4 521 521 521 521 521 521 521 519 519 520
## FA4F4
## ISMIG1 521
## ISMIG2 521
## ISMIF1 521
## ISMIF2 521
## ISMIF3 521
## ISMIF4 521
## ISMIF5 521
## FA4F1 519
## FA4F2 519
## FA4F3 520
## FA4F4 521
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## ISMIG1 ISMIG2 ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 FA4F1 FA4F2 FA4F3
## ISMIG1 0.00 0 0 0.00 0.00 0.00 0.05 0.00 0.00 0.00
## ISMIG2 0.00 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ISMIF1 0.00 0 0 0.00 0.00 0.00 0.01 0.00 0.00 0.00
## ISMIF2 0.00 0 0 0.00 0.00 0.00 0.59 0.00 0.04 0.00
## ISMIF3 0.00 0 0 0.00 0.00 0.00 0.59 0.00 0.00 0.00
## ISMIF4 0.00 0 0 0.00 0.00 0.00 0.07 0.00 0.00 0.00
## ISMIF5 0.01 0 0 0.23 0.15 0.01 0.00 0.59 0.09 0.59
## FA4F1 0.00 0 0 0.00 0.00 0.00 0.15 0.00 0.00 0.00
## FA4F2 0.00 0 0 0.00 0.00 0.00 0.01 0.00 0.00 0.00
## FA4F3 0.00 0 0 0.00 0.00 0.00 0.16 0.00 0.00 0.00
## FA4F4 0.00 0 0 0.00 0.00 0.00 0.03 0.00 0.00 0.00
## FA4F4
## ISMIG1 0.00
## ISMIG2 0.00
## ISMIF1 0.00
## ISMIF2 0.00
## ISMIF3 0.00
## ISMIF4 0.00
## ISMIF5 0.14
## FA4F1 0.00
## FA4F2 0.00
## FA4F3 0.00
## FA4F4 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#Summaries for PCA2
summary(base.dat$PCA2C1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 16.00 25.00 24.57 33.00 45.00 9summary(base.dat$PCA2C2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 2.896 5.000 6.000 5#Summaries for FA4
summary(base.dat$FA4F1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 10 16 15 20 27 5summary(base.dat$FA4F2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 2.896 5.000 6.000 5summary(base.dat$FA4F3)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 2.693 4.000 6.000 4summary(base.dat$FA4F4)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 3.000 3.251 5.000 6.000 4