CES-D
Ana Luísa Marlière Casela; Leonardo Fernandes Martins; Pollyanna Santos da Silveira; Kimber Richter; Telmo Mota Ronzani
03 de fevereiro de 2016
This is a public document with all scripts used to review the manuscript:
“Psychometric properties of the Center for Epidemiologic Studies Depression Scale (CES-D) among people in treatment for substance use disorders”
All files used here are availible in a public repository accessible by the following url:
https://github.com/crepeia/CES-D_SubstanceAbuse
Abstract
ABSTRACT Objective: Depression is an important and prevalent comorbidity for substance addiction affecting the patient’s quality of life and decreasing adherence to substance abuse treatment. Measuring depressive symptoms is considered a crucial step for treatment. Among the instruments developed that measure the level of self-reported depression symptoms, the Center for Epidemiologic Studies Depression Scale (CES-D) has been widely used in clinical and populational studies. The present study aimed to evaluate psychometric properties of CES-D for a clinical sample of persons with drug addiction diagnostic. Method: 513 outpatients from two public health services for substance abuse treatment participated of this study. We evaluated construct validity in our sample using principal components analysis (PCA) and a confirmatory factorial analysis (CFA) to compare different models. Divergent and convergent validity was evaluated using Pearson correlation test. Reliability coefficients were calculated by Cronbach’s alpha (α) and average split-half test. Results: PCA indicated a two dimensional structure explaining 47% of total variance. Based on these components we proposed a two factor structure that had a good fit for our sample (χ2(116)=176.935, p<0.001; CFI=0.995; TLI=0.994; RMSEA=0.032,). The first factor (F1) composed by negative affects and depression symptoms items had high internal consistency (α=0.88) and a second one (F2) measuring absence of positive affect had low/moderated internal consistency (α=0.67). Convergent validity was supported by moderate positive correlation between CES-D factors and Self-Stigma (F1, r=0.45, p<0.001; F2, r=0.23, p<0,001). Divergent validity was supported by negative moderated correlation with Hope (F1, r = -0.33, p< 0.001; F2, r= -0.39; p<0.001) and Self-Esteem (F1, r = 0.45, p< 0.001; F2, r= 0.24, p<0.001). Our model showed a slightly better performance compared to other structures proposed by previous Brazilian studies. Conclusion: The psychometric properties of our proposed model, considering validity and reliability measures supports the use of CES-D to evaluate depression symptoms among people in treatment for substance use disorders. Keywords: Depression; Psychometrics; Validation Studies and Substance-Related Disorders.
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: semPlotrequire(semTools) # Comparing SEM models## Loading required package: semTools
##
## ###############################################################################
## This is semTools 0.4-9
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
##
## The following object is masked from 'package:psych':
##
## skew#Setting Directory
setwd("~/CES-D_SubstanceAbuse")
#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$scaleSum <- rowSums(base.dat[,267:286])
#Recoding reversed itens
base.dat$F4r<- recode(base.dat$F4r, "0=3; 1=2; 2=1; 3=0")
base.dat$F8r<- recode(base.dat$F8r, "0=3; 1=2; 2=1; 3=0")
base.dat$F12r<- recode(base.dat$F12r, "0=3; 1=2; 2=1; 3=0")
base.dat$F16r<- recode(base.dat$F16r, "0=3; 1=2; 2=1; 3=0")
#Creating a subset for analysis without NA
base.CESD <- subset(base.dat, subset=!is.na(base.dat$scaleSum))
#Creating a subset only with CESD
fullScale <- base.CESD[ , 267:286]
#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$rho)
## Overall MSA = 0.9
## MSA for each item =
## F1r F2r F3r F4r F5r F6r F7r F8r F9r F10r F11r F12r F13r F14r F15r
## 0.92 0.89 0.93 0.75 0.92 0.92 0.95 0.79 0.95 0.91 0.91 0.83 0.88 0.94 0.84
## F16r F17r F18r F19r F20r
## 0.84 0.85 0.89 0.87 0.93# Setting a randon number generator
set.seed(234)
# Parallel Analysis
fa.parallel(fullScaleT$rho, fm="minres", fa="pc", n.obs=513, main = "Figure 1. Parallel Analysis Scree Plots")
## Parallel analysis suggests that the number of factors = NA and the number of components = 2Principal Components Analysis
#PCA - 2 components oblique rotated (assuming the components are correlated)
PCA2 <- principal(fullScaleT$rho, nfactors = 2, rotate="oblimin", method = "minres")## Loading required namespace: GPArotationprint.psych(PCA2, digits=2, cut= .4)## Principal Components Analysis
## Call: principal(r = fullScaleT$rho, nfactors = 2, rotate = "oblimin",
## method = "minres")
## 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, title="Figure 2. Principal Components Analysis (CES-D)", title(sub = "correlation matrix=polychoric; method=minimal residual solution; rotation=oblimin", font.sub = 1, cex.sub = 0.5))
#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 0splitHalf(C1_PCA2)## Split half reliabilities
## Call: splitHalf(r = C1_PCA2)
##
## Maximum split half reliability (lambda 4) = 0.92
## Guttman lambda 6 = 0.88
## Average split half reliability = 0.88
## Guttman lambda 3 (alpha) = 0.88
## Minimum split half reliability (beta) = 0.83#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 0splitHalf(C2_PCA2)## Split half reliabilities
## Call: splitHalf(r = C2_PCA2)
##
## Maximum split half reliability (lambda 4) = 0.66
## Guttman lambda 6 = 0.57
## Average split half reliability = 0.44
## Guttman lambda 3 (alpha) = 0.61
## Minimum split half reliability (beta) = 0.51Confirmatory 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, estimator="WLSMVS", mimic = "Mplus", 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 224.932 228.354
## Degrees of freedom 132 81
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.985
## for the mean and variance adjusted correction (WLSMV)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 10633.966 2367.719
## Degrees of freedom 153 34
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.991 0.937
## Tucker-Lewis Index (TLI) 0.990 0.973
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.060
## 90 Percent Confidence Interval 0.029 0.045 0.050 0.069
## P-value RMSEA <= 0.05 0.996 0.042
##
## 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 18.064 0.000 1.000 1.000
## f2 0.427 0.048 8.928 0.000 1.000 1.000
## f3 0.804 0.072 11.120 0.000 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, c("chisq","df","rmsea","rmsea.ci.lower", "rmsea.ci.upper", "srmr", "cfi", "tli", "nfi", "ecvi"))## chisq df rmsea rmsea.ci.lower rmsea.ci.upper
## 224.932 132.000 0.037 0.029 0.045
## srmr cfi tli nfi ecvi
## 0.050 0.991 0.990 0.979 NA#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 32.465 0.232 0.232 0.232 0.232#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, estimator="WLSMVS", mimic = "Mplus", 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 218.990 236.903
## Degrees of freedom 129 81
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.924
## for the mean and variance adjusted correction (WLSMV)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 12355.368 2508.265
## Degrees of freedom 153 31
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.993 0.937
## Tucker-Lewis Index (TLI) 0.991 0.976
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.061
## 90 Percent Confidence Interval 0.028 0.045 0.052 0.071
## P-value RMSEA <= 0.05 0.996 0.024
##
## 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 18.982 0.000 1.000 1.000
## f2 0.576 0.039 14.850 0.000 1.000 1.000
## f3 0.505 0.046 10.883 0.000 1.000 1.000
## f4 0.750 0.071 10.528 0.000 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, c("chisq","df","rmsea","rmsea.ci.lower", "rmsea.ci.upper", "srmr", "cfi", "tli", "nfi", "ecvi"))## chisq df rmsea rmsea.ci.lower rmsea.ci.upper
## 218.990 129.000 0.037 0.028 0.045
## srmr cfi tli nfi ecvi
## 0.046 0.993 0.991 0.982 NA#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 40.307 0.250 0.250 0.250 0.250
## 2 F19r ~~ F15r 45.897 49.652 0.254 0.254 0.254 0.254#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, estimator="WLSMVS", mimic = "Mplus", 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 354.536 330.673
## Degrees of freedom 169 96
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.072
## for the mean and variance adjusted correction (WLSMV)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 13400.870 2614.740
## Degrees of freedom 190 37
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.986 0.909
## Tucker-Lewis Index (TLI) 0.984 0.965
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046 0.069
## 90 Percent Confidence Interval 0.040 0.053 0.061 0.077
## P-value RMSEA <= 0.05 0.811 0.000
##
## 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 2.847 0.004 1.000 1.000
## f2 0.240 0.037 6.415 0.000 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, c("chisq","df","rmsea","rmsea.ci.lower", "rmsea.ci.upper", "srmr", "cfi", "tli", "nfi", "ecvi"))## chisq df rmsea rmsea.ci.lower rmsea.ci.upper
## 354.536 169.000 0.046 0.040 0.053
## srmr cfi tli nfi ecvi
## 0.055 0.986 0.984 0.974 NA#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#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 50.152 0.258 0.258 0.258 0.258
## 2 F17r ~~ F18r 33.973 31.686 0.233 0.233 0.233 0.233#Marliere - Final Solution - Principal Components Analysis - Two Components Solution Reviewed - CFA Model (removed itens - F4r, F8r, F13r) - and included error covariance
PCA2_CFArf <- '
# latent variable definitions
f2 =~ F12r + F16r
f1 =~ 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
'
fitPCA2rf <- sem(PCA2_CFArf, estimator="WLSMVS", mimic = "Mplus", 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(fitPCA2rf, 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 176.935 199.784
## Degrees of freedom 116 74
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.886
## for the mean and variance adjusted correction (WLSMV)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 12752.383 2442.717
## Degrees of freedom 136 26
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.995 0.948
## Tucker-Lewis Index (TLI) 0.994 0.982
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.032 0.058
## 90 Percent Confidence Interval 0.022 0.041 0.047 0.068
## P-value RMSEA <= 0.05 1.000 0.105
##
## 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:
## f2 =~
## F12r 1.000 0.857 0.857
## F16r 0.831 0.077 10.830 0.000 0.712 0.712
## f1 =~
## 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:
## f2 ~~
## f1 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
## f2 0.000 0.000 0.000
## f1 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
## f2 0.734 0.075 9.781 0.000 1.000 1.000
## f1 0.249 0.038 6.501 0.000 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(fitPCA2rf, c("chisq","df","rmsea","rmsea.ci.lower", "rmsea.ci.upper", "srmr", "cfi", "tli", "nfi", "ecvi"))## chisq df rmsea rmsea.ci.lower rmsea.ci.upper
## 176.935 116.000 0.032 0.022 0.041
## srmr cfi tli nfi ecvi
## 0.045 0.995 0.994 0.986 NA#Parameters Estimates
EstPCA2rf <- parameterEstimates(fitPCA2rf, standardized=T, ci=F)
subset(EstPCA2rf, op == "=~")## lhs op rhs est se z pvalue std.lv std.all std.nox
## 1 f2 =~ F12r 1.000 0.000 NA NA 0.857 0.857 0.857
## 2 f2 =~ F16r 0.831 0.077 10.830 0 0.712 0.712 0.712
## 3 f1 =~ F1r 1.000 0.000 NA NA 0.499 0.499 0.499
## 4 f1 =~ F2r 1.020 0.097 10.528 0 0.509 0.509 0.509
## 5 f1 =~ F3r 1.198 0.108 11.086 0 0.597 0.597 0.597
## 6 f1 =~ F5r 1.195 0.108 11.105 0 0.596 0.596 0.596
## 7 f1 =~ F6r 1.563 0.120 12.973 0 0.779 0.779 0.779
## 8 f1 =~ F7r 1.201 0.109 11.045 0 0.599 0.599 0.599
## 9 f1 =~ F9r 1.368 0.112 12.193 0 0.682 0.682 0.682
## 10 f1 =~ F10r 1.394 0.118 11.832 0 0.695 0.695 0.695
## 11 f1 =~ F11r 1.258 0.114 11.072 0 0.627 0.627 0.627
## 12 f1 =~ F14r 1.514 0.122 12.425 0 0.755 0.755 0.755
## 13 f1 =~ F15r 1.184 0.110 10.733 0 0.591 0.591 0.591
## 14 f1 =~ F17r 1.089 0.110 9.897 0 0.543 0.543 0.543
## 15 f1 =~ F18r 1.619 0.132 12.298 0 0.807 0.807 0.807
## 16 f1 =~ F19r 1.396 0.111 12.576 0 0.696 0.696 0.696
## 17 f1 =~ F20r 1.380 0.120 11.527 0 0.688 0.688 0.688#Modification Index
MIPCA2rf<-modindices(fitPCA2rf)
MIIPCA2rf<- MIPCA2rf[which(MIPCA2rf$mi>30),]
print(MIIPCA2rf)## [1] lhs op rhs mi mi.scaled epc sepc.lv
## [8] sepc.all sepc.nox
## <0 rows> (or 0-length row.names)#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 0splitHalf(C1_PCA2final)## Split half reliabilities
## Call: splitHalf(r = C1_PCA2final)
##
## Maximum split half reliability (lambda 4) = 0.92
## Guttman lambda 6 = 0.89
## Average split half reliability = 0.88
## Guttman lambda 3 (alpha) = 0.88
## Minimum split half reliability (beta) = 0.82#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 0splitHalf(C2_PCA2final)## Split half reliabilities
## Call: splitHalf(r = C2_PCA2final)
##
## Maximum split half reliability (lambda 4) = 0.67
## Guttman lambda 6 = 0.5
## Average split half reliability = 0
## Guttman lambda 3 (alpha) = 0.67
## Minimum split half reliability (beta) = 0.67#Model Plot
semPaths(fitPCA2rf, what="path", whatLabels ="std", edge.label.cex = 0.7, exoVar = F, exoCov = T, layout = "tree2", optimizeLatRes=T, style = "lisrel", curve= 0.9, sizeLat = 5, sizeLat2 = 5, sizeMan = 3, sizeMan2 = 3, title = T, thresholds = F, curvePivot=T, intercepts = F, residuals = T)
#Define Title
title(main = "Figure 3. CES-D - Final Model for Confirmatory Factorial Analysis", line = 1)
#Define Subtitle
title(sub = expression("Fit measures:" ~ chi^2~(116)==176.935 ~", p<0.001, n=512; CFI=0.995; TLI=0.994; NFI=0.986; RMSEA=0.032, 90%CI(0.022-0.041); SRMR=0.045"), line = 3, font.sub = 1, cex.sub = 0.5)
Comparing Fits
fit<-compareFit(fitPCA2rf,fitPCA2,fitBatistoni,fitSilveira, nested = F)
print(fit)## #################### Fit Indices Summaries ##########################
## chisq df pvalue cfi tli rmsea
## fitPCA2rf 176.935 116 .000† .995† .994† .032†
## fitPCA2 354.536 169 .000 .986 .984 .046
## fitBatistoni 224.932 132 .000 .991 .990 .037
## fitSilveira 218.990 129 .000 .993 .991 .037summary(fit, fit.measures="all")## #################### Fit Indices Summaries ##########################
## npar fmin chisq df pvalue chisq.scaled df.scaled
## fitPCA2rf 71.000 .172 176.935 116 .000† 199.784 74
## fitPCA2 81.000 .346 354.536 169 .000 330.673 96
## fitBatistoni 75.000 .219 224.932 132 .000 228.354 81
## fitSilveira 78.000 .213 218.990 129 .000 236.903 81
## pvalue.scaled chisq.scaling.factor baseline.chisq baseline.df
## fitPCA2rf .000 .886 12752.383 136
## fitPCA2 .000 1.072 13400.870 190
## fitBatistoni .000 .985 10633.966 153
## fitSilveira .000 .924 12355.368 153
## baseline.pvalue baseline.chisq.scaled baseline.df.scaled
## fitPCA2rf .000 2442.717 26
## fitPCA2 .000 2614.740 37
## fitBatistoni .000 2367.719 34
## fitSilveira .000 2508.265 31
## baseline.pvalue.scaled baseline.chisq.scaling.factor cfi
## fitPCA2rf .000 5.221 .995†
## fitPCA2 .000 5.125 .986
## fitBatistoni .000 4.491 .991
## fitSilveira .000 4.926 .993
## tli nnfi rfi nfi pnfi ifi rni cfi.scaled
## fitPCA2rf .994† .994† .984† .986† .841 .995† .995† .948
## fitPCA2 .984 .984 .970 .974 .866† .986 .986 .909
## fitBatistoni .990 .990 .975 .979 .844 .991 .991 .937
## fitSilveira .991 .991 .979 .982 .828 .993 .993 .937
## tli.scaled nnfi.scaled rfi.scaled nfi.scaled ifi.scaled
## fitPCA2rf .982 .982 .971 .918 .918
## fitPCA2 .965 .965 .951 .874 .874
## fitBatistoni .973 .973 .960 .904 .904
## fitSilveira .976 .976 .964 .906 .906
## rni.scaled rmsea rmsea.ci.lower rmsea.ci.upper rmsea.pvalue
## fitPCA2rf .990 .032† .022 .041 .000
## fitPCA2 .982 .046 .040 .053 .811
## fitBatistoni .986 .037 .029 .045 .996
## fitSilveira .987 .037 .028 .045 .996
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## fitPCA2rf .058 .047 .068
## fitPCA2 .069 .061 .077
## fitBatistoni .060 .050 .069
## fitSilveira .061 .052 .071
## rmsea.pvalue.scaled wrmr cn_05 cn_01 gfi agfi pgfi
## fitPCA2rf .105 .973 412.307† 447.627† .989† .982† .613
## fitPCA2 .000 1.191 290.310 311.036 .979 .970 .662†
## fitBatistoni .042 1.042 364.775 394.132 .984 .975 .627
## fitSilveira .024 1.029 366.916 396.775 .986 .978 .615
## mfi
## fitPCA2rf .942†
## fitPCA2 .834
## fitBatistoni .913
## fitSilveira .916More Fits
moreFitIndices(fitPCA2rf, fit.measures = "all", nPrior = 513)## gammaHat adjGammaHat baseline.rmsea
## 0.9861917 0.9817873 0.4252451moreFitIndices(fitPCA2, fit.measures = "all", nPrior = 513)## gammaHat adjGammaHat baseline.rmsea
## 0.9650297 0.9565458 0.3681546moreFitIndices(fitBatistoni, fit.measures = "all", nPrior = 513)## gammaHat adjGammaHat baseline.rmsea
## 0.9802312 0.9743904 0.3654233moreFitIndices(fitSilveira, fit.measures = "all", nPrior = 513)## gammaHat adjGammaHat baseline.rmsea
## 0.9808451 0.9746086 0.3942917Final Solution
#CFA2
#Sum CESD itens PCA2
#Component 1
base.CESD$PCA2C1 <- base.CESD$F1r+ base.CESD$F2r+ base.CESD$F3r+ base.CESD$F5r+ base.CESD$F6r+ base.CESD$F7r+ base.CESD$F9r+ base.CESD$F10r+ base.CESD$F11r+ base.CESD$F14r+ base.CESD$F15r+ base.CESD$F17r+ base.CESD$F18r+ base.CESD$F19r+ base.CESD$F20r
#Component 2
base.CESD$PCA2C2 <- base.CESD$F12r+ base.CESD$F16r#Correlation - ISMI and CES-D
#Correlation ISMI Total Socre x CESD
PCA2xISMIgeral<- base.CESD[,c(230:231, 288:289)]
corr.test(PCA2xISMIgeral, use="pairwise.complete.obs")## Call:corr.test(x = PCA2xISMIgeral, use = "pairwise.complete.obs")
## Correlation matrix
## ISMIG1 ISMIG2 PCA2C1 PCA2C2
## ISMIG1 1.00 0.97 0.45 0.23
## ISMIG2 0.97 1.00 0.45 0.24
## PCA2C1 0.45 0.45 1.00 0.44
## PCA2C2 0.23 0.24 0.44 1.00
## Sample Size
## [1] 513
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## ISMIG1 ISMIG2 PCA2C1 PCA2C2
## ISMIG1 0 0 0 0
## ISMIG2 0 0 0 0
## PCA2C1 0 0 0 0
## PCA2C2 0 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#Correlation ISMI Fatores x CESD
PCA2xISMIfatores<- base.CESD[,c(232:236, 288:289)]
corr.test(PCA2xISMIfatores, use="pairwise.complete.obs")## Call:corr.test(x = PCA2xISMIfatores, use = "pairwise.complete.obs")
## Correlation matrix
## ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 PCA2C1 PCA2C2
## ISMIF1 1.00 0.46 0.56 0.67 0.15 0.39 0.20
## ISMIF2 0.46 1.00 0.50 0.48 0.05 0.30 0.12
## ISMIF3 0.56 0.50 1.00 0.49 0.06 0.35 0.16
## ISMIF4 0.67 0.48 0.49 1.00 0.11 0.39 0.23
## ISMIF5 0.15 0.05 0.06 0.11 1.00 0.09 0.11
## PCA2C1 0.39 0.30 0.35 0.39 0.09 1.00 0.44
## PCA2C2 0.20 0.12 0.16 0.23 0.11 0.44 1.00
## Sample Size
## [1] 513
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## ISMIF1 ISMIF2 ISMIF3 ISMIF4 ISMIF5 PCA2C1 PCA2C2
## ISMIF1 0 0.00 0.00 0.00 0.01 0.00 0.00
## ISMIF2 0 0.00 0.00 0.00 0.33 0.00 0.04
## ISMIF3 0 0.00 0.00 0.00 0.33 0.00 0.00
## ISMIF4 0 0.00 0.00 0.00 0.05 0.00 0.00
## ISMIF5 0 0.24 0.17 0.01 0.00 0.13 0.05
## PCA2C1 0 0.00 0.00 0.00 0.04 0.00 0.00
## PCA2C2 0 0.01 0.00 0.00 0.01 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#Correlation Hope x CESD
PCA2xEE<- base.CESD[,c(241,288:289)]
corr.test(PCA2xEE, use="pairwise.complete.obs")## Call:corr.test(x = PCA2xEE, use = "pairwise.complete.obs")
## Correlation matrix
## EE PCA2C1 PCA2C2
## EE 1.00 -0.33 -0.39
## PCA2C1 -0.33 1.00 0.44
## PCA2C2 -0.39 0.44 1.00
## Sample Size
## [1] 513
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## EE PCA2C1 PCA2C2
## EE 0 0 0
## PCA2C1 0 0 0
## PCA2C2 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option##Correlation Self-Esteem x CESD
PCA2xEAUE<-base.CESD[,c(253,288:289)]
corr.test(PCA2xEAUE, use="pairwise.complete.obs")## Call:corr.test(x = PCA2xEAUE, use = "pairwise.complete.obs")
## Correlation matrix
## EAUE PCA2C1 PCA2C2
## EAUE 1.00 -0.45 -0.34
## PCA2C1 -0.45 1.00 0.44
## PCA2C2 -0.34 0.44 1.00
## Sample Size
## [1] 513
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## EAUE PCA2C1 PCA2C2
## EAUE 0 0 0
## PCA2C1 0 0 0
## PCA2C2 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#Summaries for PCA2
summary(base.CESD$PCA2C1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 16.00 25.00 24.61 33.00 45.00sd(base.CESD$PCA2C1)## [1] 11.04291summary(base.CESD$PCA2C2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 3.000 3.127 5.000 6.000sd(base.CESD$PCA2C2)## [1] 2.165408#Plots for each scale
par(mfrow=c(2,2))
hist(base.CESD$PCA2C1, breaks=10, axes=F, main="", xlab="", ylab="")
axis(2)
hist(base.CESD$PCA2C2, breaks=5, axes=F, main="", xlab="", ylab="")
axis(2)
boxplot(base.CESD$PCA2C1, horizontal=T, axes=F)
axis(1)
mtext("CESD - Subscale Factor 1 (M=25.0; SD=11.04)", side=1, line=2.5, font=2)
boxplot(base.CESD$PCA2C2, horizontal=T, axes=F)
axis(1)
mtext("CESD - Subscale Factor 2 (M=3.1; SD=2.17)", side=1, line=2.5, font=2)
title("Figure 4. Histogram and Boxplot for CES-D Final Solution (n=513) ", outer=T, line=-2.5, font=2)