Vivencia Teletrbalho
1 Preparing Data
1.0.0.0.1 Loading Libraries and function using auxiliary file
source("funlibs.R")1.0.0.0.2 Loading database
TDados <- read.spss(file = "Base Integrada - Servidores - Trabalho Remoto Covid-19 (dados completos)_27dez15h30.sav", to.data.frame = TRUE, use.value.labels = FALSE)
TDados<-as.data.frame(TDados)
#view(TDados)
# Criei um banco só com os itens de Suporte Gerencial
#Itens <- na.omit(TDados[c(194,196,198:202)])1.0.0.0.3 Subsetting AEC itens
data<-data.matrix(TDados[,c(193:202)])
#data<-data.matrix(TDados[,c(194,196,198:202)])
#names(TDados)2 Descriptive Statistics
2.0.0.0.1 Summary Statistics
psych::describe(data)## vars n mean sd median trimmed mad min max range skew kurtosis
## Viv_Aut 1 7608 3.66 1.06 4 3.73 1.48 1 5 4 -0.53 -0.64
## Viv_AdmT 2 7608 4.28 0.85 4 4.43 1.48 1 5 4 -1.45 2.46
## Viv_Int 3 7608 4.11 0.90 4 4.25 1.48 1 5 4 -1.26 1.72
## Viv_PrInf 4 7608 4.28 0.78 4 4.41 1.48 1 5 4 -1.34 2.57
## Viv_DiG 5 7608 4.41 0.71 4 4.50 1.48 1 5 4 -1.52 3.85
## Viv_DiF 6 7608 4.41 0.74 5 4.53 0.00 1 5 4 -1.52 3.17
## Viv_Pla 7 7608 4.44 0.66 5 4.52 0.00 1 5 4 -1.25 2.71
## Viv_EvInt 8 7608 4.11 0.92 4 4.25 1.48 1 5 4 -1.13 1.06
## Viv_HabR 9 7608 4.27 0.83 4 4.40 1.48 1 5 4 -1.20 1.42
## Viv_HabRC 10 7608 4.43 0.67 5 4.52 0.00 1 5 4 -1.27 2.54
## se
## Viv_Aut 0.01
## Viv_AdmT 0.01
## Viv_Int 0.01
## Viv_PrInf 0.01
## Viv_DiG 0.01
## Viv_DiF 0.01
## Viv_Pla 0.01
## Viv_EvInt 0.01
## Viv_HabR 0.01
## Viv_HabRC 0.012.1 Relative Frequencies
data<-as.matrix(data)
dsc<-descript(data)
porcentagem<-as.data.frame(round(dsc$perc,2)*100)
names(porcentagem)<-c("% lv1","% lv2","% lv3","% lv4","% lv5")
porcentagem## % lv1 % lv2 % lv3 % lv4 % lv5
## Viv_Aut 2 17 16 42 22
## Viv_AdmT 1 4 6 43 46
## Viv_Int 2 6 8 49 36
## Viv_PrInf 1 3 6 47 43
## Viv_DiG 1 2 4 44 50
## Viv_DiF 1 2 5 40 52
## Viv_Pla 0 1 4 43 51
## Viv_EvInt 1 7 9 45 38
## Viv_HabR 1 4 10 40 46
## Viv_HabRC 0 1 5 42 522.1.0.0.1 Plotting Itens as Likert
lbs <- c("lv1","lv2","lv3","lv4","lv5")
survey <- TDados[,c(193:202)] %>%
#survey <- TDados[,c(194,196,198:202)] %>%
dplyr::mutate_if(is.numeric, factor, levels = 1:5, labels = lbs)
plot(likert(survey[,1:10]), ordered = T, wrap= 60)
2.1.0.0.2 Histogram
dta_long <- melt(as.data.frame(data))
colnames(dta_long) <- c("Item", "Response")
Histogram <- ggplot(dta_long, aes(x = Response, fill = Item))+
geom_histogram(bins = 5)+
facet_wrap(~Item)+
theme_default()
Histogram
2.1.0.0.3 Density Plot
DensityPlot <- ggplot(dta_long, aes(x = Response, fill = Item))+
geom_density()+
facet_wrap(~Item)+
theme_default()
DensityPlot 
2.1.0.0.4 Correlation Matrix
CorMat <- psych::polychoric(data, correct=T, smooth=T,global=T)$rho2.1.0.0.5 Plotting
corrplot(CorMat,order="hclust",type="upper",method="ellipse",
tl.pos = "lt",mar = c(2,2,2,2))
corrplot(CorMat,order="hclust",type="lower",method="number",
diag=FALSE,tl.pos="n", cl.pos="n",add=TRUE,mar = c(2,2,2,2))
2.1.0.0.6 Ggcorplot
#ggcorrplot(CorMat, hc.order = T,type = "lower", lab = TRUE,
#colors = c("#E46726", "white", "#6D9EC2"))3 Splitting Data for Analysis
3.0.0.0.1 Random sampling training and test data for EFA and CFA
# Sorteio Aleatório
ss <- sample(1:2,size=nrow(TDados),replace=T,prob=c(0.3,0.7))
banco_EFA <- TDados[ss==1,]
banco_CFA <- TDados[ss==2,]3.0.0.0.2 EFA of Criativity Auto-Efficacy
data<-as.data.frame(banco_EFA)
write.csv(data,"banco_EFA.csv")3.0.0.1 Subsetting
data<-data[,c(193:202)]
#data<-data[,c(194,196,198:202)]3.0.0.1.1 Polychoric Correlation
CorMat <- psych::polychoric(data, correct=T, smooth=T,global=T)$rho3.0.0.1.2 Bartlett Sphericity
bartlett<-psych::cortest.bartlett(CorMat, n = nrow(data),diag=TRUE)
#bartlett3.0.0.1.3 Kayser Meyer - Sample Adequacy
kmo <-psych::KMO(CorMat)
#kmoIt was observed that the six items of CAEFF grouped a latent factor, Bartlett’s chi-square test 14984.9; df= 45; p< 0 and KMO = 0.91
3.0.0.1.4 Parallel Analysis using unweighted least square
parallel<-pa.plot(CorMat,n.obs = nrow(data), fm="uls", cor="poly",n.iter=1000)## Parallel analysis suggests that the number of factors = 4 and the number of components = 1print(parallel)## [[1]]
##
## [[2]]
## [1] 4#parallel[[2]][1]Number of factor by parallel analysis is equal to 4
3.0.0.1.5 Numeric Rules - Very Simples Structure based on parallel number of factor plus one
NumericRule <- VSS(CorMat,n =parallel[[2]][1]+1, plot = F, n.obs =nrow(data),rotate="oblim",cor="poly", fm="uls")## Specified rotation not found, rotate='none' used
## Specified rotation not found, rotate='none' used
## Specified rotation not found, rotate='none' used
## Specified rotation not found, rotate='none' usedtemp1 <- data.frame(nFactor = row.names(NumericRule$vss.stats),
VSS1 = NumericRule$cfit.1, VSS2 = NumericRule$cfit.2,
MAP = NumericRule$map)
temp2 <- NumericRule$vss.stats[,c(6:8,11)]
NumericRule <- cbind(temp1,temp2)
NumericRule## nFactor VSS1 VSS2 MAP RMSEA BIC SABIC SRMR
## 1 1 0.9244 0.0000 0.04806 0.17277 2189.59 2300.796 0.074089
## 2 2 0.9258 0.9508 0.04195 0.10772 524.98 607.587 0.033168
## 3 3 0.9263 0.9518 0.06859 0.08361 170.69 227.879 0.021301
## 4 4 0.9269 0.9559 0.10079 0.06603 37.12 72.073 0.012710
## 5 5 0.9272 0.9570 0.12372 0.03003 -23.28 -7.393 0.0032223.0.0.1.6 Exploratory Graph Analysis (EGA)
3.0.0.2 EGA bootstraped using non-parametric and median typical structure with Glasso regularization - with leading eigenvector for community finding and unidimensionality test
boot.ega<-EGAnet::bootEGA(
data=data,
uni.method=c("LE"),
iter=1000,
type=c("resampling"),
corr=c("cor_auto"),
model=c("glasso"),
algorithm=c("louvain"),
typicalStructure=T,
plot.typicalStructure=T,
plot.type="GGally",
ncore=c(parallel::detectCores()-1),
plot.args = list(legend.names=c("d1","d2"))
)## [1;m[4;m
## Bootstrap Exploratory Graph Analysis[0m[0m## • type = resampling
## • iterations = 1000##
## Generating data...done
## Estimating EGA networks...
## Computing results...
##
## Registered S3 method overwritten by 'statnet.common':
## method from
## sort.data.frame memisc## Warning in EGAnet::bootEGA(data = data, uni.method = c("LE"), iter = 1000, :
## Previous versions of EGAnet (<= 0.9.8) checked unidimensionality using
## [4;muni.method = "expand"[0m as the default
3.0.0.3 Item Stability
# Estimate stability statistics
res <- dimensionStability(boot.ega)## [1;m[4;m
## Item Stability Analysis[0m[0m##
## Organizing data...done
##
## Computing results...done
res$dimension.stability## $structural.consistency
## 1
## 0.801
##
## $average.item.stability
## 1
## 0.901res$item.stability$plot
# Changing plot features (ggplot2)
## Changing colors (ignore warnings)
### qgraph Defaults
res$item.stability$plot +
ggplot2::scale_color_manual(values = rainbow(length(
res$dimension.stability$structural.consistency)))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
3.0.0.4 Factorial Exploratory Analysis
EFArst <- psych::fa(r=as.matrix(data),nfactors=2,n.obs=nrow(data), rotate = "promax",fm = "wls", n.iter =1000, alpha = T,correct = T,cor="poly")The communalities were observed between 0.237 and 0.867, and the factor loadings between -0.14and 0.98 Table2. A factor was retained, with an eigenvalue of 3.57 that explained 0.36% of the variance.
3.0.0.4.1 Complete results from EFA
EFArst## Factor Analysis with confidence intervals using method = psych::fa(r = as.matrix(data), nfactors = 2, n.obs = nrow(data),
## n.iter = 1000, rotate = "promax", fm = "wls", alpha = T,
## cor = "poly", correct = T)
## Factor Analysis using method = wls
## Call: psych::fa(r = as.matrix(data), nfactors = 2, n.obs = nrow(data),
## n.iter = 1000, rotate = "promax", fm = "wls", alpha = T,
## cor = "poly", correct = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## WLS1 WLS2 h2 u2 com
## Viv_Aut 0.14 0.37 0.23 0.77 1.3
## Viv_AdmT 0.91 -0.06 0.76 0.24 1.0
## Viv_Int 0.76 -0.01 0.56 0.44 1.0
## Viv_PrInf 0.22 0.61 0.61 0.39 1.3
## Viv_DiG 0.39 0.39 0.53 0.47 2.0
## Viv_DiF 0.81 0.02 0.69 0.31 1.0
## Viv_Pla 0.63 0.19 0.61 0.39 1.2
## Viv_EvInt 0.85 -0.05 0.67 0.33 1.0
## Viv_HabR -0.14 0.92 0.68 0.32 1.0
## Viv_HabRC -0.08 0.98 0.86 0.14 1.0
##
## WLS1 WLS2
## SS loadings 3.57 2.63
## Proportion Var 0.36 0.26
## Cumulative Var 0.36 0.62
## Proportion Explained 0.58 0.42
## Cumulative Proportion 0.58 1.00
##
## With factor correlations of
## WLS1 WLS2
## WLS1 1.00 0.74
## WLS2 0.74 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 6.47 with Chi Square of 14985
## The degrees of freedom for the model are 26 and the objective function was 0.31
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 2322 with the empirical chi square 230.7 with prob < 0.0000000000000000000000000000000001
## The total number of observations was 2322 with Likelihood Chi Square = 721.3 with prob < 2.4e-135
##
## Tucker Lewis Index of factoring reliability = 0.919
## RMSEA index = 0.107 and the 0 % confidence intervals are NA 0.107
## BIC = 519.8
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## WLS1 WLS2
## Correlation of (regression) scores with factors 0.96 0.96
## Multiple R square of scores with factors 0.92 0.92
## Minimum correlation of possible factor scores 0.84 0.85
##
## Coefficients and bootstrapped confidence intervals
## low WLS1 upper low WLS2 upper
## Viv_Aut -0.35 0.14 0.42 0.11 0.37 0.84
## Viv_AdmT 0.78 0.91 0.99 -0.22 -0.06 0.23
## Viv_Int 0.68 0.76 0.83 -0.14 -0.01 0.20
## Viv_PrInf 0.08 0.22 0.45 0.46 0.61 0.69
## Viv_DiG 0.22 0.39 0.69 0.23 0.39 0.48
## Viv_DiF 0.75 0.81 0.91 -0.07 0.02 0.17
## Viv_Pla 0.51 0.63 0.84 0.12 0.19 0.25
## Viv_EvInt 0.79 0.85 0.91 -0.16 -0.05 0.16
## Viv_HabR -0.40 -0.14 0.29 0.51 0.92 1.17
## Viv_HabRC -0.35 -0.08 0.43 0.50 0.98 1.24
##
## Interfactor correlations and bootstrapped confidence intervals
## lower estimate upper
## WLS1-WLS2 0.67 0.74 0.773.0.0.4.2 Diagram
fa.diagram(EFArst,simple = F,cut = 0.33,sort = T,errors = F,e.size = 0.05)
3.0.0.4.3 Bootstrapping Factor Loadings
order <- rev(row.names(as.data.frame(printLoadings(EFArst$cis$means,sort = T,cutoff = 0.3)))) # define the order of the variable##
## Loadings:
## WLS1 WLS2
## Viv_AdmT 0.89
## Viv_EvInt 0.85
## Viv_DiF 0.83
## Viv_Int 0.75
## Viv_Pla 0.68
## Viv_DiG 0.45 0.36
## Viv_HabRC 0.87
## Viv_HabR 0.84
## Viv_PrInf 0.58
## Viv_Aut 0.47bargraph(EFArst,order = order,nf = 2,highcol = "firebrick",lowcol = "chartreuse4",ci = T)
3.0.0.4.4 Factor Loadings and Correlation Matrix
stackbar(CorMat,EFArst,order = order,highcol = "firebrick",lowcol = "chartreuse4")
3.0.0.5 Exploratory Fator Analysis using semTools and a Lavaan engine
#fa_mod1 <- efaUnrotate(data=data, nf = 1, estimator = "ULSMV",ordered=T,missing="pairwise",std.lv=T)
#fa_mod2 <- efaUnrotate(data, nf = 1, estimator = "MLR",ordered=F,missing="FIML")4 Confirmatory Factor Analysis
4.0.0.1 Changing data - all cases
data<-banco_CFA4.1 Structure Validity - Dimensionality (Leo)
4.1.0.1 Model - excluded Viv_DiG crossed factor loadings
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Int + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf + Viv_Aut
'4.1.0.2 Fitting
fit <- lavaan::cfa(model, data =data,estimator="ULSMV",ordered=T,missing="pairwise",std.lv=T)4.1.0.3 General Summary
summary(fit,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 22 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 5286
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 402.542 1248.007
## Degrees of freedom 26 26
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.324
## Shift parameter 4.489
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 58795.315 42545.682
## Degrees of freedom 36 36
## P-value NA 0.000
## Scaling correction factor 1.383
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.994 0.971
## Tucker-Lewis Index (TLI) 0.991 0.960
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.052 0.094
## 90 Percent confidence interval - lower 0.048 0.090
## 90 Percent confidence interval - upper 0.057 0.099
## P-value RMSEA <= 0.05 0.189 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.041 0.041
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 0.867 0.005 159.080 0.000 0.867 0.867
## Viv_EvInt 0.810 0.006 128.728 0.000 0.810 0.810
## Viv_DiF 0.836 0.007 126.523 0.000 0.836 0.836
## Viv_Int 0.757 0.007 103.050 0.000 0.757 0.757
## Viv_Pla 0.834 0.007 123.747 0.000 0.834 0.834
## Viv_Hard =~
## Viv_HabRC 0.851 0.007 118.413 0.000 0.851 0.851
## Viv_HabR 0.753 0.008 89.566 0.000 0.753 0.753
## Viv_PrInf 0.811 0.008 101.777 0.000 0.811 0.811
## Viv_Aut 0.544 0.012 46.461 0.000 0.544 0.544
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft ~~
## Viv_Hard 0.785 0.008 98.584 0.000 0.785 0.785
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -2.213 0.046 -48.134 0.000 -2.213 -2.213
## Viv_AdmT|t2 -1.598 0.028 -56.693 0.000 -1.598 -1.598
## Viv_AdmT|t3 -1.202 0.023 -53.130 0.000 -1.202 -1.202
## Viv_AdmT|t4 0.101 0.017 5.830 0.000 0.101 0.101
## Viv_EvInt|t1 -2.242 0.047 -47.447 0.000 -2.242 -2.242
## Viv_EvInt|t2 -1.386 0.025 -55.801 0.000 -1.386 -1.386
## Viv_EvInt|t3 -0.935 0.020 -46.095 0.000 -0.935 -0.935
## Viv_EvInt|t4 0.298 0.018 17.001 0.000 0.298 0.298
## Viv_DiF|t1 -2.568 0.067 -38.601 0.000 -2.568 -2.568
## Viv_DiF|t2 -1.877 0.034 -54.591 0.000 -1.877 -1.877
## Viv_DiF|t3 -1.439 0.026 -56.238 0.000 -1.439 -1.439
## Viv_DiF|t4 -0.057 0.017 -3.301 0.001 -0.057 -0.057
## Viv_Int|t1 -2.089 0.041 -50.922 0.000 -2.089 -2.089
## Viv_Int|t2 -1.423 0.025 -56.122 0.000 -1.423 -1.423
## Viv_Int|t3 -1.013 0.021 -48.533 0.000 -1.013 -1.013
## Viv_Int|t4 0.353 0.018 20.009 0.000 0.353 0.353
## Viv_Pla|t1 -2.639 0.072 -36.576 0.000 -2.639 -2.639
## Viv_Pla|t2 -2.162 0.044 -49.345 0.000 -2.162 -2.162
## Viv_Pla|t3 -1.583 0.028 -56.704 0.000 -1.583 -1.583
## Viv_Pla|t4 -0.041 0.017 -2.393 0.017 -0.041 -0.041
## Viv_HabRC|t1 -2.688 0.076 -35.148 0.000 -2.688 -2.688
## Viv_HabRC|t2 -2.102 0.041 -50.654 0.000 -2.102 -2.102
## Viv_HabRC|t3 -1.503 0.027 -56.576 0.000 -1.503 -1.503
## Viv_HabRC|t4 -0.033 0.017 -1.925 0.054 -0.033 -0.033
## Viv_HabR|t1 -2.448 0.058 -42.020 0.000 -2.448 -2.448
## Viv_HabR|t2 -1.685 0.030 -56.411 0.000 -1.685 -1.685
## Viv_HabR|t3 -1.069 0.021 -50.071 0.000 -1.069 -1.069
## Viv_HabR|t4 0.112 0.017 6.463 0.000 0.112 0.112
## Viv_PrInf|t1 -2.370 0.054 -44.142 0.000 -2.370 -2.370
## Viv_PrInf|t2 -1.745 0.031 -56.013 0.000 -1.745 -1.745
## Viv_PrInf|t3 -1.274 0.023 -54.379 0.000 -1.274 -1.274
## Viv_PrInf|t4 0.188 0.017 10.859 0.000 0.188 0.188
## Viv_Aut|t1 -2.053 0.040 -51.657 0.000 -2.053 -2.053
## Viv_Aut|t2 -0.870 0.020 -43.882 0.000 -0.870 -0.870
## Viv_Aut|t3 -0.384 0.018 -21.701 0.000 -0.384 -0.384
## Viv_Aut|t4 0.752 0.019 39.296 0.000 0.752 0.752
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.249 0.249 0.249
## .Viv_EvInt 0.344 0.344 0.344
## .Viv_DiF 0.302 0.302 0.302
## .Viv_Int 0.427 0.427 0.427
## .Viv_Pla 0.305 0.305 0.305
## .Viv_HabRC 0.276 0.276 0.276
## .Viv_HabR 0.433 0.433 0.433
## .Viv_PrInf 0.342 0.342 0.342
## .Viv_Aut 0.704 0.704 0.704
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 1.000 1.000 1.000
## Viv_EvInt 1.000 1.000 1.000
## Viv_DiF 1.000 1.000 1.000
## Viv_Int 1.000 1.000 1.000
## Viv_Pla 1.000 1.000 1.000
## Viv_HabRC 1.000 1.000 1.000
## Viv_HabR 1.000 1.000 1.000
## Viv_PrInf 1.000 1.000 1.000
## Viv_Aut 1.000 1.000 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.751
## Viv_EvInt 0.656
## Viv_DiF 0.698
## Viv_Int 0.573
## Viv_Pla 0.695
## Viv_HabRC 0.724
## Viv_HabR 0.567
## Viv_PrInf 0.658
## Viv_Aut 0.2964.1.0.4 Selected Robust and Scaled Fit Measures
lavaan::fitMeasures(fit,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 1248.007 26.000 0.000
## srmr cfi.scaled tli.scaled
## 0.041 0.971 0.960
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.094 0.090 0.0994.1.0.5 Factor Loadings
parameters<-lavaan::standardizedSolution(fit)
loadings<-parameters[parameters$op=="=~",]
loadings## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Viv_Soft =~ Viv_AdmT 0.867 0.005 159.08 0 0.856 0.877
## 2 Viv_Soft =~ Viv_EvInt 0.810 0.006 128.73 0 0.797 0.822
## 3 Viv_Soft =~ Viv_DiF 0.836 0.007 126.52 0 0.823 0.849
## 4 Viv_Soft =~ Viv_Int 0.757 0.007 103.05 0 0.742 0.771
## 5 Viv_Soft =~ Viv_Pla 0.834 0.007 123.75 0 0.821 0.847
## 6 Viv_Hard =~ Viv_HabRC 0.851 0.007 118.41 0 0.837 0.865
## 7 Viv_Hard =~ Viv_HabR 0.753 0.008 89.57 0 0.737 0.770
## 8 Viv_Hard =~ Viv_PrInf 0.811 0.008 101.78 0 0.796 0.827
## 9 Viv_Hard =~ Viv_Aut 0.544 0.012 46.46 0 0.521 0.5674.1.0.6 Modificantion Indices considering very bad RMSEA
modificationindices(fit, sort.=T,maximum.number = 10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 117 Viv_HabRC ~~ Viv_HabR 156.27 0.224 0.224 0.648 0.648
## 89 Viv_AdmT ~~ Viv_Int 113.54 0.179 0.179 0.549 0.549
## 80 Viv_Soft =~ Viv_PrInf 50.13 0.293 0.293 0.293 0.293
## 79 Viv_Soft =~ Viv_HabR 45.61 -0.261 -0.261 -0.261 -0.261
## 86 Viv_Hard =~ Viv_Pla 45.04 0.235 0.235 0.235 0.235
## 95 Viv_EvInt ~~ Viv_DiF 31.35 0.095 0.095 0.294 0.294
## 94 Viv_AdmT ~~ Viv_Aut 29.50 0.083 0.083 0.197 0.197
## 113 Viv_Pla ~~ Viv_HabRC 28.39 0.086 0.086 0.296 0.296
## 110 Viv_Int ~~ Viv_HabR 22.17 -0.073 -0.073 -0.169 -0.169
## 92 Viv_AdmT ~~ Viv_HabR 21.09 -0.073 -0.073 -0.222 -0.2224.1.0.7 Ordinal Alpha and Omega
semTools::reliability(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard
## alpha 0.8542 0.7243
## alpha.ord 0.9115 0.8244
## omega 0.8615 0.7438
## omega2 0.8615 0.7438
## omega3 0.8573 0.7474
## avevar 0.6747 0.56134.1.0.8 Discriminant Validity (faild if discriminant validity is false)
semMediation::discriminantValidityTable(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard AVE sqrt(AVE) discriminantValidity
## Viv_Soft 1.000 0.785 0.857 0.675 FALSE
## Viv_Hard 0.785 1.000 0.857 0.675 FALSE4.1.0.9 Discriminant Validity Robust Test (p<0.05 model tested is different from model with .90 correlation and discriminant validity is accepted)
semTools::discriminantValidity(fit,cutoff =0.9)## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff
## 1 Viv_Soft ~~ Viv_Hard 0.7852 0.7696 0.8008 27 NA NA 642 216.9
## Df diff Pr(>Chisq)
## 1 1 0.00000000000000000000000000000000000000000000000043534.1.0.10 Modified Model - after two iteration by MI searching for lower relationship between X2/df
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Int + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf + Viv_Aut
Viv_HabRC ~~ Viv_HabR
Viv_AdmT ~~ Viv_Int
'4.1.0.11 Fitting
fit <- lavaan::cfa(model, data =data,estimator="ULSMV",ordered=T,missing="pairwise",std.lv=T,orthogonal=F)4.1.0.12 General Summary
summary(fit,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 22 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 5286
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 134.863 442.280
## Degrees of freedom 24 24
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.308
## Shift parameter 3.821
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 58795.315 42545.682
## Degrees of freedom 36 36
## P-value NA 0.000
## Scaling correction factor 1.383
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.990
## Tucker-Lewis Index (TLI) 0.997 0.985
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.030 0.057
## 90 Percent confidence interval - lower 0.025 0.053
## 90 Percent confidence interval - upper 0.035 0.062
## P-value RMSEA <= 0.05 1.000 0.004
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.024 0.024
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 0.833 0.006 129.298 0.000 0.833 0.833
## Viv_EvInt 0.815 0.006 129.172 0.000 0.815 0.815
## Viv_DiF 0.842 0.007 127.206 0.000 0.842 0.842
## Viv_Int 0.721 0.008 85.152 0.000 0.721 0.721
## Viv_Pla 0.840 0.007 124.166 0.000 0.840 0.840
## Viv_Hard =~
## Viv_HabRC 0.796 0.009 87.692 0.000 0.796 0.796
## Viv_HabR 0.697 0.011 66.287 0.000 0.697 0.697
## Viv_PrInf 0.811 0.008 100.506 0.000 0.811 0.811
## Viv_Aut 0.544 0.012 46.285 0.000 0.544 0.544
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.218 0.011 19.104 0.000 0.218 0.502
## .Viv_AdmT ~~
## .Viv_Int 0.175 0.008 21.918 0.000 0.175 0.458
## Viv_Soft ~~
## Viv_Hard 0.828 0.008 99.755 0.000 0.828 0.828
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -2.213 0.046 -48.134 0.000 -2.213 -2.213
## Viv_AdmT|t2 -1.598 0.028 -56.693 0.000 -1.598 -1.598
## Viv_AdmT|t3 -1.202 0.023 -53.130 0.000 -1.202 -1.202
## Viv_AdmT|t4 0.101 0.017 5.830 0.000 0.101 0.101
## Viv_EvInt|t1 -2.242 0.047 -47.447 0.000 -2.242 -2.242
## Viv_EvInt|t2 -1.386 0.025 -55.801 0.000 -1.386 -1.386
## Viv_EvInt|t3 -0.935 0.020 -46.095 0.000 -0.935 -0.935
## Viv_EvInt|t4 0.298 0.018 17.001 0.000 0.298 0.298
## Viv_DiF|t1 -2.568 0.067 -38.601 0.000 -2.568 -2.568
## Viv_DiF|t2 -1.877 0.034 -54.591 0.000 -1.877 -1.877
## Viv_DiF|t3 -1.439 0.026 -56.238 0.000 -1.439 -1.439
## Viv_DiF|t4 -0.057 0.017 -3.301 0.001 -0.057 -0.057
## Viv_Int|t1 -2.089 0.041 -50.922 0.000 -2.089 -2.089
## Viv_Int|t2 -1.423 0.025 -56.122 0.000 -1.423 -1.423
## Viv_Int|t3 -1.013 0.021 -48.533 0.000 -1.013 -1.013
## Viv_Int|t4 0.353 0.018 20.009 0.000 0.353 0.353
## Viv_Pla|t1 -2.639 0.072 -36.576 0.000 -2.639 -2.639
## Viv_Pla|t2 -2.162 0.044 -49.345 0.000 -2.162 -2.162
## Viv_Pla|t3 -1.583 0.028 -56.704 0.000 -1.583 -1.583
## Viv_Pla|t4 -0.041 0.017 -2.393 0.017 -0.041 -0.041
## Viv_HabRC|t1 -2.688 0.076 -35.148 0.000 -2.688 -2.688
## Viv_HabRC|t2 -2.102 0.041 -50.654 0.000 -2.102 -2.102
## Viv_HabRC|t3 -1.503 0.027 -56.576 0.000 -1.503 -1.503
## Viv_HabRC|t4 -0.033 0.017 -1.925 0.054 -0.033 -0.033
## Viv_HabR|t1 -2.448 0.058 -42.020 0.000 -2.448 -2.448
## Viv_HabR|t2 -1.685 0.030 -56.411 0.000 -1.685 -1.685
## Viv_HabR|t3 -1.069 0.021 -50.071 0.000 -1.069 -1.069
## Viv_HabR|t4 0.112 0.017 6.463 0.000 0.112 0.112
## Viv_PrInf|t1 -2.370 0.054 -44.142 0.000 -2.370 -2.370
## Viv_PrInf|t2 -1.745 0.031 -56.013 0.000 -1.745 -1.745
## Viv_PrInf|t3 -1.274 0.023 -54.379 0.000 -1.274 -1.274
## Viv_PrInf|t4 0.188 0.017 10.859 0.000 0.188 0.188
## Viv_Aut|t1 -2.053 0.040 -51.657 0.000 -2.053 -2.053
## Viv_Aut|t2 -0.870 0.020 -43.882 0.000 -0.870 -0.870
## Viv_Aut|t3 -0.384 0.018 -21.701 0.000 -0.384 -0.384
## Viv_Aut|t4 0.752 0.019 39.296 0.000 0.752 0.752
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.306 0.306 0.306
## .Viv_EvInt 0.336 0.336 0.336
## .Viv_DiF 0.291 0.291 0.291
## .Viv_Int 0.481 0.481 0.481
## .Viv_Pla 0.294 0.294 0.294
## .Viv_HabRC 0.366 0.366 0.366
## .Viv_HabR 0.514 0.514 0.514
## .Viv_PrInf 0.341 0.341 0.341
## .Viv_Aut 0.704 0.704 0.704
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 1.000 1.000 1.000
## Viv_EvInt 1.000 1.000 1.000
## Viv_DiF 1.000 1.000 1.000
## Viv_Int 1.000 1.000 1.000
## Viv_Pla 1.000 1.000 1.000
## Viv_HabRC 1.000 1.000 1.000
## Viv_HabR 1.000 1.000 1.000
## Viv_PrInf 1.000 1.000 1.000
## Viv_Aut 1.000 1.000 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.694
## Viv_EvInt 0.664
## Viv_DiF 0.709
## Viv_Int 0.519
## Viv_Pla 0.706
## Viv_HabRC 0.634
## Viv_HabR 0.486
## Viv_PrInf 0.659
## Viv_Aut 0.2964.1.0.13 Selected Robust and Scaled Fit Measures
lavaan::fitMeasures(fit,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 442.280 24.000 0.000
## srmr cfi.scaled tli.scaled
## 0.024 0.990 0.985
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.057 0.053 0.0624.1.0.14 Factor Loadings
parameters<-lavaan::standardizedSolution(fit)
loadings<-parameters[parameters$op=="=~",]
loadings## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Viv_Soft =~ Viv_AdmT 0.833 0.006 129.30 0 0.821 0.846
## 2 Viv_Soft =~ Viv_EvInt 0.815 0.006 129.17 0 0.802 0.827
## 3 Viv_Soft =~ Viv_DiF 0.842 0.007 127.21 0 0.829 0.855
## 4 Viv_Soft =~ Viv_Int 0.721 0.008 85.15 0 0.704 0.737
## 5 Viv_Soft =~ Viv_Pla 0.840 0.007 124.17 0 0.827 0.853
## 6 Viv_Hard =~ Viv_HabRC 0.796 0.009 87.69 0 0.778 0.814
## 7 Viv_Hard =~ Viv_HabR 0.697 0.011 66.29 0 0.676 0.718
## 8 Viv_Hard =~ Viv_PrInf 0.811 0.008 100.51 0 0.796 0.827
## 9 Viv_Hard =~ Viv_Aut 0.544 0.012 46.28 0 0.521 0.5674.1.0.15 Modificantion Indices considering very bad RMSEA
modificationindices(fit, sort.=T,maximum.number = 10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 85 Viv_Hard =~ Viv_EvInt 39.198 -0.283 -0.283 -0.283 -0.283
## 114 Viv_Pla ~~ Viv_HabRC 32.448 0.093 0.093 0.283 0.283
## 95 Viv_AdmT ~~ Viv_Aut 25.979 0.078 0.078 0.169 0.169
## 96 Viv_EvInt ~~ Viv_DiF 23.307 0.083 0.083 0.264 0.264
## 88 Viv_Hard =~ Viv_Pla 21.319 0.212 0.212 0.212 0.212
## 101 Viv_EvInt ~~ Viv_PrInf 18.568 -0.070 -0.070 -0.207 -0.207
## 112 Viv_Int ~~ Viv_PrInf 12.253 0.056 0.056 0.138 0.138
## 108 Viv_DiF ~~ Viv_Aut 8.884 -0.046 -0.046 -0.101 -0.101
## 102 Viv_EvInt ~~ Viv_Aut 6.668 -0.040 -0.040 -0.081 -0.081
## 115 Viv_Pla ~~ Viv_HabR 5.632 0.038 0.038 0.097 0.0974.1.0.16 Ordinal Alpha and Omega
semTools::reliability(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard
## alpha 0.8542 0.7243
## alpha.ord 0.9115 0.8244
## omega 0.8323 0.6937
## omega2 0.8323 0.6937
## omega3 0.8303 0.6929
## avevar 0.6584 0.51854.1.0.17 Discriminant Validity
semMediation::discriminantValidityTable(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard AVE sqrt(AVE) discriminantValidity
## Viv_Soft 1.000 0.828 0.83 0.658 FALSE
## Viv_Hard 0.828 1.000 0.83 0.658 FALSE4.1.0.18 Discriminant Validity Robust Test
semTools::discriminantValidity(fit)## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff
## 1 Viv_Soft ~~ Viv_Hard 0.8275 0.8113 0.8438 25 NA NA 210.1 76.61
## Df diff Pr(>Chisq)
## 1 1 0.0000000000000000020834.1.0.19 Graphical Representation
semPaths(object=fit,whatLabels ="stand",residuals = F, thresholds = F,ThreshAtSide=F, cardinal = c("exogenous covariances", border.color = ("black")), intercept=F, edge.label.cex = 1,curve=2,nCharNodes=0)
4.1.1 Internal Validity
4.1.1.1 Using all data
data<-TDados4.1.1.2 Organizing Sex
data$Gen<-car::recode(data$Gen,"0=NA")
data$SexoR<-as.factor(data$Gen)
#dataSexoR1<-data[data$SexoR=="1",]
#dataSexoR2<-data[data$SexoR=="2",]4.1.1.3 Sex Invariance Model
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Int + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf + Viv_Aut
Viv_HabRC ~~ Viv_HabR
Viv_AdmT ~~ Viv_Int
'4.1.1.4 Fitting
invariance<- measurementInvarianceCat(model = model, data = data, group ="SexoR",parameterization = "theta", estimator = "ULSMV",ordered =T,missing="pairwise",std.lv=T)## Warning: The measurementInvarianceCat function is deprecated, and it will cease
## to be included in future versions of semTools. See help('semTools-deprecated)
## for details.## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.thresholds
## Model 4 : fit.means
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 48 202
## fit.loadings 55 309 19.9 7 0.0058 **
## fit.thresholds 80 509 49.5 25 0.0024 **
## fit.means 82 1186 81.7 2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi.scaled rmsea.scaled cfi.scaled.delta rmsea.scaled.delta
## fit.configural 0.990 0.057 NA NA
## fit.loadings 0.997 0.030 0.007 0.028
## fit.thresholds 0.997 0.023 0.000 0.007
## fit.means 0.993 0.036 0.004 0.0134.1.1.4.1 Configural
summary(invariance$fit.configural,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 182 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 11
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 201.634 646.736
## Degrees of freedom 48 48
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.316
## Shift parameter for each group:
## 2 3.322
## 1 4.839
## simple second-order correction
## Test statistic for each group:
## 2 93.861 300.580
## 1 107.773 346.156
##
## Model Test Baseline Model:
##
## Test statistic 83443.089 60114.774
## Degrees of freedom 72 72
## P-value NA 0.000
## Scaling correction factor 1.389
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.990
## Tucker-Lewis Index (TLI) 0.997 0.985
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.029 0.057
## 90 Percent confidence interval - lower 0.025 0.053
## 90 Percent confidence interval - upper 0.033 0.061
## P-value RMSEA <= 0.05 1.000 0.001
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.024 0.024
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.478 0.048 30.946 0.000 1.478 0.828
## Viv_EvInt 1.399 0.040 34.563 0.000 1.399 0.814
## Viv_DiF 1.589 0.055 28.794 0.000 1.589 0.846
## Viv_Int 1.023 0.032 31.761 0.000 1.023 0.715
## Viv_Pla 1.625 0.059 27.635 0.000 1.625 0.852
## Viv_Hard =~
## Viv_HabRC 1.493 0.062 24.085 0.000 1.493 0.831
## Viv_HabR 1.058 0.039 27.459 0.000 1.058 0.727
## Viv_PrInf 1.586 0.063 24.988 0.000 1.586 0.846
## Viv_Aut 0.693 0.027 26.057 0.000 0.693 0.569
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.549 0.019 29.291 0.000 0.549 0.549
## .Viv_AdmT ~~
## .Viv_Int 0.364 0.018 20.703 0.000 0.364 0.364
## Viv_Soft ~~
## Viv_Hard 0.825 0.010 80.940 0.000 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.347 0.130 -33.412 0.000 -4.347 -2.436
## Viv_AdmT|t2 -3.097 0.080 -38.800 0.000 -3.097 -1.735
## Viv_AdmT|t3 -2.257 0.065 -34.872 0.000 -2.257 -1.264
## Viv_AdmT|t4 0.070 0.040 1.756 0.079 0.070 0.039
## Viv_EvInt|t1 -4.042 0.109 -37.174 0.000 -4.042 -2.351
## Viv_EvInt|t2 -2.645 0.063 -41.712 0.000 -2.645 -1.538
## Viv_EvInt|t3 -1.773 0.052 -33.966 0.000 -1.773 -1.031
## Viv_EvInt|t4 0.427 0.038 11.190 0.000 0.427 0.248
## Viv_DiF|t1 -4.737 0.152 -31.155 0.000 -4.737 -2.523
## Viv_DiF|t2 -3.613 0.098 -37.008 0.000 -3.613 -1.925
## Viv_DiF|t3 -2.801 0.081 -34.483 0.000 -2.801 -1.492
## Viv_DiF|t4 -0.188 0.044 -4.310 0.000 -0.188 -0.100
## Viv_Int|t1 -3.309 0.089 -37.204 0.000 -3.309 -2.314
## Viv_Int|t2 -2.388 0.055 -43.111 0.000 -2.388 -1.670
## Viv_Int|t3 -1.691 0.045 -37.740 0.000 -1.691 -1.182
## Viv_Int|t4 0.355 0.032 11.115 0.000 0.355 0.248
## Viv_Pla|t1 -5.030 0.166 -30.343 0.000 -5.030 -2.636
## Viv_Pla|t2 -4.116 0.115 -35.659 0.000 -4.116 -2.157
## Viv_Pla|t3 -2.925 0.087 -33.477 0.000 -2.925 -1.533
## Viv_Pla|t4 -0.072 0.043 -1.655 0.098 -0.072 -0.038
## Viv_HabRC|t1 -4.784 0.182 -26.313 0.000 -4.784 -2.663
## Viv_HabRC|t2 -3.738 0.119 -31.420 0.000 -3.738 -2.080
## Viv_HabRC|t3 -2.827 0.092 -30.764 0.000 -2.827 -1.573
## Viv_HabRC|t4 -0.187 0.042 -4.470 0.000 -0.187 -0.104
## Viv_HabR|t1 -3.618 0.116 -31.288 0.000 -3.618 -2.486
## Viv_HabR|t2 -2.547 0.066 -38.706 0.000 -2.547 -1.750
## Viv_HabR|t3 -1.658 0.048 -34.723 0.000 -1.658 -1.139
## Viv_HabR|t4 0.004 0.033 0.126 0.900 0.004 0.003
## Viv_PrInf|t1 -4.628 0.165 -27.976 0.000 -4.628 -2.468
## Viv_PrInf|t2 -3.490 0.111 -31.444 0.000 -3.490 -1.861
## Viv_PrInf|t3 -2.608 0.087 -30.083 0.000 -2.608 -1.391
## Viv_PrInf|t4 0.036 0.042 0.849 0.396 0.036 0.019
## Viv_Aut|t1 -2.699 0.074 -36.333 0.000 -2.699 -2.219
## Viv_Aut|t2 -1.223 0.034 -35.847 0.000 -1.223 -1.005
## Viv_Aut|t3 -0.599 0.029 -20.466 0.000 -0.599 -0.493
## Viv_Aut|t4 0.757 0.029 25.823 0.000 0.757 0.622
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.314
## .Viv_EvInt 1.000 1.000 0.338
## .Viv_DiF 1.000 1.000 0.284
## .Viv_Int 1.000 1.000 0.489
## .Viv_Pla 1.000 1.000 0.275
## .Viv_HabRC 1.000 1.000 0.310
## .Viv_HabR 1.000 1.000 0.472
## .Viv_PrInf 1.000 1.000 0.285
## .Viv_Aut 1.000 1.000 0.676
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.560 0.560 1.000
## Viv_EvInt 0.582 0.582 1.000
## Viv_DiF 0.533 0.533 1.000
## Viv_Int 0.699 0.699 1.000
## Viv_Pla 0.524 0.524 1.000
## Viv_HabRC 0.557 0.557 1.000
## Viv_HabR 0.687 0.687 1.000
## Viv_PrInf 0.533 0.533 1.000
## Viv_Aut 0.822 0.822 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.686
## Viv_EvInt 0.662
## Viv_DiF 0.716
## Viv_Int 0.511
## Viv_Pla 0.725
## Viv_HabRC 0.690
## Viv_HabR 0.528
## Viv_PrInf 0.715
## Viv_Aut 0.324
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.697 0.202 8.391 0.000 1.697 0.833
## Viv_EvInt 1.520 0.155 9.811 0.000 1.520 0.812
## Viv_DiF 1.584 0.146 10.816 0.000 1.584 0.836
## Viv_Int 1.182 0.115 10.239 0.000 1.182 0.714
## Viv_Pla 1.488 0.136 10.928 0.000 1.488 0.824
## Viv_Hard =~
## Viv_HabRC 1.359 0.284 4.794 0.000 1.359 0.785
## Viv_HabR 1.010 0.179 5.636 0.000 1.010 0.679
## Viv_PrInf 1.590 0.341 4.663 0.000 1.590 0.794
## Viv_Aut 0.704 0.114 6.170 0.000 0.704 0.510
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.540 0.211 2.556 0.011 0.540 0.461
## .Viv_AdmT ~~
## .Viv_Int 0.634 0.134 4.732 0.000 0.634 0.486
## Viv_Soft ~~
## Viv_Hard 0.818 0.010 85.688 0.000 0.818 0.818
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.034 0.233 0.144 0.885 0.034 0.034
## Viv_Hard 0.065 0.592 0.110 0.913 0.065 0.065
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.347 0.130 -33.412 0.000 -4.347 -2.134
## Viv_AdmT|t2 -3.097 0.080 -38.800 0.000 -3.097 -1.520
## Viv_AdmT|t3 -2.312 0.140 -16.459 0.000 -2.312 -1.135
## Viv_AdmT|t4 0.348 0.434 0.801 0.423 0.348 0.171
## Viv_EvInt|t1 -4.042 0.109 -37.174 0.000 -4.042 -2.158
## Viv_EvInt|t2 -2.407 0.149 -16.124 0.000 -2.407 -1.285
## Viv_EvInt|t3 -1.597 0.210 -7.610 0.000 -1.597 -0.853
## Viv_EvInt|t4 0.735 0.424 1.733 0.083 0.735 0.393
## Viv_DiF|t1 -4.737 0.152 -31.155 0.000 -4.737 -2.498
## Viv_DiF|t2 -3.432 0.168 -20.463 0.000 -3.432 -1.810
## Viv_DiF|t3 -2.596 0.192 -13.512 0.000 -2.596 -1.369
## Viv_DiF|t4 0.014 0.370 0.038 0.970 0.014 0.007
## Viv_Int|t1 -3.309 0.089 -37.204 0.000 -3.309 -1.999
## Viv_Int|t2 -2.130 0.109 -19.565 0.000 -2.130 -1.287
## Viv_Int|t3 -1.494 0.150 -9.958 0.000 -1.494 -0.903
## Viv_Int|t4 0.785 0.346 2.265 0.023 0.785 0.474
## Viv_Pla|t1 -5.030 0.166 -30.343 0.000 -5.030 -2.787
## Viv_Pla|t2 -4.012 0.187 -21.406 0.000 -4.012 -2.223
## Viv_Pla|t3 -2.908 0.187 -15.569 0.000 -2.908 -1.612
## Viv_Pla|t4 -0.007 0.346 -0.021 0.983 -0.007 -0.004
## Viv_HabRC|t1 -4.784 0.182 -26.313 0.000 -4.784 -2.764
## Viv_HabRC|t2 -3.738 0.119 -31.420 0.000 -3.738 -2.160
## Viv_HabRC|t3 -2.517 0.303 -8.300 0.000 -2.517 -1.455
## Viv_HabRC|t4 0.102 0.825 0.123 0.902 0.102 0.059
## Viv_HabR|t1 -3.618 0.116 -31.288 0.000 -3.618 -2.431
## Viv_HabR|t2 -2.416 0.205 -11.786 0.000 -2.416 -1.624
## Viv_HabR|t3 -1.474 0.349 -4.226 0.000 -1.474 -0.991
## Viv_HabR|t4 0.318 0.652 0.488 0.626 0.318 0.214
## Viv_PrInf|t1 -4.628 0.165 -27.976 0.000 -4.628 -2.310
## Viv_PrInf|t2 -3.311 0.283 -11.697 0.000 -3.311 -1.653
## Viv_PrInf|t3 -2.316 0.467 -4.961 0.000 -2.316 -1.156
## Viv_PrInf|t4 0.674 1.082 0.623 0.533 0.674 0.337
## Viv_Aut|t1 -2.699 0.074 -36.333 0.000 -2.699 -1.957
## Viv_Aut|t2 -1.045 0.257 -4.069 0.000 -1.045 -0.758
## Viv_Aut|t3 -0.370 0.360 -1.029 0.303 -0.370 -0.268
## Viv_Aut|t4 1.231 0.610 2.019 0.043 1.231 0.893
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.269 0.296 4.292 0.000 1.269 0.306
## .Viv_EvInt 1.196 0.240 4.994 0.000 1.196 0.341
## .Viv_DiF 1.085 0.199 5.465 0.000 1.085 0.302
## .Viv_Int 1.341 0.254 5.290 0.000 1.341 0.490
## .Viv_Pla 1.043 0.185 5.627 0.000 1.043 0.320
## .Viv_HabRC 1.148 0.474 2.420 0.016 1.148 0.383
## .Viv_HabR 1.195 0.424 2.818 0.005 1.195 0.539
## .Viv_PrInf 1.486 0.636 2.337 0.019 1.486 0.370
## .Viv_Aut 1.407 0.448 3.142 0.002 1.407 0.740
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.491 0.491 1.000
## Viv_EvInt 0.534 0.534 1.000
## Viv_DiF 0.527 0.527 1.000
## Viv_Int 0.604 0.604 1.000
## Viv_Pla 0.554 0.554 1.000
## Viv_HabRC 0.578 0.578 1.000
## Viv_HabR 0.672 0.672 1.000
## Viv_PrInf 0.499 0.499 1.000
## Viv_Aut 0.725 0.725 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.694
## Viv_EvInt 0.659
## Viv_DiF 0.698
## Viv_Int 0.510
## Viv_Pla 0.680
## Viv_HabRC 0.617
## Viv_HabR 0.461
## Viv_PrInf 0.630
## Viv_Aut 0.260lavaan::fitMeasures(invariance$fit.configural,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 646.736 48.000 0.000
## srmr cfi.scaled tli.scaled
## 0.024 0.990 0.985
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.057 0.053 0.061modificationindices(invariance$fit.configural, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 185 Viv_AdmT ~~ Viv_Aut 1 1 1 38.03 0.269 0.269 0.269
## 218 Viv_Hard =~ Viv_EvInt 2 2 1 37.95 -0.547 -0.547 -0.292
## 221 Viv_Hard =~ Viv_Pla 2 2 1 30.30 0.475 0.475 0.263
## 247 Viv_Pla ~~ Viv_HabRC 2 2 1 25.40 0.277 0.277 0.253
## 229 Viv_EvInt ~~ Viv_DiF 2 2 1 22.91 0.317 0.317 0.278
## 204 Viv_Pla ~~ Viv_HabRC 1 1 1 18.19 0.313 0.313 0.313
## 234 Viv_EvInt ~~ Viv_PrInf 2 2 1 16.49 -0.269 -0.269 -0.202
## 175 Viv_Hard =~ Viv_EvInt 1 1 1 15.30 -0.382 -0.382 -0.222
## 186 Viv_EvInt ~~ Viv_DiF 1 1 1 15.28 0.280 0.280 0.280
## 248 Viv_Pla ~~ Viv_HabR 2 2 1 10.87 0.152 0.152 0.136
## sepc.nox
## 185 0.269
## 218 -0.292
## 221 0.263
## 247 0.253
## 229 0.278
## 204 0.313
## 234 -0.202
## 175 -0.222
## 186 0.280
## 248 0.136semTools::reliability(invariance$fit.configural)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8523 0.7517
## alpha.ord 0.9108 0.8480
## omega 0.8380 0.7211
## omega2 0.8380 0.7211
## omega3 0.8369 0.7217
## avevar 0.6744 0.6132
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8504 0.7031
## alpha.ord 0.9082 0.8067
## omega 0.8266 0.6761
## omega2 0.8266 0.6761
## omega3 0.8239 0.6738
## avevar 0.6559 0.52944.1.1.4.2 Loadings
summary(invariance$fit.loadings,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 184 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 109
## Number of equality constraints 20
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 308.962 240.339
## Degrees of freedom 55 55
## P-value (Unknown) NA 0.000
## Scaling correction factor 1.415
## Shift parameter for each group:
## 2 8.921
## 1 12.993
## simple second-order correction
## Test statistic for each group:
## 2 159.444 121.642
## 1 149.518 118.697
##
## Model Test Baseline Model:
##
## Test statistic 83443.089 60114.774
## Degrees of freedom 72 72
## P-value NA 0.000
## Scaling correction factor 1.389
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 0.997
## Tucker-Lewis Index (TLI) 0.996 0.996
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.035 0.030
## 90 Percent confidence interval - lower 0.031 0.026
## 90 Percent confidence interval - upper 0.039 0.034
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.028 0.028
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.662 0.086 19.325 0.000 1.662 0.857
## Viv_EvInt 1.458 0.059 24.757 0.000 1.458 0.825
## Viv_DiF 1.494 0.081 18.413 0.000 1.494 0.831
## Viv_Int 1.136 0.044 25.758 0.000 1.136 0.751
## Viv_Pla 1.368 0.072 19.056 0.000 1.368 0.807
## Viv_Hard =~
## Viv_HabRC 1.362 0.073 18.695 0.000 1.362 0.806
## Viv_HabR 1.038 0.044 23.526 0.000 1.038 0.720
## Viv_PrInf 1.665 0.086 19.372 0.000 1.665 0.857
## Viv_Aut 0.711 0.029 24.950 0.000 0.711 0.580
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.568 0.021 26.736 0.000 0.568 0.568
## .Viv_AdmT ~~
## .Viv_Int 0.270 0.036 7.570 0.000 0.270 0.270
## Viv_Soft ~~
## Viv_Hard 0.825 0.010 79.425 0.000 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.646 0.237 -19.585 0.000 -4.646 -2.395
## Viv_AdmT|t2 -3.416 0.151 -22.652 0.000 -3.416 -1.761
## Viv_AdmT|t3 -2.453 0.106 -23.233 0.000 -2.453 -1.264
## Viv_AdmT|t4 0.076 0.043 1.755 0.079 0.076 0.039
## Viv_EvInt|t1 -4.129 0.166 -24.885 0.000 -4.129 -2.335
## Viv_EvInt|t2 -2.719 0.093 -29.246 0.000 -2.719 -1.538
## Viv_EvInt|t3 -1.824 0.068 -26.851 0.000 -1.824 -1.031
## Viv_EvInt|t4 0.439 0.040 10.951 0.000 0.439 0.248
## Viv_DiF|t1 -4.564 0.238 -19.183 0.000 -4.564 -2.539
## Viv_DiF|t2 -3.460 0.155 -22.291 0.000 -3.460 -1.925
## Viv_DiF|t3 -2.683 0.116 -23.209 0.000 -2.683 -1.492
## Viv_DiF|t4 -0.180 0.042 -4.296 0.000 -0.180 -0.100
## Viv_Int|t1 -3.456 0.124 -27.808 0.000 -3.456 -2.284
## Viv_Int|t2 -2.527 0.078 -32.248 0.000 -2.527 -1.670
## Viv_Int|t3 -1.789 0.058 -30.901 0.000 -1.789 -1.182
## Viv_Int|t4 0.376 0.034 10.985 0.000 0.376 0.248
## Viv_Pla|t1 -4.541 0.227 -19.965 0.000 -4.541 -2.680
## Viv_Pla|t2 -3.655 0.163 -22.407 0.000 -3.655 -2.157
## Viv_Pla|t3 -2.597 0.105 -24.662 0.000 -2.597 -1.533
## Viv_Pla|t4 -0.064 0.039 -1.658 0.097 -0.064 -0.038
## Viv_HabRC|t1 -4.523 0.227 -19.887 0.000 -4.523 -2.677
## Viv_HabRC|t2 -3.522 0.153 -23.053 0.000 -3.522 -2.084
## Viv_HabRC|t3 -2.658 0.109 -24.462 0.000 -2.658 -1.573
## Viv_HabRC|t4 -0.176 0.039 -4.492 0.000 -0.176 -0.104
## Viv_HabR|t1 -3.584 0.135 -26.553 0.000 -3.584 -2.487
## Viv_HabR|t2 -2.522 0.078 -32.159 0.000 -2.522 -1.750
## Viv_HabR|t3 -1.642 0.053 -30.695 0.000 -1.642 -1.139
## Viv_HabR|t4 0.004 0.032 0.126 0.900 0.004 0.003
## Viv_PrInf|t1 -4.771 0.241 -19.836 0.000 -4.771 -2.456
## Viv_PrInf|t2 -3.615 0.155 -23.327 0.000 -3.615 -1.861
## Viv_PrInf|t3 -2.702 0.114 -23.641 0.000 -2.702 -1.391
## Viv_PrInf|t4 0.037 0.044 0.848 0.396 0.037 0.019
## Viv_Aut|t1 -2.712 0.078 -34.568 0.000 -2.712 -2.210
## Viv_Aut|t2 -1.234 0.036 -33.923 0.000 -1.234 -1.005
## Viv_Aut|t3 -0.605 0.030 -20.139 0.000 -0.605 -0.493
## Viv_Aut|t4 0.764 0.030 25.468 0.000 0.764 0.622
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.266
## .Viv_EvInt 1.000 1.000 0.320
## .Viv_DiF 1.000 1.000 0.309
## .Viv_Int 1.000 1.000 0.437
## .Viv_Pla 1.000 1.000 0.348
## .Viv_HabRC 1.000 1.000 0.350
## .Viv_HabR 1.000 1.000 0.481
## .Viv_PrInf 1.000 1.000 0.265
## .Viv_Aut 1.000 1.000 0.664
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.515 0.515 1.000
## Viv_EvInt 0.566 0.566 1.000
## Viv_DiF 0.556 0.556 1.000
## Viv_Int 0.661 0.661 1.000
## Viv_Pla 0.590 0.590 1.000
## Viv_HabRC 0.592 0.592 1.000
## Viv_HabR 0.694 0.694 1.000
## Viv_PrInf 0.515 0.515 1.000
## Viv_Aut 0.815 0.815 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.734
## Viv_EvInt 0.680
## Viv_DiF 0.691
## Viv_Int 0.563
## Viv_Pla 0.652
## Viv_HabRC 0.650
## Viv_HabR 0.519
## Viv_PrInf 0.735
## Viv_Aut 0.336
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.662 0.086 19.325 0.000 1.521 0.813
## Viv_EvInt 1.458 0.059 24.757 0.000 1.334 0.804
## Viv_DiF 1.494 0.081 18.413 0.000 1.367 0.846
## Viv_Int 1.136 0.044 25.758 0.000 1.039 0.690
## Viv_Pla 1.368 0.072 19.056 0.000 1.251 0.854
## Viv_Hard =~
## Viv_HabRC 1.362 0.073 18.695 0.000 1.351 0.805
## Viv_HabR 1.038 0.044 23.526 0.000 1.030 0.684
## Viv_PrInf 1.665 0.086 19.372 0.000 1.652 0.784
## Viv_Aut 0.711 0.029 24.950 0.000 0.706 0.502
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.485 0.164 2.958 0.003 0.485 0.443
## .Viv_AdmT ~~
## .Viv_Int 0.627 0.132 4.741 0.000 0.627 0.528
## Viv_Soft ~~
## Viv_Hard 0.742 0.141 5.252 0.000 0.817 0.817
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft -0.332 0.189 -1.757 0.079 -0.363 -0.363
## Viv_Hard 0.136 0.449 0.303 0.762 0.137 0.137
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.646 0.237 -19.585 0.000 -4.646 -2.484
## Viv_AdmT|t2 -3.416 0.151 -22.652 0.000 -3.416 -1.826
## Viv_AdmT|t3 -2.726 0.149 -18.276 0.000 -2.726 -1.458
## Viv_AdmT|t4 -0.285 0.334 -0.853 0.394 -0.285 -0.152
## Viv_EvInt|t1 -4.129 0.166 -24.885 0.000 -4.129 -2.487
## Viv_EvInt|t2 -2.662 0.153 -17.392 0.000 -2.662 -1.604
## Viv_EvInt|t3 -1.945 0.180 -10.803 0.000 -1.945 -1.172
## Viv_EvInt|t4 0.123 0.327 0.375 0.708 0.123 0.074
## Viv_DiF|t1 -4.564 0.238 -19.183 0.000 -4.564 -2.823
## Viv_DiF|t2 -3.467 0.219 -15.814 0.000 -3.467 -2.144
## Viv_DiF|t3 -2.755 0.212 -13.009 0.000 -2.755 -1.704
## Viv_DiF|t4 -0.529 0.289 -1.833 0.067 -0.529 -0.327
## Viv_Int|t1 -3.456 0.124 -27.808 0.000 -3.456 -2.294
## Viv_Int|t2 -2.352 0.108 -21.773 0.000 -2.352 -1.561
## Viv_Int|t3 -1.773 0.127 -13.978 0.000 -1.773 -1.177
## Viv_Int|t4 0.301 0.271 1.113 0.266 0.301 0.200
## Viv_Pla|t1 -4.541 0.227 -19.965 0.000 -4.541 -3.098
## Viv_Pla|t2 -3.753 0.217 -17.286 0.000 -3.753 -2.560
## Viv_Pla|t3 -2.857 0.176 -16.243 0.000 -2.857 -1.949
## Viv_Pla|t4 -0.500 0.255 -1.965 0.049 -0.500 -0.341
## Viv_HabRC|t1 -4.523 0.227 -19.887 0.000 -4.523 -2.694
## Viv_HabRC|t2 -3.522 0.153 -23.053 0.000 -3.522 -2.098
## Viv_HabRC|t3 -2.342 0.219 -10.685 0.000 -2.342 -1.395
## Viv_HabRC|t4 0.198 0.618 0.321 0.748 0.198 0.118
## Viv_HabR|t1 -3.584 0.135 -26.553 0.000 -3.584 -2.381
## Viv_HabR|t2 -2.369 0.172 -13.733 0.000 -2.369 -1.574
## Viv_HabR|t3 -1.416 0.268 -5.279 0.000 -1.416 -0.941
## Viv_HabR|t4 0.397 0.499 0.796 0.426 0.397 0.264
## Viv_PrInf|t1 -4.771 0.241 -19.836 0.000 -4.771 -2.263
## Viv_PrInf|t2 -3.367 0.265 -12.708 0.000 -3.367 -1.597
## Viv_PrInf|t3 -2.320 0.377 -6.159 0.000 -2.320 -1.100
## Viv_PrInf|t4 0.828 0.846 0.978 0.328 0.828 0.393
## Viv_Aut|t1 -2.712 0.078 -34.568 0.000 -2.712 -1.928
## Viv_Aut|t2 -1.016 0.192 -5.291 0.000 -1.016 -0.722
## Viv_Aut|t3 -0.327 0.269 -1.214 0.225 -0.327 -0.233
## Viv_Aut|t4 1.306 0.463 2.822 0.005 1.306 0.928
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.185 0.295 4.019 0.000 1.185 0.339
## .Viv_EvInt 0.975 0.193 5.066 0.000 0.975 0.354
## .Viv_DiF 0.745 0.154 4.848 0.000 0.745 0.285
## .Viv_Int 1.190 0.213 5.574 0.000 1.190 0.524
## .Viv_Pla 0.583 0.145 4.006 0.000 0.583 0.271
## .Viv_HabRC 0.992 0.383 2.589 0.010 0.992 0.352
## .Viv_HabR 1.205 0.337 3.571 0.000 1.205 0.532
## .Viv_PrInf 1.714 0.598 2.865 0.004 1.714 0.386
## .Viv_Aut 1.480 0.354 4.185 0.000 1.480 0.748
## Viv_Soft 0.837 0.152 5.519 0.000 1.000 1.000
## Viv_Hard 0.985 0.294 3.348 0.001 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.535 0.535 1.000
## Viv_EvInt 0.602 0.602 1.000
## Viv_DiF 0.619 0.619 1.000
## Viv_Int 0.664 0.664 1.000
## Viv_Pla 0.682 0.682 1.000
## Viv_HabRC 0.596 0.596 1.000
## Viv_HabR 0.664 0.664 1.000
## Viv_PrInf 0.474 0.474 1.000
## Viv_Aut 0.711 0.711 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.661
## Viv_EvInt 0.646
## Viv_DiF 0.715
## Viv_Int 0.476
## Viv_Pla 0.729
## Viv_HabRC 0.648
## Viv_HabR 0.468
## Viv_PrInf 0.614
## Viv_Aut 0.252lavaan::fitMeasures(invariance$fit.loadings,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 240.339 55.000 0.000
## srmr cfi.scaled tli.scaled
## 0.028 0.997 0.996
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.030 0.026 0.034modificationindices(invariance$fit.loadings, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 187 Viv_Hard =~ Viv_Pla 1 1 1 63.14 0.212 0.212 0.125
## 152 Viv_Pla ~1 2 2 1 56.29 0.692 0.692 0.472
## 64 Viv_Pla ~*~ Viv_Pla 1 1 1 56.29 -0.090 -0.090 -1.000
## 143 Viv_Pla ~*~ Viv_Pla 2 2 1 56.28 0.116 0.116 1.000
## 73 Viv_Pla ~1 1 1 1 56.28 -0.692 -0.692 -0.408
## 213 Viv_Pla ~~ Viv_HabRC 1 1 1 46.36 0.386 0.386 0.386
## 229 Viv_Hard =~ Viv_Int 2 2 1 24.90 0.127 0.126 0.084
## 230 Viv_Hard =~ Viv_Pla 2 2 1 23.41 -0.133 -0.132 -0.090
## 194 Viv_AdmT ~~ Viv_Aut 1 1 1 22.29 0.219 0.219 0.219
## 142 Viv_Int ~*~ Viv_Int 2 2 1 21.80 -0.076 -0.076 -1.000
## sepc.nox
## 187 0.125
## 152 0.472
## 64 -1.000
## 143 1.000
## 73 -0.408
## 213 0.386
## 229 0.084
## 230 -0.090
## 194 0.219
## 142 -1.000semTools::reliability(invariance$fit.loadings)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8523 0.7517
## alpha.ord 0.9108 0.8480
## omega 0.8476 0.7178
## omega2 0.8476 0.7178
## omega3 0.8489 0.7197
## avevar 0.6729 0.6082
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8504 0.7031
## alpha.ord 0.9082 0.8067
## omega 0.8061 0.6814
## omega2 0.8061 0.6814
## omega3 0.7997 0.6779
## avevar 0.6479 0.53154.1.1.4.3 Thresholds
summary(invariance$fit.thresholds,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 141 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 109
## Number of equality constraints 45
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 508.936 237.794
## Degrees of freedom 80 80
## P-value (Unknown) NA 0.000
## Scaling correction factor 2.453
## Shift parameter for each group:
## 2 12.340
## 1 17.973
## simple second-order correction
## Test statistic for each group:
## 2 283.974 128.109
## 1 224.961 109.685
##
## Model Test Baseline Model:
##
## Test statistic 83443.089 60114.774
## Degrees of freedom 72 72
## P-value NA 0.000
## Scaling correction factor 1.389
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.995 0.997
## Tucker-Lewis Index (TLI) 0.995 0.998
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.038 0.023
## 90 Percent confidence interval - lower 0.034 0.019
## 90 Percent confidence interval - upper 0.041 0.026
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.030 0.030
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.654 0.073 22.634 0.000 1.654 0.856
## Viv_EvInt 1.490 0.055 26.989 0.000 1.490 0.830
## Viv_DiF 1.495 0.065 22.862 0.000 1.495 0.831
## Viv_Int 1.175 0.042 28.073 0.000 1.175 0.761
## Viv_Pla 1.306 0.056 23.425 0.000 1.306 0.794
## Viv_Hard =~
## Viv_HabRC 1.330 0.061 21.720 0.000 1.330 0.799
## Viv_HabR 1.039 0.039 26.664 0.000 1.039 0.720
## Viv_PrInf 1.650 0.080 20.620 0.000 1.650 0.855
## Viv_Aut 0.725 0.025 28.876 0.000 0.725 0.587
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.570 0.020 28.015 0.000 0.570 0.570
## .Viv_AdmT ~~
## .Viv_Int 0.248 0.034 7.403 0.000 0.248 0.248
## Viv_Soft ~~
## Viv_Hard 0.826 0.010 79.463 0.000 0.826 0.826
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.633 0.194 -23.876 0.000 -4.633 -2.397
## Viv_AdmT|t2 -3.351 0.134 -24.969 0.000 -3.351 -1.734
## Viv_AdmT|t3 -2.521 0.103 -24.582 0.000 -2.521 -1.304
## Viv_AdmT|t4 0.054 0.041 1.302 0.193 0.054 0.028
## Viv_EvInt|t1 -4.228 0.145 -29.079 0.000 -4.228 -2.356
## Viv_EvInt|t2 -2.685 0.091 -29.531 0.000 -2.685 -1.496
## Viv_EvInt|t3 -1.844 0.067 -27.534 0.000 -1.844 -1.027
## Viv_EvInt|t4 0.446 0.041 10.888 0.000 0.446 0.249
## Viv_DiF|t1 -4.588 0.190 -24.187 0.000 -4.588 -2.552
## Viv_DiF|t2 -3.438 0.139 -24.721 0.000 -3.438 -1.912
## Viv_DiF|t3 -2.667 0.109 -24.485 0.000 -2.667 -1.483
## Viv_DiF|t4 -0.229 0.039 -5.945 0.000 -0.229 -0.127
## Viv_Int|t1 -3.583 0.113 -31.580 0.000 -3.583 -2.323
## Viv_Int|t2 -2.475 0.075 -33.133 0.000 -2.475 -1.604
## Viv_Int|t3 -1.779 0.056 -31.846 0.000 -1.779 -1.153
## Viv_Int|t4 0.493 0.034 14.309 0.000 0.493 0.320
## Viv_Pla|t1 -4.376 0.175 -25.004 0.000 -4.376 -2.660
## Viv_Pla|t2 -3.553 0.144 -24.746 0.000 -3.553 -2.160
## Viv_Pla|t3 -2.599 0.105 -24.751 0.000 -2.599 -1.580
## Viv_Pla|t4 -0.178 0.034 -5.280 0.000 -0.178 -0.108
## Viv_HabRC|t1 -4.437 0.185 -24.042 0.000 -4.437 -2.666
## Viv_HabRC|t2 -3.530 0.145 -24.260 0.000 -3.530 -2.121
## Viv_HabRC|t3 -2.569 0.109 -23.578 0.000 -2.569 -1.544
## Viv_HabRC|t4 -0.291 0.036 -8.041 0.000 -0.291 -0.175
## Viv_HabR|t1 -3.584 0.116 -30.999 0.000 -3.584 -2.486
## Viv_HabR|t2 -2.513 0.077 -32.668 0.000 -2.513 -1.743
## Viv_HabR|t3 -1.656 0.053 -31.321 0.000 -1.656 -1.148
## Viv_HabR|t4 -0.035 0.030 -1.168 0.243 -0.035 -0.024
## Viv_PrInf|t1 -4.764 0.205 -23.279 0.000 -4.764 -2.470
## Viv_PrInf|t2 -3.575 0.150 -23.791 0.000 -3.575 -1.853
## Viv_PrInf|t3 -2.667 0.115 -23.243 0.000 -2.667 -1.383
## Viv_PrInf|t4 0.058 0.044 1.322 0.186 0.058 0.030
## Viv_Aut|t1 -2.717 0.072 -37.530 0.000 -2.717 -2.200
## Viv_Aut|t2 -1.213 0.033 -36.448 0.000 -1.213 -0.982
## Viv_Aut|t3 -0.590 0.025 -23.474 0.000 -0.590 -0.478
## Viv_Aut|t4 0.835 0.030 27.851 0.000 0.835 0.676
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.268
## .Viv_EvInt 1.000 1.000 0.311
## .Viv_DiF 1.000 1.000 0.309
## .Viv_Int 1.000 1.000 0.420
## .Viv_Pla 1.000 1.000 0.370
## .Viv_HabRC 1.000 1.000 0.361
## .Viv_HabR 1.000 1.000 0.481
## .Viv_PrInf 1.000 1.000 0.269
## .Viv_Aut 1.000 1.000 0.656
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.517 0.517 1.000
## Viv_EvInt 0.557 0.557 1.000
## Viv_DiF 0.556 0.556 1.000
## Viv_Int 0.648 0.648 1.000
## Viv_Pla 0.608 0.608 1.000
## Viv_HabRC 0.601 0.601 1.000
## Viv_HabR 0.693 0.693 1.000
## Viv_PrInf 0.518 0.518 1.000
## Viv_Aut 0.810 0.810 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.732
## Viv_EvInt 0.689
## Viv_DiF 0.691
## Viv_Int 0.580
## Viv_Pla 0.630
## Viv_HabRC 0.639
## Viv_HabR 0.519
## Viv_PrInf 0.731
## Viv_Aut 0.344
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.654 0.073 22.634 0.000 1.628 0.813
## Viv_EvInt 1.490 0.055 26.989 0.000 1.467 0.799
## Viv_DiF 1.495 0.065 22.862 0.000 1.471 0.845
## Viv_Int 1.175 0.042 28.073 0.000 1.156 0.684
## Viv_Pla 1.306 0.056 23.425 0.000 1.286 0.861
## Viv_Hard =~
## Viv_HabRC 1.330 0.061 21.720 0.000 1.161 0.802
## Viv_HabR 1.039 0.039 26.664 0.000 0.907 0.680
## Viv_PrInf 1.650 0.080 20.620 0.000 1.439 0.788
## Viv_Aut 0.725 0.025 28.876 0.000 0.632 0.503
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.382 0.045 8.442 0.000 0.382 0.451
## .Viv_AdmT ~~
## .Viv_Int 0.768 0.070 10.936 0.000 0.768 0.535
## Viv_Soft ~~
## Viv_Hard 0.703 0.038 18.436 0.000 0.818 0.818
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft -0.150 0.029 -5.164 0.000 -0.153 -0.153
## Viv_Hard -0.273 0.029 -9.366 0.000 -0.313 -0.313
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.633 0.194 -23.876 0.000 -4.633 -2.314
## Viv_AdmT|t2 -3.351 0.134 -24.969 0.000 -3.351 -1.674
## Viv_AdmT|t3 -2.521 0.103 -24.582 0.000 -2.521 -1.259
## Viv_AdmT|t4 0.054 0.041 1.302 0.193 0.054 0.027
## Viv_EvInt|t1 -4.228 0.145 -29.079 0.000 -4.228 -2.304
## Viv_EvInt|t2 -2.685 0.091 -29.531 0.000 -2.685 -1.463
## Viv_EvInt|t3 -1.844 0.067 -27.534 0.000 -1.844 -1.005
## Viv_EvInt|t4 0.446 0.041 10.888 0.000 0.446 0.243
## Viv_DiF|t1 -4.588 0.190 -24.187 0.000 -4.588 -2.636
## Viv_DiF|t2 -3.438 0.139 -24.721 0.000 -3.438 -1.975
## Viv_DiF|t3 -2.667 0.109 -24.485 0.000 -2.667 -1.532
## Viv_DiF|t4 -0.229 0.039 -5.945 0.000 -0.229 -0.132
## Viv_Int|t1 -3.583 0.113 -31.580 0.000 -3.583 -2.121
## Viv_Int|t2 -2.475 0.075 -33.133 0.000 -2.475 -1.465
## Viv_Int|t3 -1.779 0.056 -31.846 0.000 -1.779 -1.053
## Viv_Int|t4 0.493 0.034 14.309 0.000 0.493 0.292
## Viv_Pla|t1 -4.376 0.175 -25.004 0.000 -4.376 -2.931
## Viv_Pla|t2 -3.553 0.144 -24.746 0.000 -3.553 -2.380
## Viv_Pla|t3 -2.599 0.105 -24.751 0.000 -2.599 -1.741
## Viv_Pla|t4 -0.178 0.034 -5.280 0.000 -0.178 -0.119
## Viv_HabRC|t1 -4.437 0.185 -24.042 0.000 -4.437 -3.064
## Viv_HabRC|t2 -3.530 0.145 -24.260 0.000 -3.530 -2.437
## Viv_HabRC|t3 -2.569 0.109 -23.578 0.000 -2.569 -1.774
## Viv_HabRC|t4 -0.291 0.036 -8.041 0.000 -0.291 -0.201
## Viv_HabR|t1 -3.584 0.116 -30.999 0.000 -3.584 -2.688
## Viv_HabR|t2 -2.513 0.077 -32.668 0.000 -2.513 -1.885
## Viv_HabR|t3 -1.656 0.053 -31.321 0.000 -1.656 -1.242
## Viv_HabR|t4 -0.035 0.030 -1.168 0.243 -0.035 -0.026
## Viv_PrInf|t1 -4.764 0.205 -23.279 0.000 -4.764 -2.607
## Viv_PrInf|t2 -3.575 0.150 -23.791 0.000 -3.575 -1.956
## Viv_PrInf|t3 -2.667 0.115 -23.243 0.000 -2.667 -1.460
## Viv_PrInf|t4 0.058 0.044 1.322 0.186 0.058 0.032
## Viv_Aut|t1 -2.717 0.072 -37.530 0.000 -2.717 -2.161
## Viv_Aut|t2 -1.213 0.033 -36.448 0.000 -1.213 -0.965
## Viv_Aut|t3 -0.590 0.025 -23.474 0.000 -0.590 -0.469
## Viv_Aut|t4 0.835 0.030 27.851 0.000 0.835 0.664
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.358 0.148 9.147 0.000 1.358 0.339
## .Viv_EvInt 1.216 0.114 10.669 0.000 1.216 0.361
## .Viv_DiF 0.865 0.111 7.819 0.000 0.865 0.286
## .Viv_Int 1.517 0.120 12.596 0.000 1.517 0.532
## .Viv_Pla 0.575 0.085 6.810 0.000 0.575 0.258
## .Viv_HabRC 0.750 0.100 7.481 0.000 0.750 0.358
## .Viv_HabR 0.956 0.085 11.288 0.000 0.956 0.538
## .Viv_PrInf 1.267 0.141 8.961 0.000 1.267 0.379
## .Viv_Aut 1.181 0.074 16.017 0.000 1.181 0.747
## Viv_Soft 0.969 0.058 16.774 0.000 1.000 1.000
## Viv_Hard 0.761 0.050 15.238 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.499 0.499 1.000
## Viv_EvInt 0.545 0.545 1.000
## Viv_DiF 0.575 0.575 1.000
## Viv_Int 0.592 0.592 1.000
## Viv_Pla 0.670 0.670 1.000
## Viv_HabRC 0.691 0.691 1.000
## Viv_HabR 0.750 0.750 1.000
## Viv_PrInf 0.547 0.547 1.000
## Viv_Aut 0.795 0.795 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.661
## Viv_EvInt 0.639
## Viv_DiF 0.714
## Viv_Int 0.468
## Viv_Pla 0.742
## Viv_HabRC 0.642
## Viv_HabR 0.462
## Viv_PrInf 0.621
## Viv_Aut 0.253lavaan::fitMeasures(invariance$fit.thresholds,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 237.794 80.000 0.000
## srmr cfi.scaled tli.scaled
## 0.030 0.997 0.998
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.023 0.019 0.026modificationindices(invariance$fit.thresholds, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 212 Viv_Hard =~ Viv_Pla 1 1 1 86.69 0.221 0.221 0.134
## 73 Viv_Pla ~1 1 1 1 85.05 -0.311 -0.311 -0.189
## 152 Viv_Pla ~1 2 2 1 85.05 0.311 0.311 0.208
## 151 Viv_Int ~1 2 2 1 82.84 -0.263 -0.263 -0.156
## 72 Viv_Int ~1 1 1 1 82.84 0.263 0.263 0.171
## 64 Viv_Pla ~*~ Viv_Pla 1 1 1 79.67 -0.101 -0.101 -1.000
## 143 Viv_Pla ~*~ Viv_Pla 2 2 1 70.17 0.111 0.111 1.000
## 238 Viv_Pla ~~ Viv_HabRC 1 1 1 56.20 0.400 0.400 0.400
## 255 Viv_Hard =~ Viv_Pla 2 2 1 53.97 -0.182 -0.159 -0.106
## 254 Viv_Hard =~ Viv_Int 2 2 1 50.51 0.178 0.156 0.092
## sepc.nox
## 212 0.134
## 73 -0.189
## 152 0.208
## 151 -0.156
## 72 0.171
## 64 -1.000
## 143 1.000
## 238 0.400
## 255 -0.106
## 254 0.092semTools::reliability(invariance$fit.thresholds)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8523 0.7517
## alpha.ord 0.9108 0.8480
## omega 0.8488 0.7179
## omega2 0.8488 0.7179
## omega3 0.8507 0.7206
## avevar 0.6727 0.6038
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8504 0.7031
## alpha.ord 0.9082 0.8067
## omega 0.8121 0.6581
## omega2 0.8121 0.6581
## omega3 0.8049 0.6543
## avevar 0.6428 0.52774.1.1.5 Partial Invariance - Sex Means
summary(invariance$fit.means,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 142 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 45
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 1186.248 483.326
## Degrees of freedom 82 82
## P-value (Unknown) NA 0.000
## Scaling correction factor 2.618
## Shift parameter for each group:
## 2 12.312
## 1 17.933
## simple second-order correction
## Test statistic for each group:
## 2 641.428 257.302
## 1 544.820 226.024
##
## Model Test Baseline Model:
##
## Test statistic 83443.089 60114.774
## Degrees of freedom 72 72
## P-value NA 0.000
## Scaling correction factor 1.389
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.987 0.993
## Tucker-Lewis Index (TLI) 0.988 0.994
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060 0.036
## 90 Percent confidence interval - lower 0.057 0.033
## 90 Percent confidence interval - upper 0.063 0.039
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.030 0.030
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.667 0.074 22.396 0.000 1.667 0.858
## Viv_EvInt 1.494 0.055 26.948 0.000 1.494 0.831
## Viv_DiF 1.501 0.066 22.838 0.000 1.501 0.832
## Viv_Int 1.157 0.040 28.594 0.000 1.157 0.757
## Viv_Pla 1.307 0.056 23.398 0.000 1.307 0.794
## Viv_Hard =~
## Viv_HabRC 1.356 0.064 21.174 0.000 1.356 0.805
## Viv_HabR 1.047 0.040 26.291 0.000 1.047 0.723
## Viv_PrInf 1.690 0.085 19.921 0.000 1.690 0.861
## Viv_Aut 0.703 0.024 28.997 0.000 0.703 0.575
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.565 0.021 27.094 0.000 0.565 0.565
## .Viv_AdmT ~~
## .Viv_Int 0.256 0.033 7.797 0.000 0.256 0.256
## Viv_Soft ~~
## Viv_Hard 0.824 0.010 79.613 0.000 0.824 0.824
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.690 0.198 -23.743 0.000 -4.690 -2.413
## Viv_AdmT|t2 -3.351 0.134 -24.960 0.000 -3.351 -1.724
## Viv_AdmT|t3 -2.482 0.100 -24.809 0.000 -2.482 -1.277
## Viv_AdmT|t4 0.202 0.030 6.742 0.000 0.202 0.104
## Viv_EvInt|t1 -4.259 0.146 -29.207 0.000 -4.259 -2.369
## Viv_EvInt|t2 -2.660 0.089 -29.936 0.000 -2.660 -1.479
## Viv_EvInt|t3 -1.783 0.063 -28.424 0.000 -1.783 -0.992
## Viv_EvInt|t4 0.595 0.032 18.765 0.000 0.595 0.331
## Viv_DiF|t1 -4.634 0.191 -24.199 0.000 -4.634 -2.569
## Viv_DiF|t2 -3.439 0.139 -24.793 0.000 -3.439 -1.906
## Viv_DiF|t3 -2.636 0.107 -24.665 0.000 -2.636 -1.461
## Viv_DiF|t4 -0.098 0.027 -3.640 0.000 -0.098 -0.054
## Viv_Int|t1 -3.568 0.111 -32.214 0.000 -3.568 -2.333
## Viv_Int|t2 -2.437 0.071 -34.136 0.000 -2.437 -1.593
## Viv_Int|t3 -1.724 0.052 -33.207 0.000 -1.724 -1.127
## Viv_Int|t4 0.598 0.028 21.658 0.000 0.598 0.391
## Viv_Pla|t1 -4.405 0.175 -25.117 0.000 -4.405 -2.677
## Viv_Pla|t2 -3.554 0.144 -24.758 0.000 -3.554 -2.159
## Viv_Pla|t3 -2.564 0.104 -24.716 0.000 -2.564 -1.558
## Viv_Pla|t4 -0.055 0.023 -2.344 0.019 -0.055 -0.033
## Viv_HabRC|t1 -4.543 0.191 -23.740 0.000 -4.543 -2.697
## Viv_HabRC|t2 -3.561 0.150 -23.756 0.000 -3.561 -2.114
## Viv_HabRC|t3 -2.528 0.111 -22.851 0.000 -2.528 -1.501
## Viv_HabRC|t4 -0.061 0.024 -2.529 0.011 -0.061 -0.036
## Viv_HabR|t1 -3.643 0.118 -30.866 0.000 -3.643 -2.516
## Viv_HabR|t2 -2.500 0.077 -32.489 0.000 -2.500 -1.726
## Viv_HabR|t3 -1.583 0.051 -31.232 0.000 -1.583 -1.094
## Viv_HabR|t4 0.148 0.021 6.959 0.000 0.148 0.102
## Viv_PrInf|t1 -4.896 0.215 -22.742 0.000 -4.896 -2.493
## Viv_PrInf|t2 -3.602 0.155 -23.193 0.000 -3.602 -1.835
## Viv_PrInf|t3 -2.615 0.115 -22.733 0.000 -2.615 -1.332
## Viv_PrInf|t4 0.352 0.032 11.138 0.000 0.352 0.179
## Viv_Aut|t1 -2.688 0.071 -38.102 0.000 -2.688 -2.199
## Viv_Aut|t2 -1.137 0.030 -37.813 0.000 -1.137 -0.930
## Viv_Aut|t3 -0.494 0.021 -23.235 0.000 -0.494 -0.404
## Viv_Aut|t4 0.975 0.027 36.641 0.000 0.975 0.798
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.265
## .Viv_EvInt 1.000 1.000 0.309
## .Viv_DiF 1.000 1.000 0.307
## .Viv_Int 1.000 1.000 0.427
## .Viv_Pla 1.000 1.000 0.369
## .Viv_HabRC 1.000 1.000 0.352
## .Viv_HabR 1.000 1.000 0.477
## .Viv_PrInf 1.000 1.000 0.259
## .Viv_Aut 1.000 1.000 0.670
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.514 0.514 1.000
## Viv_EvInt 0.556 0.556 1.000
## Viv_DiF 0.554 0.554 1.000
## Viv_Int 0.654 0.654 1.000
## Viv_Pla 0.608 0.608 1.000
## Viv_HabRC 0.594 0.594 1.000
## Viv_HabR 0.691 0.691 1.000
## Viv_PrInf 0.509 0.509 1.000
## Viv_Aut 0.818 0.818 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.735
## Viv_EvInt 0.691
## Viv_DiF 0.693
## Viv_Int 0.573
## Viv_Pla 0.631
## Viv_HabRC 0.648
## Viv_HabR 0.523
## Viv_PrInf 0.741
## Viv_Aut 0.330
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.667 0.074 22.396 0.000 1.745 0.811
## Viv_EvInt 1.494 0.055 26.948 0.000 1.564 0.798
## Viv_DiF 1.501 0.066 22.838 0.000 1.571 0.846
## Viv_Int 1.157 0.040 28.594 0.000 1.211 0.682
## Viv_Pla 1.307 0.056 23.398 0.000 1.368 0.866
## Viv_Hard =~
## Viv_HabRC 1.356 0.064 21.174 0.000 1.310 0.805
## Viv_HabR 1.047 0.040 26.291 0.000 1.012 0.682
## Viv_PrInf 1.690 0.085 19.921 0.000 1.633 0.782
## Viv_Aut 0.703 0.024 28.997 0.000 0.679 0.506
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.468 0.055 8.554 0.000 0.468 0.447
## .Viv_AdmT ~~
## .Viv_Int 0.882 0.081 10.942 0.000 0.882 0.539
## Viv_Soft ~~
## Viv_Hard 0.827 0.040 20.506 0.000 0.818 0.818
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.690 0.198 -23.743 0.000 -4.690 -2.179
## Viv_AdmT|t2 -3.351 0.134 -24.960 0.000 -3.351 -1.557
## Viv_AdmT|t3 -2.482 0.100 -24.809 0.000 -2.482 -1.153
## Viv_AdmT|t4 0.202 0.030 6.742 0.000 0.202 0.094
## Viv_EvInt|t1 -4.259 0.146 -29.207 0.000 -4.259 -2.172
## Viv_EvInt|t2 -2.660 0.089 -29.936 0.000 -2.660 -1.356
## Viv_EvInt|t3 -1.783 0.063 -28.424 0.000 -1.783 -0.909
## Viv_EvInt|t4 0.595 0.032 18.765 0.000 0.595 0.304
## Viv_DiF|t1 -4.634 0.191 -24.199 0.000 -4.634 -2.494
## Viv_DiF|t2 -3.439 0.139 -24.793 0.000 -3.439 -1.851
## Viv_DiF|t3 -2.636 0.107 -24.665 0.000 -2.636 -1.419
## Viv_DiF|t4 -0.098 0.027 -3.640 0.000 -0.098 -0.053
## Viv_Int|t1 -3.568 0.111 -32.214 0.000 -3.568 -2.008
## Viv_Int|t2 -2.437 0.071 -34.136 0.000 -2.437 -1.372
## Viv_Int|t3 -1.724 0.052 -33.207 0.000 -1.724 -0.970
## Viv_Int|t4 0.598 0.028 21.658 0.000 0.598 0.336
## Viv_Pla|t1 -4.405 0.175 -25.117 0.000 -4.405 -2.788
## Viv_Pla|t2 -3.554 0.144 -24.758 0.000 -3.554 -2.249
## Viv_Pla|t3 -2.564 0.104 -24.716 0.000 -2.564 -1.623
## Viv_Pla|t4 -0.055 0.023 -2.344 0.019 -0.055 -0.035
## Viv_HabRC|t1 -4.543 0.191 -23.740 0.000 -4.543 -2.792
## Viv_HabRC|t2 -3.561 0.150 -23.756 0.000 -3.561 -2.189
## Viv_HabRC|t3 -2.528 0.111 -22.851 0.000 -2.528 -1.554
## Viv_HabRC|t4 -0.061 0.024 -2.529 0.011 -0.061 -0.037
## Viv_HabR|t1 -3.643 0.118 -30.866 0.000 -3.643 -2.454
## Viv_HabR|t2 -2.500 0.077 -32.489 0.000 -2.500 -1.684
## Viv_HabR|t3 -1.583 0.051 -31.232 0.000 -1.583 -1.067
## Viv_HabR|t4 0.148 0.021 6.959 0.000 0.148 0.100
## Viv_PrInf|t1 -4.896 0.215 -22.742 0.000 -4.896 -2.343
## Viv_PrInf|t2 -3.602 0.155 -23.193 0.000 -3.602 -1.724
## Viv_PrInf|t3 -2.615 0.115 -22.733 0.000 -2.615 -1.251
## Viv_PrInf|t4 0.352 0.032 11.138 0.000 0.352 0.168
## Viv_Aut|t1 -2.688 0.071 -38.102 0.000 -2.688 -2.005
## Viv_Aut|t2 -1.137 0.030 -37.813 0.000 -1.137 -0.848
## Viv_Aut|t3 -0.494 0.021 -23.235 0.000 -0.494 -0.368
## Viv_Aut|t4 0.975 0.027 36.641 0.000 0.975 0.727
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.588 0.176 9.046 0.000 1.588 0.343
## .Viv_EvInt 1.399 0.131 10.687 0.000 1.399 0.364
## .Viv_DiF 0.984 0.126 7.796 0.000 0.984 0.285
## .Viv_Int 1.690 0.131 12.879 0.000 1.690 0.535
## .Viv_Pla 0.625 0.093 6.748 0.000 0.625 0.250
## .Viv_HabRC 0.931 0.124 7.495 0.000 0.931 0.352
## .Viv_HabR 1.179 0.102 11.525 0.000 1.179 0.535
## .Viv_PrInf 1.699 0.192 8.846 0.000 1.699 0.389
## .Viv_Aut 1.337 0.082 16.277 0.000 1.337 0.744
## Viv_Soft 1.096 0.058 18.732 0.000 1.000 1.000
## Viv_Hard 0.934 0.055 16.871 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.465 0.465 1.000
## Viv_EvInt 0.510 0.510 1.000
## Viv_DiF 0.538 0.538 1.000
## Viv_Int 0.563 0.563 1.000
## Viv_Pla 0.633 0.633 1.000
## Viv_HabRC 0.615 0.615 1.000
## Viv_HabR 0.674 0.674 1.000
## Viv_PrInf 0.479 0.479 1.000
## Viv_Aut 0.746 0.746 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.657
## Viv_EvInt 0.636
## Viv_DiF 0.715
## Viv_Int 0.465
## Viv_Pla 0.750
## Viv_HabRC 0.648
## Viv_HabR 0.465
## Viv_PrInf 0.611
## Viv_Aut 0.256lavaan::fitMeasures(invariance$fit.means,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 483.326 82.000 0.000
## srmr cfi.scaled tli.scaled
## 0.030 0.993 0.994
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.036 0.033 0.039modificationindices(invariance$fit.means, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox
## 158 Viv_Hard ~1 2 2 1 488.6 -0.284 -0.294 -0.294 -0.294
## 79 Viv_Hard ~1 1 1 1 488.6 0.284 0.284 0.284 0.284
## 156 Viv_Aut ~1 2 2 1 243.5 -0.268 -0.268 -0.200 -0.200
## 77 Viv_Aut ~1 1 1 1 243.5 0.268 0.268 0.219 0.219
## 72 Viv_Int ~1 1 1 1 191.2 0.372 0.372 0.243 0.243
## 151 Viv_Int ~1 2 2 1 191.2 -0.372 -0.372 -0.209 -0.209
## 157 Viv_Soft ~1 2 2 1 185.9 -0.154 -0.147 -0.147 -0.147
## 78 Viv_Soft ~1 1 1 1 185.9 0.154 0.154 0.154 0.154
## 155 Viv_PrInf ~1 2 2 1 165.7 -0.474 -0.474 -0.227 -0.227
## 76 Viv_PrInf ~1 1 1 1 165.7 0.474 0.474 0.242 0.242semTools::reliability(invariance$fit.means)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8523 0.7517
## alpha.ord 0.9108 0.8480
## omega 0.8487 0.7205
## omega2 0.8487 0.7205
## omega3 0.8505 0.7218
## avevar 0.6735 0.6110
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8504 0.7031
## alpha.ord 0.9082 0.8067
## omega 0.8147 0.6732
## omega2 0.8147 0.6732
## omega3 0.8070 0.6700
## avevar 0.6426 0.53284.1.1.6 Organizing Education
data$Esc<-car::recode(data$Esc,"5=4")
data$EscClasseR<-as.factor(data$Esc)
summary(data$EscClasseR)## 1 2 3 4
## 503 2506 4006 5934.1.1.7 Education Invariance Model
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Int + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf + Viv_Aut
Viv_HabRC ~~ Viv_HabR
Viv_AdmT ~~ Viv_Int
'4.1.1.8 Fitting
invariance<- measurementInvarianceCat(model = model, data = data, group = "EscClasseR",parameterization = "theta", estimator = "ULSMV",ordered = T,missing="pairwise",std.lv=T)## Warning: The measurementInvarianceCat function is deprecated, and it will cease
## to be included in future versions of semTools. See help('semTools-deprecated)
## for details.## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.200944e-14) is close to zero. This may be a symptom that the
## model is not identified.##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.thresholds
## Model 4 : fit.means
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 96 214
## fit.loadings 117 348 22.8 21 0.35
## fit.thresholds 192 911 87.7 75 0.15
## fit.means 198 1683 35.4 6 0.0000037 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi.scaled rmsea.scaled cfi.scaled.delta rmsea.scaled.delta
## fit.configural 0.991 0.056 NA NA
## fit.loadings 0.998 0.021 0.007 0.035
## fit.thresholds 0.998 0.017 0.000 0.004
## fit.means 0.996 0.025 0.002 0.0084.1.1.8.1 Configural
summary(invariance$fit.configural,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 614 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 225
## Number of equality constraints 33
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 214.450 669.683
## Degrees of freedom 96 96
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.329
## Shift parameter for each group:
## 2 6.155
## 3 9.840
## 1 1.235
## 4 1.457
## simple second-order correction
## Test statistic for each group:
## 2 50.053 158.098
## 3 114.127 356.290
## 1 26.162 80.654
## 4 24.108 74.640
##
## Model Test Baseline Model:
##
## Test statistic 83696.523 64274.637
## Degrees of freedom 144 144
## P-value NA 0.000
## Scaling correction factor 1.304
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.991
## Tucker-Lewis Index (TLI) 0.998 0.987
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.025 0.056
## 90 Percent confidence interval - lower 0.021 0.052
## 90 Percent confidence interval - upper 0.030 0.060
## P-value RMSEA <= 0.05 1.000 0.006
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.025 0.025
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.458 0.053 27.663 0.000 1.458 0.825
## Viv_EvInt 1.410 0.047 30.170 0.000 1.410 0.816
## Viv_DiF 1.491 0.055 27.238 0.000 1.491 0.831
## Viv_Int 1.002 0.035 28.346 0.000 1.002 0.708
## Viv_Pla 1.491 0.057 26.271 0.000 1.491 0.830
## Viv_Hard =~
## Viv_HabRC 1.475 0.067 21.997 0.000 1.475 0.828
## Viv_HabR 1.047 0.043 24.396 0.000 1.047 0.723
## Viv_PrInf 1.434 0.056 25.392 0.000 1.434 0.820
## Viv_Aut 0.583 0.027 21.361 0.000 0.583 0.504
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.456 0.025 17.913 0.000 0.456 0.456
## .Viv_AdmT ~~
## .Viv_Int 0.441 0.019 23.302 0.000 0.441 0.441
## Viv_Soft ~~
## Viv_Hard 0.835 0.011 76.781 0.000 0.835 0.835
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.039 0.123 -32.814 0.000 -4.039 -2.284
## Viv_AdmT|t2 -2.856 0.081 -35.362 0.000 -2.856 -1.616
## Viv_AdmT|t3 -2.076 0.067 -31.183 0.000 -2.076 -1.174
## Viv_AdmT|t4 0.325 0.044 7.428 0.000 0.325 0.184
## Viv_EvInt|t1 -3.861 0.105 -36.632 0.000 -3.861 -2.233
## Viv_EvInt|t2 -2.474 0.066 -37.206 0.000 -2.474 -1.431
## Viv_EvInt|t3 -1.663 0.057 -29.422 0.000 -1.663 -0.962
## Viv_EvInt|t4 0.611 0.043 14.046 0.000 0.611 0.353
## Viv_DiF|t1 -4.602 0.171 -26.919 0.000 -4.602 -2.563
## Viv_DiF|t2 -3.260 0.092 -35.311 0.000 -3.260 -1.816
## Viv_DiF|t3 -2.540 0.077 -33.053 0.000 -2.540 -1.414
## Viv_DiF|t4 -0.005 0.045 -0.120 0.905 -0.005 -0.003
## Viv_Int|t1 -3.024 0.082 -37.047 0.000 -3.024 -2.135
## Viv_Int|t2 -2.062 0.054 -38.209 0.000 -2.062 -1.457
## Viv_Int|t3 -1.431 0.045 -31.597 0.000 -1.431 -1.011
## Viv_Int|t4 0.621 0.036 17.102 0.000 0.621 0.439
## Viv_Pla|t1 -4.825 0.183 -26.326 0.000 -4.825 -2.688
## Viv_Pla|t2 -3.851 0.116 -33.206 0.000 -3.851 -2.145
## Viv_Pla|t3 -2.799 0.086 -32.553 0.000 -2.799 -1.559
## Viv_Pla|t4 0.032 0.045 0.722 0.471 0.032 0.018
## Viv_HabRC|t1 -4.669 0.194 -24.015 0.000 -4.669 -2.621
## Viv_HabRC|t2 -3.979 0.144 -27.638 0.000 -3.979 -2.233
## Viv_HabRC|t3 -2.709 0.098 -27.560 0.000 -2.709 -1.520
## Viv_HabRC|t4 0.032 0.044 0.722 0.471 0.032 0.018
## Viv_HabR|t1 -3.574 0.133 -26.822 0.000 -3.574 -2.469
## Viv_HabR|t2 -2.430 0.071 -34.411 0.000 -2.430 -1.678
## Viv_HabR|t3 -1.483 0.050 -29.544 0.000 -1.483 -1.024
## Viv_HabR|t4 0.268 0.036 7.457 0.000 0.268 0.185
## Viv_PrInf|t1 -4.122 0.148 -27.799 0.000 -4.122 -2.358
## Viv_PrInf|t2 -3.070 0.094 -32.709 0.000 -3.070 -1.756
## Viv_PrInf|t3 -2.215 0.074 -30.115 0.000 -2.215 -1.267
## Viv_PrInf|t4 0.402 0.043 9.343 0.000 0.402 0.230
## Viv_Aut|t1 -2.251 0.062 -36.478 0.000 -2.251 -1.944
## Viv_Aut|t2 -0.893 0.033 -27.243 0.000 -0.893 -0.771
## Viv_Aut|t3 -0.257 0.029 -8.740 0.000 -0.257 -0.222
## Viv_Aut|t4 1.067 0.034 31.157 0.000 1.067 0.922
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.320
## .Viv_EvInt 1.000 1.000 0.335
## .Viv_DiF 1.000 1.000 0.310
## .Viv_Int 1.000 1.000 0.499
## .Viv_Pla 1.000 1.000 0.310
## .Viv_HabRC 1.000 1.000 0.315
## .Viv_HabR 1.000 1.000 0.477
## .Viv_PrInf 1.000 1.000 0.327
## .Viv_Aut 1.000 1.000 0.746
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.566 0.566 1.000
## Viv_EvInt 0.578 0.578 1.000
## Viv_DiF 0.557 0.557 1.000
## Viv_Int 0.706 0.706 1.000
## Viv_Pla 0.557 0.557 1.000
## Viv_HabRC 0.561 0.561 1.000
## Viv_HabR 0.691 0.691 1.000
## Viv_PrInf 0.572 0.572 1.000
## Viv_Aut 0.864 0.864 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.680
## Viv_EvInt 0.665
## Viv_DiF 0.690
## Viv_Int 0.501
## Viv_Pla 0.690
## Viv_HabRC 0.685
## Viv_HabR 0.523
## Viv_PrInf 0.673
## Viv_Aut 0.254
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.642 0.203 8.086 0.000 1.642 0.840
## Viv_EvInt 1.524 0.161 9.457 0.000 1.524 0.815
## Viv_DiF 1.626 0.166 9.774 0.000 1.626 0.837
## Viv_Int 1.143 0.118 9.665 0.000 1.143 0.737
## Viv_Pla 1.556 0.154 10.114 0.000 1.556 0.835
## Viv_Hard =~
## Viv_HabRC 0.835 0.216 3.860 0.000 0.835 0.794
## Viv_HabR 0.622 0.144 4.330 0.000 0.622 0.689
## Viv_PrInf 0.818 0.211 3.870 0.000 0.818 0.811
## Viv_Aut 0.381 0.082 4.630 0.000 0.381 0.554
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.216 0.106 2.028 0.043 0.216 0.516
## .Viv_AdmT ~~
## .Viv_Int 0.469 0.105 4.470 0.000 0.469 0.424
## Viv_Soft ~~
## Viv_Hard 0.814 0.010 80.962 0.000 0.814 0.814
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.190 0.232 0.820 0.412 0.190 0.190
## Viv_Hard -2.055 1.262 -1.628 0.104 -2.055 -2.055
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.039 0.123 -32.814 0.000 -4.039 -2.067
## Viv_AdmT|t2 -2.856 0.081 -35.362 0.000 -2.856 -1.462
## Viv_AdmT|t3 -2.101 0.142 -14.788 0.000 -2.101 -1.075
## Viv_AdmT|t4 0.383 0.426 0.900 0.368 0.383 0.196
## Viv_EvInt|t1 -3.861 0.105 -36.632 0.000 -3.861 -2.064
## Viv_EvInt|t2 -2.293 0.153 -14.950 0.000 -2.293 -1.226
## Viv_EvInt|t3 -1.463 0.218 -6.718 0.000 -1.463 -0.782
## Viv_EvInt|t4 0.835 0.436 1.913 0.056 0.835 0.446
## Viv_DiF|t1 -4.602 0.171 -26.919 0.000 -4.602 -2.368
## Viv_DiF|t2 -3.452 0.175 -19.715 0.000 -3.452 -1.776
## Viv_DiF|t3 -2.535 0.194 -13.033 0.000 -2.535 -1.304
## Viv_DiF|t4 0.099 0.386 0.256 0.798 0.099 0.051
## Viv_Int|t1 -3.024 0.082 -37.047 0.000 -3.024 -1.952
## Viv_Int|t2 -1.963 0.103 -19.138 0.000 -1.963 -1.267
## Viv_Int|t3 -1.375 0.143 -9.622 0.000 -1.375 -0.887
## Viv_Int|t4 0.703 0.333 2.114 0.035 0.703 0.454
## Viv_Pla|t1 -4.825 0.183 -26.326 0.000 -4.825 -2.589
## Viv_Pla|t2 -3.941 0.201 -19.607 0.000 -3.941 -2.115
## Viv_Pla|t3 -2.752 0.201 -13.663 0.000 -2.752 -1.477
## Viv_Pla|t4 0.135 0.372 0.363 0.717 0.135 0.072
## Viv_HabRC|t1 -4.669 0.194 -24.015 0.000 -4.669 -4.439
## Viv_HabRC|t2 -3.979 0.144 -27.638 0.000 -3.979 -3.783
## Viv_HabRC|t3 -3.350 0.233 -14.406 0.000 -3.350 -3.185
## Viv_HabRC|t4 -1.800 0.597 -3.013 0.003 -1.800 -1.711
## Viv_HabR|t1 -3.574 0.133 -26.822 0.000 -3.574 -3.959
## Viv_HabR|t2 -2.849 0.181 -15.782 0.000 -2.849 -3.156
## Viv_HabR|t3 -2.287 0.283 -8.092 0.000 -2.287 -2.533
## Viv_HabR|t4 -1.238 0.507 -2.442 0.015 -1.238 -1.372
## Viv_PrInf|t1 -4.122 0.148 -27.799 0.000 -4.122 -4.087
## Viv_PrInf|t2 -3.489 0.197 -17.756 0.000 -3.489 -3.460
## Viv_PrInf|t3 -3.009 0.290 -10.379 0.000 -3.009 -2.983
## Viv_PrInf|t4 -1.563 0.637 -2.455 0.014 -1.563 -1.549
## Viv_Aut|t1 -2.251 0.062 -36.478 0.000 -2.251 -3.271
## Viv_Aut|t2 -1.447 0.179 -8.094 0.000 -1.447 -2.103
## Viv_Aut|t3 -1.126 0.244 -4.612 0.000 -1.126 -1.637
## Viv_Aut|t4 -0.319 0.414 -0.770 0.441 -0.319 -0.463
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.122 0.271 4.134 0.000 1.122 0.294
## .Viv_EvInt 1.175 0.244 4.822 0.000 1.175 0.336
## .Viv_DiF 1.133 0.228 4.965 0.000 1.133 0.300
## .Viv_Int 1.095 0.219 4.995 0.000 1.095 0.456
## .Viv_Pla 1.053 0.199 5.283 0.000 1.053 0.303
## .Viv_HabRC 0.409 0.210 1.948 0.051 0.409 0.369
## .Viv_HabR 0.428 0.197 2.167 0.030 0.428 0.525
## .Viv_PrInf 0.348 0.179 1.941 0.052 0.348 0.342
## .Viv_Aut 0.328 0.142 2.316 0.021 0.328 0.693
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.512 0.512 1.000
## Viv_EvInt 0.535 0.535 1.000
## Viv_DiF 0.515 0.515 1.000
## Viv_Int 0.645 0.645 1.000
## Viv_Pla 0.537 0.537 1.000
## Viv_HabRC 0.951 0.951 1.000
## Viv_HabR 1.108 1.108 1.000
## Viv_PrInf 0.991 0.991 1.000
## Viv_Aut 1.453 1.453 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.706
## Viv_EvInt 0.664
## Viv_DiF 0.700
## Viv_Int 0.544
## Viv_Pla 0.697
## Viv_HabRC 0.631
## Viv_HabR 0.475
## Viv_PrInf 0.658
## Viv_Aut 0.307
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.650 0.403 4.093 0.000 1.650 0.823
## Viv_EvInt 1.185 0.241 4.911 0.000 1.185 0.827
## Viv_DiF 1.823 0.404 4.514 0.000 1.823 0.914
## Viv_Int 0.982 0.193 5.092 0.000 0.982 0.683
## Viv_Pla 1.530 0.319 4.802 0.000 1.530 0.852
## Viv_Hard =~
## Viv_HabRC 0.622 0.243 2.561 0.010 0.622 0.842
## Viv_HabR 0.482 0.173 2.785 0.005 0.482 0.679
## Viv_PrInf 0.603 0.232 2.596 0.009 0.603 0.846
## Viv_Aut 0.296 0.102 2.900 0.004 0.296 0.574
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.094 0.072 1.314 0.189 0.094 0.455
## .Viv_AdmT ~~
## .Viv_Int 0.454 0.191 2.385 0.017 0.454 0.380
## Viv_Soft ~~
## Viv_Hard 0.854 0.025 34.460 0.000 0.854 0.854
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.226 0.447 0.506 0.613 0.226 0.226
## Viv_Hard -4.084 2.532 -1.613 0.107 -4.084 -4.084
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.039 0.123 -32.814 0.000 -4.039 -2.014
## Viv_AdmT|t2 -2.856 0.081 -35.362 0.000 -2.856 -1.424
## Viv_AdmT|t3 -1.971 0.282 -6.983 0.000 -1.971 -0.983
## Viv_AdmT|t4 0.811 0.925 0.876 0.381 0.811 0.404
## Viv_EvInt|t1 -3.861 0.105 -36.632 0.000 -3.861 -2.693
## Viv_EvInt|t2 -1.918 0.325 -5.898 0.000 -1.918 -1.338
## Viv_EvInt|t3 -1.211 0.369 -3.287 0.001 -1.211 -0.845
## Viv_EvInt|t4 0.775 0.661 1.173 0.241 0.775 0.540
## Viv_DiF|t1 -4.602 0.171 -26.919 0.000 -4.602 -2.308
## Viv_DiF|t2 -3.610 0.325 -11.097 0.000 -3.610 -1.810
## Viv_DiF|t3 -2.729 0.394 -6.922 0.000 -2.729 -1.368
## Viv_DiF|t4 0.576 0.927 0.622 0.534 0.576 0.289
## Viv_Int|t1 -3.024 0.082 -37.047 0.000 -3.024 -2.105
## Viv_Int|t2 -2.297 0.176 -13.017 0.000 -2.297 -1.599
## Viv_Int|t3 -1.471 0.232 -6.337 0.000 -1.471 -1.024
## Viv_Int|t4 0.901 0.595 1.514 0.130 0.901 0.627
## Viv_Pla|t1 -4.825 0.183 -26.326 0.000 -4.825 -2.687
## Viv_Pla|t2 -3.711 0.434 -8.556 0.000 -3.711 -2.067
## Viv_Pla|t3 -2.685 0.408 -6.578 0.000 -2.685 -1.495
## Viv_Pla|t4 0.620 0.789 0.786 0.432 0.620 0.345
## Viv_HabRC|t1 -4.669 0.194 -24.015 0.000 -4.669 -6.320
## Viv_HabRC|t2 -3.979 0.144 -27.638 0.000 -3.979 -5.386
## Viv_HabRC|t3 -3.592 0.230 -15.620 0.000 -3.592 -4.862
## Viv_HabRC|t4 -2.418 0.647 -3.734 0.000 -2.418 -3.273
## Viv_HabR|t1 -3.574 0.133 -26.822 0.000 -3.574 -5.033
## Viv_HabR|t2 -2.951 0.224 -13.150 0.000 -2.951 -4.156
## Viv_HabR|t3 -2.488 0.361 -6.891 0.000 -2.488 -3.504
## Viv_HabR|t4 -1.634 0.648 -2.520 0.012 -1.634 -2.301
## Viv_PrInf|t1 -4.122 0.148 -27.799 0.000 -4.122 -5.785
## Viv_PrInf|t2 -3.506 0.247 -14.195 0.000 -3.506 -4.920
## Viv_PrInf|t3 -3.168 0.350 -9.058 0.000 -3.168 -4.446
## Viv_PrInf|t4 -2.165 0.707 -3.062 0.002 -2.165 -3.038
## Viv_Aut|t1 -2.251 0.062 -36.478 0.000 -2.251 -4.359
## Viv_Aut|t2 -1.498 0.250 -5.983 0.000 -1.498 -2.901
## Viv_Aut|t3 -1.193 0.349 -3.414 0.001 -1.193 -2.310
## Viv_Aut|t4 -0.690 0.517 -1.334 0.182 -0.690 -1.336
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.300 0.601 2.164 0.030 1.300 0.323
## .Viv_EvInt 0.650 0.261 2.486 0.013 0.650 0.316
## .Viv_DiF 0.655 0.254 2.579 0.010 0.655 0.165
## .Viv_Int 1.100 0.391 2.812 0.005 1.100 0.533
## .Viv_Pla 0.882 0.368 2.395 0.017 0.882 0.274
## .Viv_HabRC 0.158 0.126 1.258 0.208 0.158 0.290
## .Viv_HabR 0.272 0.193 1.409 0.159 0.272 0.539
## .Viv_PrInf 0.144 0.109 1.320 0.187 0.144 0.284
## .Viv_Aut 0.179 0.122 1.471 0.141 0.179 0.671
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.499 0.499 1.000
## Viv_EvInt 0.698 0.698 1.000
## Viv_DiF 0.501 0.501 1.000
## Viv_Int 0.696 0.696 1.000
## Viv_Pla 0.557 0.557 1.000
## Viv_HabRC 1.354 1.354 1.000
## Viv_HabR 1.408 1.408 1.000
## Viv_PrInf 1.403 1.403 1.000
## Viv_Aut 1.937 1.937 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.677
## Viv_EvInt 0.684
## Viv_DiF 0.835
## Viv_Int 0.467
## Viv_Pla 0.726
## Viv_HabRC 0.710
## Viv_HabR 0.461
## Viv_PrInf 0.716
## Viv_Aut 0.329
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.186 0.260 4.558 0.000 1.186 0.800
## Viv_EvInt 1.239 0.240 5.164 0.000 1.239 0.819
## Viv_DiF 1.406 0.265 5.314 0.000 1.406 0.837
## Viv_Int 0.816 0.146 5.609 0.000 0.816 0.646
## Viv_Pla 1.236 0.213 5.796 0.000 1.236 0.777
## Viv_Hard =~
## Viv_HabRC 0.574 0.242 2.375 0.018 0.574 0.704
## Viv_HabR 0.476 0.189 2.523 0.012 0.476 0.676
## Viv_PrInf 0.566 0.231 2.451 0.014 0.566 0.778
## Viv_Aut 0.255 0.086 2.964 0.003 0.255 0.507
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.172 0.141 1.213 0.225 0.172 0.571
## .Viv_AdmT ~~
## .Viv_Int 0.462 0.178 2.602 0.009 0.462 0.539
## Viv_Soft ~~
## Viv_Hard 0.823 0.027 29.973 0.000 0.823 0.823
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft -0.418 0.549 -0.761 0.447 -0.418 -0.418
## Viv_Hard -3.971 2.928 -1.356 0.175 -3.971 -3.971
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.039 0.123 -32.814 0.000 -4.039 -2.724
## Viv_AdmT|t2 -2.856 0.081 -35.362 0.000 -2.856 -1.927
## Viv_AdmT|t3 -2.179 0.204 -10.691 0.000 -2.179 -1.469
## Viv_AdmT|t4 -0.367 0.573 -0.640 0.522 -0.367 -0.247
## Viv_EvInt|t1 -3.861 0.105 -36.632 0.000 -3.861 -2.554
## Viv_EvInt|t2 -2.416 0.281 -8.590 0.000 -2.416 -1.598
## Viv_EvInt|t3 -1.697 0.387 -4.385 0.000 -1.697 -1.123
## Viv_EvInt|t4 -0.040 0.674 -0.059 0.953 -0.040 -0.026
## Viv_DiF|t1 -4.602 0.171 -26.919 0.000 -4.602 -2.739
## Viv_DiF|t2 -3.369 0.303 -11.109 0.000 -3.369 -2.005
## Viv_DiF|t3 -2.667 0.369 -7.233 0.000 -2.667 -1.587
## Viv_DiF|t4 -0.598 0.670 -0.893 0.372 -0.598 -0.356
## Viv_Int|t1 -3.024 0.082 -37.047 0.000 -3.024 -2.393
## Viv_Int|t2 -2.014 0.168 -12.005 0.000 -2.014 -1.594
## Viv_Int|t3 -1.495 0.231 -6.484 0.000 -1.495 -1.184
## Viv_Int|t4 0.086 0.463 0.186 0.852 0.086 0.068
## Viv_Pla|t1 -4.825 0.183 -26.326 0.000 -4.825 -3.034
## Viv_Pla|t2 -3.775 0.347 -10.893 0.000 -3.775 -2.374
## Viv_Pla|t3 -2.759 0.369 -7.474 0.000 -2.759 -1.735
## Viv_Pla|t4 -0.601 0.599 -1.003 0.316 -0.601 -0.378
## Viv_HabRC|t1 -4.669 0.194 -24.015 0.000 -4.669 -5.729
## Viv_HabRC|t2 -3.979 0.144 -27.638 0.000 -3.979 -4.882
## Viv_HabRC|t3 -3.542 0.255 -13.881 0.000 -3.542 -4.346
## Viv_HabRC|t4 -2.424 0.679 -3.567 0.000 -2.424 -2.974
## Viv_HabR|t1 -3.574 0.133 -26.822 0.000 -3.574 -5.073
## Viv_HabR|t2 -3.249 0.189 -17.220 0.000 -3.249 -4.611
## Viv_HabR|t3 -2.918 0.287 -10.166 0.000 -2.918 -4.141
## Viv_HabR|t4 -2.015 0.613 -3.286 0.001 -2.015 -2.860
## Viv_PrInf|t1 -4.122 0.148 -27.799 0.000 -4.122 -5.661
## Viv_PrInf|t2 -3.673 0.248 -14.821 0.000 -3.673 -5.044
## Viv_PrInf|t3 -3.236 0.385 -8.400 0.000 -3.236 -4.444
## Viv_PrInf|t4 -2.146 0.799 -2.687 0.007 -2.146 -2.947
## Viv_Aut|t1 -2.251 0.062 -36.478 0.000 -2.251 -4.485
## Viv_Aut|t2 -1.538 0.249 -6.186 0.000 -1.538 -3.066
## Viv_Aut|t3 -1.332 0.313 -4.258 0.000 -1.332 -2.654
## Viv_Aut|t4 -0.753 0.500 -1.506 0.132 -0.753 -1.501
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.792 0.333 2.379 0.017 0.792 0.360
## .Viv_EvInt 0.751 0.284 2.641 0.008 0.751 0.329
## .Viv_DiF 0.845 0.314 2.687 0.007 0.845 0.299
## .Viv_Int 0.930 0.311 2.994 0.003 0.930 0.583
## .Viv_Pla 1.002 0.330 3.038 0.002 1.002 0.396
## .Viv_HabRC 0.335 0.272 1.230 0.219 0.335 0.504
## .Viv_HabR 0.270 0.213 1.268 0.205 0.270 0.543
## .Viv_PrInf 0.209 0.172 1.216 0.224 0.209 0.395
## .Viv_Aut 0.187 0.126 1.480 0.139 0.187 0.743
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.674 0.674 1.000
## Viv_EvInt 0.662 0.662 1.000
## Viv_DiF 0.595 0.595 1.000
## Viv_Int 0.792 0.792 1.000
## Viv_Pla 0.629 0.629 1.000
## Viv_HabRC 1.227 1.227 1.000
## Viv_HabR 1.419 1.419 1.000
## Viv_PrInf 1.373 1.373 1.000
## Viv_Aut 1.993 1.993 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.640
## Viv_EvInt 0.671
## Viv_DiF 0.701
## Viv_Int 0.417
## Viv_Pla 0.604
## Viv_HabRC 0.496
## Viv_HabR 0.457
## Viv_PrInf 0.605
## Viv_Aut 0.257lavaan::fitMeasures(invariance$fit.configural,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 669.683 96.000 0.000
## srmr cfi.scaled tli.scaled
## 0.025 0.991 0.987
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.056 0.052 0.060modificationindices(invariance$fit.configural, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 398 Viv_Hard =~ Viv_EvInt 2 2 1 34.587 -0.531 -0.531 -0.284
## 427 Viv_Pla ~~ Viv_HabRC 2 2 1 26.985 0.190 0.190 0.289
## 409 Viv_EvInt ~~ Viv_DiF 2 2 1 24.343 0.352 0.352 0.305
## 408 Viv_AdmT ~~ Viv_Aut 2 2 1 22.438 0.113 0.113 0.186
## 401 Viv_Hard =~ Viv_Pla 2 2 1 21.992 0.428 0.428 0.229
## 384 Viv_Pla ~~ Viv_HabRC 1 1 1 15.712 0.302 0.302 0.302
## 414 Viv_EvInt ~~ Viv_PrInf 2 2 1 11.300 -0.118 -0.118 -0.185
## 366 Viv_EvInt ~~ Viv_DiF 1 1 1 10.731 0.252 0.252 0.252
## 358 Viv_Hard =~ Viv_Pla 1 1 1 10.223 0.399 0.399 0.222
## 428 Viv_Pla ~~ Viv_HabR 2 2 1 9.528 0.095 0.095 0.141
## sepc.nox
## 398 -0.284
## 427 0.289
## 409 0.305
## 408 0.186
## 401 0.229
## 384 0.302
## 414 -0.185
## 366 0.252
## 358 0.222
## 428 0.141semTools::reliability(invariance$fit.configural)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8484 0.7256
## alpha.ord 0.9065 0.8243
## omega 0.8274 0.7003
## omega2 0.8274 0.7003
## omega3 0.8264 0.7004
## avevar 0.6567 0.5862
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8543 0.7213
## alpha.ord 0.9124 0.8254
## omega 0.8403 0.4894
## omega2 0.8403 0.4894
## omega3 0.8379 0.4885
## avevar 0.6712 0.5565
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8575 0.7587
## alpha.ord 0.9168 0.8391
## omega 0.8423 0.2206
## omega2 0.8423 0.2206
## omega3 0.8408 0.2195
## avevar 0.7011 0.5873
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8357 0.6651
## alpha.ord 0.8926 0.7947
## omega 0.7929 0.1392
## omega2 0.7929 0.1392
## omega3 0.7920 0.1390
## avevar 0.6222 0.48494.1.1.8.2 Loadings
summary(invariance$fit.loadings,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 463 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 231
## Number of equality constraints 60
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 347.499 215.078
## Degrees of freedom 117 117
## P-value (Unknown) NA 0.000
## Scaling correction factor 2.241
## Shift parameter for each group:
## 2 19.761
## 3 31.590
## 1 3.966
## 4 4.676
## simple second-order correction
## Test statistic for each group:
## 2 76.903 54.082
## 3 126.120 87.876
## 1 89.398 43.864
## 4 55.077 29.256
##
## Model Test Baseline Model:
##
## Test statistic 83696.523 64274.637
## Degrees of freedom 144 144
## P-value NA 0.000
## Scaling correction factor 1.304
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 0.998
## Tucker-Lewis Index (TLI) 0.997 0.998
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.032 0.021
## 90 Percent confidence interval - lower 0.028 0.017
## 90 Percent confidence interval - upper 0.036 0.025
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.029 0.029
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.510 0.075 20.161 0.000 1.510 0.834
## Viv_EvInt 1.358 0.057 23.976 0.000 1.358 0.805
## Viv_DiF 1.563 0.100 15.665 0.000 1.563 0.842
## Viv_Int 1.027 0.040 25.942 0.000 1.027 0.716
## Viv_Pla 1.409 0.088 15.964 0.000 1.409 0.816
## Viv_Hard =~
## Viv_HabRC 1.358 0.089 15.283 0.000 1.358 0.805
## Viv_HabR 1.067 0.053 20.175 0.000 1.067 0.730
## Viv_PrInf 1.366 0.067 20.304 0.000 1.366 0.807
## Viv_Aut 0.629 0.027 23.080 0.000 0.629 0.532
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.463 0.033 14.235 0.000 0.463 0.463
## .Viv_AdmT ~~
## .Viv_Int 0.423 0.030 14.093 0.000 0.423 0.423
## Viv_Soft ~~
## Viv_Hard 0.837 0.011 74.239 0.000 0.837 0.837
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.119 0.192 -21.447 0.000 -4.119 -2.274
## Viv_AdmT|t2 -2.934 0.126 -23.337 0.000 -2.934 -1.620
## Viv_AdmT|t3 -2.127 0.089 -24.024 0.000 -2.127 -1.174
## Viv_AdmT|t4 0.333 0.046 7.309 0.000 0.333 0.184
## Viv_EvInt|t1 -3.788 0.153 -24.808 0.000 -3.788 -2.246
## Viv_EvInt|t2 -2.414 0.086 -28.140 0.000 -2.414 -1.431
## Viv_EvInt|t3 -1.623 0.064 -25.167 0.000 -1.623 -0.962
## Viv_EvInt|t4 0.596 0.044 13.647 0.000 0.596 0.353
## Viv_DiF|t1 -4.731 0.306 -15.453 0.000 -4.731 -2.549
## Viv_DiF|t2 -3.369 0.174 -19.381 0.000 -3.369 -1.816
## Viv_DiF|t3 -2.625 0.132 -19.874 0.000 -2.625 -1.414
## Viv_DiF|t4 -0.006 0.047 -0.120 0.905 -0.006 -0.003
## Viv_Int|t1 -3.051 0.103 -29.540 0.000 -3.051 -2.129
## Viv_Int|t2 -2.088 0.067 -31.064 0.000 -2.088 -1.457
## Viv_Int|t3 -1.449 0.052 -28.111 0.000 -1.449 -1.011
## Viv_Int|t4 0.629 0.037 16.845 0.000 0.629 0.439
## Viv_Pla|t1 -4.670 0.304 -15.384 0.000 -4.670 -2.702
## Viv_Pla|t2 -3.707 0.199 -18.644 0.000 -3.707 -2.145
## Viv_Pla|t3 -2.695 0.132 -20.395 0.000 -2.695 -1.559
## Viv_Pla|t4 0.031 0.043 0.721 0.471 0.031 0.018
## Viv_HabRC|t1 -4.480 0.287 -15.599 0.000 -4.480 -2.656
## Viv_HabRC|t2 -3.732 0.212 -17.633 0.000 -3.732 -2.212
## Viv_HabRC|t3 -2.564 0.127 -20.188 0.000 -2.564 -1.520
## Viv_HabRC|t4 0.030 0.042 0.720 0.472 0.030 0.018
## Viv_HabR|t1 -3.597 0.171 -21.086 0.000 -3.597 -2.459
## Viv_HabR|t2 -2.454 0.091 -26.984 0.000 -2.454 -1.678
## Viv_HabR|t3 -1.498 0.059 -25.490 0.000 -1.498 -1.024
## Viv_HabR|t4 0.271 0.037 7.349 0.000 0.271 0.185
## Viv_PrInf|t1 -4.016 0.193 -20.814 0.000 -4.016 -2.372
## Viv_PrInf|t2 -2.973 0.122 -24.332 0.000 -2.973 -1.756
## Viv_PrInf|t3 -2.145 0.087 -24.675 0.000 -2.145 -1.267
## Viv_PrInf|t4 0.389 0.043 9.051 0.000 0.389 0.230
## Viv_Aut|t1 -2.261 0.065 -34.714 0.000 -2.261 -1.914
## Viv_Aut|t2 -0.911 0.035 -26.089 0.000 -0.911 -0.771
## Viv_Aut|t3 -0.262 0.030 -8.693 0.000 -0.262 -0.222
## Viv_Aut|t4 1.089 0.035 30.704 0.000 1.089 0.922
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.305
## .Viv_EvInt 1.000 1.000 0.351
## .Viv_DiF 1.000 1.000 0.290
## .Viv_Int 1.000 1.000 0.487
## .Viv_Pla 1.000 1.000 0.335
## .Viv_HabRC 1.000 1.000 0.351
## .Viv_HabR 1.000 1.000 0.467
## .Viv_PrInf 1.000 1.000 0.349
## .Viv_Aut 1.000 1.000 0.717
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.552 0.552 1.000
## Viv_EvInt 0.593 0.593 1.000
## Viv_DiF 0.539 0.539 1.000
## Viv_Int 0.698 0.698 1.000
## Viv_Pla 0.579 0.579 1.000
## Viv_HabRC 0.593 0.593 1.000
## Viv_HabR 0.684 0.684 1.000
## Viv_PrInf 0.591 0.591 1.000
## Viv_Aut 0.846 0.846 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.695
## Viv_EvInt 0.649
## Viv_DiF 0.710
## Viv_Int 0.513
## Viv_Pla 0.665
## Viv_HabRC 0.649
## Viv_HabR 0.533
## Viv_PrInf 0.651
## Viv_Aut 0.283
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.510 0.075 20.161 0.000 1.588 0.833
## Viv_EvInt 1.358 0.057 23.976 0.000 1.428 0.811
## Viv_DiF 1.563 0.100 15.665 0.000 1.644 0.847
## Viv_Int 1.027 0.040 25.942 0.000 1.080 0.722
## Viv_Pla 1.409 0.088 15.964 0.000 1.482 0.845
## Viv_Hard =~
## Viv_HabRC 1.358 0.089 15.283 0.000 0.920 0.798
## Viv_HabR 1.067 0.053 20.175 0.000 0.723 0.701
## Viv_PrInf 1.366 0.067 20.304 0.000 0.924 0.811
## Viv_Aut 0.629 0.027 23.080 0.000 0.426 0.542
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.256 0.100 2.551 0.011 0.256 0.501
## .Viv_AdmT ~~
## .Viv_Int 0.495 0.107 4.608 0.000 0.495 0.453
## Viv_Soft ~~
## Viv_Hard 0.579 0.122 4.725 0.000 0.813 0.813
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.096 0.220 0.437 0.662 0.092 0.092
## Viv_Hard -0.920 0.367 -2.506 0.012 -1.359 -1.359
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.119 0.192 -21.447 0.000 -4.119 -2.160
## Viv_AdmT|t2 -2.934 0.126 -23.337 0.000 -2.934 -1.539
## Viv_AdmT|t3 -2.211 0.139 -15.878 0.000 -2.211 -1.159
## Viv_AdmT|t4 0.215 0.341 0.628 0.530 0.215 0.113
## Viv_EvInt|t1 -3.788 0.153 -24.808 0.000 -3.788 -2.151
## Viv_EvInt|t2 -2.302 0.150 -15.331 0.000 -2.302 -1.307
## Viv_EvInt|t3 -1.521 0.184 -8.284 0.000 -1.521 -0.863
## Viv_EvInt|t4 0.644 0.340 1.891 0.059 0.644 0.365
## Viv_DiF|t1 -4.731 0.306 -15.453 0.000 -4.731 -2.439
## Viv_DiF|t2 -3.604 0.269 -13.372 0.000 -3.604 -1.858
## Viv_DiF|t3 -2.689 0.245 -10.984 0.000 -2.689 -1.386
## Viv_DiF|t4 -0.060 0.328 -0.183 0.855 -0.060 -0.031
## Viv_Int|t1 -3.051 0.103 -29.540 0.000 -3.051 -2.040
## Viv_Int|t2 -2.007 0.097 -20.750 0.000 -2.007 -1.341
## Viv_Int|t3 -1.438 0.119 -12.117 0.000 -1.438 -0.962
## Viv_Int|t4 0.568 0.264 2.153 0.031 0.568 0.380
## Viv_Pla|t1 -4.670 0.304 -15.384 0.000 -4.670 -2.662
## Viv_Pla|t2 -3.854 0.270 -14.267 0.000 -3.854 -2.197
## Viv_Pla|t3 -2.734 0.205 -13.341 0.000 -2.734 -1.559
## Viv_Pla|t4 -0.016 0.298 -0.054 0.957 -0.016 -0.009
## Viv_HabRC|t1 -4.480 0.287 -15.599 0.000 -4.480 -3.890
## Viv_HabRC|t2 -3.732 0.212 -17.633 0.000 -3.732 -3.240
## Viv_HabRC|t3 -3.039 0.209 -14.509 0.000 -3.039 -2.639
## Viv_HabRC|t4 -1.342 0.458 -2.932 0.003 -1.342 -1.165
## Viv_HabR|t1 -3.597 0.171 -21.086 0.000 -3.597 -3.488
## Viv_HabR|t2 -2.777 0.177 -15.685 0.000 -2.777 -2.692
## Viv_HabR|t3 -2.134 0.237 -9.003 0.000 -2.134 -2.069
## Viv_HabR|t4 -0.936 0.408 -2.295 0.022 -0.936 -0.908
## Viv_PrInf|t1 -4.016 0.193 -20.814 0.000 -4.016 -3.523
## Viv_PrInf|t2 -3.301 0.204 -16.143 0.000 -3.301 -2.896
## Viv_PrInf|t3 -2.758 0.257 -10.711 0.000 -2.758 -2.420
## Viv_PrInf|t4 -1.124 0.517 -2.175 0.030 -1.124 -0.986
## Viv_Aut|t1 -2.261 0.065 -34.714 0.000 -2.261 -2.880
## Viv_Aut|t2 -1.336 0.138 -9.665 0.000 -1.336 -1.702
## Viv_Aut|t3 -0.970 0.187 -5.179 0.000 -0.970 -1.236
## Viv_Aut|t4 -0.049 0.319 -0.154 0.878 -0.049 -0.062
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.115 0.269 4.139 0.000 1.115 0.307
## .Viv_EvInt 1.063 0.204 5.208 0.000 1.063 0.343
## .Viv_DiF 1.061 0.244 4.352 0.000 1.061 0.282
## .Viv_Int 1.071 0.192 5.573 0.000 1.071 0.479
## .Viv_Pla 0.882 0.239 3.688 0.000 0.882 0.287
## .Viv_HabRC 0.481 0.211 2.282 0.022 0.481 0.363
## .Viv_HabR 0.541 0.186 2.913 0.004 0.541 0.509
## .Viv_PrInf 0.444 0.174 2.553 0.011 0.444 0.342
## .Viv_Aut 0.435 0.128 3.386 0.001 0.435 0.706
## Viv_Soft 1.105 0.199 5.545 0.000 1.000 1.000
## Viv_Hard 0.458 0.161 2.848 0.004 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.524 0.524 1.000
## Viv_EvInt 0.568 0.568 1.000
## Viv_DiF 0.516 0.516 1.000
## Viv_Int 0.669 0.669 1.000
## Viv_Pla 0.570 0.570 1.000
## Viv_HabRC 0.868 0.868 1.000
## Viv_HabR 0.970 0.970 1.000
## Viv_PrInf 0.877 0.877 1.000
## Viv_Aut 1.274 1.274 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.693
## Viv_EvInt 0.657
## Viv_DiF 0.718
## Viv_Int 0.521
## Viv_Pla 0.713
## Viv_HabRC 0.637
## Viv_HabR 0.491
## Viv_PrInf 0.658
## Viv_Aut 0.294
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.510 0.075 20.161 0.000 1.544 0.778
## Viv_EvInt 1.358 0.057 23.976 0.000 1.389 0.938
## Viv_DiF 1.563 0.100 15.665 0.000 1.598 0.825
## Viv_Int 1.027 0.040 25.942 0.000 1.050 0.713
## Viv_Pla 1.409 0.088 15.964 0.000 1.441 0.839
## Viv_Hard =~
## Viv_HabRC 1.358 0.089 15.283 0.000 0.751 0.863
## Viv_HabR 1.067 0.053 20.175 0.000 0.590 0.665
## Viv_PrInf 1.366 0.067 20.304 0.000 0.755 0.870
## Viv_Aut 0.629 0.027 23.080 0.000 0.348 0.537
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.138 0.088 1.572 0.116 0.138 0.473
## .Viv_AdmT ~~
## .Viv_Int 0.483 0.219 2.208 0.027 0.483 0.375
## Viv_Soft ~~
## Viv_Hard 0.478 0.219 2.185 0.029 0.847 0.847
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.212 0.459 0.462 0.644 0.207 0.207
## Viv_Hard -1.479 0.431 -3.432 0.001 -2.677 -2.677
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.119 0.192 -21.447 0.000 -4.119 -2.075
## Viv_AdmT|t2 -2.934 0.126 -23.337 0.000 -2.934 -1.478
## Viv_AdmT|t3 -2.000 0.265 -7.533 0.000 -2.000 -1.007
## Viv_AdmT|t4 0.754 0.776 0.972 0.331 0.754 0.380
## Viv_EvInt|t1 -3.788 0.153 -24.808 0.000 -3.788 -2.560
## Viv_EvInt|t2 -1.969 0.282 -6.990 0.000 -1.969 -1.331
## Viv_EvInt|t3 -1.239 0.347 -3.568 0.000 -1.239 -0.837
## Viv_EvInt|t4 0.811 0.734 1.106 0.269 0.811 0.548
## Viv_DiF|t1 -4.731 0.306 -15.453 0.000 -4.731 -2.443
## Viv_DiF|t2 -3.574 0.331 -10.807 0.000 -3.574 -1.845
## Viv_DiF|t3 -2.719 0.336 -8.089 0.000 -2.719 -1.404
## Viv_DiF|t4 0.491 0.745 0.659 0.510 0.491 0.253
## Viv_Int|t1 -3.051 0.103 -29.540 0.000 -3.051 -2.073
## Viv_Int|t2 -2.363 0.166 -14.223 0.000 -2.363 -1.606
## Viv_Int|t3 -1.517 0.215 -7.056 0.000 -1.517 -1.031
## Viv_Int|t4 0.914 0.593 1.540 0.123 0.914 0.621
## Viv_Pla|t1 -4.670 0.304 -15.384 0.000 -4.670 -2.721
## Viv_Pla|t2 -3.579 0.406 -8.816 0.000 -3.579 -2.085
## Viv_Pla|t3 -2.598 0.326 -7.974 0.000 -2.598 -1.514
## Viv_Pla|t4 0.561 0.695 0.806 0.420 0.561 0.327
## Viv_HabRC|t1 -4.480 0.287 -15.599 0.000 -4.480 -5.148
## Viv_HabRC|t2 -3.732 0.212 -17.633 0.000 -3.732 -4.288
## Viv_HabRC|t3 -3.247 0.243 -13.387 0.000 -3.247 -3.731
## Viv_HabRC|t4 -1.864 0.612 -3.045 0.002 -1.864 -2.142
## Viv_HabR|t1 -3.597 0.171 -21.086 0.000 -3.597 -4.056
## Viv_HabR|t2 -2.805 0.214 -13.117 0.000 -2.805 -3.163
## Viv_HabR|t3 -2.227 0.322 -6.924 0.000 -2.227 -2.511
## Viv_HabR|t4 -1.160 0.582 -1.992 0.046 -1.160 -1.308
## Viv_PrInf|t1 -4.016 0.193 -20.814 0.000 -4.016 -4.630
## Viv_PrInf|t2 -3.290 0.259 -12.717 0.000 -3.290 -3.794
## Viv_PrInf|t3 -2.879 0.347 -8.299 0.000 -2.879 -3.320
## Viv_PrInf|t4 -1.658 0.688 -2.409 0.016 -1.658 -1.911
## Viv_Aut|t1 -2.261 0.065 -34.714 0.000 -2.261 -3.493
## Viv_Aut|t2 -1.292 0.205 -6.304 0.000 -1.292 -1.996
## Viv_Aut|t3 -0.910 0.290 -3.138 0.002 -0.910 -1.405
## Viv_Aut|t4 -0.279 0.438 -0.637 0.524 -0.279 -0.431
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.557 0.634 2.458 0.014 1.557 0.395
## .Viv_EvInt 0.263 0.230 1.142 0.253 0.263 0.120
## .Viv_DiF 1.198 0.489 2.450 0.014 1.198 0.319
## .Viv_Int 1.065 0.386 2.756 0.006 1.065 0.491
## .Viv_Pla 0.870 0.433 2.011 0.044 0.870 0.295
## .Viv_HabRC 0.194 0.165 1.178 0.239 0.194 0.256
## .Viv_HabR 0.439 0.227 1.937 0.053 0.439 0.558
## .Viv_PrInf 0.183 0.131 1.398 0.162 0.183 0.243
## .Viv_Aut 0.298 0.136 2.199 0.028 0.298 0.712
## Viv_Soft 1.045 0.427 2.446 0.014 1.000 1.000
## Viv_Hard 0.305 0.181 1.685 0.092 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.504 0.504 1.000
## Viv_EvInt 0.676 0.676 1.000
## Viv_DiF 0.516 0.516 1.000
## Viv_Int 0.679 0.679 1.000
## Viv_Pla 0.583 0.583 1.000
## Viv_HabRC 1.149 1.149 1.000
## Viv_HabR 1.127 1.127 1.000
## Viv_PrInf 1.153 1.153 1.000
## Viv_Aut 1.545 1.545 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.605
## Viv_EvInt 0.880
## Viv_DiF 0.681
## Viv_Int 0.509
## Viv_Pla 0.705
## Viv_HabRC 0.744
## Viv_HabR 0.442
## Viv_PrInf 0.757
## Viv_Aut 0.288
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.510 0.075 20.161 0.000 1.332 0.850
## Viv_EvInt 1.358 0.057 23.976 0.000 1.198 0.789
## Viv_DiF 1.563 0.100 15.665 0.000 1.379 0.784
## Viv_Int 1.027 0.040 25.942 0.000 0.906 0.688
## Viv_Pla 1.409 0.088 15.964 0.000 1.243 0.786
## Viv_Hard =~
## Viv_HabRC 1.358 0.089 15.283 0.000 0.667 0.729
## Viv_HabR 1.067 0.053 20.175 0.000 0.524 0.592
## Viv_PrInf 1.366 0.067 20.304 0.000 0.671 0.787
## Viv_Aut 0.629 0.027 23.080 0.000 0.309 0.534
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.279 0.185 1.511 0.131 0.279 0.623
## .Viv_AdmT ~~
## .Viv_Int 0.368 0.164 2.250 0.024 0.368 0.467
## Viv_Soft ~~
## Viv_Hard 0.357 0.122 2.923 0.003 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft -0.287 0.309 -0.927 0.354 -0.325 -0.325
## Viv_Hard -1.340 0.502 -2.672 0.008 -2.729 -2.729
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.119 0.192 -21.447 0.000 -4.119 -2.628
## Viv_AdmT|t2 -2.934 0.126 -23.337 0.000 -2.934 -1.872
## Viv_AdmT|t3 -2.213 0.195 -11.329 0.000 -2.213 -1.412
## Viv_AdmT|t4 -0.297 0.484 -0.614 0.539 -0.297 -0.190
## Viv_EvInt|t1 -3.788 0.153 -24.808 0.000 -3.788 -2.496
## Viv_EvInt|t2 -2.296 0.217 -10.601 0.000 -2.296 -1.513
## Viv_EvInt|t3 -1.574 0.278 -5.660 0.000 -1.574 -1.037
## Viv_EvInt|t4 0.090 0.483 0.186 0.853 0.090 0.059
## Viv_DiF|t1 -4.731 0.306 -15.453 0.000 -4.731 -2.691
## Viv_DiF|t2 -3.359 0.311 -10.786 0.000 -3.359 -1.911
## Viv_DiF|t3 -2.624 0.319 -8.231 0.000 -2.624 -1.493
## Viv_DiF|t4 -0.460 0.483 -0.952 0.341 -0.460 -0.261
## Viv_Int|t1 -3.051 0.103 -29.540 0.000 -3.051 -2.319
## Viv_Int|t2 -2.037 0.146 -13.945 0.000 -2.037 -1.548
## Viv_Int|t3 -1.497 0.186 -8.050 0.000 -1.497 -1.138
## Viv_Int|t4 0.150 0.376 0.400 0.689 0.150 0.114
## Viv_Pla|t1 -4.670 0.304 -15.384 0.000 -4.670 -2.953
## Viv_Pla|t2 -3.644 0.332 -10.988 0.000 -3.644 -2.305
## Viv_Pla|t3 -2.634 0.270 -9.763 0.000 -2.634 -1.666
## Viv_Pla|t4 -0.488 0.425 -1.147 0.251 -0.488 -0.309
## Viv_HabRC|t1 -4.480 0.287 -15.599 0.000 -4.480 -4.894
## Viv_HabRC|t2 -3.732 0.212 -17.633 0.000 -3.732 -4.076
## Viv_HabRC|t3 -3.239 0.254 -12.765 0.000 -3.239 -3.538
## Viv_HabRC|t4 -1.982 0.607 -3.267 0.001 -1.982 -2.165
## Viv_HabR|t1 -3.597 0.171 -21.086 0.000 -3.597 -4.059
## Viv_HabR|t2 -3.139 0.195 -16.096 0.000 -3.139 -3.542
## Viv_HabR|t3 -2.722 0.254 -10.713 0.000 -2.722 -3.072
## Viv_HabR|t4 -1.587 0.505 -3.145 0.002 -1.587 -1.791
## Viv_PrInf|t1 -4.016 0.193 -20.814 0.000 -4.016 -4.715
## Viv_PrInf|t2 -3.495 0.263 -13.277 0.000 -3.495 -4.104
## Viv_PrInf|t3 -2.984 0.365 -8.184 0.000 -2.984 -3.504
## Viv_PrInf|t4 -1.709 0.714 -2.393 0.017 -1.709 -2.007
## Viv_Aut|t1 -2.261 0.065 -34.714 0.000 -2.261 -3.909
## Viv_Aut|t2 -1.451 0.188 -7.697 0.000 -1.451 -2.508
## Viv_Aut|t3 -1.213 0.240 -5.057 0.000 -1.213 -2.097
## Viv_Aut|t4 -0.546 0.396 -1.377 0.168 -0.546 -0.943
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.683 0.341 2.000 0.046 0.683 0.278
## .Viv_EvInt 0.868 0.278 3.117 0.002 0.868 0.377
## .Viv_DiF 1.190 0.385 3.089 0.002 1.190 0.385
## .Viv_Int 0.911 0.267 3.408 0.001 0.911 0.526
## .Viv_Pla 0.956 0.412 2.321 0.020 0.956 0.382
## .Viv_HabRC 0.393 0.314 1.250 0.211 0.393 0.469
## .Viv_HabR 0.510 0.267 1.912 0.056 0.510 0.650
## .Viv_PrInf 0.276 0.187 1.470 0.142 0.276 0.380
## .Viv_Aut 0.239 0.116 2.066 0.039 0.239 0.715
## Viv_Soft 0.778 0.226 3.445 0.001 1.000 1.000
## Viv_Hard 0.241 0.143 1.685 0.092 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.638 0.638 1.000
## Viv_EvInt 0.659 0.659 1.000
## Viv_DiF 0.569 0.569 1.000
## Viv_Int 0.760 0.760 1.000
## Viv_Pla 0.632 0.632 1.000
## Viv_HabRC 1.092 1.092 1.000
## Viv_HabR 1.128 1.128 1.000
## Viv_PrInf 1.174 1.174 1.000
## Viv_Aut 1.729 1.729 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.722
## Viv_EvInt 0.623
## Viv_DiF 0.615
## Viv_Int 0.474
## Viv_Pla 0.618
## Viv_HabRC 0.531
## Viv_HabR 0.350
## Viv_PrInf 0.620
## Viv_Aut 0.285lavaan::fitMeasures(invariance$fit.loadings,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 215.078 117.000 0.000
## srmr cfi.scaled tli.scaled
## 0.029 0.998 0.998
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.021 0.017 0.025modificationindices(invariance$fit.loadings, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 468 Viv_Hard =~ Viv_EvInt 3 3 1 43.26 -0.319 -0.176 -0.119
## 228 Viv_EvInt ~1 3 3 1 42.47 0.994 0.994 0.672
## 219 Viv_EvInt ~*~ Viv_EvInt 3 3 1 42.47 0.165 0.165 1.000
## 435 Viv_AdmT ~~ Viv_Aut 2 2 1 28.89 0.137 0.137 0.197
## 469 Viv_Hard =~ Viv_DiF 3 3 1 27.13 0.317 0.175 0.090
## 220 Viv_DiF ~*~ Viv_DiF 3 3 1 25.84 -0.102 -0.102 -1.000
## 229 Viv_DiF ~1 3 3 1 25.84 -1.003 -1.003 -0.518
## 411 Viv_Pla ~~ Viv_HabRC 1 1 1 24.22 0.313 0.313 0.313
## 454 Viv_Pla ~~ Viv_HabRC 2 2 1 18.92 0.156 0.156 0.240
## 436 Viv_EvInt ~~ Viv_DiF 2 2 1 18.09 0.269 0.269 0.253
## sepc.nox
## 468 -0.119
## 228 0.672
## 219 1.000
## 435 0.197
## 469 0.090
## 220 -1.000
## 229 -0.518
## 411 0.313
## 454 0.240
## 436 0.253semTools::reliability(invariance$fit.loadings)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8484 0.7256
## alpha.ord 0.9065 0.8243
## omega 0.8298 0.7020
## omega2 0.8298 0.7020
## omega3 0.8292 0.7068
## avevar 0.6578 0.5674
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8543 0.7213
## alpha.ord 0.9124 0.8254
## omega 0.8339 0.5852
## omega2 0.8339 0.5852
## omega3 0.8300 0.5831
## avevar 0.6717 0.5583
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8575 0.7587
## alpha.ord 0.9168 0.8391
## omega 0.8442 0.4222
## omega2 0.8442 0.4222
## omega3 0.8447 0.4139
## avevar 0.6697 0.5899
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8357 0.6651
## alpha.ord 0.8926 0.7947
## omega 0.8064 0.3342
## omega2 0.8064 0.3342
## omega3 0.8043 0.3321
## avevar 0.6187 0.47164.1.1.8.3 Thresholds
summary(invariance$fit.thresholds,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 165 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 231
## Number of equality constraints 135
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 911.178 292.970
## Degrees of freedom 192 192
## P-value (Unknown) NA 0.000
## Scaling correction factor 5.222
## Shift parameter for each group:
## 2 39.027
## 3 62.388
## 1 7.834
## 4 9.235
## simple second-order correction
## Test statistic for each group:
## 2 230.747 83.214
## 3 206.734 101.976
## 1 232.042 52.269
## 4 241.655 55.511
##
## Model Test Baseline Model:
##
## Test statistic 83696.523 64274.637
## Degrees of freedom 144 144
## P-value NA 0.000
## Scaling correction factor 1.304
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.991 0.998
## Tucker-Lewis Index (TLI) 0.994 0.999
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.044 0.017
## 90 Percent confidence interval - lower 0.042 0.013
## 90 Percent confidence interval - upper 0.047 0.020
## P-value RMSEA <= 0.05 0.999 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.028 0.028
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.516 0.065 23.157 0.000 1.516 0.835
## Viv_EvInt 1.405 0.054 26.025 0.000 1.405 0.815
## Viv_DiF 1.512 0.075 20.194 0.000 1.512 0.834
## Viv_Int 1.035 0.036 28.547 0.000 1.035 0.719
## Viv_Pla 1.392 0.073 19.004 0.000 1.392 0.812
## Viv_Hard =~
## Viv_HabRC 1.374 0.077 17.948 0.000 1.374 0.808
## Viv_HabR 1.015 0.043 23.747 0.000 1.015 0.712
## Viv_PrInf 1.465 0.070 20.963 0.000 1.465 0.826
## Viv_Aut 0.608 0.023 26.369 0.000 0.608 0.520
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.483 0.029 16.645 0.000 0.483 0.483
## .Viv_AdmT ~~
## .Viv_Int 0.417 0.028 14.662 0.000 0.417 0.417
## Viv_Soft ~~
## Viv_Hard 0.838 0.011 75.412 0.000 0.838 0.838
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.128 0.166 -24.881 0.000 -4.128 -2.273
## Viv_AdmT|t2 -2.931 0.117 -25.141 0.000 -2.931 -1.614
## Viv_AdmT|t3 -2.157 0.089 -24.165 0.000 -2.157 -1.188
## Viv_AdmT|t4 0.280 0.042 6.621 0.000 0.280 0.154
## Viv_EvInt|t1 -3.929 0.139 -28.264 0.000 -3.929 -2.278
## Viv_EvInt|t2 -2.386 0.088 -27.225 0.000 -2.386 -1.384
## Viv_EvInt|t3 -1.579 0.064 -24.712 0.000 -1.579 -0.916
## Viv_EvInt|t4 0.640 0.045 14.150 0.000 0.640 0.371
## Viv_DiF|t1 -4.574 0.223 -20.514 0.000 -4.574 -2.523
## Viv_DiF|t2 -3.379 0.155 -21.819 0.000 -3.379 -1.864
## Viv_DiF|t3 -2.563 0.121 -21.175 0.000 -2.563 -1.414
## Viv_DiF|t4 -0.006 0.041 -0.146 0.884 -0.006 -0.003
## Viv_Int|t1 -3.079 0.092 -33.542 0.000 -3.079 -2.139
## Viv_Int|t2 -2.076 0.062 -33.280 0.000 -2.076 -1.442
## Viv_Int|t3 -1.456 0.048 -30.403 0.000 -1.456 -1.012
## Viv_Int|t4 0.606 0.035 17.347 0.000 0.606 0.421
## Viv_Pla|t1 -4.612 0.243 -18.990 0.000 -4.612 -2.691
## Viv_Pla|t2 -3.717 0.187 -19.842 0.000 -3.717 -2.169
## Viv_Pla|t3 -2.659 0.136 -19.568 0.000 -2.659 -1.552
## Viv_Pla|t4 0.028 0.038 0.745 0.456 0.028 0.017
## Viv_HabRC|t1 -4.652 0.240 -19.391 0.000 -4.652 -2.738
## Viv_HabRC|t2 -3.608 0.191 -18.889 0.000 -3.608 -2.123
## Viv_HabRC|t3 -2.517 0.131 -19.255 0.000 -2.517 -1.482
## Viv_HabRC|t4 0.087 0.039 2.245 0.025 0.087 0.051
## Viv_HabR|t1 -3.525 0.131 -26.894 0.000 -3.525 -2.473
## Viv_HabR|t2 -2.384 0.084 -28.433 0.000 -2.384 -1.673
## Viv_HabR|t3 -1.471 0.055 -26.568 0.000 -1.471 -1.032
## Viv_HabR|t4 0.272 0.032 8.480 0.000 0.272 0.191
## Viv_PrInf|t1 -4.232 0.177 -23.887 0.000 -4.232 -2.386
## Viv_PrInf|t2 -3.071 0.128 -24.000 0.000 -3.071 -1.731
## Viv_PrInf|t3 -2.176 0.095 -23.019 0.000 -2.176 -1.227
## Viv_PrInf|t4 0.491 0.046 10.773 0.000 0.491 0.277
## Viv_Aut|t1 -2.291 0.057 -40.065 0.000 -2.291 -1.957
## Viv_Aut|t2 -0.921 0.029 -31.618 0.000 -0.921 -0.787
## Viv_Aut|t3 -0.359 0.022 -16.035 0.000 -0.359 -0.307
## Viv_Aut|t4 0.940 0.031 30.359 0.000 0.940 0.803
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.303
## .Viv_EvInt 1.000 1.000 0.336
## .Viv_DiF 1.000 1.000 0.304
## .Viv_Int 1.000 1.000 0.483
## .Viv_Pla 1.000 1.000 0.341
## .Viv_HabRC 1.000 1.000 0.346
## .Viv_HabR 1.000 1.000 0.492
## .Viv_PrInf 1.000 1.000 0.318
## .Viv_Aut 1.000 1.000 0.730
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.551 0.551 1.000
## Viv_EvInt 0.580 0.580 1.000
## Viv_DiF 0.552 0.552 1.000
## Viv_Int 0.695 0.695 1.000
## Viv_Pla 0.584 0.584 1.000
## Viv_HabRC 0.589 0.589 1.000
## Viv_HabR 0.702 0.702 1.000
## Viv_PrInf 0.564 0.564 1.000
## Viv_Aut 0.854 0.854 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.697
## Viv_EvInt 0.664
## Viv_DiF 0.696
## Viv_Int 0.517
## Viv_Pla 0.659
## Viv_HabRC 0.654
## Viv_HabR 0.508
## Viv_PrInf 0.682
## Viv_Aut 0.270
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.516 0.065 23.157 0.000 1.601 0.833
## Viv_EvInt 1.405 0.054 26.025 0.000 1.483 0.806
## Viv_DiF 1.512 0.075 20.194 0.000 1.597 0.852
## Viv_Int 1.035 0.036 28.547 0.000 1.093 0.714
## Viv_Pla 1.392 0.073 19.004 0.000 1.469 0.849
## Viv_Hard =~
## Viv_HabRC 1.374 0.077 17.948 0.000 1.413 0.792
## Viv_HabR 1.015 0.043 23.747 0.000 1.045 0.703
## Viv_PrInf 1.465 0.070 20.963 0.000 1.507 0.803
## Viv_Aut 0.608 0.023 26.369 0.000 0.626 0.555
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.575 0.072 7.958 0.000 0.575 0.501
## .Viv_AdmT ~~
## .Viv_Int 0.530 0.056 9.508 0.000 0.530 0.465
## Viv_Soft ~~
## Viv_Hard 0.885 0.050 17.831 0.000 0.815 0.815
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.119 0.034 3.485 0.000 0.113 0.113
## Viv_Hard 0.211 0.037 5.738 0.000 0.205 0.205
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.128 0.166 -24.881 0.000 -4.128 -2.148
## Viv_AdmT|t2 -2.931 0.117 -25.141 0.000 -2.931 -1.525
## Viv_AdmT|t3 -2.157 0.089 -24.165 0.000 -2.157 -1.122
## Viv_AdmT|t4 0.280 0.042 6.621 0.000 0.280 0.146
## Viv_EvInt|t1 -3.929 0.139 -28.264 0.000 -3.929 -2.135
## Viv_EvInt|t2 -2.386 0.088 -27.225 0.000 -2.386 -1.297
## Viv_EvInt|t3 -1.579 0.064 -24.712 0.000 -1.579 -0.858
## Viv_EvInt|t4 0.640 0.045 14.150 0.000 0.640 0.348
## Viv_DiF|t1 -4.574 0.223 -20.514 0.000 -4.574 -2.442
## Viv_DiF|t2 -3.379 0.155 -21.819 0.000 -3.379 -1.804
## Viv_DiF|t3 -2.563 0.121 -21.175 0.000 -2.563 -1.368
## Viv_DiF|t4 -0.006 0.041 -0.146 0.884 -0.006 -0.003
## Viv_Int|t1 -3.079 0.092 -33.542 0.000 -3.079 -2.011
## Viv_Int|t2 -2.076 0.062 -33.280 0.000 -2.076 -1.356
## Viv_Int|t3 -1.456 0.048 -30.403 0.000 -1.456 -0.951
## Viv_Int|t4 0.606 0.035 17.347 0.000 0.606 0.396
## Viv_Pla|t1 -4.612 0.243 -18.990 0.000 -4.612 -2.666
## Viv_Pla|t2 -3.717 0.187 -19.842 0.000 -3.717 -2.148
## Viv_Pla|t3 -2.659 0.136 -19.568 0.000 -2.659 -1.537
## Viv_Pla|t4 0.028 0.038 0.745 0.456 0.028 0.016
## Viv_HabRC|t1 -4.652 0.240 -19.391 0.000 -4.652 -2.609
## Viv_HabRC|t2 -3.608 0.191 -18.889 0.000 -3.608 -2.023
## Viv_HabRC|t3 -2.517 0.131 -19.255 0.000 -2.517 -1.412
## Viv_HabRC|t4 0.087 0.039 2.245 0.025 0.087 0.049
## Viv_HabR|t1 -3.525 0.131 -26.894 0.000 -3.525 -2.373
## Viv_HabR|t2 -2.384 0.084 -28.433 0.000 -2.384 -1.606
## Viv_HabR|t3 -1.471 0.055 -26.568 0.000 -1.471 -0.990
## Viv_HabR|t4 0.272 0.032 8.480 0.000 0.272 0.183
## Viv_PrInf|t1 -4.232 0.177 -23.887 0.000 -4.232 -2.255
## Viv_PrInf|t2 -3.071 0.128 -24.000 0.000 -3.071 -1.637
## Viv_PrInf|t3 -2.176 0.095 -23.019 0.000 -2.176 -1.159
## Viv_PrInf|t4 0.491 0.046 10.773 0.000 0.491 0.262
## Viv_Aut|t1 -2.291 0.057 -40.065 0.000 -2.291 -2.031
## Viv_Aut|t2 -0.921 0.029 -31.618 0.000 -0.921 -0.817
## Viv_Aut|t3 -0.359 0.022 -16.035 0.000 -0.359 -0.318
## Viv_Aut|t4 0.940 0.031 30.359 0.000 0.940 0.833
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.132 0.124 9.126 0.000 1.132 0.306
## .Viv_EvInt 1.186 0.116 10.267 0.000 1.186 0.350
## .Viv_DiF 0.960 0.137 6.993 0.000 0.960 0.274
## .Viv_Int 1.148 0.090 12.762 0.000 1.148 0.490
## .Viv_Pla 0.835 0.138 6.064 0.000 0.835 0.279
## .Viv_HabRC 1.183 0.177 6.687 0.000 1.183 0.372
## .Viv_HabR 1.114 0.111 10.004 0.000 1.114 0.505
## .Viv_PrInf 1.250 0.142 8.812 0.000 1.250 0.355
## .Viv_Aut 0.881 0.056 15.835 0.000 0.881 0.692
## Viv_Soft 1.115 0.069 16.124 0.000 1.000 1.000
## Viv_Hard 1.058 0.072 14.704 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.520 0.520 1.000
## Viv_EvInt 0.543 0.543 1.000
## Viv_DiF 0.534 0.534 1.000
## Viv_Int 0.653 0.653 1.000
## Viv_Pla 0.578 0.578 1.000
## Viv_HabRC 0.561 0.561 1.000
## Viv_HabR 0.673 0.673 1.000
## Viv_PrInf 0.533 0.533 1.000
## Viv_Aut 0.887 0.887 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.694
## Viv_EvInt 0.650
## Viv_DiF 0.726
## Viv_Int 0.510
## Viv_Pla 0.721
## Viv_HabRC 0.628
## Viv_HabR 0.495
## Viv_PrInf 0.645
## Viv_Aut 0.308
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.516 0.065 23.157 0.000 1.426 0.787
## Viv_EvInt 1.405 0.054 26.025 0.000 1.321 0.911
## Viv_DiF 1.512 0.075 20.194 0.000 1.422 0.838
## Viv_Int 1.035 0.036 28.547 0.000 0.974 0.747
## Viv_Pla 1.392 0.073 19.004 0.000 1.309 0.823
## Viv_Hard =~
## Viv_HabRC 1.374 0.077 17.948 0.000 1.344 0.873
## Viv_HabR 1.015 0.043 23.747 0.000 0.993 0.682
## Viv_PrInf 1.465 0.070 20.963 0.000 1.433 0.841
## Viv_Aut 0.608 0.023 26.369 0.000 0.595 0.553
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.352 0.101 3.484 0.000 0.352 0.440
## .Viv_AdmT ~~
## .Viv_Int 0.313 0.115 2.728 0.006 0.313 0.322
## Viv_Soft ~~
## Viv_Hard 0.778 0.105 7.424 0.000 0.845 0.845
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft -0.021 0.060 -0.355 0.723 -0.023 -0.023
## Viv_Hard -0.294 0.059 -5.011 0.000 -0.300 -0.300
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.128 0.166 -24.881 0.000 -4.128 -2.279
## Viv_AdmT|t2 -2.931 0.117 -25.141 0.000 -2.931 -1.618
## Viv_AdmT|t3 -2.157 0.089 -24.165 0.000 -2.157 -1.191
## Viv_AdmT|t4 0.280 0.042 6.621 0.000 0.280 0.155
## Viv_EvInt|t1 -3.929 0.139 -28.264 0.000 -3.929 -2.710
## Viv_EvInt|t2 -2.386 0.088 -27.225 0.000 -2.386 -1.646
## Viv_EvInt|t3 -1.579 0.064 -24.712 0.000 -1.579 -1.089
## Viv_EvInt|t4 0.640 0.045 14.150 0.000 0.640 0.441
## Viv_DiF|t1 -4.574 0.223 -20.514 0.000 -4.574 -2.696
## Viv_DiF|t2 -3.379 0.155 -21.819 0.000 -3.379 -1.992
## Viv_DiF|t3 -2.563 0.121 -21.175 0.000 -2.563 -1.511
## Viv_DiF|t4 -0.006 0.041 -0.146 0.884 -0.006 -0.003
## Viv_Int|t1 -3.079 0.092 -33.542 0.000 -3.079 -2.360
## Viv_Int|t2 -2.076 0.062 -33.280 0.000 -2.076 -1.591
## Viv_Int|t3 -1.456 0.048 -30.403 0.000 -1.456 -1.116
## Viv_Int|t4 0.606 0.035 17.347 0.000 0.606 0.464
## Viv_Pla|t1 -4.612 0.243 -18.990 0.000 -4.612 -2.902
## Viv_Pla|t2 -3.717 0.187 -19.842 0.000 -3.717 -2.338
## Viv_Pla|t3 -2.659 0.136 -19.568 0.000 -2.659 -1.673
## Viv_Pla|t4 0.028 0.038 0.745 0.456 0.028 0.018
## Viv_HabRC|t1 -4.652 0.240 -19.391 0.000 -4.652 -3.021
## Viv_HabRC|t2 -3.608 0.191 -18.889 0.000 -3.608 -2.343
## Viv_HabRC|t3 -2.517 0.131 -19.255 0.000 -2.517 -1.635
## Viv_HabRC|t4 0.087 0.039 2.245 0.025 0.087 0.057
## Viv_HabR|t1 -3.525 0.131 -26.894 0.000 -3.525 -2.421
## Viv_HabR|t2 -2.384 0.084 -28.433 0.000 -2.384 -1.637
## Viv_HabR|t3 -1.471 0.055 -26.568 0.000 -1.471 -1.010
## Viv_HabR|t4 0.272 0.032 8.480 0.000 0.272 0.187
## Viv_PrInf|t1 -4.232 0.177 -23.887 0.000 -4.232 -2.484
## Viv_PrInf|t2 -3.071 0.128 -24.000 0.000 -3.071 -1.803
## Viv_PrInf|t3 -2.176 0.095 -23.019 0.000 -2.176 -1.277
## Viv_PrInf|t4 0.491 0.046 10.773 0.000 0.491 0.288
## Viv_Aut|t1 -2.291 0.057 -40.065 0.000 -2.291 -2.127
## Viv_Aut|t2 -0.921 0.029 -31.618 0.000 -0.921 -0.856
## Viv_Aut|t3 -0.359 0.022 -16.035 0.000 -0.359 -0.333
## Viv_Aut|t4 0.940 0.031 30.359 0.000 0.940 0.873
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.247 0.236 5.282 0.000 1.247 0.380
## .Viv_EvInt 0.356 0.148 2.410 0.016 0.356 0.169
## .Viv_DiF 0.856 0.215 3.988 0.000 0.856 0.297
## .Viv_Int 0.754 0.130 5.803 0.000 0.754 0.443
## .Viv_Pla 0.813 0.206 3.954 0.000 0.813 0.322
## .Viv_HabRC 0.565 0.217 2.608 0.009 0.565 0.238
## .Viv_HabR 1.134 0.167 6.783 0.000 1.134 0.535
## .Viv_PrInf 0.849 0.163 5.199 0.000 0.849 0.292
## .Viv_Aut 0.806 0.090 8.906 0.000 0.806 0.695
## Viv_Soft 0.885 0.130 6.804 0.000 1.000 1.000
## Viv_Hard 0.957 0.125 7.648 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.552 0.552 1.000
## Viv_EvInt 0.690 0.690 1.000
## Viv_DiF 0.589 0.589 1.000
## Viv_Int 0.766 0.766 1.000
## Viv_Pla 0.629 0.629 1.000
## Viv_HabRC 0.649 0.649 1.000
## Viv_HabR 0.687 0.687 1.000
## Viv_PrInf 0.587 0.587 1.000
## Viv_Aut 0.928 0.928 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.620
## Viv_EvInt 0.831
## Viv_DiF 0.703
## Viv_Int 0.557
## Viv_Pla 0.678
## Viv_HabRC 0.762
## Viv_HabR 0.465
## Viv_PrInf 0.708
## Viv_Aut 0.305
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.516 0.065 23.157 0.000 1.532 0.850
## Viv_EvInt 1.405 0.054 26.025 0.000 1.420 0.783
## Viv_DiF 1.512 0.075 20.194 0.000 1.528 0.782
## Viv_Int 1.035 0.036 28.547 0.000 1.046 0.689
## Viv_Pla 1.392 0.073 19.004 0.000 1.406 0.793
## Viv_Hard =~
## Viv_HabRC 1.374 0.077 17.948 0.000 1.332 0.708
## Viv_HabR 1.015 0.043 23.747 0.000 0.984 0.647
## Viv_PrInf 1.465 0.070 20.963 0.000 1.420 0.750
## Viv_Aut 0.608 0.023 26.369 0.000 0.590 0.556
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.906 0.209 4.338 0.000 0.906 0.588
## .Viv_AdmT ~~
## .Viv_Int 0.486 0.135 3.595 0.000 0.486 0.465
## Viv_Soft ~~
## Viv_Hard 0.808 0.075 10.799 0.000 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.001 0.059 0.017 0.986 0.001 0.001
## Viv_Hard 0.401 0.071 5.621 0.000 0.414 0.414
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.128 0.166 -24.881 0.000 -4.128 -2.290
## Viv_AdmT|t2 -2.931 0.117 -25.141 0.000 -2.931 -1.626
## Viv_AdmT|t3 -2.157 0.089 -24.165 0.000 -2.157 -1.197
## Viv_AdmT|t4 0.280 0.042 6.621 0.000 0.280 0.155
## Viv_EvInt|t1 -3.929 0.139 -28.264 0.000 -3.929 -2.167
## Viv_EvInt|t2 -2.386 0.088 -27.225 0.000 -2.386 -1.316
## Viv_EvInt|t3 -1.579 0.064 -24.712 0.000 -1.579 -0.871
## Viv_EvInt|t4 0.640 0.045 14.150 0.000 0.640 0.353
## Viv_DiF|t1 -4.574 0.223 -20.514 0.000 -4.574 -2.341
## Viv_DiF|t2 -3.379 0.155 -21.819 0.000 -3.379 -1.730
## Viv_DiF|t3 -2.563 0.121 -21.175 0.000 -2.563 -1.312
## Viv_DiF|t4 -0.006 0.041 -0.146 0.884 -0.006 -0.003
## Viv_Int|t1 -3.079 0.092 -33.542 0.000 -3.079 -2.028
## Viv_Int|t2 -2.076 0.062 -33.280 0.000 -2.076 -1.368
## Viv_Int|t3 -1.456 0.048 -30.403 0.000 -1.456 -0.959
## Viv_Int|t4 0.606 0.035 17.347 0.000 0.606 0.399
## Viv_Pla|t1 -4.612 0.243 -18.990 0.000 -4.612 -2.600
## Viv_Pla|t2 -3.717 0.187 -19.842 0.000 -3.717 -2.096
## Viv_Pla|t3 -2.659 0.136 -19.568 0.000 -2.659 -1.499
## Viv_Pla|t4 0.028 0.038 0.745 0.456 0.028 0.016
## Viv_HabRC|t1 -4.652 0.240 -19.391 0.000 -4.652 -2.474
## Viv_HabRC|t2 -3.608 0.191 -18.889 0.000 -3.608 -1.919
## Viv_HabRC|t3 -2.517 0.131 -19.255 0.000 -2.517 -1.339
## Viv_HabRC|t4 0.087 0.039 2.245 0.025 0.087 0.046
## Viv_HabR|t1 -3.525 0.131 -26.894 0.000 -3.525 -2.316
## Viv_HabR|t2 -2.384 0.084 -28.433 0.000 -2.384 -1.567
## Viv_HabR|t3 -1.471 0.055 -26.568 0.000 -1.471 -0.966
## Viv_HabR|t4 0.272 0.032 8.480 0.000 0.272 0.179
## Viv_PrInf|t1 -4.232 0.177 -23.887 0.000 -4.232 -2.236
## Viv_PrInf|t2 -3.071 0.128 -24.000 0.000 -3.071 -1.623
## Viv_PrInf|t3 -2.176 0.095 -23.019 0.000 -2.176 -1.150
## Viv_PrInf|t4 0.491 0.046 10.773 0.000 0.491 0.260
## Viv_Aut|t1 -2.291 0.057 -40.065 0.000 -2.291 -2.160
## Viv_Aut|t2 -0.921 0.029 -31.618 0.000 -0.921 -0.869
## Viv_Aut|t3 -0.359 0.022 -16.035 0.000 -0.359 -0.339
## Viv_Aut|t4 0.940 0.031 30.359 0.000 0.940 0.886
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.902 0.222 4.058 0.000 0.902 0.277
## .Viv_EvInt 1.271 0.211 6.030 0.000 1.271 0.387
## .Viv_DiF 1.482 0.284 5.225 0.000 1.482 0.388
## .Viv_Int 1.210 0.174 6.973 0.000 1.210 0.525
## .Viv_Pla 1.168 0.304 3.840 0.000 1.168 0.371
## .Viv_HabRC 1.761 0.457 3.855 0.000 1.761 0.498
## .Viv_HabR 1.347 0.235 5.737 0.000 1.347 0.582
## .Viv_PrInf 1.564 0.333 4.700 0.000 1.564 0.437
## .Viv_Aut 0.777 0.117 6.644 0.000 0.777 0.691
## Viv_Soft 1.021 0.109 9.338 0.000 1.000 1.000
## Viv_Hard 0.940 0.109 8.645 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.555 0.555 1.000
## Viv_EvInt 0.552 0.552 1.000
## Viv_DiF 0.512 0.512 1.000
## Viv_Int 0.659 0.659 1.000
## Viv_Pla 0.564 0.564 1.000
## Viv_HabRC 0.532 0.532 1.000
## Viv_HabR 0.657 0.657 1.000
## Viv_PrInf 0.528 0.528 1.000
## Viv_Aut 0.943 0.943 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.723
## Viv_EvInt 0.613
## Viv_DiF 0.612
## Viv_Int 0.475
## Viv_Pla 0.629
## Viv_HabRC 0.502
## Viv_HabR 0.418
## Viv_PrInf 0.563
## Viv_Aut 0.309lavaan::fitMeasures(invariance$fit.thresholds,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 292.970 192.000 0.000
## srmr cfi.scaled tli.scaled
## 0.028 0.998 0.999
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.017 0.013 0.020modificationindices(invariance$fit.thresholds, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 77 Viv_Aut ~1 1 1 1 82.36 -0.166 -0.166 -0.142
## 156 Viv_Aut ~1 2 2 1 66.89 0.141 0.141 0.125
## 313 Viv_PrInf ~1 4 4 1 40.12 -0.510 -0.510 -0.269
## 312 Viv_HabR ~1 4 4 1 24.84 0.305 0.305 0.200
## 543 Viv_Hard =~ Viv_EvInt 3 3 1 24.26 -0.205 -0.201 -0.138
## 486 Viv_Pla ~~ Viv_HabRC 1 1 1 24.23 0.312 0.312 0.312
## 219 Viv_EvInt ~*~ Viv_EvInt 3 3 1 22.32 0.117 0.117 1.000
## 76 Viv_PrInf ~1 1 1 1 22.31 0.186 0.186 0.105
## 510 Viv_AdmT ~~ Viv_Aut 2 2 1 22.24 0.173 0.173 0.173
## 314 Viv_Aut ~1 4 4 1 20.88 0.153 0.153 0.144
## sepc.nox
## 77 -0.142
## 156 0.125
## 313 -0.269
## 312 0.200
## 543 -0.138
## 486 0.312
## 219 1.000
## 76 0.105
## 510 0.173
## 314 0.144semTools::reliability(invariance$fit.thresholds)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8484 0.7256
## alpha.ord 0.9065 0.8243
## omega 0.8310 0.6973
## omega2 0.8310 0.6973
## omega3 0.8311 0.6996
## avevar 0.6568 0.5760
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8543 0.7213
## alpha.ord 0.9124 0.8254
## omega 0.8334 0.7027
## omega2 0.8334 0.7027
## omega3 0.8286 0.7026
## avevar 0.6697 0.5650
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8575 0.7587
## alpha.ord 0.9168 0.8391
## omega 0.8431 0.7167
## omega2 0.8431 0.7167
## omega3 0.8443 0.7121
## avevar 0.6777 0.6080
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8357 0.6651
## alpha.ord 0.8926 0.7947
## omega 0.8129 0.6489
## omega2 0.8129 0.6489
## omega3 0.8106 0.6510
## avevar 0.6183 0.48384.1.1.8.4 Partial Invariance - Means
summary(invariance$fit.means,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 186 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 225
## Number of equality constraints 135
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 1682.934 426.271
## Degrees of freedom 198 198
## P-value (Unknown) NA 0.000
## Scaling correction factor 5.511
## Shift parameter for each group:
## 2 39.829
## 3 63.670
## 1 7.994
## 4 9.425
## simple second-order correction
## Test statistic for each group:
## 2 407.101 113.694
## 3 337.886 124.976
## 1 564.474 110.413
## 4 373.473 77.188
##
## Model Test Baseline Model:
##
## Test statistic 83696.523 64274.637
## Degrees of freedom 144 144
## P-value NA 0.000
## Scaling correction factor 1.304
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.982 0.996
## Tucker-Lewis Index (TLI) 0.987 0.997
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063 0.025
## 90 Percent confidence interval - lower 0.060 0.021
## 90 Percent confidence interval - upper 0.066 0.028
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.028 0.028
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.503 0.065 23.209 0.000 1.503 0.832
## Viv_EvInt 1.407 0.054 25.821 0.000 1.407 0.815
## Viv_DiF 1.508 0.074 20.263 0.000 1.508 0.833
## Viv_Int 1.039 0.036 28.550 0.000 1.039 0.720
## Viv_Pla 1.399 0.074 18.833 0.000 1.399 0.813
## Viv_Hard =~
## Viv_HabRC 1.395 0.079 17.699 0.000 1.395 0.813
## Viv_HabR 1.009 0.042 23.884 0.000 1.009 0.710
## Viv_PrInf 1.466 0.071 20.694 0.000 1.466 0.826
## Viv_Aut 0.605 0.023 26.614 0.000 0.605 0.518
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.483 0.029 16.537 0.000 0.483 0.483
## .Viv_AdmT ~~
## .Viv_Int 0.417 0.028 14.668 0.000 0.417 0.417
## Viv_Soft ~~
## Viv_Hard 0.838 0.011 75.481 0.000 0.838 0.838
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.083 0.165 -24.709 0.000 -4.083 -2.262
## Viv_AdmT|t2 -2.925 0.116 -25.298 0.000 -2.925 -1.621
## Viv_AdmT|t3 -2.177 0.087 -24.935 0.000 -2.177 -1.206
## Viv_AdmT|t4 0.183 0.027 6.889 0.000 0.183 0.101
## Viv_EvInt|t1 -3.917 0.141 -27.808 0.000 -3.917 -2.269
## Viv_EvInt|t2 -2.410 0.088 -27.510 0.000 -2.410 -1.396
## Viv_EvInt|t3 -1.621 0.062 -26.199 0.000 -1.621 -0.939
## Viv_EvInt|t4 0.548 0.030 17.995 0.000 0.548 0.318
## Viv_DiF|t1 -4.542 0.221 -20.516 0.000 -4.542 -2.511
## Viv_DiF|t2 -3.381 0.155 -21.858 0.000 -3.381 -1.869
## Viv_DiF|t3 -2.585 0.121 -21.417 0.000 -2.585 -1.429
## Viv_DiF|t4 -0.096 0.027 -3.611 0.000 -0.096 -0.053
## Viv_Int|t1 -3.074 0.093 -33.187 0.000 -3.074 -2.132
## Viv_Int|t2 -2.091 0.062 -33.629 0.000 -2.091 -1.450
## Viv_Int|t3 -1.485 0.046 -32.087 0.000 -1.485 -1.030
## Viv_Int|t4 0.534 0.025 21.311 0.000 0.534 0.370
## Viv_Pla|t1 -4.608 0.245 -18.841 0.000 -4.608 -2.680
## Viv_Pla|t2 -3.732 0.190 -19.660 0.000 -3.732 -2.170
## Viv_Pla|t3 -2.693 0.138 -19.475 0.000 -2.693 -1.566
## Viv_Pla|t4 -0.057 0.025 -2.310 0.021 -0.057 -0.033
## Viv_HabRC|t1 -4.665 0.244 -19.090 0.000 -4.665 -2.718
## Viv_HabRC|t2 -3.648 0.196 -18.639 0.000 -3.648 -2.126
## Viv_HabRC|t3 -2.591 0.136 -19.007 0.000 -2.591 -1.510
## Viv_HabRC|t4 -0.067 0.025 -2.712 0.007 -0.067 -0.039
## Viv_HabR|t1 -3.481 0.129 -27.037 0.000 -3.481 -2.451
## Viv_HabR|t2 -2.397 0.084 -28.666 0.000 -2.397 -1.687
## Viv_HabR|t3 -1.528 0.055 -27.698 0.000 -1.528 -1.075
## Viv_HabR|t4 0.135 0.020 6.568 0.000 0.135 0.095
## Viv_PrInf|t1 -4.201 0.179 -23.509 0.000 -4.201 -2.367
## Viv_PrInf|t2 -3.098 0.130 -23.802 0.000 -3.098 -1.745
## Viv_PrInf|t3 -2.241 0.096 -23.351 0.000 -2.241 -1.263
## Viv_PrInf|t4 0.303 0.027 11.073 0.000 0.303 0.171
## Viv_Aut|t1 -2.287 0.057 -40.099 0.000 -2.287 -1.957
## Viv_Aut|t2 -0.968 0.028 -35.128 0.000 -0.968 -0.828
## Viv_Aut|t3 -0.429 0.019 -22.929 0.000 -0.429 -0.367
## Viv_Aut|t4 0.826 0.025 32.916 0.000 0.826 0.706
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.307
## .Viv_EvInt 1.000 1.000 0.336
## .Viv_DiF 1.000 1.000 0.306
## .Viv_Int 1.000 1.000 0.481
## .Viv_Pla 1.000 1.000 0.338
## .Viv_HabRC 1.000 1.000 0.340
## .Viv_HabR 1.000 1.000 0.496
## .Viv_PrInf 1.000 1.000 0.317
## .Viv_Aut 1.000 1.000 0.732
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.554 0.554 1.000
## Viv_EvInt 0.579 0.579 1.000
## Viv_DiF 0.553 0.553 1.000
## Viv_Int 0.693 0.693 1.000
## Viv_Pla 0.582 0.582 1.000
## Viv_HabRC 0.583 0.583 1.000
## Viv_HabR 0.704 0.704 1.000
## Viv_PrInf 0.563 0.563 1.000
## Viv_Aut 0.856 0.856 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.693
## Viv_EvInt 0.664
## Viv_DiF 0.694
## Viv_Int 0.519
## Viv_Pla 0.662
## Viv_HabRC 0.660
## Viv_HabR 0.504
## Viv_PrInf 0.683
## Viv_Aut 0.268
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.503 0.065 23.209 0.000 1.513 0.834
## Viv_EvInt 1.407 0.054 25.821 0.000 1.416 0.808
## Viv_DiF 1.508 0.074 20.263 0.000 1.518 0.852
## Viv_Int 1.039 0.036 28.550 0.000 1.046 0.714
## Viv_Pla 1.399 0.074 18.833 0.000 1.408 0.847
## Viv_Hard =~
## Viv_HabRC 1.395 0.079 17.699 0.000 1.333 0.797
## Viv_HabR 1.009 0.042 23.884 0.000 0.965 0.702
## Viv_PrInf 1.466 0.071 20.694 0.000 1.402 0.811
## Viv_Aut 0.605 0.023 26.614 0.000 0.578 0.543
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.495 0.060 8.217 0.000 0.495 0.500
## .Viv_AdmT ~~
## .Viv_Int 0.476 0.050 9.455 0.000 0.476 0.464
## Viv_Soft ~~
## Viv_Hard 0.783 0.040 19.344 0.000 0.814 0.814
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.083 0.165 -24.709 0.000 -4.083 -2.251
## Viv_AdmT|t2 -2.925 0.116 -25.298 0.000 -2.925 -1.613
## Viv_AdmT|t3 -2.177 0.087 -24.935 0.000 -2.177 -1.200
## Viv_AdmT|t4 0.183 0.027 6.889 0.000 0.183 0.101
## Viv_EvInt|t1 -3.917 0.141 -27.808 0.000 -3.917 -2.235
## Viv_EvInt|t2 -2.410 0.088 -27.510 0.000 -2.410 -1.375
## Viv_EvInt|t3 -1.621 0.062 -26.199 0.000 -1.621 -0.925
## Viv_EvInt|t4 0.548 0.030 17.995 0.000 0.548 0.313
## Viv_DiF|t1 -4.542 0.221 -20.516 0.000 -4.542 -2.549
## Viv_DiF|t2 -3.381 0.155 -21.858 0.000 -3.381 -1.897
## Viv_DiF|t3 -2.585 0.121 -21.417 0.000 -2.585 -1.451
## Viv_DiF|t4 -0.096 0.027 -3.611 0.000 -0.096 -0.054
## Viv_Int|t1 -3.074 0.093 -33.187 0.000 -3.074 -2.098
## Viv_Int|t2 -2.091 0.062 -33.629 0.000 -2.091 -1.427
## Viv_Int|t3 -1.485 0.046 -32.087 0.000 -1.485 -1.014
## Viv_Int|t4 0.534 0.025 21.311 0.000 0.534 0.365
## Viv_Pla|t1 -4.608 0.245 -18.841 0.000 -4.608 -2.772
## Viv_Pla|t2 -3.732 0.190 -19.660 0.000 -3.732 -2.244
## Viv_Pla|t3 -2.693 0.138 -19.475 0.000 -2.693 -1.619
## Viv_Pla|t4 -0.057 0.025 -2.310 0.021 -0.057 -0.034
## Viv_HabRC|t1 -4.665 0.244 -19.090 0.000 -4.665 -2.788
## Viv_HabRC|t2 -3.648 0.196 -18.639 0.000 -3.648 -2.180
## Viv_HabRC|t3 -2.591 0.136 -19.007 0.000 -2.591 -1.549
## Viv_HabRC|t4 -0.067 0.025 -2.712 0.007 -0.067 -0.040
## Viv_HabR|t1 -3.481 0.129 -27.037 0.000 -3.481 -2.532
## Viv_HabR|t2 -2.397 0.084 -28.666 0.000 -2.397 -1.744
## Viv_HabR|t3 -1.528 0.055 -27.698 0.000 -1.528 -1.111
## Viv_HabR|t4 0.135 0.020 6.568 0.000 0.135 0.098
## Viv_PrInf|t1 -4.201 0.179 -23.509 0.000 -4.201 -2.430
## Viv_PrInf|t2 -3.098 0.130 -23.802 0.000 -3.098 -1.791
## Viv_PrInf|t3 -2.241 0.096 -23.351 0.000 -2.241 -1.296
## Viv_PrInf|t4 0.303 0.027 11.073 0.000 0.303 0.175
## Viv_Aut|t1 -2.287 0.057 -40.099 0.000 -2.287 -2.148
## Viv_Aut|t2 -0.968 0.028 -35.128 0.000 -0.968 -0.909
## Viv_Aut|t3 -0.429 0.019 -22.929 0.000 -0.429 -0.402
## Viv_Aut|t4 0.826 0.025 32.916 0.000 0.826 0.775
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.002 0.112 8.979 0.000 1.002 0.305
## .Viv_EvInt 1.066 0.105 10.115 0.000 1.066 0.347
## .Viv_DiF 0.871 0.124 7.044 0.000 0.871 0.274
## .Viv_Int 1.051 0.083 12.698 0.000 1.051 0.490
## .Viv_Pla 0.781 0.127 6.134 0.000 0.781 0.283
## .Viv_HabRC 1.021 0.151 6.757 0.000 1.021 0.365
## .Viv_HabR 0.959 0.093 10.346 0.000 0.959 0.508
## .Viv_PrInf 1.024 0.118 8.705 0.000 1.024 0.343
## .Viv_Aut 0.799 0.050 15.835 0.000 0.799 0.705
## Viv_Soft 1.014 0.057 17.707 0.000 1.000 1.000
## Viv_Hard 0.914 0.058 15.887 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.551 0.551 1.000
## Viv_EvInt 0.571 0.571 1.000
## Viv_DiF 0.561 0.561 1.000
## Viv_Int 0.683 0.683 1.000
## Viv_Pla 0.601 0.601 1.000
## Viv_HabRC 0.598 0.598 1.000
## Viv_HabR 0.727 0.727 1.000
## Viv_PrInf 0.578 0.578 1.000
## Viv_Aut 0.939 0.939 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.695
## Viv_EvInt 0.653
## Viv_DiF 0.726
## Viv_Int 0.510
## Viv_Pla 0.717
## Viv_HabRC 0.635
## Viv_HabR 0.492
## Viv_PrInf 0.657
## Viv_Aut 0.295
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.503 0.065 23.209 0.000 1.426 0.784
## Viv_EvInt 1.407 0.054 25.821 0.000 1.334 0.911
## Viv_DiF 1.508 0.074 20.263 0.000 1.430 0.837
## Viv_Int 1.039 0.036 28.550 0.000 0.986 0.748
## Viv_Pla 1.399 0.074 18.833 0.000 1.327 0.825
## Viv_Hard =~
## Viv_HabRC 1.395 0.079 17.699 0.000 1.525 0.878
## Viv_HabR 1.009 0.042 23.884 0.000 1.103 0.676
## Viv_PrInf 1.466 0.071 20.694 0.000 1.604 0.830
## Viv_Aut 0.605 0.023 26.614 0.000 0.662 0.564
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.450 0.126 3.561 0.000 0.450 0.450
## .Viv_AdmT ~~
## .Viv_Int 0.318 0.119 2.669 0.008 0.318 0.323
## Viv_Soft ~~
## Viv_Hard 0.878 0.110 7.999 0.000 0.846 0.846
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.083 0.165 -24.709 0.000 -4.083 -2.247
## Viv_AdmT|t2 -2.925 0.116 -25.298 0.000 -2.925 -1.610
## Viv_AdmT|t3 -2.177 0.087 -24.935 0.000 -2.177 -1.198
## Viv_AdmT|t4 0.183 0.027 6.889 0.000 0.183 0.101
## Viv_EvInt|t1 -3.917 0.141 -27.808 0.000 -3.917 -2.675
## Viv_EvInt|t2 -2.410 0.088 -27.510 0.000 -2.410 -1.645
## Viv_EvInt|t3 -1.621 0.062 -26.199 0.000 -1.621 -1.107
## Viv_EvInt|t4 0.548 0.030 17.995 0.000 0.548 0.374
## Viv_DiF|t1 -4.542 0.221 -20.516 0.000 -4.542 -2.659
## Viv_DiF|t2 -3.381 0.155 -21.858 0.000 -3.381 -1.979
## Viv_DiF|t3 -2.585 0.121 -21.417 0.000 -2.585 -1.513
## Viv_DiF|t4 -0.096 0.027 -3.611 0.000 -0.096 -0.056
## Viv_Int|t1 -3.074 0.093 -33.187 0.000 -3.074 -2.334
## Viv_Int|t2 -2.091 0.062 -33.629 0.000 -2.091 -1.587
## Viv_Int|t3 -1.485 0.046 -32.087 0.000 -1.485 -1.127
## Viv_Int|t4 0.534 0.025 21.311 0.000 0.534 0.405
## Viv_Pla|t1 -4.608 0.245 -18.841 0.000 -4.608 -2.865
## Viv_Pla|t2 -3.732 0.190 -19.660 0.000 -3.732 -2.320
## Viv_Pla|t3 -2.693 0.138 -19.475 0.000 -2.693 -1.674
## Viv_Pla|t4 -0.057 0.025 -2.310 0.021 -0.057 -0.035
## Viv_HabRC|t1 -4.665 0.244 -19.090 0.000 -4.665 -2.685
## Viv_HabRC|t2 -3.648 0.196 -18.639 0.000 -3.648 -2.100
## Viv_HabRC|t3 -2.591 0.136 -19.007 0.000 -2.591 -1.492
## Viv_HabRC|t4 -0.067 0.025 -2.712 0.007 -0.067 -0.039
## Viv_HabR|t1 -3.481 0.129 -27.037 0.000 -3.481 -2.133
## Viv_HabR|t2 -2.397 0.084 -28.666 0.000 -2.397 -1.469
## Viv_HabR|t3 -1.528 0.055 -27.698 0.000 -1.528 -0.936
## Viv_HabR|t4 0.135 0.020 6.568 0.000 0.135 0.082
## Viv_PrInf|t1 -4.201 0.179 -23.509 0.000 -4.201 -2.175
## Viv_PrInf|t2 -3.098 0.130 -23.802 0.000 -3.098 -1.604
## Viv_PrInf|t3 -2.241 0.096 -23.351 0.000 -2.241 -1.160
## Viv_PrInf|t4 0.303 0.027 11.073 0.000 0.303 0.157
## Viv_Aut|t1 -2.287 0.057 -40.099 0.000 -2.287 -1.950
## Viv_Aut|t2 -0.968 0.028 -35.128 0.000 -0.968 -0.825
## Viv_Aut|t3 -0.429 0.019 -22.929 0.000 -0.429 -0.365
## Viv_Aut|t4 0.826 0.025 32.916 0.000 0.826 0.704
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.270 0.252 5.038 0.000 1.270 0.385
## .Viv_EvInt 0.364 0.145 2.511 0.012 0.364 0.170
## .Viv_DiF 0.872 0.217 4.020 0.000 0.872 0.299
## .Viv_Int 0.763 0.134 5.678 0.000 0.763 0.440
## .Viv_Pla 0.826 0.204 4.056 0.000 0.826 0.319
## .Viv_HabRC 0.691 0.267 2.584 0.010 0.691 0.229
## .Viv_HabR 1.446 0.216 6.704 0.000 1.446 0.543
## .Viv_PrInf 1.159 0.221 5.241 0.000 1.159 0.311
## .Viv_Aut 0.938 0.113 8.329 0.000 0.938 0.682
## Viv_Soft 0.900 0.117 7.666 0.000 1.000 1.000
## Viv_Hard 1.196 0.153 7.814 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.550 0.550 1.000
## Viv_EvInt 0.683 0.683 1.000
## Viv_DiF 0.585 0.585 1.000
## Viv_Int 0.759 0.759 1.000
## Viv_Pla 0.622 0.622 1.000
## Viv_HabRC 0.576 0.576 1.000
## Viv_HabR 0.613 0.613 1.000
## Viv_PrInf 0.518 0.518 1.000
## Viv_Aut 0.853 0.853 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.615
## Viv_EvInt 0.830
## Viv_DiF 0.701
## Viv_Int 0.560
## Viv_Pla 0.681
## Viv_HabRC 0.771
## Viv_HabR 0.457
## Viv_PrInf 0.689
## Viv_Aut 0.318
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.503 0.065 23.209 0.000 1.518 0.848
## Viv_EvInt 1.407 0.054 25.821 0.000 1.421 0.783
## Viv_DiF 1.508 0.074 20.263 0.000 1.523 0.782
## Viv_Int 1.039 0.036 28.550 0.000 1.050 0.691
## Viv_Pla 1.399 0.074 18.833 0.000 1.413 0.794
## Viv_Hard =~
## Viv_HabRC 1.395 0.079 17.699 0.000 1.183 0.715
## Viv_HabR 1.009 0.042 23.884 0.000 0.856 0.644
## Viv_PrInf 1.466 0.071 20.694 0.000 1.244 0.768
## Viv_Aut 0.605 0.023 26.614 0.000 0.513 0.534
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.692 0.152 4.541 0.000 0.692 0.588
## .Viv_AdmT ~~
## .Viv_Int 0.484 0.135 3.575 0.000 0.484 0.464
## Viv_Soft ~~
## Viv_Hard 0.705 0.059 11.907 0.000 0.823 0.823
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Int 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## .Viv_Aut 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.083 0.165 -24.709 0.000 -4.083 -2.281
## Viv_AdmT|t2 -2.925 0.116 -25.298 0.000 -2.925 -1.634
## Viv_AdmT|t3 -2.177 0.087 -24.935 0.000 -2.177 -1.216
## Viv_AdmT|t4 0.183 0.027 6.889 0.000 0.183 0.102
## Viv_EvInt|t1 -3.917 0.141 -27.808 0.000 -3.917 -2.159
## Viv_EvInt|t2 -2.410 0.088 -27.510 0.000 -2.410 -1.328
## Viv_EvInt|t3 -1.621 0.062 -26.199 0.000 -1.621 -0.893
## Viv_EvInt|t4 0.548 0.030 17.995 0.000 0.548 0.302
## Viv_DiF|t1 -4.542 0.221 -20.516 0.000 -4.542 -2.331
## Viv_DiF|t2 -3.381 0.155 -21.858 0.000 -3.381 -1.735
## Viv_DiF|t3 -2.585 0.121 -21.417 0.000 -2.585 -1.326
## Viv_DiF|t4 -0.096 0.027 -3.611 0.000 -0.096 -0.049
## Viv_Int|t1 -3.074 0.093 -33.187 0.000 -3.074 -2.022
## Viv_Int|t2 -2.091 0.062 -33.629 0.000 -2.091 -1.376
## Viv_Int|t3 -1.485 0.046 -32.087 0.000 -1.485 -0.977
## Viv_Int|t4 0.534 0.025 21.311 0.000 0.534 0.351
## Viv_Pla|t1 -4.608 0.245 -18.841 0.000 -4.608 -2.590
## Viv_Pla|t2 -3.732 0.190 -19.660 0.000 -3.732 -2.098
## Viv_Pla|t3 -2.693 0.138 -19.475 0.000 -2.693 -1.514
## Viv_Pla|t4 -0.057 0.025 -2.310 0.021 -0.057 -0.032
## Viv_HabRC|t1 -4.665 0.244 -19.090 0.000 -4.665 -2.819
## Viv_HabRC|t2 -3.648 0.196 -18.639 0.000 -3.648 -2.205
## Viv_HabRC|t3 -2.591 0.136 -19.007 0.000 -2.591 -1.566
## Viv_HabRC|t4 -0.067 0.025 -2.712 0.007 -0.067 -0.041
## Viv_HabR|t1 -3.481 0.129 -27.037 0.000 -3.481 -2.619
## Viv_HabR|t2 -2.397 0.084 -28.666 0.000 -2.397 -1.804
## Viv_HabR|t3 -1.528 0.055 -27.698 0.000 -1.528 -1.149
## Viv_HabR|t4 0.135 0.020 6.568 0.000 0.135 0.101
## Viv_PrInf|t1 -4.201 0.179 -23.509 0.000 -4.201 -2.594
## Viv_PrInf|t2 -3.098 0.130 -23.802 0.000 -3.098 -1.912
## Viv_PrInf|t3 -2.241 0.096 -23.351 0.000 -2.241 -1.384
## Viv_PrInf|t4 0.303 0.027 11.073 0.000 0.303 0.187
## Viv_Aut|t1 -2.287 0.057 -40.099 0.000 -2.287 -2.381
## Viv_Aut|t2 -0.968 0.028 -35.128 0.000 -0.968 -1.008
## Viv_Aut|t3 -0.429 0.019 -22.929 0.000 -0.429 -0.446
## Viv_Aut|t4 0.826 0.025 32.916 0.000 0.826 0.860
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.901 0.224 4.014 0.000 0.901 0.281
## .Viv_EvInt 1.273 0.213 5.970 0.000 1.273 0.387
## .Viv_DiF 1.479 0.286 5.177 0.000 1.479 0.389
## .Viv_Int 1.208 0.175 6.915 0.000 1.208 0.523
## .Viv_Pla 1.168 0.300 3.893 0.000 1.168 0.369
## .Viv_HabRC 1.339 0.338 3.961 0.000 1.339 0.489
## .Viv_HabR 1.034 0.177 5.837 0.000 1.034 0.585
## .Viv_PrInf 1.077 0.237 4.537 0.000 1.077 0.410
## .Viv_Aut 0.659 0.096 6.866 0.000 0.659 0.715
## Viv_Soft 1.021 0.100 10.182 0.000 1.000 1.000
## Viv_Hard 0.719 0.073 9.915 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.559 0.559 1.000
## Viv_EvInt 0.551 0.551 1.000
## Viv_DiF 0.513 0.513 1.000
## Viv_Int 0.658 0.658 1.000
## Viv_Pla 0.562 0.562 1.000
## Viv_HabRC 0.604 0.604 1.000
## Viv_HabR 0.752 0.752 1.000
## Viv_PrInf 0.617 0.617 1.000
## Viv_Aut 1.041 1.041 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.719
## Viv_EvInt 0.613
## Viv_DiF 0.611
## Viv_Int 0.477
## Viv_Pla 0.631
## Viv_HabRC 0.511
## Viv_HabR 0.415
## Viv_PrInf 0.590
## Viv_Aut 0.285lavaan::fitMeasures(invariance$fit.means,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 426.271 198.000 0.000
## srmr cfi.scaled tli.scaled
## 0.028 0.996 0.997
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.025 0.021 0.028modificationindices(invariance$fit.means, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox
## 237 Viv_Hard ~1 3 3 1 326.41 -0.495 -0.452 -0.452 -0.452
## 77 Viv_Aut ~1 1 1 1 217.10 -0.236 -0.236 -0.202 -0.202
## 158 Viv_Hard ~1 2 2 1 191.24 0.174 0.182 0.182 0.182
## 79 Viv_Hard ~1 1 1 1 176.01 -0.180 -0.180 -0.180 -0.180
## 156 Viv_Aut ~1 2 2 1 169.81 0.190 0.190 0.179 0.179
## 235 Viv_Aut ~1 3 3 1 142.69 -0.370 -0.370 -0.316 -0.316
## 316 Viv_Hard ~1 4 4 1 140.69 0.260 0.307 0.307 0.307
## 157 Viv_Soft ~1 2 2 1 121.22 0.120 0.119 0.119 0.119
## 314 Viv_Aut ~1 4 4 1 100.45 0.243 0.243 0.253 0.253
## 233 Viv_HabR ~1 3 3 1 94.74 -0.553 -0.553 -0.339 -0.339semTools::reliability(invariance$fit.means)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8484 0.7256
## alpha.ord 0.9065 0.8243
## omega 0.8306 0.6956
## omega2 0.8306 0.6956
## omega3 0.8306 0.6974
## avevar 0.6562 0.5780
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8543 0.7213
## alpha.ord 0.9124 0.8254
## omega 0.8311 0.6938
## omega2 0.8311 0.6938
## omega3 0.8265 0.6921
## avevar 0.6697 0.5684
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8575 0.7587
## alpha.ord 0.9168 0.8391
## omega 0.8433 0.7254
## omega2 0.8433 0.7254
## omega3 0.8445 0.7212
## avevar 0.6772 0.6075
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8357 0.6651
## alpha.ord 0.8926 0.7947
## omega 0.8123 0.6354
## omega2 0.8123 0.6354
## omega3 0.8100 0.6357
## avevar 0.6177 0.48964.1.1.8.5 Checking Latent Differences
partial<-partialInvarianceCat(invariance,type="means",return.fit = F)
partial## $estimates
## poolest mean:2 mean:3 mean:1 mean:4 std:2 std:3 std:1
## Viv_Soft~1 0 0 0.1193 -0.02146 0.000988 0 0.1161 -0.0209
## Viv_Hard~1 0 0 0.2110 -0.29376 0.401145 0 0.2086 -0.2904
## std:4 diff_std:3 vs. 2 diff_std:1 vs. 2 diff_std:4 vs. 2
## Viv_Soft~1 0.0009621 0.1161 -0.0209 0.0009621
## Viv_Hard~1 0.3965774 0.2086 -0.2904 0.3965774
##
## $results
## free.chi free.df free.p free.cfi fix.chi fix.df
## Viv_Soft~1 6.658 3 0.0836430264054 -0.001430 7.789 3
## Viv_Hard~1 46.996 3 0.0000000003482 -0.007735 58.273 3
## fix.p fix.cfi wald.chi wald.df wald.p
## Viv_Soft~1 0.050584514799289 -0.001430 NA NA NA
## Viv_Hard~1 0.000000000001374 -0.007735 NA NA NA4.1.2 Predicting Latent as Factor Scores
#data_predict <- predict(fit)
#data <- cbind(data,data_predict)
write.csv(data,"data_CFA_leo.csv")
#names(data)5 Confirmatory Factor Analysis - Cleyton
5.0.0.1 Changing data - all cases
data<-banco_CFA5.1 Structure Validity - Dimensionality (Cleyton)
5.1.0.1 Model - excluded Viv_DiG crossed factor loadings
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf
'5.1.0.2 Fitting
fit <- lavaan::cfa(model, data =data,estimator="ULSMV",ordered=T,missing="pairwise",std.lv=T)5.1.0.3 General Summary
summary(fit,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 17 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 36
##
## Number of observations 5286
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 168.805 731.514
## Degrees of freedom 13 13
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.231
## Shift parameter 1.429
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 40289.802 37258.115
## Degrees of freedom 21 21
## P-value NA 0.000
## Scaling correction factor 1.082
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.996 0.981
## Tucker-Lewis Index (TLI) 0.994 0.969
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048 0.102
## 90 Percent confidence interval - lower 0.041 0.096
## 90 Percent confidence interval - upper 0.054 0.109
## P-value RMSEA <= 0.05 0.717 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.034 0.034
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 0.821 0.007 124.885 0.000 0.821 0.821
## Viv_EvInt 0.814 0.006 129.195 0.000 0.814 0.814
## Viv_DiF 0.851 0.006 135.378 0.000 0.851 0.851
## Viv_Pla 0.850 0.007 129.771 0.000 0.850 0.850
## Viv_Hard =~
## Viv_HabRC 0.877 0.007 134.844 0.000 0.877 0.877
## Viv_HabR 0.771 0.008 97.582 0.000 0.771 0.771
## Viv_PrInf 0.796 0.008 96.109 0.000 0.796 0.796
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft ~~
## Viv_Hard 0.778 0.008 97.413 0.000 0.778 0.778
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -2.213 0.046 -48.134 0.000 -2.213 -2.213
## Viv_AdmT|t2 -1.598 0.028 -56.693 0.000 -1.598 -1.598
## Viv_AdmT|t3 -1.202 0.023 -53.130 0.000 -1.202 -1.202
## Viv_AdmT|t4 0.101 0.017 5.830 0.000 0.101 0.101
## Viv_EvInt|t1 -2.242 0.047 -47.447 0.000 -2.242 -2.242
## Viv_EvInt|t2 -1.386 0.025 -55.801 0.000 -1.386 -1.386
## Viv_EvInt|t3 -0.935 0.020 -46.095 0.000 -0.935 -0.935
## Viv_EvInt|t4 0.298 0.018 17.001 0.000 0.298 0.298
## Viv_DiF|t1 -2.568 0.067 -38.601 0.000 -2.568 -2.568
## Viv_DiF|t2 -1.877 0.034 -54.591 0.000 -1.877 -1.877
## Viv_DiF|t3 -1.439 0.026 -56.238 0.000 -1.439 -1.439
## Viv_DiF|t4 -0.057 0.017 -3.301 0.001 -0.057 -0.057
## Viv_Pla|t1 -2.639 0.072 -36.576 0.000 -2.639 -2.639
## Viv_Pla|t2 -2.162 0.044 -49.345 0.000 -2.162 -2.162
## Viv_Pla|t3 -1.583 0.028 -56.704 0.000 -1.583 -1.583
## Viv_Pla|t4 -0.041 0.017 -2.393 0.017 -0.041 -0.041
## Viv_HabRC|t1 -2.688 0.076 -35.148 0.000 -2.688 -2.688
## Viv_HabRC|t2 -2.102 0.041 -50.654 0.000 -2.102 -2.102
## Viv_HabRC|t3 -1.503 0.027 -56.576 0.000 -1.503 -1.503
## Viv_HabRC|t4 -0.033 0.017 -1.925 0.054 -0.033 -0.033
## Viv_HabR|t1 -2.448 0.058 -42.020 0.000 -2.448 -2.448
## Viv_HabR|t2 -1.685 0.030 -56.411 0.000 -1.685 -1.685
## Viv_HabR|t3 -1.069 0.021 -50.071 0.000 -1.069 -1.069
## Viv_HabR|t4 0.112 0.017 6.463 0.000 0.112 0.112
## Viv_PrInf|t1 -2.370 0.054 -44.142 0.000 -2.370 -2.370
## Viv_PrInf|t2 -1.745 0.031 -56.013 0.000 -1.745 -1.745
## Viv_PrInf|t3 -1.274 0.023 -54.379 0.000 -1.274 -1.274
## Viv_PrInf|t4 0.188 0.017 10.859 0.000 0.188 0.188
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.327 0.327 0.327
## .Viv_EvInt 0.338 0.338 0.338
## .Viv_DiF 0.275 0.275 0.275
## .Viv_Pla 0.277 0.277 0.277
## .Viv_HabRC 0.231 0.231 0.231
## .Viv_HabR 0.405 0.405 0.405
## .Viv_PrInf 0.367 0.367 0.367
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 1.000 1.000 1.000
## Viv_EvInt 1.000 1.000 1.000
## Viv_DiF 1.000 1.000 1.000
## Viv_Pla 1.000 1.000 1.000
## Viv_HabRC 1.000 1.000 1.000
## Viv_HabR 1.000 1.000 1.000
## Viv_PrInf 1.000 1.000 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.673
## Viv_EvInt 0.662
## Viv_DiF 0.725
## Viv_Pla 0.723
## Viv_HabRC 0.769
## Viv_HabR 0.595
## Viv_PrInf 0.6335.1.0.4 Selected Robust and Scaled Fit Measures
lavaan::fitMeasures(fit,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 731.514 13.000 0.000
## srmr cfi.scaled tli.scaled
## 0.034 0.981 0.969
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.102 0.096 0.1095.1.0.5 Factor Loadings
parameters<-lavaan::standardizedSolution(fit)
loadings<-parameters[parameters$op=="=~",]
loadings## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Viv_Soft =~ Viv_AdmT 0.821 0.007 124.89 0 0.808 0.833
## 2 Viv_Soft =~ Viv_EvInt 0.814 0.006 129.19 0 0.801 0.826
## 3 Viv_Soft =~ Viv_DiF 0.851 0.006 135.38 0 0.839 0.864
## 4 Viv_Soft =~ Viv_Pla 0.850 0.007 129.77 0 0.837 0.863
## 5 Viv_Hard =~ Viv_HabRC 0.877 0.007 134.84 0 0.864 0.890
## 6 Viv_Hard =~ Viv_HabR 0.771 0.008 97.58 0 0.756 0.787
## 7 Viv_Hard =~ Viv_PrInf 0.796 0.008 96.11 0 0.780 0.8125.1.0.6 Modificantion Indices considering very bad RMSEA
modificationindices(fit, sort.=T,maximum.number = 10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 64 Viv_Soft =~ Viv_PrInf 105.86 0.484 0.484 0.484 0.484
## 87 Viv_HabRC ~~ Viv_HabR 105.86 0.208 0.208 0.681 0.681
## 74 Viv_AdmT ~~ Viv_PrInf 34.76 0.097 0.097 0.281 0.281
## 88 Viv_HabRC ~~ Viv_PrInf 32.98 -0.119 -0.119 -0.410 -0.410
## 63 Viv_Soft =~ Viv_HabR 32.98 -0.261 -0.261 -0.261 -0.261
## 68 Viv_Hard =~ Viv_Pla 26.88 0.205 0.205 0.205 0.205
## 66 Viv_Hard =~ Viv_EvInt 26.03 -0.196 -0.196 -0.196 -0.196
## 62 Viv_Soft =~ Viv_HabRC 21.38 -0.246 -0.246 -0.246 -0.246
## 89 Viv_HabR ~~ Viv_PrInf 21.38 -0.087 -0.087 -0.226 -0.226
## 75 Viv_EvInt ~~ Viv_DiF 20.24 0.082 0.082 0.269 0.2695.1.0.7 Ordinal Alpha and Omega
semTools::reliability(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard
## alpha 0.8338 0.7662
## alpha.ord 0.9014 0.8545
## omega 0.8416 0.7792
## omega2 0.8416 0.7792
## omega3 0.8384 0.7797
## avevar 0.6957 0.66585.1.0.8 Discriminant Validity (faild if discriminant validity is false)
semMediation::discriminantValidityTable(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard AVE sqrt(AVE) discriminantValidity
## Viv_Soft 1.000 0.778 0.838 0.696 FALSE
## Viv_Hard 0.778 1.000 0.838 0.696 FALSE5.1.0.9 Discriminant Validity Robust Test (p<0.05 model tested is different from model with .90 correlation and discriminant validity is accepted)
semTools::discriminantValidity(fit,cutoff =0.9)## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff
## 1 Viv_Soft ~~ Viv_Hard 0.7785 0.7628 0.7941 14 NA NA 360.1 241
## Df diff Pr(>Chisq)
## 1 1 0.0000000000000000000000000000000000000000000000000000023265.1.0.10 Modified Model - after two iteration by MI searching for lower relationship between X2/df
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf
Viv_HabRC ~~ Viv_HabR
Viv_EvInt ~~ Viv_DiF
'5.1.0.11 Fitting
fit <- lavaan::cfa(model, data =data,estimator="ULSMV",ordered=T,missing="pairwise",std.lv=T,orthogonal=F)5.1.0.12 General Summary
summary(fit,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 19 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 38
##
## Number of observations 5286
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 42.450 214.566
## Degrees of freedom 11 11
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.199
## Shift parameter 0.772
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 40289.802 37258.115
## Degrees of freedom 21 21
## P-value NA 0.000
## Scaling correction factor 1.082
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.995
## Tucker-Lewis Index (TLI) 0.999 0.990
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.023 0.059
## 90 Percent confidence interval - lower 0.016 0.052
## 90 Percent confidence interval - upper 0.031 0.066
## P-value RMSEA <= 0.05 1.000 0.013
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.017 0.017
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 0.826 0.007 124.365 0.000 0.826 0.826
## Viv_EvInt 0.792 0.007 107.864 0.000 0.792 0.792
## Viv_DiF 0.830 0.007 114.079 0.000 0.830 0.830
## Viv_Pla 0.855 0.007 128.261 0.000 0.855 0.855
## Viv_Hard =~
## Viv_HabRC 0.811 0.009 92.607 0.000 0.811 0.811
## Viv_HabR 0.705 0.010 67.225 0.000 0.705 0.705
## Viv_PrInf 0.800 0.008 94.941 0.000 0.800 0.800
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.201 0.011 17.579 0.000 0.201 0.484
## .Viv_EvInt ~~
## .Viv_DiF 0.082 0.008 9.662 0.000 0.082 0.240
## Viv_Soft ~~
## Viv_Hard 0.830 0.008 100.267 0.000 0.830 0.830
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -2.213 0.046 -48.134 0.000 -2.213 -2.213
## Viv_AdmT|t2 -1.598 0.028 -56.693 0.000 -1.598 -1.598
## Viv_AdmT|t3 -1.202 0.023 -53.130 0.000 -1.202 -1.202
## Viv_AdmT|t4 0.101 0.017 5.830 0.000 0.101 0.101
## Viv_EvInt|t1 -2.242 0.047 -47.447 0.000 -2.242 -2.242
## Viv_EvInt|t2 -1.386 0.025 -55.801 0.000 -1.386 -1.386
## Viv_EvInt|t3 -0.935 0.020 -46.095 0.000 -0.935 -0.935
## Viv_EvInt|t4 0.298 0.018 17.001 0.000 0.298 0.298
## Viv_DiF|t1 -2.568 0.067 -38.601 0.000 -2.568 -2.568
## Viv_DiF|t2 -1.877 0.034 -54.591 0.000 -1.877 -1.877
## Viv_DiF|t3 -1.439 0.026 -56.238 0.000 -1.439 -1.439
## Viv_DiF|t4 -0.057 0.017 -3.301 0.001 -0.057 -0.057
## Viv_Pla|t1 -2.639 0.072 -36.576 0.000 -2.639 -2.639
## Viv_Pla|t2 -2.162 0.044 -49.345 0.000 -2.162 -2.162
## Viv_Pla|t3 -1.583 0.028 -56.704 0.000 -1.583 -1.583
## Viv_Pla|t4 -0.041 0.017 -2.393 0.017 -0.041 -0.041
## Viv_HabRC|t1 -2.688 0.076 -35.148 0.000 -2.688 -2.688
## Viv_HabRC|t2 -2.102 0.041 -50.654 0.000 -2.102 -2.102
## Viv_HabRC|t3 -1.503 0.027 -56.576 0.000 -1.503 -1.503
## Viv_HabRC|t4 -0.033 0.017 -1.925 0.054 -0.033 -0.033
## Viv_HabR|t1 -2.448 0.058 -42.020 0.000 -2.448 -2.448
## Viv_HabR|t2 -1.685 0.030 -56.411 0.000 -1.685 -1.685
## Viv_HabR|t3 -1.069 0.021 -50.071 0.000 -1.069 -1.069
## Viv_HabR|t4 0.112 0.017 6.463 0.000 0.112 0.112
## Viv_PrInf|t1 -2.370 0.054 -44.142 0.000 -2.370 -2.370
## Viv_PrInf|t2 -1.745 0.031 -56.013 0.000 -1.745 -1.745
## Viv_PrInf|t3 -1.274 0.023 -54.379 0.000 -1.274 -1.274
## Viv_PrInf|t4 0.188 0.017 10.859 0.000 0.188 0.188
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.318 0.318 0.318
## .Viv_EvInt 0.373 0.373 0.373
## .Viv_DiF 0.310 0.310 0.310
## .Viv_Pla 0.269 0.269 0.269
## .Viv_HabRC 0.342 0.342 0.342
## .Viv_HabR 0.503 0.503 0.503
## .Viv_PrInf 0.361 0.361 0.361
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 1.000 1.000 1.000
## Viv_EvInt 1.000 1.000 1.000
## Viv_DiF 1.000 1.000 1.000
## Viv_Pla 1.000 1.000 1.000
## Viv_HabRC 1.000 1.000 1.000
## Viv_HabR 1.000 1.000 1.000
## Viv_PrInf 1.000 1.000 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.682
## Viv_EvInt 0.627
## Viv_DiF 0.690
## Viv_Pla 0.731
## Viv_HabRC 0.658
## Viv_HabR 0.497
## Viv_PrInf 0.6395.1.0.13 Selected Robust and Scaled Fit Measures
lavaan::fitMeasures(fit,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 214.566 11.000 0.000
## srmr cfi.scaled tli.scaled
## 0.017 0.995 0.990
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.059 0.052 0.0665.1.0.14 Factor Loadings
parameters<-lavaan::standardizedSolution(fit)
loadings<-parameters[parameters$op=="=~",]
loadings## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Viv_Soft =~ Viv_AdmT 0.826 0.007 124.36 0 0.813 0.839
## 2 Viv_Soft =~ Viv_EvInt 0.792 0.007 107.86 0 0.777 0.806
## 3 Viv_Soft =~ Viv_DiF 0.830 0.007 114.08 0 0.816 0.845
## 4 Viv_Soft =~ Viv_Pla 0.855 0.007 128.26 0 0.842 0.868
## 5 Viv_Hard =~ Viv_HabRC 0.811 0.009 92.61 0 0.794 0.828
## 6 Viv_Hard =~ Viv_HabR 0.705 0.010 67.22 0 0.685 0.726
## 7 Viv_Hard =~ Viv_PrInf 0.800 0.008 94.94 0 0.783 0.8165.1.0.15 Modificantion Indices considering very bad RMSEA
modificationindices(fit, sort.=T,maximum.number = 10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 85 Viv_Pla ~~ Viv_HabRC 16.797 0.072 0.072 0.237 0.237
## 71 Viv_AdmT ~~ Viv_EvInt 15.152 0.073 0.073 0.211 0.211
## 68 Viv_Hard =~ Viv_EvInt 13.861 -0.206 -0.206 -0.206 -0.206
## 70 Viv_Hard =~ Viv_Pla 13.422 0.213 0.213 0.213 0.213
## 80 Viv_EvInt ~~ Viv_PrInf 6.933 -0.046 -0.046 -0.125 -0.125
## 76 Viv_AdmT ~~ Viv_PrInf 6.728 0.046 0.046 0.135 0.135
## 73 Viv_AdmT ~~ Viv_Pla 5.719 -0.045 -0.045 -0.153 -0.153
## 75 Viv_AdmT ~~ Viv_HabR 5.204 -0.038 -0.038 -0.096 -0.096
## 74 Viv_AdmT ~~ Viv_HabRC 4.577 -0.037 -0.037 -0.112 -0.112
## 78 Viv_EvInt ~~ Viv_HabRC 4.298 -0.036 -0.036 -0.099 -0.0995.1.0.16 Ordinal Alpha and Omega
semTools::reliability(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard
## alpha 0.8338 0.7662
## alpha.ord 0.9014 0.8545
## omega 0.8214 0.6932
## omega2 0.8214 0.6932
## omega3 0.8187 0.6930
## avevar 0.6822 0.59815.1.0.17 Discriminant Validity
semMediation::discriminantValidityTable(fit)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## Viv_Soft Viv_Hard AVE sqrt(AVE) discriminantValidity
## Viv_Soft 1.00 0.83 0.819 0.682 FALSE
## Viv_Hard 0.83 1.00 0.819 0.682 FALSE5.1.0.18 Discriminant Validity Robust Test
semTools::discriminantValidity(fit)## lhs op rhs est ci.lower ci.upper Df AIC BIC Chisq Chisq diff
## 1 Viv_Soft ~~ Viv_Hard 0.8303 0.8141 0.8465 12 NA NA 85.74 70.21
## Df diff Pr(>Chisq)
## 1 1 0.00000000000000005335.1.0.19 Graphical Representation
semPaths(object=fit,whatLabels ="stand",residuals = F, thresholds = F,ThreshAtSide=F, cardinal = c("exogenous covariances", border.color = ("black")), intercept=F, edge.label.cex = 1,curve=2,nCharNodes=0)
5.1.1 Internal Validity
5.1.1.1 Using all data
data<-TDados5.1.1.2 Organizing Sex
data$Gen<-car::recode(data$Gen,"0=NA")
data$SexoR<-as.factor(data$Gen)
#dataSexoR1<-data[data$SexoR=="1",]
#dataSexoR2<-data[data$SexoR=="2",]5.1.1.3 Sex Invariance Model
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf
Viv_HabRC ~~ Viv_HabR
Viv_EvInt ~~ Viv_DiF
'5.1.1.4 Fitting
invariance<- measurementInvarianceCat(model = model, data = data, group ="SexoR",parameterization = "theta", estimator = "ULSMV",ordered =T,missing="pairwise",std.lv=F)## Warning: The measurementInvarianceCat function is deprecated, and it will cease
## to be included in future versions of semTools. See help('semTools-deprecated)
## for details.## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'SexoR' contains missing values##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.thresholds
## Model 4 : fit.means
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 22 63
## fit.loadings 27 143 18.1 5 0.0028 **
## fit.thresholds 46 234 23.2 19 0.2290
## fit.means 48 580 48.3 2 0.000000000032 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi.scaled rmsea.scaled cfi.scaled.delta rmsea.scaled.delta
## fit.configural 0.994 0.059 NA NA
## fit.loadings 0.998 0.029 0.004 0.030
## fit.thresholds 0.999 0.017 0.001 0.013
## fit.means 0.997 0.029 0.002 0.0125.1.1.4.1 Configural
summary(invariance$fit.configural,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 210 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 85
## Number of equality constraints 9
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 63.036 316.808
## Degrees of freedom 22 22
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.200
## Shift parameter for each group:
## 2 0.790
## 1 1.151
## simple second-order correction
## Test statistic for each group:
## 2 17.079 86.099
## 1 45.957 230.709
##
## Model Test Baseline Model:
##
## Test statistic 57583.946 53150.718
## Degrees of freedom 42 42
## P-value NA 0.000
## Scaling correction factor 1.084
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.994
## Tucker-Lewis Index (TLI) 0.999 0.989
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.022 0.059
## 90 Percent confidence interval - lower 0.016 0.054
## 90 Percent confidence interval - upper 0.029 0.065
## P-value RMSEA <= 0.05 1.000 0.004
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.017 0.017
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.408 0.815
## Viv_EvInt 0.929 0.037 24.836 0.000 1.309 0.795
## Viv_DiF 1.077 0.049 22.137 0.000 1.517 0.835
## Viv_Pla 1.251 0.059 21.364 0.000 1.763 0.870
## Viv_Hard =~
## Viv_HabRC 1.000 1.604 0.849
## Viv_HabR 0.678 0.026 25.865 0.000 1.088 0.736
## Viv_PrInf 0.982 0.060 16.245 0.000 1.576 0.844
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.527 0.020 25.944 0.000 0.527 0.527
## .Viv_EvInt ~~
## .Viv_DiF 0.245 0.026 9.549 0.000 0.245 0.245
## Viv_Soft ~~
## Viv_Hard 1.852 0.107 17.236 0.000 0.820 0.820
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.207 0.127 -33.213 0.000 -4.207 -2.436
## Viv_AdmT|t2 -2.997 0.076 -39.235 0.000 -2.997 -1.735
## Viv_AdmT|t3 -2.184 0.062 -35.479 0.000 -2.184 -1.264
## Viv_AdmT|t4 0.068 0.039 1.755 0.079 0.068 0.039
## Viv_EvInt|t1 -3.872 0.107 -36.186 0.000 -3.872 -2.351
## Viv_EvInt|t2 -2.533 0.063 -40.477 0.000 -2.533 -1.538
## Viv_EvInt|t3 -1.699 0.051 -33.426 0.000 -1.699 -1.031
## Viv_EvInt|t4 0.409 0.037 11.134 0.000 0.409 0.248
## Viv_DiF|t1 -4.584 0.150 -30.631 0.000 -4.584 -2.523
## Viv_DiF|t2 -3.497 0.095 -36.839 0.000 -3.497 -1.925
## Viv_DiF|t3 -2.711 0.079 -34.320 0.000 -2.711 -1.492
## Viv_DiF|t4 -0.182 0.042 -4.317 0.000 -0.182 -0.100
## Viv_Pla|t1 -5.341 0.179 -29.855 0.000 -5.341 -2.636
## Viv_Pla|t2 -4.371 0.130 -33.594 0.000 -4.371 -2.157
## Viv_Pla|t3 -3.106 0.100 -31.049 0.000 -3.106 -1.533
## Viv_Pla|t4 -0.076 0.046 -1.654 0.098 -0.076 -0.038
## Viv_HabRC|t1 -5.033 0.195 -25.780 0.000 -5.033 -2.663
## Viv_HabRC|t2 -3.932 0.131 -29.951 0.000 -3.932 -2.080
## Viv_HabRC|t3 -2.974 0.103 -28.996 0.000 -2.974 -1.573
## Viv_HabRC|t4 -0.197 0.044 -4.457 0.000 -0.197 -0.104
## Viv_HabR|t1 -3.673 0.117 -31.406 0.000 -3.673 -2.486
## Viv_HabR|t2 -2.586 0.067 -38.687 0.000 -2.586 -1.750
## Viv_HabR|t3 -1.683 0.049 -34.488 0.000 -1.683 -1.139
## Viv_HabR|t4 0.004 0.033 0.126 0.900 0.004 0.003
## Viv_PrInf|t1 -4.607 0.165 -27.859 0.000 -4.607 -2.468
## Viv_PrInf|t2 -3.474 0.111 -31.190 0.000 -3.474 -1.861
## Viv_PrInf|t3 -2.597 0.087 -29.783 0.000 -2.597 -1.391
## Viv_PrInf|t4 0.036 0.042 0.848 0.396 0.036 0.019
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.335
## .Viv_EvInt 1.000 1.000 0.369
## .Viv_DiF 1.000 1.000 0.303
## .Viv_Pla 1.000 1.000 0.244
## .Viv_HabRC 1.000 1.000 0.280
## .Viv_HabR 1.000 1.000 0.458
## .Viv_PrInf 1.000 1.000 0.287
## Viv_Soft 1.984 0.127 15.661 0.000 1.000 1.000
## Viv_Hard 2.573 0.224 11.480 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.579 0.579 1.000
## Viv_EvInt 0.607 0.607 1.000
## Viv_DiF 0.550 0.550 1.000
## Viv_Pla 0.493 0.493 1.000
## Viv_HabRC 0.529 0.529 1.000
## Viv_HabR 0.677 0.677 1.000
## Viv_PrInf 0.536 0.536 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.665
## Viv_EvInt 0.631
## Viv_DiF 0.697
## Viv_Pla 0.756
## Viv_HabRC 0.720
## Viv_HabR 0.542
## Viv_PrInf 0.713
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.637 0.830
## Viv_EvInt 0.860 0.045 19.223 0.000 1.407 0.785
## Viv_DiF 0.922 0.059 15.681 0.000 1.509 0.822
## Viv_Pla 0.981 0.067 14.690 0.000 1.606 0.838
## Viv_Hard =~
## Viv_HabRC 1.000 1.440 0.791
## Viv_HabR 0.715 0.044 16.098 0.000 1.030 0.682
## Viv_PrInf 1.080 0.069 15.693 0.000 1.556 0.781
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.559 0.218 2.562 0.010 0.559 0.454
## .Viv_EvInt ~~
## .Viv_DiF 0.295 0.062 4.730 0.000 0.295 0.255
## Viv_Soft ~~
## Viv_Hard 1.951 0.482 4.050 0.000 0.827 0.827
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.055 0.388 0.142 0.887 0.034 0.034
## Viv_Hard 0.093 0.865 0.107 0.915 0.064 0.064
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.207 0.127 -33.213 0.000 -4.207 -2.134
## Viv_AdmT|t2 -2.997 0.076 -39.235 0.000 -2.997 -1.520
## Viv_AdmT|t3 -2.237 0.135 -16.559 0.000 -2.237 -1.135
## Viv_AdmT|t4 0.337 0.421 0.800 0.423 0.337 0.171
## Viv_EvInt|t1 -3.872 0.107 -36.186 0.000 -3.872 -2.159
## Viv_EvInt|t2 -2.306 0.141 -16.362 0.000 -2.306 -1.286
## Viv_EvInt|t3 -1.531 0.196 -7.818 0.000 -1.531 -0.854
## Viv_EvInt|t4 0.703 0.394 1.783 0.075 0.703 0.392
## Viv_DiF|t1 -4.584 0.150 -30.631 0.000 -4.584 -2.498
## Viv_DiF|t2 -3.321 0.163 -20.391 0.000 -3.321 -1.810
## Viv_DiF|t3 -2.513 0.185 -13.586 0.000 -2.513 -1.369
## Viv_DiF|t4 0.013 0.353 0.037 0.971 0.013 0.007
## Viv_Pla|t1 -5.341 0.179 -29.855 0.000 -5.341 -2.787
## Viv_Pla|t2 -4.260 0.201 -21.154 0.000 -4.260 -2.223
## Viv_Pla|t3 -3.088 0.202 -15.295 0.000 -3.088 -1.611
## Viv_Pla|t4 -0.007 0.375 -0.018 0.986 -0.007 -0.004
## Viv_HabRC|t1 -5.033 0.195 -25.780 0.000 -5.033 -2.764
## Viv_HabRC|t2 -3.932 0.131 -29.951 0.000 -3.932 -2.160
## Viv_HabRC|t3 -2.648 0.321 -8.245 0.000 -2.648 -1.455
## Viv_HabRC|t4 0.107 0.868 0.123 0.902 0.107 0.059
## Viv_HabR|t1 -3.673 0.117 -31.406 0.000 -3.673 -2.431
## Viv_HabR|t2 -2.453 0.208 -11.801 0.000 -2.453 -1.624
## Viv_HabR|t3 -1.497 0.353 -4.234 0.000 -1.497 -0.991
## Viv_HabR|t4 0.323 0.661 0.489 0.625 0.323 0.214
## Viv_PrInf|t1 -4.607 0.165 -27.859 0.000 -4.607 -2.311
## Viv_PrInf|t2 -3.297 0.278 -11.877 0.000 -3.297 -1.654
## Viv_PrInf|t3 -2.307 0.455 -5.074 0.000 -2.307 -1.157
## Viv_PrInf|t4 0.669 1.051 0.636 0.525 0.669 0.335
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.208 0.283 4.277 0.000 1.208 0.311
## .Viv_EvInt 1.235 0.242 5.103 0.000 1.235 0.384
## .Viv_DiF 1.090 0.198 5.496 0.000 1.090 0.324
## .Viv_Pla 1.094 0.197 5.549 0.000 1.094 0.298
## .Viv_HabRC 1.241 0.512 2.425 0.015 1.241 0.374
## .Viv_HabR 1.220 0.432 2.828 0.005 1.220 0.535
## .Viv_PrInf 1.552 0.648 2.396 0.017 1.552 0.391
## Viv_Soft 2.678 0.640 4.185 0.000 1.000 1.000
## Viv_Hard 2.075 0.865 2.399 0.016 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.507 0.507 1.000
## Viv_EvInt 0.558 0.558 1.000
## Viv_DiF 0.545 0.545 1.000
## Viv_Pla 0.522 0.522 1.000
## Viv_HabRC 0.549 0.549 1.000
## Viv_HabR 0.662 0.662 1.000
## Viv_PrInf 0.502 0.502 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.689
## Viv_EvInt 0.616
## Viv_DiF 0.676
## Viv_Pla 0.702
## Viv_HabRC 0.626
## Viv_HabR 0.465
## Viv_PrInf 0.609lavaan::fitMeasures(invariance$fit.configural,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 316.808 22.000 0.000
## srmr cfi.scaled tli.scaled
## 0.017 0.994 0.989
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.059 0.054 0.065modificationindices(invariance$fit.configural, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 168 Viv_Hard =~ Viv_Pla 2 2 1 21.653 0.385 0.555 0.290
## 169 Viv_AdmT ~~ Viv_EvInt 2 2 1 20.287 0.326 0.326 0.267
## 166 Viv_Hard =~ Viv_EvInt 2 2 1 16.380 -0.302 -0.435 -0.242
## 183 Viv_Pla ~~ Viv_HabRC 2 2 1 14.573 0.252 0.252 0.216
## 171 Viv_AdmT ~~ Viv_Pla 2 2 1 11.488 -0.262 -0.262 -0.228
## 157 Viv_Pla ~~ Viv_HabRC 1 1 1 9.559 0.274 0.274 0.274
## 178 Viv_EvInt ~~ Viv_PrInf 2 2 1 6.923 -0.177 -0.177 -0.128
## 184 Viv_Pla ~~ Viv_HabR 2 2 1 5.061 0.119 0.119 0.103
## 143 Viv_AdmT ~~ Viv_EvInt 1 1 1 4.845 0.150 0.150 0.150
## 173 Viv_AdmT ~~ Viv_HabR 2 2 1 4.736 -0.118 -0.118 -0.097
## sepc.nox
## 168 0.290
## 169 0.267
## 166 -0.242
## 183 0.216
## 171 -0.228
## 157 0.274
## 178 -0.128
## 184 0.103
## 143 0.150
## 173 -0.097semTools::reliability(invariance$fit.configural)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8368 0.8072
## alpha.ord 0.9035 0.8841
## omega 0.8234 0.7296
## omega2 0.8234 0.7296
## omega3 0.8218 0.7288
## avevar 0.6948 0.6753
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8292 0.7421
## alpha.ord 0.8972 0.8368
## omega 0.8155 0.6749
## omega2 0.8155 0.6749
## omega3 0.8116 0.6748
## avevar 0.6728 0.58075.1.1.4.2 Loadings
summary(invariance$fit.loadings,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 173 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 85
## Number of equality constraints 14
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 143.072 114.882
## Degrees of freedom 27 27
## P-value (Unknown) NA 0.000
## Scaling correction factor 1.353
## Shift parameter for each group:
## 2 3.725
## 1 5.426
## simple second-order correction
## Test statistic for each group:
## 2 66.448 52.830
## 1 76.624 62.052
##
## Model Test Baseline Model:
##
## Test statistic 57583.946 53150.718
## Degrees of freedom 42 42
## P-value NA 0.000
## Scaling correction factor 1.084
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.998
## Tucker-Lewis Index (TLI) 0.997 0.997
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.034 0.029
## 90 Percent confidence interval - lower 0.028 0.024
## 90 Percent confidence interval - upper 0.039 0.035
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.024 0.024
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.640 0.854
## Viv_EvInt 0.844 0.058 14.429 0.000 1.385 0.811
## Viv_DiF 0.892 0.077 11.543 0.000 1.464 0.826
## Viv_Pla 0.897 0.075 11.962 0.000 1.471 0.827
## Viv_Hard =~
## Viv_HabRC 1.000 1.462 0.825
## Viv_HabR 0.740 0.053 14.055 0.000 1.082 0.735
## Viv_PrInf 1.145 0.097 11.768 0.000 1.675 0.859
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.541 0.023 23.115 0.000 0.541 0.541
## .Viv_EvInt ~~
## .Viv_DiF 0.230 0.049 4.680 0.000 0.230 0.230
## Viv_Soft ~~
## Viv_Hard 1.971 0.153 12.870 0.000 0.822 0.822
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.579 0.232 -19.697 0.000 -4.579 -2.383
## Viv_AdmT|t2 -3.376 0.147 -23.032 0.000 -3.376 -1.757
## Viv_AdmT|t3 -2.429 0.103 -23.618 0.000 -2.429 -1.264
## Viv_AdmT|t4 0.075 0.043 1.755 0.079 0.075 0.039
## Viv_EvInt|t1 -3.992 0.172 -23.243 0.000 -3.992 -2.337
## Viv_EvInt|t2 -2.627 0.096 -27.304 0.000 -2.627 -1.538
## Viv_EvInt|t3 -1.761 0.069 -25.416 0.000 -1.761 -1.031
## Viv_EvInt|t4 0.424 0.039 10.820 0.000 0.424 0.248
## Viv_DiF|t1 -4.490 0.262 -17.155 0.000 -4.490 -2.533
## Viv_DiF|t2 -3.412 0.170 -20.062 0.000 -3.412 -1.925
## Viv_DiF|t3 -2.645 0.127 -20.793 0.000 -2.645 -1.492
## Viv_DiF|t4 -0.178 0.041 -4.284 0.000 -0.178 -0.100
## Viv_Pla|t1 -4.749 0.260 -18.295 0.000 -4.749 -2.670
## Viv_Pla|t2 -3.836 0.186 -20.670 0.000 -3.836 -2.157
## Viv_Pla|t3 -2.726 0.120 -22.639 0.000 -2.726 -1.533
## Viv_Pla|t4 -0.067 0.040 -1.657 0.098 -0.067 -0.038
## Viv_HabRC|t1 -4.732 0.249 -19.040 0.000 -4.732 -2.671
## Viv_HabRC|t2 -3.696 0.167 -22.070 0.000 -3.696 -2.086
## Viv_HabRC|t3 -2.787 0.123 -22.727 0.000 -2.787 -1.573
## Viv_HabRC|t4 -0.185 0.041 -4.472 0.000 -0.185 -0.104
## Viv_HabR|t1 -3.659 0.142 -25.733 0.000 -3.659 -2.483
## Viv_HabR|t2 -2.579 0.088 -29.244 0.000 -2.579 -1.750
## Viv_HabR|t3 -1.679 0.060 -28.171 0.000 -1.679 -1.139
## Viv_HabR|t4 0.004 0.033 0.126 0.900 0.004 0.003
## Viv_PrInf|t1 -4.793 0.236 -20.303 0.000 -4.793 -2.457
## Viv_PrInf|t2 -3.631 0.153 -23.711 0.000 -3.631 -1.861
## Viv_PrInf|t3 -2.714 0.114 -23.853 0.000 -2.714 -1.391
## Viv_PrInf|t4 0.037 0.044 0.848 0.396 0.037 0.019
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.271
## .Viv_EvInt 1.000 1.000 0.343
## .Viv_DiF 1.000 1.000 0.318
## .Viv_Pla 1.000 1.000 0.316
## .Viv_HabRC 1.000 1.000 0.319
## .Viv_HabR 1.000 1.000 0.460
## .Viv_PrInf 1.000 1.000 0.263
## Viv_Soft 2.691 0.275 9.793 0.000 1.000 1.000
## Viv_Hard 2.138 0.241 8.875 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.521 0.521 1.000
## Viv_EvInt 0.586 0.586 1.000
## Viv_DiF 0.564 0.564 1.000
## Viv_Pla 0.562 0.562 1.000
## Viv_HabRC 0.564 0.564 1.000
## Viv_HabR 0.679 0.679 1.000
## Viv_PrInf 0.513 0.513 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.729
## Viv_EvInt 0.657
## Viv_DiF 0.682
## Viv_Pla 0.684
## Viv_HabRC 0.681
## Viv_HabR 0.540
## Viv_PrInf 0.737
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.459 0.803
## Viv_EvInt 0.844 0.058 14.429 0.000 1.231 0.773
## Viv_DiF 0.892 0.077 11.543 0.000 1.302 0.828
## Viv_Pla 0.897 0.075 11.962 0.000 1.308 0.866
## Viv_Hard =~
## Viv_HabRC 1.000 1.393 0.809
## Viv_HabR 0.740 0.053 14.055 0.000 1.031 0.684
## Viv_PrInf 1.145 0.097 11.768 0.000 1.595 0.770
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.492 0.179 2.739 0.006 0.492 0.442
## .Viv_EvInt ~~
## .Viv_DiF 0.238 0.058 4.093 0.000 0.238 0.267
## Viv_Soft ~~
## Viv_Hard 1.676 0.403 4.157 0.000 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.591 0.313 -1.886 0.059 -0.405 -0.405
## Viv_Hard 0.103 0.746 0.138 0.890 0.074 0.074
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.579 0.232 -19.697 0.000 -4.579 -2.521
## Viv_AdmT|t2 -3.376 0.147 -23.032 0.000 -3.376 -1.859
## Viv_AdmT|t3 -2.702 0.147 -18.399 0.000 -2.702 -1.488
## Viv_AdmT|t4 -0.331 0.340 -0.973 0.330 -0.331 -0.182
## Viv_EvInt|t1 -3.992 0.172 -23.243 0.000 -3.992 -2.508
## Viv_EvInt|t2 -2.587 0.159 -16.318 0.000 -2.587 -1.625
## Viv_EvInt|t3 -1.899 0.182 -10.421 0.000 -1.899 -1.193
## Viv_EvInt|t4 0.083 0.323 0.258 0.796 0.083 0.052
## Viv_DiF|t1 -4.490 0.262 -17.155 0.000 -4.490 -2.855
## Viv_DiF|t2 -3.417 0.241 -14.204 0.000 -3.417 -2.173
## Viv_DiF|t3 -2.724 0.229 -11.898 0.000 -2.724 -1.732
## Viv_DiF|t4 -0.559 0.295 -1.895 0.058 -0.559 -0.356
## Viv_Pla|t1 -4.749 0.260 -18.295 0.000 -4.749 -3.145
## Viv_Pla|t2 -3.928 0.251 -15.644 0.000 -3.928 -2.601
## Viv_Pla|t3 -3.005 0.209 -14.360 0.000 -3.005 -1.990
## Viv_Pla|t4 -0.577 0.286 -2.019 0.044 -0.577 -0.382
## Viv_HabRC|t1 -4.732 0.249 -19.040 0.000 -4.732 -2.749
## Viv_HabRC|t2 -3.696 0.167 -22.070 0.000 -3.696 -2.147
## Viv_HabRC|t3 -2.488 0.267 -9.316 0.000 -2.488 -1.446
## Viv_HabRC|t4 0.116 0.748 0.156 0.876 0.116 0.068
## Viv_HabR|t1 -3.659 0.142 -25.733 0.000 -3.659 -2.427
## Viv_HabR|t2 -2.438 0.205 -11.871 0.000 -2.438 -1.617
## Viv_HabR|t3 -1.484 0.322 -4.604 0.000 -1.484 -0.984
## Viv_HabR|t4 0.332 0.588 0.566 0.572 0.332 0.220
## Viv_PrInf|t1 -4.793 0.236 -20.303 0.000 -4.793 -2.313
## Viv_PrInf|t2 -3.414 0.291 -11.734 0.000 -3.414 -1.648
## Viv_PrInf|t3 -2.385 0.428 -5.577 0.000 -2.385 -1.151
## Viv_PrInf|t4 0.709 0.959 0.740 0.460 0.709 0.342
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.169 0.299 3.914 0.000 1.169 0.355
## .Viv_EvInt 1.018 0.206 4.937 0.000 1.018 0.402
## .Viv_DiF 0.779 0.171 4.565 0.000 0.779 0.315
## .Viv_Pla 0.569 0.150 3.802 0.000 0.569 0.250
## .Viv_HabRC 1.023 0.443 2.311 0.021 1.023 0.345
## .Viv_HabR 1.210 0.375 3.232 0.001 1.210 0.533
## .Viv_PrInf 1.751 0.669 2.617 0.009 1.751 0.408
## Viv_Soft 2.128 0.491 4.338 0.000 1.000 1.000
## Viv_Hard 1.940 0.775 2.504 0.012 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.551 0.551 1.000
## Viv_EvInt 0.628 0.628 1.000
## Viv_DiF 0.636 0.636 1.000
## Viv_Pla 0.662 0.662 1.000
## Viv_HabRC 0.581 0.581 1.000
## Viv_HabR 0.663 0.663 1.000
## Viv_PrInf 0.483 0.483 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.645
## Viv_EvInt 0.598
## Viv_DiF 0.685
## Viv_Pla 0.750
## Viv_HabRC 0.655
## Viv_HabR 0.467
## Viv_PrInf 0.592lavaan::fitMeasures(invariance$fit.loadings,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 114.882 27.000 0.000
## srmr cfi.scaled tli.scaled
## 0.024 0.998 0.997
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.029 0.024 0.035modificationindices(invariance$fit.loadings, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 147 Viv_Hard =~ Viv_Pla 1 1 1 48.00 0.152 0.222 0.125
## 58 Viv_Pla ~1 1 1 1 45.57 -0.752 -0.752 -0.423
## 51 Viv_Pla ~*~ Viv_Pla 1 1 1 45.57 -0.089 -0.089 -1.000
## 114 Viv_Pla ~*~ Viv_Pla 2 2 1 45.57 0.118 0.118 1.000
## 121 Viv_Pla ~1 2 2 1 45.57 0.752 0.752 0.498
## 174 Viv_AdmT ~~ Viv_EvInt 2 2 1 44.85 0.364 0.364 0.333
## 111 Viv_AdmT ~*~ Viv_AdmT 2 2 1 43.04 -0.100 -0.100 -1.000
## 64 Viv_Soft =~ Viv_AdmT 2 2 1 42.18 0.158 0.231 0.127
## 48 Viv_AdmT ~*~ Viv_AdmT 1 1 1 42.18 0.082 0.082 1.000
## 1 Viv_Soft =~ Viv_AdmT 1 1 1 42.18 -0.158 -0.259 -0.135
## sepc.nox
## 147 0.125
## 58 -0.423
## 51 -1.000
## 114 1.000
## 121 0.498
## 174 0.333
## 111 -1.000
## 64 0.127
## 48 1.000
## 1 -0.135semTools::reliability(invariance$fit.loadings)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8368 0.8072
## alpha.ord 0.9035 0.8841
## omega 0.8279 0.7235
## omega2 0.8279 0.7235
## omega3 0.8287 0.7233
## avevar 0.6902 0.6709
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8292 0.7421
## alpha.ord 0.8972 0.8368
## omega 0.7953 0.6796
## omega2 0.7953 0.6796
## omega3 0.7880 0.6790
## avevar 0.6660 0.58195.1.1.4.3 Thresholds
summary(invariance$fit.thresholds,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 154 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 85
## Number of equality constraints 33
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 234.485 94.795
## Degrees of freedom 46 46
## P-value (Unknown) NA 0.000
## Scaling correction factor 2.942
## Shift parameter for each group:
## 2 6.147
## 1 8.953
## simple second-order correction
## Test statistic for each group:
## 2 122.571 47.805
## 1 111.914 46.990
##
## Model Test Baseline Model:
##
## Test statistic 57583.946 53150.718
## Degrees of freedom 42 42
## P-value NA 0.000
## Scaling correction factor 1.084
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 0.999
## Tucker-Lewis Index (TLI) 0.997 0.999
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.033 0.017
## 90 Percent confidence interval - lower 0.029 0.012
## 90 Percent confidence interval - upper 0.037 0.021
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.025 0.025
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.651 0.855
## Viv_EvInt 0.864 0.051 17.089 0.000 1.427 0.819
## Viv_DiF 0.899 0.059 15.130 0.000 1.484 0.829
## Viv_Pla 0.859 0.057 15.147 0.000 1.418 0.817
## Viv_Hard =~
## Viv_HabRC 1.000 1.436 0.821
## Viv_HabR 0.758 0.041 18.665 0.000 1.088 0.736
## Viv_PrInf 1.176 0.089 13.151 0.000 1.689 0.860
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.541 0.022 24.684 0.000 0.541 0.541
## .Viv_EvInt ~~
## .Viv_DiF 0.207 0.044 4.750 0.000 0.207 0.207
## Viv_Soft ~~
## Viv_Hard 1.947 0.133 14.624 0.000 0.821 0.821
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.616 0.191 -24.195 0.000 -4.616 -2.391
## Viv_AdmT|t2 -3.328 0.131 -25.331 0.000 -3.328 -1.724
## Viv_AdmT|t3 -2.493 0.100 -24.887 0.000 -2.493 -1.291
## Viv_AdmT|t4 0.094 0.042 2.229 0.026 0.094 0.049
## Viv_EvInt|t1 -4.117 0.150 -27.517 0.000 -4.117 -2.363
## Viv_EvInt|t2 -2.602 0.094 -27.798 0.000 -2.602 -1.493
## Viv_EvInt|t3 -1.774 0.068 -26.001 0.000 -1.774 -1.018
## Viv_EvInt|t4 0.476 0.041 11.617 0.000 0.476 0.273
## Viv_DiF|t1 -4.570 0.206 -22.201 0.000 -4.570 -2.554
## Viv_DiF|t2 -3.415 0.152 -22.503 0.000 -3.415 -1.908
## Viv_DiF|t3 -2.640 0.119 -22.275 0.000 -2.640 -1.475
## Viv_DiF|t4 -0.191 0.039 -4.918 0.000 -0.191 -0.107
## Viv_Pla|t1 -4.609 0.192 -23.989 0.000 -4.609 -2.657
## Viv_Pla|t2 -3.737 0.159 -23.435 0.000 -3.737 -2.154
## Viv_Pla|t3 -2.724 0.116 -23.388 0.000 -2.724 -1.570
## Viv_Pla|t4 -0.154 0.037 -4.196 0.000 -0.154 -0.089
## Viv_HabRC|t1 -4.667 0.201 -23.171 0.000 -4.667 -2.667
## Viv_HabRC|t2 -3.706 0.161 -23.063 0.000 -3.706 -2.118
## Viv_HabRC|t3 -2.688 0.121 -22.298 0.000 -2.688 -1.536
## Viv_HabRC|t4 -0.274 0.040 -6.776 0.000 -0.274 -0.157
## Viv_HabR|t1 -3.677 0.122 -30.252 0.000 -3.677 -2.488
## Viv_HabR|t2 -2.571 0.082 -31.447 0.000 -2.571 -1.739
## Viv_HabR|t3 -1.685 0.056 -29.994 0.000 -1.685 -1.140
## Viv_HabR|t4 -0.010 0.032 -0.321 0.749 -0.010 -0.007
## Viv_PrInf|t1 -4.854 0.205 -23.639 0.000 -4.854 -2.473
## Viv_PrInf|t2 -3.631 0.152 -23.887 0.000 -3.631 -1.850
## Viv_PrInf|t3 -2.698 0.116 -23.220 0.000 -2.698 -1.375
## Viv_PrInf|t4 0.103 0.047 2.182 0.029 0.103 0.052
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.268
## .Viv_EvInt 1.000 1.000 0.329
## .Viv_DiF 1.000 1.000 0.312
## .Viv_Pla 1.000 1.000 0.332
## .Viv_HabRC 1.000 1.000 0.327
## .Viv_HabR 1.000 1.000 0.458
## .Viv_PrInf 1.000 1.000 0.260
## Viv_Soft 2.727 0.239 11.398 0.000 1.000 1.000
## Viv_Hard 2.062 0.201 10.271 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.518 0.518 1.000
## Viv_EvInt 0.574 0.574 1.000
## Viv_DiF 0.559 0.559 1.000
## Viv_Pla 0.576 0.576 1.000
## Viv_HabRC 0.572 0.572 1.000
## Viv_HabR 0.677 0.677 1.000
## Viv_PrInf 0.510 0.510 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.732
## Viv_EvInt 0.671
## Viv_DiF 0.688
## Viv_Pla 0.668
## Viv_HabRC 0.673
## Viv_HabR 0.542
## Viv_PrInf 0.740
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.617 0.800
## Viv_EvInt 0.864 0.051 17.089 0.000 1.397 0.768
## Viv_DiF 0.899 0.059 15.130 0.000 1.453 0.826
## Viv_Pla 0.859 0.057 15.147 0.000 1.388 0.873
## Viv_Hard =~
## Viv_HabRC 1.000 1.242 0.806
## Viv_HabR 0.758 0.041 18.665 0.000 0.941 0.680
## Viv_PrInf 1.176 0.089 13.151 0.000 1.461 0.773
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.413 0.049 8.356 0.000 0.413 0.448
## .Viv_EvInt ~~
## .Viv_DiF 0.322 0.055 5.868 0.000 0.322 0.279
## Viv_Soft ~~
## Viv_Hard 1.660 0.120 13.880 0.000 0.827 0.827
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.181 0.052 -3.500 0.000 -0.112 -0.112
## Viv_Hard -0.335 0.048 -7.020 0.000 -0.270 -0.270
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.616 0.191 -24.195 0.000 -4.616 -2.284
## Viv_AdmT|t2 -3.328 0.131 -25.331 0.000 -3.328 -1.646
## Viv_AdmT|t3 -2.493 0.100 -24.887 0.000 -2.493 -1.233
## Viv_AdmT|t4 0.094 0.042 2.229 0.026 0.094 0.047
## Viv_EvInt|t1 -4.117 0.150 -27.517 0.000 -4.117 -2.263
## Viv_EvInt|t2 -2.602 0.094 -27.798 0.000 -2.602 -1.430
## Viv_EvInt|t3 -1.774 0.068 -26.001 0.000 -1.774 -0.975
## Viv_EvInt|t4 0.476 0.041 11.617 0.000 0.476 0.262
## Viv_DiF|t1 -4.570 0.206 -22.201 0.000 -4.570 -2.598
## Viv_DiF|t2 -3.415 0.152 -22.503 0.000 -3.415 -1.941
## Viv_DiF|t3 -2.640 0.119 -22.275 0.000 -2.640 -1.501
## Viv_DiF|t4 -0.191 0.039 -4.918 0.000 -0.191 -0.109
## Viv_Pla|t1 -4.609 0.192 -23.989 0.000 -4.609 -2.899
## Viv_Pla|t2 -3.737 0.159 -23.435 0.000 -3.737 -2.350
## Viv_Pla|t3 -2.724 0.116 -23.388 0.000 -2.724 -1.713
## Viv_Pla|t4 -0.154 0.037 -4.196 0.000 -0.154 -0.097
## Viv_HabRC|t1 -4.667 0.201 -23.171 0.000 -4.667 -3.030
## Viv_HabRC|t2 -3.706 0.161 -23.063 0.000 -3.706 -2.406
## Viv_HabRC|t3 -2.688 0.121 -22.298 0.000 -2.688 -1.745
## Viv_HabRC|t4 -0.274 0.040 -6.776 0.000 -0.274 -0.178
## Viv_HabR|t1 -3.677 0.122 -30.252 0.000 -3.677 -2.658
## Viv_HabR|t2 -2.571 0.082 -31.447 0.000 -2.571 -1.858
## Viv_HabR|t3 -1.685 0.056 -29.994 0.000 -1.685 -1.218
## Viv_HabR|t4 -0.010 0.032 -0.321 0.749 -0.010 -0.007
## Viv_PrInf|t1 -4.854 0.205 -23.639 0.000 -4.854 -2.567
## Viv_PrInf|t2 -3.631 0.152 -23.887 0.000 -3.631 -1.921
## Viv_PrInf|t3 -2.698 0.116 -23.220 0.000 -2.698 -1.427
## Viv_PrInf|t4 0.103 0.047 2.182 0.029 0.103 0.055
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.472 0.156 9.411 0.000 1.472 0.360
## .Viv_EvInt 1.357 0.134 10.122 0.000 1.357 0.410
## .Viv_DiF 0.982 0.134 7.330 0.000 0.982 0.317
## .Viv_Pla 0.600 0.094 6.372 0.000 0.600 0.237
## .Viv_HabRC 0.830 0.116 7.172 0.000 0.830 0.350
## .Viv_HabR 1.028 0.095 10.862 0.000 1.028 0.537
## .Viv_PrInf 1.440 0.152 9.456 0.000 1.440 0.403
## Viv_Soft 2.615 0.235 11.121 0.000 1.000 1.000
## Viv_Hard 1.543 0.161 9.556 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.495 0.495 1.000
## Viv_EvInt 0.550 0.550 1.000
## Viv_DiF 0.569 0.569 1.000
## Viv_Pla 0.629 0.629 1.000
## Viv_HabRC 0.649 0.649 1.000
## Viv_HabR 0.723 0.723 1.000
## Viv_PrInf 0.529 0.529 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.640
## Viv_EvInt 0.590
## Viv_DiF 0.683
## Viv_Pla 0.763
## Viv_HabRC 0.650
## Viv_HabR 0.463
## Viv_PrInf 0.597lavaan::fitMeasures(invariance$fit.thresholds,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 94.795 46.000 0.000
## srmr cfi.scaled tli.scaled
## 0.025 0.999 0.999
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.017 0.012 0.021modificationindices(invariance$fit.thresholds, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 166 Viv_Hard =~ Viv_Pla 1 1 1 62.76 0.162 0.233 0.134
## 51 Viv_Pla ~*~ Viv_Pla 1 1 1 59.88 -0.099 -0.099 -1.000
## 114 Viv_Pla ~*~ Viv_Pla 2 2 1 54.57 0.107 0.107 1.000
## 58 Viv_Pla ~1 1 1 1 52.04 -0.273 -0.273 -0.157
## 121 Viv_Pla ~1 2 2 1 52.04 0.273 0.273 0.172
## 193 Viv_AdmT ~~ Viv_EvInt 2 2 1 50.61 0.481 0.481 0.340
## 192 Viv_Hard =~ Viv_Pla 2 2 1 41.90 -0.140 -0.174 -0.109
## 111 Viv_AdmT ~*~ Viv_AdmT 2 2 1 40.10 -0.074 -0.074 -1.000
## 64 Viv_Soft =~ Viv_AdmT 2 2 1 39.56 0.141 0.228 0.113
## 1 Viv_Soft =~ Viv_AdmT 1 1 1 39.56 -0.141 -0.233 -0.121
## sepc.nox
## 166 0.134
## 51 -1.000
## 114 1.000
## 58 -0.157
## 121 0.172
## 193 0.340
## 192 -0.109
## 111 -1.000
## 64 0.113
## 1 -0.121semTools::reliability(invariance$fit.thresholds)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8368 0.8072
## alpha.ord 0.9035 0.8841
## omega 0.8301 0.7234
## omega2 0.8301 0.7234
## omega3 0.8313 0.7234
## avevar 0.6917 0.6703
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8292 0.7421
## alpha.ord 0.8972 0.8368
## omega 0.8045 0.6640
## omega2 0.8045 0.6640
## omega3 0.7964 0.6634
## avevar 0.6612 0.58055.1.1.5 Partial Invariance - Sex Means
summary(invariance$fit.means,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 153 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 83
## Number of equality constraints 33
##
## Number of observations per group:
## 2 3095
## 1 4508
## Number of missing patterns per group:
## 2 1
## 1 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 580.229 200.166
## Degrees of freedom 48 48
## P-value (Unknown) NA 0.000
## Scaling correction factor 3.137
## Shift parameter for each group:
## 2 6.188
## 1 9.014
## simple second-order correction
## Test statistic for each group:
## 2 311.951 105.631
## 1 268.278 94.535
##
## Model Test Baseline Model:
##
## Test statistic 57583.946 53150.718
## Degrees of freedom 42 42
## P-value NA 0.000
## Scaling correction factor 1.084
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.991 0.997
## Tucker-Lewis Index (TLI) 0.992 0.997
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.054 0.029
## 90 Percent confidence interval - lower 0.050 0.025
## 90 Percent confidence interval - upper 0.058 0.033
## P-value RMSEA <= 0.05 0.045 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.026 0.026
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.657 0.856
## Viv_EvInt 0.857 0.050 17.143 0.000 1.420 0.818
## Viv_DiF 0.894 0.059 15.129 0.000 1.481 0.829
## Viv_Pla 0.857 0.057 15.045 0.000 1.419 0.818
## Viv_Hard =~
## Viv_HabRC 1.000 1.444 0.822
## Viv_HabR 0.751 0.041 18.299 0.000 1.085 0.735
## Viv_PrInf 1.168 0.091 12.901 0.000 1.686 0.860
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.542 0.022 24.536 0.000 0.542 0.542
## .Viv_EvInt ~~
## .Viv_DiF 0.210 0.043 4.878 0.000 0.210 0.210
## Viv_Soft ~~
## Viv_Hard 1.965 0.136 14.495 0.000 0.821 0.821
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.652 0.193 -24.091 0.000 -4.652 -2.403
## Viv_AdmT|t2 -3.323 0.131 -25.356 0.000 -3.323 -1.717
## Viv_AdmT|t3 -2.462 0.098 -25.201 0.000 -2.462 -1.272
## Viv_AdmT|t4 0.201 0.030 6.751 0.000 0.201 0.104
## Viv_EvInt|t1 -4.121 0.148 -27.763 0.000 -4.121 -2.372
## Viv_EvInt|t2 -2.573 0.091 -28.341 0.000 -2.573 -1.481
## Viv_EvInt|t3 -1.725 0.064 -27.067 0.000 -1.725 -0.993
## Viv_EvInt|t4 0.576 0.031 18.308 0.000 0.576 0.331
## Viv_DiF|t1 -4.587 0.206 -22.273 0.000 -4.587 -2.566
## Viv_DiF|t2 -3.404 0.150 -22.628 0.000 -3.404 -1.904
## Viv_DiF|t3 -2.609 0.116 -22.499 0.000 -2.609 -1.460
## Viv_DiF|t4 -0.097 0.027 -3.631 0.000 -0.097 -0.054
## Viv_Pla|t1 -4.634 0.193 -24.048 0.000 -4.634 -2.669
## Viv_Pla|t2 -3.738 0.160 -23.398 0.000 -3.738 -2.153
## Viv_Pla|t3 -2.697 0.116 -23.327 0.000 -2.697 -1.553
## Viv_Pla|t4 -0.058 0.025 -2.341 0.019 -0.058 -0.033
## Viv_HabRC|t1 -4.733 0.205 -23.066 0.000 -4.733 -2.695
## Viv_HabRC|t2 -3.710 0.163 -22.775 0.000 -3.710 -2.112
## Viv_HabRC|t3 -2.633 0.121 -21.826 0.000 -2.633 -1.499
## Viv_HabRC|t4 -0.063 0.025 -2.524 0.012 -0.063 -0.036
## Viv_HabR|t1 -3.710 0.122 -30.355 0.000 -3.710 -2.515
## Viv_HabR|t2 -2.546 0.081 -31.564 0.000 -2.546 -1.726
## Viv_HabR|t3 -1.613 0.053 -30.377 0.000 -1.613 -1.093
## Viv_HabR|t4 0.151 0.022 6.954 0.000 0.151 0.102
## Viv_PrInf|t1 -4.892 0.207 -23.643 0.000 -4.892 -2.495
## Viv_PrInf|t2 -3.599 0.150 -23.941 0.000 -3.599 -1.836
## Viv_PrInf|t3 -2.612 0.111 -23.473 0.000 -2.612 -1.332
## Viv_PrInf|t4 0.351 0.031 11.188 0.000 0.351 0.179
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.267
## .Viv_EvInt 1.000 1.000 0.331
## .Viv_DiF 1.000 1.000 0.313
## .Viv_Pla 1.000 1.000 0.332
## .Viv_HabRC 1.000 1.000 0.324
## .Viv_HabR 1.000 1.000 0.459
## .Viv_PrInf 1.000 1.000 0.260
## Viv_Soft 2.746 0.243 11.301 0.000 1.000 1.000
## Viv_Hard 2.085 0.206 10.117 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.517 0.517 1.000
## Viv_EvInt 0.576 0.576 1.000
## Viv_DiF 0.559 0.559 1.000
## Viv_Pla 0.576 0.576 1.000
## Viv_HabRC 0.569 0.569 1.000
## Viv_HabR 0.678 0.678 1.000
## Viv_PrInf 0.510 0.510 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.733
## Viv_EvInt 0.669
## Viv_DiF 0.687
## Viv_Pla 0.668
## Viv_HabRC 0.676
## Viv_HabR 0.541
## Viv_PrInf 0.740
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.000 1.698 0.798
## Viv_EvInt 0.857 0.050 17.143 0.000 1.455 0.766
## Viv_DiF 0.894 0.059 15.129 0.000 1.518 0.826
## Viv_Pla 0.857 0.057 15.045 0.000 1.454 0.877
## Viv_Hard =~
## Viv_HabRC 1.000 1.374 0.811
## Viv_HabR 0.751 0.041 18.299 0.000 1.033 0.683
## Viv_PrInf 1.168 0.091 12.901 0.000 1.605 0.769
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.482 0.057 8.455 0.000 0.482 0.441
## .Viv_EvInt ~~
## .Viv_DiF 0.358 0.060 5.947 0.000 0.358 0.283
## Viv_Soft ~~
## Viv_Hard 1.926 0.134 14.349 0.000 0.825 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.652 0.193 -24.091 0.000 -4.652 -2.186
## Viv_AdmT|t2 -3.323 0.131 -25.356 0.000 -3.323 -1.562
## Viv_AdmT|t3 -2.462 0.098 -25.201 0.000 -2.462 -1.157
## Viv_AdmT|t4 0.201 0.030 6.751 0.000 0.201 0.094
## Viv_EvInt|t1 -4.121 0.148 -27.763 0.000 -4.121 -2.169
## Viv_EvInt|t2 -2.573 0.091 -28.341 0.000 -2.573 -1.355
## Viv_EvInt|t3 -1.725 0.064 -27.067 0.000 -1.725 -0.908
## Viv_EvInt|t4 0.576 0.031 18.308 0.000 0.576 0.303
## Viv_DiF|t1 -4.587 0.206 -22.273 0.000 -4.587 -2.496
## Viv_DiF|t2 -3.404 0.150 -22.628 0.000 -3.404 -1.852
## Viv_DiF|t3 -2.609 0.116 -22.499 0.000 -2.609 -1.420
## Viv_DiF|t4 -0.097 0.027 -3.631 0.000 -0.097 -0.053
## Viv_Pla|t1 -4.634 0.193 -24.048 0.000 -4.634 -2.793
## Viv_Pla|t2 -3.738 0.160 -23.398 0.000 -3.738 -2.253
## Viv_Pla|t3 -2.697 0.116 -23.327 0.000 -2.697 -1.626
## Viv_Pla|t4 -0.058 0.025 -2.341 0.019 -0.058 -0.035
## Viv_HabRC|t1 -4.733 0.205 -23.066 0.000 -4.733 -2.794
## Viv_HabRC|t2 -3.710 0.163 -22.775 0.000 -3.710 -2.190
## Viv_HabRC|t3 -2.633 0.121 -21.826 0.000 -2.633 -1.554
## Viv_HabRC|t4 -0.063 0.025 -2.524 0.012 -0.063 -0.037
## Viv_HabR|t1 -3.710 0.122 -30.355 0.000 -3.710 -2.455
## Viv_HabR|t2 -2.546 0.081 -31.564 0.000 -2.546 -1.685
## Viv_HabR|t3 -1.613 0.053 -30.377 0.000 -1.613 -1.067
## Viv_HabR|t4 0.151 0.022 6.954 0.000 0.151 0.100
## Viv_PrInf|t1 -4.892 0.207 -23.643 0.000 -4.892 -2.342
## Viv_PrInf|t2 -3.599 0.150 -23.941 0.000 -3.599 -1.723
## Viv_PrInf|t3 -2.612 0.111 -23.473 0.000 -2.612 -1.251
## Viv_PrInf|t4 0.351 0.031 11.188 0.000 0.351 0.168
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.644 0.176 9.353 0.000 1.644 0.363
## .Viv_EvInt 1.490 0.146 10.207 0.000 1.490 0.413
## .Viv_DiF 1.074 0.146 7.363 0.000 1.074 0.318
## .Viv_Pla 0.638 0.100 6.356 0.000 0.638 0.232
## .Viv_HabRC 0.981 0.136 7.229 0.000 0.981 0.342
## .Viv_HabR 1.217 0.109 11.214 0.000 1.217 0.533
## .Viv_PrInf 1.785 0.188 9.484 0.000 1.785 0.409
## Viv_Soft 2.882 0.256 11.261 0.000 1.000 1.000
## Viv_Hard 1.889 0.191 9.894 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.470 0.470 1.000
## Viv_EvInt 0.526 0.526 1.000
## Viv_DiF 0.544 0.544 1.000
## Viv_Pla 0.603 0.603 1.000
## Viv_HabRC 0.590 0.590 1.000
## Viv_HabR 0.662 0.662 1.000
## Viv_PrInf 0.479 0.479 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.637
## Viv_EvInt 0.587
## Viv_DiF 0.682
## Viv_Pla 0.768
## Viv_HabRC 0.658
## Viv_HabR 0.467
## Viv_PrInf 0.591lavaan::fitMeasures(invariance$fit.means,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 200.166 48.000 0.000
## srmr cfi.scaled tli.scaled
## 0.026 0.997 0.997
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.029 0.025 0.033modificationindices(invariance$fit.means, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox
## 63 Viv_Hard ~1 1 1 1 266.41 0.350 0.242 0.242 0.242
## 126 Viv_Hard ~1 2 2 1 266.41 -0.350 -0.254 -0.254 -0.254
## 61 Viv_PrInf ~1 1 1 1 170.01 0.495 0.495 0.252 0.252
## 124 Viv_PrInf ~1 2 2 1 170.01 -0.495 -0.495 -0.237 -0.237
## 125 Viv_Soft ~1 2 2 1 78.53 -0.185 -0.109 -0.109 -0.109
## 62 Viv_Soft ~1 1 1 1 78.53 0.185 0.111 0.111 0.111
## 56 Viv_EvInt ~1 1 1 1 68.99 0.248 0.248 0.143 0.143
## 119 Viv_EvInt ~1 2 2 1 68.99 -0.248 -0.248 -0.131 -0.131
## 60 Viv_HabR ~1 1 1 1 66.48 0.225 0.225 0.152 0.152
## 123 Viv_HabR ~1 2 2 1 66.48 -0.225 -0.225 -0.149 -0.149semTools::reliability(invariance$fit.means)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8368 0.8072
## alpha.ord 0.9035 0.8841
## omega 0.8299 0.7249
## omega2 0.8299 0.7249
## omega3 0.8311 0.7248
## avevar 0.6917 0.6705
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8292 0.7421
## alpha.ord 0.8972 0.8368
## omega 0.8067 0.6757
## omega2 0.8067 0.6757
## omega3 0.7981 0.6751
## avevar 0.6603 0.58145.1.1.6 Organizing Education
data$Esc<-car::recode(data$Esc,"5=4")
data$EscClasseR<-as.factor(data$Esc)
summary(data$EscClasseR)## 1 2 3 4
## 503 2506 4006 5935.1.1.7 Education Invariance Model
model <- '
Viv_Soft =~ Viv_AdmT + Viv_EvInt + Viv_DiF + Viv_Pla
Viv_Hard =~ Viv_HabRC + Viv_HabR + Viv_PrInf
Viv_HabRC ~~ Viv_HabR
Viv_EvInt ~~ Viv_DiF
'5.1.1.8 Fitting
invariance<- measurementInvarianceCat(model = model, data = data, group = "EscClasseR",parameterization = "theta", estimator = "ULSMV",ordered = T,missing="pairwise",std.lv=T)## Warning: The measurementInvarianceCat function is deprecated, and it will cease
## to be included in future versions of semTools. See help('semTools-deprecated)
## for details.## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.243154e-13) is close to zero. This may be a symptom that the
## model is not identified.##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.thresholds
## Model 4 : fit.means
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 44 71.5
## fit.loadings 59 150.0 16.3 15 0.3648
## fit.thresholds 116 511.2 61.6 57 0.3151
## fit.means 122 910.1 22.2 6 0.0011 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi.scaled rmsea.scaled cfi.scaled.delta rmsea.scaled.delta
## fit.configural 0.995 0.059 NA NA
## fit.loadings 0.999 0.018 0.005 0.041
## fit.thresholds 0.999 0.012 0.000 0.006
## fit.means 0.998 0.019 0.001 0.0075.1.1.8.1 Configural
summary(invariance$fit.configural,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 598 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 179
## Number of equality constraints 27
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 71.511 339.451
## Degrees of freedom 44 44
## P-value (Unknown) NA 0.000
## Scaling correction factor 0.214
## Shift parameter for each group:
## 2 1.613
## 3 2.579
## 1 0.324
## 4 0.382
## simple second-order correction
## Test statistic for each group:
## 2 17.308 82.587
## 3 39.420 186.999
## 1 7.015 33.140
## 4 7.768 36.725
##
## Model Test Baseline Model:
##
## Test statistic 57688.409 56390.095
## Degrees of freedom 84 84
## P-value NA 0.000
## Scaling correction factor 1.024
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.995
## Tucker-Lewis Index (TLI) 0.999 0.990
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.018 0.059
## 90 Percent confidence interval - lower 0.010 0.054
## 90 Percent confidence interval - upper 0.026 0.065
## P-value RMSEA <= 0.05 1.000 0.004
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.018 0.018
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.435 0.052 27.427 0.000 1.435 0.821
## Viv_EvInt 1.322 0.047 28.174 0.000 1.322 0.798
## Viv_DiF 1.438 0.056 25.830 0.000 1.438 0.821
## Viv_Pla 1.569 0.065 24.290 0.000 1.569 0.843
## Viv_Hard =~
## Viv_HabRC 1.565 0.075 20.915 0.000 1.565 0.843
## Viv_HabR 1.075 0.045 23.804 0.000 1.075 0.732
## Viv_PrInf 1.390 0.055 25.128 0.000 1.390 0.812
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.432 0.028 15.535 0.000 0.432 0.432
## .Viv_EvInt ~~
## .Viv_DiF 0.220 0.030 7.352 0.000 0.220 0.220
## Viv_Soft ~~
## Viv_Hard 0.832 0.011 73.615 0.000 0.832 0.832
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -3.996 0.123 -32.448 0.000 -3.996 -2.284
## Viv_AdmT|t2 -2.827 0.081 -35.109 0.000 -2.827 -1.616
## Viv_AdmT|t3 -2.055 0.066 -30.997 0.000 -2.055 -1.174
## Viv_AdmT|t4 0.322 0.043 7.416 0.000 0.322 0.184
## Viv_EvInt|t1 -3.702 0.103 -36.016 0.000 -3.702 -2.233
## Viv_EvInt|t2 -2.372 0.066 -36.203 0.000 -2.372 -1.431
## Viv_EvInt|t3 -1.595 0.055 -29.030 0.000 -1.595 -0.962
## Viv_EvInt|t4 0.586 0.042 13.967 0.000 0.586 0.353
## Viv_DiF|t1 -4.489 0.168 -26.746 0.000 -4.489 -2.563
## Viv_DiF|t2 -3.180 0.092 -34.722 0.000 -3.180 -1.816
## Viv_DiF|t3 -2.477 0.077 -32.353 0.000 -2.477 -1.414
## Viv_DiF|t4 -0.005 0.044 -0.120 0.905 -0.005 -0.003
## Viv_Pla|t1 -5.002 0.192 -26.006 0.000 -5.002 -2.688
## Viv_Pla|t2 -3.992 0.128 -31.231 0.000 -3.992 -2.145
## Viv_Pla|t3 -2.902 0.095 -30.411 0.000 -2.902 -1.559
## Viv_Pla|t4 0.034 0.046 0.722 0.470 0.034 0.018
## Viv_HabRC|t1 -4.867 0.208 -23.433 0.000 -4.867 -2.621
## Viv_HabRC|t2 -4.148 0.155 -26.676 0.000 -4.148 -2.233
## Viv_HabRC|t3 -2.823 0.108 -26.148 0.000 -2.823 -1.520
## Viv_HabRC|t4 0.033 0.046 0.722 0.470 0.033 0.018
## Viv_HabR|t1 -3.624 0.135 -26.833 0.000 -3.624 -2.469
## Viv_HabR|t2 -2.463 0.072 -34.117 0.000 -2.463 -1.678
## Viv_HabR|t3 -1.504 0.052 -29.048 0.000 -1.504 -1.024
## Viv_HabR|t4 0.272 0.036 7.456 0.000 0.272 0.185
## Viv_PrInf|t1 -4.038 0.144 -27.979 0.000 -4.038 -2.358
## Viv_PrInf|t2 -3.007 0.091 -32.941 0.000 -3.007 -1.756
## Viv_PrInf|t3 -2.169 0.071 -30.429 0.000 -2.169 -1.267
## Viv_PrInf|t4 0.394 0.042 9.294 0.000 0.394 0.230
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.327
## .Viv_EvInt 1.000 1.000 0.364
## .Viv_DiF 1.000 1.000 0.326
## .Viv_Pla 1.000 1.000 0.289
## .Viv_HabRC 1.000 1.000 0.290
## .Viv_HabR 1.000 1.000 0.464
## .Viv_PrInf 1.000 1.000 0.341
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.572 0.572 1.000
## Viv_EvInt 0.603 0.603 1.000
## Viv_DiF 0.571 0.571 1.000
## Viv_Pla 0.537 0.537 1.000
## Viv_HabRC 0.538 0.538 1.000
## Viv_HabR 0.681 0.681 1.000
## Viv_PrInf 0.584 0.584 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.673
## Viv_EvInt 0.636
## Viv_DiF 0.674
## Viv_Pla 0.711
## Viv_HabRC 0.710
## Viv_HabR 0.536
## Viv_PrInf 0.659
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.610 0.199 8.075 0.000 1.610 0.833
## Viv_EvInt 1.411 0.147 9.571 0.000 1.411 0.788
## Viv_DiF 1.558 0.159 9.809 0.000 1.558 0.823
## Viv_Pla 1.651 0.167 9.898 0.000 1.651 0.853
## Viv_Hard =~
## Viv_HabRC 0.882 0.229 3.859 0.000 0.882 0.804
## Viv_HabR 0.638 0.147 4.335 0.000 0.638 0.696
## Viv_PrInf 0.801 0.204 3.929 0.000 0.801 0.803
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.216 0.106 2.031 0.042 0.216 0.504
## .Viv_EvInt ~~
## .Viv_DiF 0.325 0.072 4.487 0.000 0.325 0.274
## Viv_Soft ~~
## Viv_Hard 0.817 0.010 81.626 0.000 0.817 0.817
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.192 0.234 0.820 0.412 0.192 0.192
## Viv_Hard -2.029 1.246 -1.628 0.104 -2.029 -2.029
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -3.996 0.123 -32.448 0.000 -3.996 -2.067
## Viv_AdmT|t2 -2.827 0.081 -35.109 0.000 -2.827 -1.462
## Viv_AdmT|t3 -2.079 0.141 -14.789 0.000 -2.079 -1.075
## Viv_AdmT|t4 0.379 0.422 0.900 0.368 0.379 0.196
## Viv_EvInt|t1 -3.702 0.103 -36.016 0.000 -3.702 -2.068
## Viv_EvInt|t2 -2.201 0.143 -15.367 0.000 -2.201 -1.230
## Viv_EvInt|t3 -1.407 0.203 -6.942 0.000 -1.407 -0.786
## Viv_EvInt|t4 0.792 0.408 1.944 0.052 0.792 0.443
## Viv_DiF|t1 -4.489 0.168 -26.746 0.000 -4.489 -2.370
## Viv_DiF|t2 -3.367 0.171 -19.696 0.000 -3.367 -1.778
## Viv_DiF|t3 -2.473 0.189 -13.102 0.000 -2.473 -1.306
## Viv_DiF|t4 0.094 0.373 0.252 0.801 0.094 0.050
## Viv_Pla|t1 -5.002 0.192 -26.006 0.000 -5.002 -2.584
## Viv_Pla|t2 -4.084 0.211 -19.381 0.000 -4.084 -2.110
## Viv_Pla|t3 -2.849 0.214 -13.323 0.000 -2.849 -1.472
## Viv_Pla|t4 0.150 0.400 0.375 0.708 0.150 0.077
## Viv_HabRC|t1 -4.867 0.208 -23.433 0.000 -4.867 -4.439
## Viv_HabRC|t2 -4.148 0.155 -26.676 0.000 -4.148 -3.783
## Viv_HabRC|t3 -3.492 0.245 -14.248 0.000 -3.492 -3.185
## Viv_HabRC|t4 -1.876 0.623 -3.011 0.003 -1.876 -1.711
## Viv_HabR|t1 -3.624 0.135 -26.833 0.000 -3.624 -3.954
## Viv_HabR|t2 -2.888 0.183 -15.808 0.000 -2.888 -3.151
## Viv_HabR|t3 -2.317 0.286 -8.101 0.000 -2.317 -2.528
## Viv_HabR|t4 -1.253 0.513 -2.439 0.015 -1.253 -1.367
## Viv_PrInf|t1 -4.038 0.144 -27.979 0.000 -4.038 -4.050
## Viv_PrInf|t2 -3.412 0.193 -17.659 0.000 -3.412 -3.423
## Viv_PrInf|t3 -2.937 0.284 -10.329 0.000 -2.937 -2.946
## Viv_PrInf|t4 -1.508 0.622 -2.425 0.015 -1.508 -1.512
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.146 0.278 4.122 0.000 1.146 0.307
## .Viv_EvInt 1.212 0.248 4.881 0.000 1.212 0.378
## .Viv_DiF 1.160 0.233 4.970 0.000 1.160 0.323
## .Viv_Pla 1.020 0.197 5.179 0.000 1.020 0.272
## .Viv_HabRC 0.425 0.218 1.949 0.051 0.425 0.353
## .Viv_HabR 0.433 0.200 2.173 0.030 0.433 0.516
## .Viv_PrInf 0.353 0.179 1.970 0.049 0.353 0.355
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.517 0.517 1.000
## Viv_EvInt 0.559 0.559 1.000
## Viv_DiF 0.528 0.528 1.000
## Viv_Pla 0.517 0.517 1.000
## Viv_HabRC 0.912 0.912 1.000
## Viv_HabR 1.091 1.091 1.000
## Viv_PrInf 1.003 1.003 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.693
## Viv_EvInt 0.622
## Viv_DiF 0.677
## Viv_Pla 0.728
## Viv_HabRC 0.647
## Viv_HabR 0.484
## Viv_PrInf 0.645
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.597 0.391 4.081 0.000 1.597 0.805
## Viv_EvInt 1.130 0.232 4.861 0.000 1.130 0.821
## Viv_DiF 1.792 0.406 4.414 0.000 1.792 0.919
## Viv_Pla 1.614 0.343 4.704 0.000 1.614 0.865
## Viv_Hard =~
## Viv_HabRC 0.671 0.262 2.560 0.010 0.671 0.871
## Viv_HabR 0.506 0.182 2.784 0.005 0.506 0.703
## Viv_PrInf 0.599 0.227 2.639 0.008 0.599 0.835
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.078 0.060 1.304 0.192 0.078 0.401
## .Viv_EvInt ~~
## .Viv_DiF -0.037 0.057 -0.649 0.516 -0.037 -0.061
## Viv_Soft ~~
## Viv_Hard 0.844 0.022 37.579 0.000 0.844 0.844
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.231 0.457 0.506 0.613 0.231 0.231
## Viv_Hard -3.949 2.450 -1.612 0.107 -3.949 -3.949
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -3.996 0.123 -32.448 0.000 -3.996 -2.014
## Viv_AdmT|t2 -2.827 0.081 -35.109 0.000 -2.827 -1.424
## Viv_AdmT|t3 -1.950 0.279 -6.983 0.000 -1.950 -0.983
## Viv_AdmT|t4 0.802 0.916 0.876 0.381 0.802 0.404
## Viv_EvInt|t1 -3.702 0.103 -36.016 0.000 -3.702 -2.690
## Viv_EvInt|t2 -1.837 0.315 -5.838 0.000 -1.837 -1.335
## Viv_EvInt|t3 -1.159 0.359 -3.232 0.001 -1.159 -0.842
## Viv_EvInt|t4 0.747 0.644 1.161 0.246 0.747 0.543
## Viv_DiF|t1 -4.489 0.168 -26.746 0.000 -4.489 -2.302
## Viv_DiF|t2 -3.518 0.320 -11.000 0.000 -3.518 -1.804
## Viv_DiF|t3 -2.657 0.393 -6.769 0.000 -2.657 -1.363
## Viv_DiF|t4 0.574 0.933 0.616 0.538 0.574 0.294
## Viv_Pla|t1 -5.002 0.192 -26.006 0.000 -5.002 -2.680
## Viv_Pla|t2 -3.844 0.452 -8.496 0.000 -3.844 -2.060
## Viv_Pla|t3 -2.777 0.432 -6.434 0.000 -2.777 -1.488
## Viv_Pla|t4 0.658 0.852 0.772 0.440 0.658 0.352
## Viv_HabRC|t1 -4.867 0.208 -23.433 0.000 -4.867 -6.320
## Viv_HabRC|t2 -4.148 0.155 -26.676 0.000 -4.148 -5.386
## Viv_HabRC|t3 -3.744 0.243 -15.428 0.000 -3.744 -4.862
## Viv_HabRC|t4 -2.520 0.676 -3.731 0.000 -2.520 -3.273
## Viv_HabR|t1 -3.624 0.135 -26.833 0.000 -3.624 -5.035
## Viv_HabR|t2 -2.992 0.227 -13.155 0.000 -2.992 -4.157
## Viv_HabR|t3 -2.523 0.366 -6.891 0.000 -2.523 -3.506
## Viv_HabR|t4 -1.657 0.658 -2.520 0.012 -1.657 -2.302
## Viv_PrInf|t1 -4.038 0.144 -27.979 0.000 -4.038 -5.627
## Viv_PrInf|t2 -3.417 0.244 -14.005 0.000 -3.417 -4.762
## Viv_PrInf|t3 -3.077 0.346 -8.902 0.000 -3.077 -4.289
## Viv_PrInf|t4 -2.067 0.698 -2.959 0.003 -2.067 -2.880
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.387 0.642 2.159 0.031 1.387 0.352
## .Viv_EvInt 0.617 0.252 2.443 0.015 0.617 0.326
## .Viv_DiF 0.592 0.239 2.478 0.013 0.592 0.156
## .Viv_Pla 0.877 0.377 2.329 0.020 0.877 0.252
## .Viv_HabRC 0.143 0.114 1.258 0.208 0.143 0.241
## .Viv_HabR 0.262 0.186 1.408 0.159 0.262 0.506
## .Viv_PrInf 0.156 0.115 1.355 0.175 0.156 0.302
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.504 0.504 1.000
## Viv_EvInt 0.727 0.727 1.000
## Viv_DiF 0.513 0.513 1.000
## Viv_Pla 0.536 0.536 1.000
## Viv_HabRC 1.299 1.299 1.000
## Viv_HabR 1.389 1.389 1.000
## Viv_PrInf 1.394 1.394 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.648
## Viv_EvInt 0.674
## Viv_DiF 0.844
## Viv_Pla 0.748
## Viv_HabRC 0.759
## Viv_HabR 0.494
## Viv_PrInf 0.698
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.164 0.256 4.553 0.000 1.164 0.794
## Viv_EvInt 1.148 0.218 5.258 0.000 1.148 0.789
## Viv_DiF 1.359 0.255 5.332 0.000 1.359 0.829
## Viv_Pla 1.314 0.231 5.691 0.000 1.314 0.800
## Viv_Hard =~
## Viv_HabRC 0.605 0.255 2.377 0.017 0.605 0.712
## Viv_HabR 0.486 0.192 2.532 0.011 0.486 0.678
## Viv_PrInf 0.553 0.222 2.490 0.013 0.553 0.762
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.178 0.147 1.215 0.224 0.178 0.566
## .Viv_EvInt ~~
## .Viv_DiF 0.219 0.107 2.045 0.041 0.219 0.267
## Viv_Soft ~~
## Viv_Hard 0.828 0.029 28.563 0.000 0.828 0.828
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.421 0.553 -0.761 0.447 -0.421 -0.421
## Viv_Hard -3.928 2.895 -1.357 0.175 -3.928 -3.928
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -3.996 0.123 -32.448 0.000 -3.996 -2.724
## Viv_AdmT|t2 -2.827 0.081 -35.109 0.000 -2.827 -1.927
## Viv_AdmT|t3 -2.156 0.202 -10.690 0.000 -2.156 -1.469
## Viv_AdmT|t4 -0.363 0.567 -0.640 0.522 -0.363 -0.247
## Viv_EvInt|t1 -3.702 0.103 -36.016 0.000 -3.702 -2.544
## Viv_EvInt|t2 -2.311 0.265 -8.720 0.000 -2.311 -1.588
## Viv_EvInt|t3 -1.619 0.363 -4.456 0.000 -1.619 -1.113
## Viv_EvInt|t4 -0.024 0.633 -0.037 0.970 -0.024 -0.016
## Viv_DiF|t1 -4.489 0.168 -26.746 0.000 -4.489 -2.739
## Viv_DiF|t2 -3.286 0.296 -11.096 0.000 -3.286 -2.005
## Viv_DiF|t3 -2.601 0.360 -7.228 0.000 -2.601 -1.587
## Viv_DiF|t4 -0.583 0.653 -0.892 0.372 -0.583 -0.355
## Viv_Pla|t1 -5.002 0.192 -26.006 0.000 -5.002 -3.046
## Viv_Pla|t2 -3.918 0.362 -10.834 0.000 -3.918 -2.386
## Viv_Pla|t3 -2.869 0.390 -7.355 0.000 -2.869 -1.747
## Viv_Pla|t4 -0.640 0.639 -1.002 0.316 -0.640 -0.390
## Viv_HabRC|t1 -4.867 0.208 -23.433 0.000 -4.867 -5.729
## Viv_HabRC|t2 -4.148 0.155 -26.676 0.000 -4.148 -4.882
## Viv_HabRC|t3 -3.693 0.269 -13.744 0.000 -3.693 -4.346
## Viv_HabRC|t4 -2.527 0.709 -3.564 0.000 -2.527 -2.974
## Viv_HabR|t1 -3.624 0.135 -26.833 0.000 -3.624 -5.052
## Viv_HabR|t2 -3.292 0.191 -17.209 0.000 -3.292 -4.589
## Viv_HabR|t3 -2.955 0.291 -10.161 0.000 -2.955 -4.120
## Viv_HabR|t4 -2.036 0.621 -3.277 0.001 -2.036 -2.839
## Viv_PrInf|t1 -4.038 0.144 -27.979 0.000 -4.038 -5.566
## Viv_PrInf|t2 -3.590 0.244 -14.685 0.000 -3.590 -4.949
## Viv_PrInf|t3 -3.155 0.379 -8.314 0.000 -3.155 -4.349
## Viv_PrInf|t4 -2.069 0.785 -2.636 0.008 -2.069 -2.852
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.797 0.335 2.376 0.018 0.797 0.370
## .Viv_EvInt 0.800 0.300 2.666 0.008 0.800 0.378
## .Viv_DiF 0.840 0.318 2.645 0.008 0.840 0.313
## .Viv_Pla 0.971 0.328 2.959 0.003 0.971 0.360
## .Viv_HabRC 0.356 0.289 1.230 0.219 0.356 0.493
## .Viv_HabR 0.278 0.218 1.274 0.203 0.278 0.541
## .Viv_PrInf 0.221 0.179 1.234 0.217 0.221 0.419
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.682 0.682 1.000
## Viv_EvInt 0.687 0.687 1.000
## Viv_DiF 0.610 0.610 1.000
## Viv_Pla 0.609 0.609 1.000
## Viv_HabRC 1.177 1.177 1.000
## Viv_HabR 1.394 1.394 1.000
## Viv_PrInf 1.378 1.378 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.630
## Viv_EvInt 0.622
## Viv_DiF 0.687
## Viv_Pla 0.640
## Viv_HabRC 0.507
## Viv_HabR 0.459
## Viv_PrInf 0.581lavaan::fitMeasures(invariance$fit.configural,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 339.451 44.000 0.000
## srmr cfi.scaled tli.scaled
## 0.018 0.995 0.990
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.059 0.054 0.065modificationindices(invariance$fit.configural, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 313 Viv_AdmT ~~ Viv_EvInt 2 2 1 16.743 0.306 0.306 0.260
## 312 Viv_Hard =~ Viv_Pla 2 2 1 16.472 0.487 0.487 0.252
## 327 Viv_Pla ~~ Viv_HabRC 2 2 1 15.373 0.167 0.167 0.254
## 310 Viv_Hard =~ Viv_EvInt 2 2 1 10.785 -0.347 -0.347 -0.194
## 301 Viv_Pla ~~ Viv_HabRC 1 1 1 9.957 0.279 0.279 0.279
## 286 Viv_Hard =~ Viv_Pla 1 1 1 8.883 0.465 0.465 0.250
## 317 Viv_AdmT ~~ Viv_HabR 2 2 1 6.553 -0.087 -0.087 -0.124
## 315 Viv_AdmT ~~ Viv_Pla 2 2 1 5.311 -0.188 -0.188 -0.174
## 287 Viv_AdmT ~~ Viv_EvInt 1 1 1 4.563 0.167 0.167 0.167
## 289 Viv_AdmT ~~ Viv_Pla 1 1 1 4.553 -0.186 -0.186 -0.186
## sepc.nox
## 313 0.260
## 312 0.252
## 327 0.254
## 310 -0.194
## 301 0.279
## 286 0.250
## 317 -0.124
## 315 -0.174
## 287 0.167
## 289 -0.186semTools::reliability(invariance$fit.configural)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8310 0.7855
## alpha.ord 0.8974 0.8670
## omega 0.8191 0.7252
## omega2 0.8191 0.7252
## omega3 0.8169 0.7248
## avevar 0.6758 0.6486
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8319 0.7612
## alpha.ord 0.9014 0.8536
## omega 0.8223 0.5081
## omega2 0.8223 0.5081
## omega3 0.8189 0.5074
## avevar 0.6822 0.6012
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8446 0.7938
## alpha.ord 0.9133 0.8695
## omega 0.8556 0.2466
## omega2 0.8556 0.2466
## omega3 0.8532 0.2461
## avevar 0.7353 0.6551
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8247 0.7078
## alpha.ord 0.8860 0.8249
## omega 0.7928 0.1783
## omega2 0.7928 0.1783
## omega3 0.7913 0.1784
## avevar 0.6470 0.51535.1.1.8.2 Loadings
summary(invariance$fit.loadings,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 465 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 185
## Number of equality constraints 48
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 149.969 96.565
## Degrees of freedom 59 59
## P-value (Unknown) NA 0.001
## Scaling correction factor 2.141
## Shift parameter for each group:
## 2 8.732
## 3 13.959
## 1 1.753
## 4 2.066
## simple second-order correction
## Test statistic for each group:
## 2 27.750 21.695
## 3 44.041 34.532
## 1 50.647 25.412
## 4 27.531 14.927
##
## Model Test Baseline Model:
##
## Test statistic 57688.409 56390.095
## Degrees of freedom 84 84
## P-value NA 0.000
## Scaling correction factor 1.024
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.999
## Tucker-Lewis Index (TLI) 0.998 0.999
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.028 0.018
## 90 Percent confidence interval - lower 0.023 0.011
## 90 Percent confidence interval - upper 0.034 0.025
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.023 0.023
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.487 0.073 20.295 0.000 1.487 0.830
## Viv_EvInt 1.266 0.058 21.989 0.000 1.266 0.785
## Viv_DiF 1.517 0.109 13.917 0.000 1.517 0.835
## Viv_Pla 1.505 0.100 15.084 0.000 1.505 0.833
## Viv_Hard =~
## Viv_HabRC 1.475 0.106 13.877 0.000 1.475 0.828
## Viv_HabR 1.146 0.066 17.451 0.000 1.146 0.754
## Viv_PrInf 1.378 0.065 21.304 0.000 1.378 0.809
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.411 0.039 10.550 0.000 0.411 0.411
## .Viv_EvInt ~~
## .Viv_DiF 0.221 0.051 4.287 0.000 0.221 0.221
## Viv_Soft ~~
## Viv_Hard 0.831 0.012 68.843 0.000 0.831 0.831
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.075 0.190 -21.436 0.000 -4.075 -2.274
## Viv_AdmT|t2 -2.900 0.123 -23.546 0.000 -2.900 -1.618
## Viv_AdmT|t3 -2.105 0.087 -24.119 0.000 -2.105 -1.174
## Viv_AdmT|t4 0.330 0.045 7.312 0.000 0.330 0.184
## Viv_EvInt|t1 -3.623 0.154 -23.466 0.000 -3.623 -2.246
## Viv_EvInt|t2 -2.308 0.085 -27.044 0.000 -2.308 -1.431
## Viv_EvInt|t3 -1.552 0.063 -24.484 0.000 -1.552 -0.962
## Viv_EvInt|t4 0.570 0.042 13.497 0.000 0.570 0.353
## Viv_DiF|t1 -4.635 0.326 -14.206 0.000 -4.635 -2.551
## Viv_DiF|t2 -3.298 0.185 -17.791 0.000 -3.298 -1.816
## Viv_DiF|t3 -2.570 0.141 -18.213 0.000 -2.570 -1.414
## Viv_DiF|t4 -0.005 0.046 -0.120 0.905 -0.005 -0.003
## Viv_Pla|t1 -4.872 0.341 -14.309 0.000 -4.872 -2.696
## Viv_Pla|t2 -3.877 0.222 -17.459 0.000 -3.877 -2.145
## Viv_Pla|t3 -2.818 0.149 -18.966 0.000 -2.818 -1.559
## Viv_Pla|t4 0.033 0.045 0.721 0.471 0.033 0.018
## Viv_HabRC|t1 -4.698 0.332 -14.136 0.000 -4.698 -2.636
## Viv_HabRC|t2 -3.974 0.248 -16.001 0.000 -3.974 -2.230
## Viv_HabRC|t3 -2.709 0.152 -17.849 0.000 -2.709 -1.520
## Viv_HabRC|t4 0.032 0.045 0.720 0.472 0.032 0.018
## Viv_HabR|t1 -3.731 0.190 -19.617 0.000 -3.731 -2.453
## Viv_HabR|t2 -2.552 0.107 -23.828 0.000 -2.552 -1.678
## Viv_HabR|t3 -1.558 0.068 -22.824 0.000 -1.558 -1.024
## Viv_HabR|t4 0.282 0.039 7.294 0.000 0.282 0.185
## Viv_PrInf|t1 -4.020 0.192 -20.937 0.000 -4.020 -2.360
## Viv_PrInf|t2 -2.991 0.119 -25.142 0.000 -2.991 -1.756
## Viv_PrInf|t3 -2.158 0.085 -25.428 0.000 -2.158 -1.267
## Viv_PrInf|t4 0.392 0.043 9.065 0.000 0.392 0.230
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.311
## .Viv_EvInt 1.000 1.000 0.384
## .Viv_DiF 1.000 1.000 0.303
## .Viv_Pla 1.000 1.000 0.306
## .Viv_HabRC 1.000 1.000 0.315
## .Viv_HabR 1.000 1.000 0.432
## .Viv_PrInf 1.000 1.000 0.345
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.558 0.558 1.000
## Viv_EvInt 0.620 0.620 1.000
## Viv_DiF 0.550 0.550 1.000
## Viv_Pla 0.553 0.553 1.000
## Viv_HabRC 0.561 0.561 1.000
## Viv_HabR 0.657 0.657 1.000
## Viv_PrInf 0.587 0.587 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.689
## Viv_EvInt 0.616
## Viv_DiF 0.697
## Viv_Pla 0.694
## Viv_HabRC 0.685
## Viv_HabR 0.568
## Viv_PrInf 0.655
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.487 0.073 20.295 0.000 1.554 0.823
## Viv_EvInt 1.266 0.058 21.989 0.000 1.322 0.784
## Viv_DiF 1.517 0.109 13.917 0.000 1.584 0.833
## Viv_Pla 1.505 0.100 15.084 0.000 1.572 0.858
## Viv_Hard =~
## Viv_HabRC 1.475 0.106 13.877 0.000 0.881 0.806
## Viv_HabR 1.146 0.066 17.451 0.000 0.685 0.704
## Viv_PrInf 1.378 0.065 21.304 0.000 0.824 0.798
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.220 0.099 2.216 0.027 0.220 0.493
## .Viv_EvInt ~~
## .Viv_DiF 0.293 0.080 3.660 0.000 0.293 0.266
## Viv_Soft ~~
## Viv_Hard 0.509 0.129 3.936 0.000 0.816 0.816
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.103 0.228 0.450 0.653 0.098 0.098
## Viv_Hard -1.101 0.391 -2.813 0.005 -1.842 -1.842
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.075 0.190 -21.436 0.000 -4.075 -2.158
## Viv_AdmT|t2 -2.900 0.123 -23.546 0.000 -2.900 -1.536
## Viv_AdmT|t3 -2.180 0.138 -15.797 0.000 -2.180 -1.155
## Viv_AdmT|t4 0.221 0.348 0.636 0.525 0.221 0.117
## Viv_EvInt|t1 -3.623 0.154 -23.466 0.000 -3.623 -2.148
## Viv_EvInt|t2 -2.200 0.148 -14.844 0.000 -2.200 -1.304
## Viv_EvInt|t3 -1.452 0.178 -8.164 0.000 -1.452 -0.861
## Viv_EvInt|t4 0.621 0.328 1.894 0.058 0.621 0.368
## Viv_DiF|t1 -4.635 0.326 -14.206 0.000 -4.635 -2.438
## Viv_DiF|t2 -3.524 0.287 -12.270 0.000 -3.524 -1.854
## Viv_DiF|t3 -2.627 0.257 -10.210 0.000 -2.627 -1.382
## Viv_DiF|t4 -0.051 0.329 -0.154 0.877 -0.051 -0.027
## Viv_Pla|t1 -4.872 0.341 -14.309 0.000 -4.872 -2.660
## Viv_Pla|t2 -4.011 0.308 -13.038 0.000 -4.011 -2.190
## Viv_Pla|t3 -2.842 0.240 -11.845 0.000 -2.842 -1.552
## Viv_Pla|t4 -0.004 0.329 -0.012 0.990 -0.004 -0.002
## Viv_HabRC|t1 -4.698 0.332 -14.136 0.000 -4.698 -4.295
## Viv_HabRC|t2 -3.974 0.248 -16.001 0.000 -3.974 -3.633
## Viv_HabRC|t3 -3.323 0.252 -13.164 0.000 -3.323 -3.038
## Viv_HabRC|t4 -1.711 0.535 -3.197 0.001 -1.711 -1.564
## Viv_HabR|t1 -3.731 0.190 -19.617 0.000 -3.731 -3.836
## Viv_HabR|t2 -2.954 0.214 -13.809 0.000 -2.954 -3.037
## Viv_HabR|t3 -2.348 0.289 -8.117 0.000 -2.348 -2.414
## Viv_HabR|t4 -1.218 0.483 -2.520 0.012 -1.218 -1.253
## Viv_PrInf|t1 -4.020 0.192 -20.937 0.000 -4.020 -3.895
## Viv_PrInf|t2 -3.368 0.215 -15.662 0.000 -3.368 -3.264
## Viv_PrInf|t3 -2.877 0.280 -10.274 0.000 -2.877 -2.788
## Viv_PrInf|t4 -1.397 0.567 -2.463 0.014 -1.397 -1.354
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.151 0.282 4.083 0.000 1.151 0.323
## .Viv_EvInt 1.098 0.215 5.096 0.000 1.098 0.386
## .Viv_DiF 1.105 0.270 4.095 0.000 1.105 0.306
## .Viv_Pla 0.884 0.251 3.515 0.000 0.884 0.263
## .Viv_HabRC 0.419 0.215 1.948 0.051 0.419 0.351
## .Viv_HabR 0.477 0.191 2.501 0.012 0.477 0.504
## .Viv_PrInf 0.387 0.175 2.204 0.028 0.387 0.363
## Viv_Soft 1.091 0.206 5.292 0.000 1.000 1.000
## Viv_Hard 0.357 0.158 2.261 0.024 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.530 0.530 1.000
## Viv_EvInt 0.593 0.593 1.000
## Viv_DiF 0.526 0.526 1.000
## Viv_Pla 0.546 0.546 1.000
## Viv_HabRC 0.914 0.914 1.000
## Viv_HabR 1.028 1.028 1.000
## Viv_PrInf 0.969 0.969 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.677
## Viv_EvInt 0.614
## Viv_DiF 0.694
## Viv_Pla 0.737
## Viv_HabRC 0.649
## Viv_HabR 0.496
## Viv_PrInf 0.637
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.487 0.073 20.295 0.000 1.554 0.780
## Viv_EvInt 1.266 0.058 21.989 0.000 1.322 0.939
## Viv_DiF 1.517 0.109 13.917 0.000 1.585 0.828
## Viv_Pla 1.505 0.100 15.084 0.000 1.573 0.867
## Viv_Hard =~
## Viv_HabRC 1.475 0.106 13.877 0.000 0.695 0.872
## Viv_HabR 1.146 0.066 17.451 0.000 0.540 0.665
## Viv_PrInf 1.378 0.065 21.304 0.000 0.650 0.852
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.111 0.072 1.555 0.120 0.111 0.471
## .Viv_EvInt ~~
## .Viv_DiF -0.099 0.110 -0.895 0.371 -0.099 -0.190
## Viv_Soft ~~
## Viv_Hard 0.415 0.213 1.946 0.052 0.842 0.842
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.229 0.494 0.463 0.643 0.219 0.219
## Viv_Hard -1.637 0.412 -3.969 0.000 -3.473 -3.473
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.075 0.190 -21.436 0.000 -4.075 -2.046
## Viv_AdmT|t2 -2.900 0.123 -23.546 0.000 -2.900 -1.456
## Viv_AdmT|t3 -1.988 0.274 -7.264 0.000 -1.988 -0.998
## Viv_AdmT|t4 0.776 0.823 0.943 0.346 0.776 0.389
## Viv_EvInt|t1 -3.623 0.154 -23.466 0.000 -3.623 -2.573
## Viv_EvInt|t2 -1.858 0.274 -6.777 0.000 -1.858 -1.319
## Viv_EvInt|t3 -1.163 0.345 -3.372 0.001 -1.163 -0.826
## Viv_EvInt|t4 0.787 0.735 1.071 0.284 0.787 0.559
## Viv_DiF|t1 -4.635 0.326 -14.206 0.000 -4.635 -2.420
## Viv_DiF|t2 -3.515 0.346 -10.163 0.000 -3.515 -1.835
## Viv_DiF|t3 -2.669 0.347 -7.682 0.000 -2.669 -1.394
## Viv_DiF|t4 0.505 0.777 0.649 0.516 0.505 0.263
## Viv_Pla|t1 -4.872 0.341 -14.309 0.000 -4.872 -2.687
## Viv_Pla|t2 -3.752 0.442 -8.480 0.000 -3.752 -2.070
## Viv_Pla|t3 -2.716 0.360 -7.553 0.000 -2.716 -1.498
## Viv_Pla|t4 0.621 0.799 0.778 0.437 0.621 0.342
## Viv_HabRC|t1 -4.698 0.332 -14.136 0.000 -4.698 -5.894
## Viv_HabRC|t2 -3.974 0.248 -16.001 0.000 -3.974 -4.986
## Viv_HabRC|t3 -3.548 0.276 -12.857 0.000 -3.548 -4.451
## Viv_HabRC|t4 -2.281 0.639 -3.570 0.000 -2.281 -2.861
## Viv_HabR|t1 -3.731 0.190 -19.617 0.000 -3.731 -4.595
## Viv_HabR|t2 -2.998 0.241 -12.430 0.000 -2.998 -3.692
## Viv_HabR|t3 -2.469 0.352 -7.010 0.000 -2.469 -3.041
## Viv_HabR|t4 -1.492 0.615 -2.427 0.015 -1.492 -1.837
## Viv_PrInf|t1 -4.020 0.192 -20.937 0.000 -4.020 -5.270
## Viv_PrInf|t2 -3.373 0.257 -13.139 0.000 -3.373 -4.422
## Viv_PrInf|t3 -3.011 0.344 -8.752 0.000 -3.011 -3.948
## Viv_PrInf|t4 -1.937 0.677 -2.859 0.004 -1.937 -2.540
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.554 0.667 2.332 0.020 1.554 0.392
## .Viv_EvInt 0.234 0.203 1.153 0.249 0.234 0.118
## .Viv_DiF 1.156 0.512 2.259 0.024 1.156 0.315
## .Viv_Pla 0.814 0.440 1.850 0.064 0.814 0.248
## .Viv_HabRC 0.152 0.136 1.116 0.264 0.152 0.239
## .Viv_HabR 0.367 0.205 1.795 0.073 0.367 0.557
## .Viv_PrInf 0.160 0.115 1.395 0.163 0.160 0.275
## Viv_Soft 1.092 0.476 2.295 0.022 1.000 1.000
## Viv_Hard 0.222 0.153 1.448 0.148 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.502 0.502 1.000
## Viv_EvInt 0.710 0.710 1.000
## Viv_DiF 0.522 0.522 1.000
## Viv_Pla 0.552 0.552 1.000
## Viv_HabRC 1.255 1.255 1.000
## Viv_HabR 1.232 1.232 1.000
## Viv_PrInf 1.311 1.311 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.608
## Viv_EvInt 0.882
## Viv_DiF 0.685
## Viv_Pla 0.752
## Viv_HabRC 0.761
## Viv_HabR 0.443
## Viv_PrInf 0.725
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.487 0.073 20.295 0.000 1.329 0.842
## Viv_EvInt 1.266 0.058 21.989 0.000 1.131 0.760
## Viv_DiF 1.517 0.109 13.917 0.000 1.355 0.771
## Viv_Pla 1.505 0.100 15.084 0.000 1.345 0.809
## Viv_Hard =~
## Viv_HabRC 1.475 0.106 13.877 0.000 0.674 0.749
## Viv_HabR 1.146 0.066 17.451 0.000 0.524 0.604
## Viv_PrInf 1.378 0.065 21.304 0.000 0.630 0.780
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.252 0.185 1.367 0.172 0.252 0.611
## .Viv_EvInt ~~
## .Viv_DiF 0.419 0.165 2.534 0.011 0.419 0.386
## Viv_Soft ~~
## Viv_Hard 0.341 0.137 2.491 0.013 0.835 0.835
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.250 0.334 -0.749 0.454 -0.280 -0.280
## Viv_Hard -1.415 0.531 -2.666 0.008 -3.096 -3.096
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.075 0.190 -21.436 0.000 -4.075 -2.583
## Viv_AdmT|t2 -2.900 0.123 -23.546 0.000 -2.900 -1.838
## Viv_AdmT|t3 -2.163 0.200 -10.795 0.000 -2.163 -1.371
## Viv_AdmT|t4 -0.235 0.516 -0.456 0.649 -0.235 -0.149
## Viv_EvInt|t1 -3.623 0.154 -23.466 0.000 -3.623 -2.434
## Viv_EvInt|t2 -2.186 0.218 -10.014 0.000 -2.186 -1.469
## Viv_EvInt|t3 -1.479 0.280 -5.282 0.000 -1.479 -0.993
## Viv_EvInt|t4 0.154 0.487 0.315 0.753 0.154 0.103
## Viv_DiF|t1 -4.635 0.326 -14.206 0.000 -4.635 -2.636
## Viv_DiF|t2 -3.290 0.332 -9.910 0.000 -3.290 -1.871
## Viv_DiF|t3 -2.556 0.338 -7.570 0.000 -2.556 -1.453
## Viv_DiF|t4 -0.391 0.506 -0.772 0.440 -0.391 -0.222
## Viv_Pla|t1 -4.872 0.341 -14.309 0.000 -4.872 -2.932
## Viv_Pla|t2 -3.782 0.373 -10.151 0.000 -3.782 -2.275
## Viv_Pla|t3 -2.720 0.314 -8.666 0.000 -2.720 -1.637
## Viv_Pla|t4 -0.464 0.492 -0.943 0.346 -0.464 -0.279
## Viv_HabRC|t1 -4.698 0.332 -14.136 0.000 -4.698 -5.217
## Viv_HabRC|t2 -3.974 0.248 -16.001 0.000 -3.974 -4.413
## Viv_HabRC|t3 -3.482 0.298 -11.675 0.000 -3.482 -3.867
## Viv_HabRC|t4 -2.246 0.703 -3.197 0.001 -2.246 -2.495
## Viv_HabR|t1 -3.731 0.190 -19.617 0.000 -3.731 -4.300
## Viv_HabR|t2 -3.294 0.223 -14.781 0.000 -3.294 -3.796
## Viv_HabR|t3 -2.886 0.296 -9.764 0.000 -2.886 -3.327
## Viv_HabR|t4 -1.775 0.586 -3.027 0.002 -1.775 -2.046
## Viv_PrInf|t1 -4.020 0.192 -20.937 0.000 -4.020 -4.977
## Viv_PrInf|t2 -3.530 0.267 -13.230 0.000 -3.530 -4.370
## Viv_PrInf|t3 -3.045 0.383 -7.955 0.000 -3.045 -3.771
## Viv_PrInf|t4 -1.836 0.773 -2.374 0.018 -1.836 -2.273
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.722 0.368 1.964 0.050 0.722 0.290
## .Viv_EvInt 0.937 0.306 3.063 0.002 0.937 0.423
## .Viv_DiF 1.255 0.430 2.916 0.004 1.255 0.406
## .Viv_Pla 0.953 0.434 2.196 0.028 0.953 0.345
## .Viv_HabRC 0.356 0.320 1.112 0.266 0.356 0.439
## .Viv_HabR 0.478 0.282 1.694 0.090 0.478 0.636
## .Viv_PrInf 0.255 0.189 1.354 0.176 0.255 0.391
## Viv_Soft 0.798 0.250 3.194 0.001 1.000 1.000
## Viv_Hard 0.209 0.151 1.386 0.166 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.634 0.634 1.000
## Viv_EvInt 0.672 0.672 1.000
## Viv_DiF 0.569 0.569 1.000
## Viv_Pla 0.602 0.602 1.000
## Viv_HabRC 1.111 1.111 1.000
## Viv_HabR 1.153 1.153 1.000
## Viv_PrInf 1.238 1.238 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.710
## Viv_EvInt 0.577
## Viv_DiF 0.594
## Viv_Pla 0.655
## Viv_HabRC 0.561
## Viv_HabR 0.364
## Viv_PrInf 0.609lavaan::fitMeasures(invariance$fit.loadings,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 96.565 59.000 0.001
## srmr cfi.scaled tli.scaled
## 0.023 0.999 0.999
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.018 0.011 0.025modificationindices(invariance$fit.loadings, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 357 Viv_Hard =~ Viv_EvInt 3 3 1 37.42 -0.331 -0.156 -0.111
## 182 Viv_EvInt ~1 3 3 1 36.80 1.017 1.017 0.723
## 175 Viv_EvInt ~*~ Viv_EvInt 3 3 1 36.80 0.185 0.185 1.000
## 358 Viv_Hard =~ Viv_DiF 3 3 1 27.40 0.374 0.176 0.092
## 176 Viv_DiF ~*~ Viv_DiF 3 3 1 26.94 -0.123 -0.123 -1.000
## 183 Viv_DiF ~1 3 3 1 26.94 -1.178 -1.178 -0.615
## 334 Viv_AdmT ~~ Viv_EvInt 2 2 1 21.68 0.292 0.292 0.260
## 322 Viv_Pla ~~ Viv_HabRC 1 1 1 14.42 0.274 0.274 0.274
## 380 Viv_Soft =~ Viv_HabR 4 4 1 13.09 0.105 0.094 0.108
## 242 Viv_HabR ~*~ Viv_HabR 4 4 1 12.26 -0.240 -0.240 -1.000
## sepc.nox
## 357 -0.111
## 182 0.723
## 175 1.000
## 358 0.092
## 176 -1.000
## 183 -0.615
## 334 0.260
## 322 0.274
## 380 0.108
## 242 -1.000semTools::reliability(invariance$fit.loadings)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8310 0.7855
## alpha.ord 0.8974 0.8670
## omega 0.8192 0.7295
## omega2 0.8192 0.7295
## omega3 0.8170 0.7301
## avevar 0.6769 0.6424
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8319 0.7612
## alpha.ord 0.9014 0.8536
## omega 0.8195 0.5377
## omega2 0.8195 0.5377
## omega3 0.8149 0.5372
## avevar 0.6833 0.6000
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8446 0.7938
## alpha.ord 0.9133 0.8695
## omega 0.8658 0.3007
## omega2 0.8658 0.3007
## omega3 0.8638 0.2987
## avevar 0.7088 0.6380
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8247 0.7078
## alpha.ord 0.8860 0.8249
## omega 0.7795 0.2659
## omega2 0.7795 0.2659
## omega3 0.7781 0.2653
## avevar 0.6337 0.50815.1.1.8.3 Thresholds
summary(invariance$fit.thresholds,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 221 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 185
## Number of equality constraints 105
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 511.232 149.511
## Degrees of freedom 116 116
## P-value (Unknown) NA 0.020
## Scaling correction factor 6.338
## Shift parameter for each group:
## 2 22.680
## 3 36.256
## 1 4.552
## 4 5.367
## simple second-order correction
## Test statistic for each group:
## 2 120.516 41.694
## 3 78.220 48.596
## 1 136.439 26.078
## 4 176.056 33.143
##
## Model Test Baseline Model:
##
## Test statistic 57688.409 56390.095
## Degrees of freedom 84 84
## P-value NA 0.000
## Scaling correction factor 1.024
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.993 0.999
## Tucker-Lewis Index (TLI) 0.995 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.042 0.012
## 90 Percent confidence interval - lower 0.039 0.005
## 90 Percent confidence interval - upper 0.046 0.018
## P-value RMSEA <= 0.05 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.022 0.022
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.493 0.064 23.437 0.000 1.493 0.831
## Viv_EvInt 1.308 0.053 24.664 0.000 1.308 0.794
## Viv_DiF 1.470 0.080 18.410 0.000 1.470 0.827
## Viv_Pla 1.496 0.081 18.484 0.000 1.496 0.831
## Viv_Hard =~
## Viv_HabRC 1.482 0.089 16.689 0.000 1.482 0.829
## Viv_HabR 1.060 0.046 22.851 0.000 1.060 0.728
## Viv_PrInf 1.441 0.065 22.040 0.000 1.441 0.822
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.449 0.032 13.992 0.000 0.449 0.449
## .Viv_EvInt ~~
## .Viv_DiF 0.215 0.045 4.764 0.000 0.215 0.215
## Viv_Soft ~~
## Viv_Hard 0.834 0.012 70.818 0.000 0.834 0.834
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.081 0.162 -25.153 0.000 -4.081 -2.271
## Viv_AdmT|t2 -2.899 0.113 -25.561 0.000 -2.899 -1.613
## Viv_AdmT|t3 -2.134 0.087 -24.547 0.000 -2.134 -1.188
## Viv_AdmT|t4 0.272 0.043 6.384 0.000 0.272 0.152
## Viv_EvInt|t1 -3.753 0.137 -27.344 0.000 -3.753 -2.280
## Viv_EvInt|t2 -2.281 0.086 -26.459 0.000 -2.281 -1.386
## Viv_EvInt|t3 -1.512 0.063 -24.174 0.000 -1.512 -0.918
## Viv_EvInt|t4 0.604 0.044 13.770 0.000 0.604 0.367
## Viv_DiF|t1 -4.485 0.233 -19.255 0.000 -4.485 -2.522
## Viv_DiF|t2 -3.315 0.163 -20.362 0.000 -3.315 -1.864
## Viv_DiF|t3 -2.516 0.127 -19.823 0.000 -2.516 -1.415
## Viv_DiF|t4 -0.010 0.040 -0.258 0.797 -0.010 -0.006
## Viv_Pla|t1 -4.835 0.265 -18.212 0.000 -4.835 -2.687
## Viv_Pla|t2 -3.897 0.206 -18.885 0.000 -3.897 -2.166
## Viv_Pla|t3 -2.788 0.150 -18.608 0.000 -2.788 -1.549
## Viv_Pla|t4 0.028 0.041 0.673 0.501 0.028 0.015
## Viv_HabRC|t1 -4.878 0.270 -18.036 0.000 -4.878 -2.728
## Viv_HabRC|t2 -3.796 0.216 -17.600 0.000 -3.796 -2.123
## Viv_HabRC|t3 -2.671 0.149 -17.932 0.000 -2.671 -1.494
## Viv_HabRC|t4 0.018 0.043 0.421 0.674 0.018 0.010
## Viv_HabR|t1 -3.587 0.137 -26.135 0.000 -3.587 -2.461
## Viv_HabR|t2 -2.444 0.089 -27.571 0.000 -2.444 -1.677
## Viv_HabR|t3 -1.529 0.059 -25.935 0.000 -1.529 -1.049
## Viv_HabR|t4 0.218 0.034 6.381 0.000 0.218 0.150
## Viv_PrInf|t1 -4.170 0.166 -25.162 0.000 -4.170 -2.377
## Viv_PrInf|t2 -3.047 0.120 -25.371 0.000 -3.047 -1.737
## Viv_PrInf|t3 -2.179 0.090 -24.290 0.000 -2.179 -1.243
## Viv_PrInf|t4 0.401 0.046 8.807 0.000 0.401 0.229
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.310
## .Viv_EvInt 1.000 1.000 0.369
## .Viv_DiF 1.000 1.000 0.316
## .Viv_Pla 1.000 1.000 0.309
## .Viv_HabRC 1.000 1.000 0.313
## .Viv_HabR 1.000 1.000 0.471
## .Viv_PrInf 1.000 1.000 0.325
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.557 0.557 1.000
## Viv_EvInt 0.607 0.607 1.000
## Viv_DiF 0.562 0.562 1.000
## Viv_Pla 0.556 0.556 1.000
## Viv_HabRC 0.559 0.559 1.000
## Viv_HabR 0.686 0.686 1.000
## Viv_PrInf 0.570 0.570 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.690
## Viv_EvInt 0.631
## Viv_DiF 0.684
## Viv_Pla 0.691
## Viv_HabRC 0.687
## Viv_HabR 0.529
## Viv_PrInf 0.675
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.493 0.064 23.437 0.000 1.558 0.822
## Viv_EvInt 1.308 0.053 24.664 0.000 1.365 0.777
## Viv_DiF 1.470 0.080 18.410 0.000 1.535 0.837
## Viv_Pla 1.496 0.081 18.484 0.000 1.561 0.861
## Viv_Hard =~
## Viv_HabRC 1.482 0.089 16.689 0.000 1.465 0.805
## Viv_HabR 1.060 0.046 22.851 0.000 1.048 0.712
## Viv_PrInf 1.441 0.065 22.040 0.000 1.423 0.795
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.540 0.071 7.571 0.000 0.540 0.484
## .Viv_EvInt ~~
## .Viv_DiF 0.304 0.060 5.106 0.000 0.304 0.274
## Viv_Soft ~~
## Viv_Hard 0.840 0.050 16.697 0.000 0.815 0.815
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.118 0.036 3.326 0.001 0.113 0.113
## Viv_Hard 0.120 0.039 3.104 0.002 0.122 0.122
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.081 0.162 -25.153 0.000 -4.081 -2.152
## Viv_AdmT|t2 -2.899 0.113 -25.561 0.000 -2.899 -1.529
## Viv_AdmT|t3 -2.134 0.087 -24.547 0.000 -2.134 -1.126
## Viv_AdmT|t4 0.272 0.043 6.384 0.000 0.272 0.144
## Viv_EvInt|t1 -3.753 0.137 -27.344 0.000 -3.753 -2.137
## Viv_EvInt|t2 -2.281 0.086 -26.459 0.000 -2.281 -1.299
## Viv_EvInt|t3 -1.512 0.063 -24.174 0.000 -1.512 -0.861
## Viv_EvInt|t4 0.604 0.044 13.770 0.000 0.604 0.344
## Viv_DiF|t1 -4.485 0.233 -19.255 0.000 -4.485 -2.445
## Viv_DiF|t2 -3.315 0.163 -20.362 0.000 -3.315 -1.807
## Viv_DiF|t3 -2.516 0.127 -19.823 0.000 -2.516 -1.372
## Viv_DiF|t4 -0.010 0.040 -0.258 0.797 -0.010 -0.006
## Viv_Pla|t1 -4.835 0.265 -18.212 0.000 -4.835 -2.667
## Viv_Pla|t2 -3.897 0.206 -18.885 0.000 -3.897 -2.150
## Viv_Pla|t3 -2.788 0.150 -18.608 0.000 -2.788 -1.538
## Viv_Pla|t4 0.028 0.041 0.673 0.501 0.028 0.015
## Viv_HabRC|t1 -4.878 0.270 -18.036 0.000 -4.878 -2.680
## Viv_HabRC|t2 -3.796 0.216 -17.600 0.000 -3.796 -2.086
## Viv_HabRC|t3 -2.671 0.149 -17.932 0.000 -2.671 -1.468
## Viv_HabRC|t4 0.018 0.043 0.421 0.674 0.018 0.010
## Viv_HabR|t1 -3.587 0.137 -26.135 0.000 -3.587 -2.438
## Viv_HabR|t2 -2.444 0.089 -27.571 0.000 -2.444 -1.661
## Viv_HabR|t3 -1.529 0.059 -25.935 0.000 -1.529 -1.039
## Viv_HabR|t4 0.218 0.034 6.381 0.000 0.218 0.148
## Viv_PrInf|t1 -4.170 0.166 -25.162 0.000 -4.170 -2.329
## Viv_PrInf|t2 -3.047 0.120 -25.371 0.000 -3.047 -1.701
## Viv_PrInf|t3 -2.179 0.090 -24.290 0.000 -2.179 -1.217
## Viv_PrInf|t4 0.401 0.046 8.807 0.000 0.401 0.224
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.169 0.125 9.353 0.000 1.169 0.325
## .Viv_EvInt 1.221 0.124 9.875 0.000 1.221 0.396
## .Viv_DiF 1.010 0.154 6.576 0.000 1.010 0.300
## .Viv_Pla 0.849 0.148 5.742 0.000 0.849 0.258
## .Viv_HabRC 1.168 0.187 6.230 0.000 1.168 0.352
## .Viv_HabR 1.068 0.112 9.549 0.000 1.068 0.493
## .Viv_PrInf 1.180 0.124 9.551 0.000 1.180 0.368
## Viv_Soft 1.089 0.071 15.375 0.000 1.000 1.000
## Viv_Hard 0.976 0.074 13.200 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.527 0.527 1.000
## Viv_EvInt 0.570 0.570 1.000
## Viv_DiF 0.545 0.545 1.000
## Viv_Pla 0.552 0.552 1.000
## Viv_HabRC 0.549 0.549 1.000
## Viv_HabR 0.680 0.680 1.000
## Viv_PrInf 0.558 0.558 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.675
## Viv_EvInt 0.604
## Viv_DiF 0.700
## Viv_Pla 0.742
## Viv_HabRC 0.648
## Viv_HabR 0.507
## Viv_PrInf 0.632
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.493 0.064 23.437 0.000 1.425 0.795
## Viv_EvInt 1.308 0.053 24.664 0.000 1.249 0.913
## Viv_DiF 1.470 0.080 18.410 0.000 1.404 0.848
## Viv_Pla 1.496 0.081 18.484 0.000 1.429 0.860
## Viv_Hard =~
## Viv_HabRC 1.482 0.089 16.689 0.000 1.428 0.891
## Viv_HabR 1.060 0.046 22.851 0.000 1.022 0.693
## Viv_PrInf 1.441 0.065 22.040 0.000 1.388 0.828
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.319 0.099 3.212 0.001 0.319 0.412
## .Viv_EvInt ~~
## .Viv_DiF -0.077 0.085 -0.906 0.365 -0.077 -0.157
## Viv_Soft ~~
## Viv_Hard 0.770 0.111 6.918 0.000 0.837 0.837
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.043 0.064 -0.675 0.500 -0.045 -0.045
## Viv_Hard -0.302 0.061 -4.967 0.000 -0.313 -0.313
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.081 0.162 -25.153 0.000 -4.081 -2.277
## Viv_AdmT|t2 -2.899 0.113 -25.561 0.000 -2.899 -1.617
## Viv_AdmT|t3 -2.134 0.087 -24.547 0.000 -2.134 -1.191
## Viv_AdmT|t4 0.272 0.043 6.384 0.000 0.272 0.152
## Viv_EvInt|t1 -3.753 0.137 -27.344 0.000 -3.753 -2.744
## Viv_EvInt|t2 -2.281 0.086 -26.459 0.000 -2.281 -1.668
## Viv_EvInt|t3 -1.512 0.063 -24.174 0.000 -1.512 -1.105
## Viv_EvInt|t4 0.604 0.044 13.770 0.000 0.604 0.442
## Viv_DiF|t1 -4.485 0.233 -19.255 0.000 -4.485 -2.710
## Viv_DiF|t2 -3.315 0.163 -20.362 0.000 -3.315 -2.003
## Viv_DiF|t3 -2.516 0.127 -19.823 0.000 -2.516 -1.520
## Viv_DiF|t4 -0.010 0.040 -0.258 0.797 -0.010 -0.006
## Viv_Pla|t1 -4.835 0.265 -18.212 0.000 -4.835 -2.912
## Viv_Pla|t2 -3.897 0.206 -18.885 0.000 -3.897 -2.347
## Viv_Pla|t3 -2.788 0.150 -18.608 0.000 -2.788 -1.679
## Viv_Pla|t4 0.028 0.041 0.673 0.501 0.028 0.017
## Viv_HabRC|t1 -4.878 0.270 -18.036 0.000 -4.878 -3.042
## Viv_HabRC|t2 -3.796 0.216 -17.600 0.000 -3.796 -2.367
## Viv_HabRC|t3 -2.671 0.149 -17.932 0.000 -2.671 -1.666
## Viv_HabRC|t4 0.018 0.043 0.421 0.674 0.018 0.011
## Viv_HabR|t1 -3.587 0.137 -26.135 0.000 -3.587 -2.433
## Viv_HabR|t2 -2.444 0.089 -27.571 0.000 -2.444 -1.658
## Viv_HabR|t3 -1.529 0.059 -25.935 0.000 -1.529 -1.037
## Viv_HabR|t4 0.218 0.034 6.381 0.000 0.218 0.148
## Viv_PrInf|t1 -4.170 0.166 -25.162 0.000 -4.170 -2.487
## Viv_PrInf|t2 -3.047 0.120 -25.371 0.000 -3.047 -1.817
## Viv_PrInf|t3 -2.179 0.090 -24.290 0.000 -2.179 -1.300
## Viv_PrInf|t4 0.401 0.046 8.807 0.000 0.401 0.239
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.181 0.227 5.212 0.000 1.181 0.368
## .Viv_EvInt 0.311 0.135 2.311 0.021 0.311 0.166
## .Viv_DiF 0.768 0.222 3.453 0.001 0.768 0.280
## .Viv_Pla 0.717 0.200 3.574 0.000 0.717 0.260
## .Viv_HabRC 0.531 0.224 2.369 0.018 0.531 0.207
## .Viv_HabR 1.130 0.174 6.509 0.000 1.130 0.520
## .Viv_PrInf 0.883 0.159 5.553 0.000 0.883 0.314
## Viv_Soft 0.912 0.140 6.528 0.000 1.000 1.000
## Viv_Hard 0.928 0.132 7.051 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.558 0.558 1.000
## Viv_EvInt 0.731 0.731 1.000
## Viv_DiF 0.604 0.604 1.000
## Viv_Pla 0.602 0.602 1.000
## Viv_HabRC 0.624 0.624 1.000
## Viv_HabR 0.678 0.678 1.000
## Viv_PrInf 0.597 0.597 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.632
## Viv_EvInt 0.834
## Viv_DiF 0.720
## Viv_Pla 0.740
## Viv_HabRC 0.793
## Viv_HabR 0.480
## Viv_PrInf 0.686
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.493 0.064 23.437 0.000 1.499 0.844
## Viv_EvInt 1.308 0.053 24.664 0.000 1.313 0.755
## Viv_DiF 1.470 0.080 18.410 0.000 1.477 0.770
## Viv_Pla 1.496 0.081 18.484 0.000 1.502 0.812
## Viv_Hard =~
## Viv_HabRC 1.482 0.089 16.689 0.000 1.395 0.733
## Viv_HabR 1.060 0.046 22.851 0.000 0.998 0.665
## Viv_PrInf 1.441 0.065 22.040 0.000 1.356 0.758
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.819 0.202 4.063 0.000 0.819 0.565
## .Viv_EvInt ~~
## .Viv_DiF 0.549 0.157 3.490 0.000 0.549 0.393
## Viv_Soft ~~
## Viv_Hard 0.787 0.079 10.009 0.000 0.832 0.832
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft -0.006 0.060 -0.102 0.919 -0.006 -0.006
## Viv_Hard 0.276 0.077 3.571 0.000 0.293 0.293
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.081 0.162 -25.153 0.000 -4.081 -2.297
## Viv_AdmT|t2 -2.899 0.113 -25.561 0.000 -2.899 -1.631
## Viv_AdmT|t3 -2.134 0.087 -24.547 0.000 -2.134 -1.201
## Viv_AdmT|t4 0.272 0.043 6.384 0.000 0.272 0.153
## Viv_EvInt|t1 -3.753 0.137 -27.344 0.000 -3.753 -2.158
## Viv_EvInt|t2 -2.281 0.086 -26.459 0.000 -2.281 -1.312
## Viv_EvInt|t3 -1.512 0.063 -24.174 0.000 -1.512 -0.869
## Viv_EvInt|t4 0.604 0.044 13.770 0.000 0.604 0.347
## Viv_DiF|t1 -4.485 0.233 -19.255 0.000 -4.485 -2.338
## Viv_DiF|t2 -3.315 0.163 -20.362 0.000 -3.315 -1.728
## Viv_DiF|t3 -2.516 0.127 -19.823 0.000 -2.516 -1.311
## Viv_DiF|t4 -0.010 0.040 -0.258 0.797 -0.010 -0.005
## Viv_Pla|t1 -4.835 0.265 -18.212 0.000 -4.835 -2.613
## Viv_Pla|t2 -3.897 0.206 -18.885 0.000 -3.897 -2.106
## Viv_Pla|t3 -2.788 0.150 -18.608 0.000 -2.788 -1.507
## Viv_Pla|t4 0.028 0.041 0.673 0.501 0.028 0.015
## Viv_HabRC|t1 -4.878 0.270 -18.036 0.000 -4.878 -2.563
## Viv_HabRC|t2 -3.796 0.216 -17.600 0.000 -3.796 -1.994
## Viv_HabRC|t3 -2.671 0.149 -17.932 0.000 -2.671 -1.403
## Viv_HabRC|t4 0.018 0.043 0.421 0.674 0.018 0.010
## Viv_HabR|t1 -3.587 0.137 -26.135 0.000 -3.587 -2.392
## Viv_HabR|t2 -2.444 0.089 -27.571 0.000 -2.444 -1.630
## Viv_HabR|t3 -1.529 0.059 -25.935 0.000 -1.529 -1.019
## Viv_HabR|t4 0.218 0.034 6.381 0.000 0.218 0.146
## Viv_PrInf|t1 -4.170 0.166 -25.162 0.000 -4.170 -2.329
## Viv_PrInf|t2 -3.047 0.120 -25.371 0.000 -3.047 -1.702
## Viv_PrInf|t3 -2.179 0.090 -24.290 0.000 -2.179 -1.217
## Viv_PrInf|t4 0.401 0.046 8.807 0.000 0.401 0.224
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.911 0.220 4.146 0.000 0.911 0.288
## .Viv_EvInt 1.299 0.227 5.730 0.000 1.299 0.430
## .Viv_DiF 1.502 0.307 4.895 0.000 1.502 0.408
## .Viv_Pla 1.167 0.317 3.677 0.000 1.167 0.341
## .Viv_HabRC 1.675 0.468 3.577 0.000 1.675 0.462
## .Viv_HabR 1.253 0.228 5.490 0.000 1.253 0.557
## .Viv_PrInf 1.366 0.276 4.942 0.000 1.366 0.426
## Viv_Soft 1.008 0.112 8.965 0.000 1.000 1.000
## Viv_Hard 0.886 0.118 7.536 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.563 0.563 1.000
## Viv_EvInt 0.575 0.575 1.000
## Viv_DiF 0.521 0.521 1.000
## Viv_Pla 0.540 0.540 1.000
## Viv_HabRC 0.525 0.525 1.000
## Viv_HabR 0.667 0.667 1.000
## Viv_PrInf 0.559 0.559 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.712
## Viv_EvInt 0.570
## Viv_DiF 0.592
## Viv_Pla 0.659
## Viv_HabRC 0.538
## Viv_HabR 0.443
## Viv_PrInf 0.574lavaan::fitMeasures(invariance$fit.thresholds,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 149.511 116.000 0.020
## srmr cfi.scaled tli.scaled
## 0.022 0.999 1.000
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.012 0.005 0.018modificationindices(invariance$fit.thresholds, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all
## 249 Viv_HabR ~1 4 4 1 48.31 0.446 0.446 0.297
## 391 Viv_AdmT ~~ Viv_EvInt 2 2 1 25.56 0.325 0.325 0.272
## 414 Viv_Hard =~ Viv_EvInt 3 3 1 24.18 -0.221 -0.213 -0.156
## 250 Viv_PrInf ~1 4 4 1 22.84 -0.398 -0.398 -0.223
## 175 Viv_EvInt ~*~ Viv_EvInt 3 3 1 20.62 0.139 0.139 1.000
## 119 Viv_EvInt ~1 2 2 1 18.43 -0.137 -0.137 -0.078
## 186 Viv_HabR ~1 3 3 1 15.97 -0.220 -0.220 -0.149
## 176 Viv_DiF ~*~ Viv_DiF 3 3 1 14.88 -0.100 -0.100 -1.000
## 415 Viv_Hard =~ Viv_DiF 3 3 1 14.29 0.197 0.190 0.115
## 379 Viv_Pla ~~ Viv_HabRC 1 1 1 13.60 0.267 0.267 0.267
## sepc.nox
## 249 0.297
## 391 0.272
## 414 -0.156
## 250 -0.223
## 175 1.000
## 119 -0.078
## 186 -0.149
## 176 -1.000
## 415 0.115
## 379 0.267semTools::reliability(invariance$fit.thresholds)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8310 0.7855
## alpha.ord 0.8974 0.8670
## omega 0.8206 0.7216
## omega2 0.8206 0.7216
## omega3 0.8189 0.7215
## avevar 0.6758 0.6428
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8319 0.7612
## alpha.ord 0.9014 0.8536
## omega 0.8187 0.6946
## omega2 0.8187 0.6946
## omega3 0.8136 0.6945
## avevar 0.6813 0.6067
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8446 0.7938
## alpha.ord 0.9133 0.8695
## omega 0.8583 0.7278
## omega2 0.8583 0.7278
## omega3 0.8571 0.7255
## avevar 0.7186 0.6633
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8247 0.7078
## alpha.ord 0.8860 0.8249
## omega 0.7844 0.6248
## omega2 0.7844 0.6248
## omega3 0.7831 0.6257
## avevar 0.6328 0.52705.1.1.8.4 Partial Invariance - Means
summary(invariance$fit.means,rsquare=T,fit=T,standardized=T)## lavaan 0.6-8 ended normally after 197 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 179
## Number of equality constraints 105
##
## Number of observations per group:
## 2 2506
## 3 4006
## 1 503
## 4 593
## Number of missing patterns per group:
## 2 1
## 3 1
## 1 1
## 4 1
##
## Model Test User Model:
## Standard Robust
## Test Statistic 910.089 207.916
## Degrees of freedom 122 122
## P-value (Unknown) NA 0.000
## Scaling correction factor 6.688
## Shift parameter for each group:
## 2 23.664
## 3 37.829
## 1 4.750
## 4 5.600
## simple second-order correction
## Test statistic for each group:
## 2 179.581 50.514
## 3 149.081 60.119
## 1 342.171 55.910
## 4 239.256 41.372
##
## Model Test Baseline Model:
##
## Test statistic 57688.409 56390.095
## Degrees of freedom 84 84
## P-value NA 0.000
## Scaling correction factor 1.024
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.986 0.998
## Tucker-Lewis Index (TLI) 0.991 0.999
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058 0.019
## 90 Percent confidence interval - lower 0.055 0.015
## 90 Percent confidence interval - upper 0.062 0.024
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.021 0.021
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.482 0.063 23.437 0.000 1.482 0.829
## Viv_EvInt 1.314 0.054 24.493 0.000 1.314 0.796
## Viv_DiF 1.469 0.079 18.490 0.000 1.469 0.827
## Viv_Pla 1.502 0.082 18.367 0.000 1.502 0.832
## Viv_Hard =~
## Viv_HabRC 1.495 0.090 16.565 0.000 1.495 0.831
## Viv_HabR 1.047 0.045 23.127 0.000 1.047 0.723
## Viv_PrInf 1.446 0.066 21.846 0.000 1.446 0.822
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.454 0.032 14.194 0.000 0.454 0.454
## .Viv_EvInt ~~
## .Viv_DiF 0.213 0.045 4.682 0.000 0.213 0.213
## Viv_Soft ~~
## Viv_Hard 0.834 0.012 70.828 0.000 0.834 0.834
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.043 0.162 -24.961 0.000 -4.043 -2.261
## Viv_AdmT|t2 -2.896 0.113 -25.687 0.000 -2.896 -1.620
## Viv_AdmT|t3 -2.155 0.085 -25.305 0.000 -2.155 -1.205
## Viv_AdmT|t4 0.181 0.026 6.879 0.000 0.181 0.101
## Viv_EvInt|t1 -3.750 0.139 -26.891 0.000 -3.750 -2.271
## Viv_EvInt|t2 -2.307 0.086 -26.670 0.000 -2.307 -1.397
## Viv_EvInt|t3 -1.552 0.061 -25.490 0.000 -1.552 -0.940
## Viv_EvInt|t4 0.525 0.030 17.724 0.000 0.525 0.318
## Viv_DiF|t1 -4.461 0.232 -19.262 0.000 -4.461 -2.510
## Viv_DiF|t2 -3.320 0.163 -20.399 0.000 -3.320 -1.868
## Viv_DiF|t3 -2.539 0.127 -20.024 0.000 -2.539 -1.429
## Viv_DiF|t4 -0.094 0.026 -3.612 0.000 -0.094 -0.053
## Viv_Pla|t1 -4.829 0.267 -18.097 0.000 -4.829 -2.676
## Viv_Pla|t2 -3.911 0.209 -18.747 0.000 -3.911 -2.167
## Viv_Pla|t3 -2.822 0.152 -18.545 0.000 -2.822 -1.564
## Viv_Pla|t4 -0.060 0.026 -2.315 0.021 -0.060 -0.033
## Viv_HabRC|t1 -4.885 0.273 -17.903 0.000 -4.885 -2.716
## Viv_HabRC|t2 -3.820 0.219 -17.454 0.000 -3.820 -2.124
## Viv_HabRC|t3 -2.713 0.153 -17.753 0.000 -2.713 -1.509
## Viv_HabRC|t4 -0.070 0.026 -2.706 0.007 -0.070 -0.039
## Viv_HabR|t1 -3.545 0.134 -26.411 0.000 -3.545 -2.448
## Viv_HabR|t2 -2.441 0.087 -27.913 0.000 -2.441 -1.686
## Viv_HabR|t3 -1.555 0.058 -26.947 0.000 -1.555 -1.074
## Viv_HabR|t4 0.137 0.021 6.562 0.000 0.137 0.095
## Viv_PrInf|t1 -4.158 0.167 -24.859 0.000 -4.158 -2.365
## Viv_PrInf|t2 -3.066 0.121 -25.293 0.000 -3.066 -1.744
## Viv_PrInf|t3 -2.219 0.089 -24.798 0.000 -2.219 -1.262
## Viv_PrInf|t4 0.300 0.027 11.177 0.000 0.300 0.170
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.000 1.000 0.313
## .Viv_EvInt 1.000 1.000 0.367
## .Viv_DiF 1.000 1.000 0.317
## .Viv_Pla 1.000 1.000 0.307
## .Viv_HabRC 1.000 1.000 0.309
## .Viv_HabR 1.000 1.000 0.477
## .Viv_PrInf 1.000 1.000 0.324
## Viv_Soft 1.000 1.000 1.000
## Viv_Hard 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.559 0.559 1.000
## Viv_EvInt 0.606 0.606 1.000
## Viv_DiF 0.563 0.563 1.000
## Viv_Pla 0.554 0.554 1.000
## Viv_HabRC 0.556 0.556 1.000
## Viv_HabR 0.691 0.691 1.000
## Viv_PrInf 0.569 0.569 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.687
## Viv_EvInt 0.633
## Viv_DiF 0.683
## Viv_Pla 0.693
## Viv_HabRC 0.691
## Viv_HabR 0.523
## Viv_PrInf 0.676
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.482 0.063 23.437 0.000 1.475 0.823
## Viv_EvInt 1.314 0.054 24.493 0.000 1.308 0.779
## Viv_DiF 1.469 0.079 18.490 0.000 1.462 0.836
## Viv_Pla 1.502 0.082 18.367 0.000 1.495 0.859
## Viv_Hard =~
## Viv_HabRC 1.495 0.090 16.565 0.000 1.410 0.805
## Viv_HabR 1.047 0.045 23.127 0.000 0.988 0.706
## Viv_PrInf 1.446 0.066 21.846 0.000 1.364 0.798
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.505 0.063 7.993 0.000 0.505 0.491
## .Viv_EvInt ~~
## .Viv_DiF 0.273 0.054 5.074 0.000 0.273 0.270
## Viv_Soft ~~
## Viv_Hard 0.766 0.041 18.735 0.000 0.816 0.816
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.043 0.162 -24.961 0.000 -4.043 -2.255
## Viv_AdmT|t2 -2.896 0.113 -25.687 0.000 -2.896 -1.615
## Viv_AdmT|t3 -2.155 0.085 -25.305 0.000 -2.155 -1.202
## Viv_AdmT|t4 0.181 0.026 6.879 0.000 0.181 0.101
## Viv_EvInt|t1 -3.750 0.139 -26.891 0.000 -3.750 -2.235
## Viv_EvInt|t2 -2.307 0.086 -26.670 0.000 -2.307 -1.375
## Viv_EvInt|t3 -1.552 0.061 -25.490 0.000 -1.552 -0.925
## Viv_EvInt|t4 0.525 0.030 17.724 0.000 0.525 0.313
## Viv_DiF|t1 -4.461 0.232 -19.262 0.000 -4.461 -2.552
## Viv_DiF|t2 -3.320 0.163 -20.399 0.000 -3.320 -1.899
## Viv_DiF|t3 -2.539 0.127 -20.024 0.000 -2.539 -1.452
## Viv_DiF|t4 -0.094 0.026 -3.612 0.000 -0.094 -0.054
## Viv_Pla|t1 -4.829 0.267 -18.097 0.000 -4.829 -2.775
## Viv_Pla|t2 -3.911 0.209 -18.747 0.000 -3.911 -2.247
## Viv_Pla|t3 -2.822 0.152 -18.545 0.000 -2.822 -1.621
## Viv_Pla|t4 -0.060 0.026 -2.315 0.021 -0.060 -0.034
## Viv_HabRC|t1 -4.885 0.273 -17.903 0.000 -4.885 -2.789
## Viv_HabRC|t2 -3.820 0.219 -17.454 0.000 -3.820 -2.181
## Viv_HabRC|t3 -2.713 0.153 -17.753 0.000 -2.713 -1.549
## Viv_HabRC|t4 -0.070 0.026 -2.706 0.007 -0.070 -0.040
## Viv_HabR|t1 -3.545 0.134 -26.411 0.000 -3.545 -2.534
## Viv_HabR|t2 -2.441 0.087 -27.913 0.000 -2.441 -1.745
## Viv_HabR|t3 -1.555 0.058 -26.947 0.000 -1.555 -1.112
## Viv_HabR|t4 0.137 0.021 6.562 0.000 0.137 0.098
## Viv_PrInf|t1 -4.158 0.167 -24.859 0.000 -4.158 -2.432
## Viv_PrInf|t2 -3.066 0.121 -25.293 0.000 -3.066 -1.793
## Viv_PrInf|t3 -2.219 0.089 -24.798 0.000 -2.219 -1.298
## Viv_PrInf|t4 0.300 0.027 11.177 0.000 0.300 0.175
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.038 0.112 9.237 0.000 1.038 0.323
## .Viv_EvInt 1.106 0.113 9.745 0.000 1.106 0.393
## .Viv_DiF 0.919 0.139 6.626 0.000 0.919 0.301
## .Viv_Pla 0.794 0.137 5.813 0.000 0.794 0.262
## .Viv_HabRC 1.080 0.169 6.374 0.000 1.080 0.352
## .Viv_HabR 0.981 0.098 10.009 0.000 0.981 0.501
## .Viv_PrInf 1.064 0.112 9.495 0.000 1.064 0.364
## Viv_Soft 0.990 0.057 17.238 0.000 1.000 1.000
## Viv_Hard 0.890 0.059 14.986 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.558 0.558 1.000
## Viv_EvInt 0.596 0.596 1.000
## Viv_DiF 0.572 0.572 1.000
## Viv_Pla 0.575 0.575 1.000
## Viv_HabRC 0.571 0.571 1.000
## Viv_HabR 0.715 0.715 1.000
## Viv_PrInf 0.585 0.585 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.677
## Viv_EvInt 0.607
## Viv_DiF 0.699
## Viv_Pla 0.738
## Viv_HabRC 0.648
## Viv_HabR 0.499
## Viv_PrInf 0.636
##
##
## Group 3 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.482 0.063 23.437 0.000 1.441 0.792
## Viv_EvInt 1.314 0.054 24.493 0.000 1.277 0.914
## Viv_DiF 1.469 0.079 18.490 0.000 1.428 0.848
## Viv_Pla 1.502 0.082 18.367 0.000 1.460 0.862
## Viv_Hard =~
## Viv_HabRC 1.495 0.090 16.565 0.000 1.633 0.900
## Viv_HabR 1.047 0.045 23.127 0.000 1.144 0.690
## Viv_PrInf 1.446 0.066 21.846 0.000 1.580 0.824
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.396 0.124 3.189 0.001 0.396 0.417
## .Viv_EvInt ~~
## .Viv_DiF -0.082 0.088 -0.929 0.353 -0.082 -0.163
## Viv_Soft ~~
## Viv_Hard 0.888 0.115 7.728 0.000 0.836 0.836
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.043 0.162 -24.961 0.000 -4.043 -2.223
## Viv_AdmT|t2 -2.896 0.113 -25.687 0.000 -2.896 -1.593
## Viv_AdmT|t3 -2.155 0.085 -25.305 0.000 -2.155 -1.185
## Viv_AdmT|t4 0.181 0.026 6.879 0.000 0.181 0.099
## Viv_EvInt|t1 -3.750 0.139 -26.891 0.000 -3.750 -2.685
## Viv_EvInt|t2 -2.307 0.086 -26.670 0.000 -2.307 -1.652
## Viv_EvInt|t3 -1.552 0.061 -25.490 0.000 -1.552 -1.111
## Viv_EvInt|t4 0.525 0.030 17.724 0.000 0.525 0.376
## Viv_DiF|t1 -4.461 0.232 -19.262 0.000 -4.461 -2.650
## Viv_DiF|t2 -3.320 0.163 -20.399 0.000 -3.320 -1.972
## Viv_DiF|t3 -2.539 0.127 -20.024 0.000 -2.539 -1.508
## Viv_DiF|t4 -0.094 0.026 -3.612 0.000 -0.094 -0.056
## Viv_Pla|t1 -4.829 0.267 -18.097 0.000 -4.829 -2.852
## Viv_Pla|t2 -3.911 0.209 -18.747 0.000 -3.911 -2.309
## Viv_Pla|t3 -2.822 0.152 -18.545 0.000 -2.822 -1.666
## Viv_Pla|t4 -0.060 0.026 -2.315 0.021 -0.060 -0.035
## Viv_HabRC|t1 -4.885 0.273 -17.903 0.000 -4.885 -2.691
## Viv_HabRC|t2 -3.820 0.219 -17.454 0.000 -3.820 -2.104
## Viv_HabRC|t3 -2.713 0.153 -17.753 0.000 -2.713 -1.495
## Viv_HabRC|t4 -0.070 0.026 -2.706 0.007 -0.070 -0.039
## Viv_HabR|t1 -3.545 0.134 -26.411 0.000 -3.545 -2.137
## Viv_HabR|t2 -2.441 0.087 -27.913 0.000 -2.441 -1.472
## Viv_HabR|t3 -1.555 0.058 -26.947 0.000 -1.555 -0.938
## Viv_HabR|t4 0.137 0.021 6.562 0.000 0.137 0.083
## Viv_PrInf|t1 -4.158 0.167 -24.859 0.000 -4.158 -2.169
## Viv_PrInf|t2 -3.066 0.121 -25.293 0.000 -3.066 -1.600
## Viv_PrInf|t3 -2.219 0.089 -24.798 0.000 -2.219 -1.158
## Viv_PrInf|t4 0.300 0.027 11.177 0.000 0.300 0.156
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 1.231 0.248 4.971 0.000 1.231 0.372
## .Viv_EvInt 0.320 0.134 2.383 0.017 0.320 0.164
## .Viv_DiF 0.795 0.229 3.471 0.001 0.795 0.281
## .Viv_Pla 0.736 0.200 3.678 0.000 0.736 0.257
## .Viv_HabRC 0.627 0.276 2.272 0.023 0.627 0.190
## .Viv_HabR 1.442 0.221 6.525 0.000 1.442 0.524
## .Viv_PrInf 1.179 0.214 5.508 0.000 1.179 0.321
## Viv_Soft 0.945 0.126 7.498 0.000 1.000 1.000
## Viv_Hard 1.194 0.157 7.584 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.550 0.550 1.000
## Viv_EvInt 0.716 0.716 1.000
## Viv_DiF 0.594 0.594 1.000
## Viv_Pla 0.591 0.591 1.000
## Viv_HabRC 0.551 0.551 1.000
## Viv_HabR 0.603 0.603 1.000
## Viv_PrInf 0.522 0.522 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.628
## Viv_EvInt 0.836
## Viv_DiF 0.719
## Viv_Pla 0.743
## Viv_HabRC 0.810
## Viv_HabR 0.476
## Viv_PrInf 0.679
##
##
## Group 4 [4]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_Soft =~
## Viv_AdmT 1.482 0.063 23.437 0.000 1.492 0.842
## Viv_EvInt 1.314 0.054 24.493 0.000 1.323 0.756
## Viv_DiF 1.469 0.079 18.490 0.000 1.479 0.769
## Viv_Pla 1.502 0.082 18.367 0.000 1.512 0.813
## Viv_Hard =~
## Viv_HabRC 1.495 0.090 16.565 0.000 1.268 0.732
## Viv_HabR 1.047 0.045 23.127 0.000 0.889 0.656
## Viv_PrInf 1.446 0.066 21.846 0.000 1.227 0.764
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_HabRC ~~
## .Viv_HabR 0.693 0.159 4.372 0.000 0.693 0.573
## .Viv_EvInt ~~
## .Viv_DiF 0.551 0.160 3.453 0.001 0.551 0.392
## Viv_Soft ~~
## Viv_Hard 0.712 0.063 11.395 0.000 0.834 0.834
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.000 0.000 0.000
## .Viv_EvInt 0.000 0.000 0.000
## .Viv_DiF 0.000 0.000 0.000
## .Viv_Pla 0.000 0.000 0.000
## .Viv_HabRC 0.000 0.000 0.000
## .Viv_HabR 0.000 0.000 0.000
## .Viv_PrInf 0.000 0.000 0.000
## Viv_Soft 0.000 0.000 0.000
## Viv_Hard 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT|t1 -4.043 0.162 -24.961 0.000 -4.043 -2.280
## Viv_AdmT|t2 -2.896 0.113 -25.687 0.000 -2.896 -1.634
## Viv_AdmT|t3 -2.155 0.085 -25.305 0.000 -2.155 -1.216
## Viv_AdmT|t4 0.181 0.026 6.879 0.000 0.181 0.102
## Viv_EvInt|t1 -3.750 0.139 -26.891 0.000 -3.750 -2.143
## Viv_EvInt|t2 -2.307 0.086 -26.670 0.000 -2.307 -1.319
## Viv_EvInt|t3 -1.552 0.061 -25.490 0.000 -1.552 -0.887
## Viv_EvInt|t4 0.525 0.030 17.724 0.000 0.525 0.300
## Viv_DiF|t1 -4.461 0.232 -19.262 0.000 -4.461 -2.320
## Viv_DiF|t2 -3.320 0.163 -20.399 0.000 -3.320 -1.727
## Viv_DiF|t3 -2.539 0.127 -20.024 0.000 -2.539 -1.321
## Viv_DiF|t4 -0.094 0.026 -3.612 0.000 -0.094 -0.049
## Viv_Pla|t1 -4.829 0.267 -18.097 0.000 -4.829 -2.597
## Viv_Pla|t2 -3.911 0.209 -18.747 0.000 -3.911 -2.103
## Viv_Pla|t3 -2.822 0.152 -18.545 0.000 -2.822 -1.517
## Viv_Pla|t4 -0.060 0.026 -2.315 0.021 -0.060 -0.032
## Viv_HabRC|t1 -4.885 0.273 -17.903 0.000 -4.885 -2.819
## Viv_HabRC|t2 -3.820 0.219 -17.454 0.000 -3.820 -2.204
## Viv_HabRC|t3 -2.713 0.153 -17.753 0.000 -2.713 -1.566
## Viv_HabRC|t4 -0.070 0.026 -2.706 0.007 -0.070 -0.041
## Viv_HabR|t1 -3.545 0.134 -26.411 0.000 -3.545 -2.615
## Viv_HabR|t2 -2.441 0.087 -27.913 0.000 -2.441 -1.801
## Viv_HabR|t3 -1.555 0.058 -26.947 0.000 -1.555 -1.147
## Viv_HabR|t4 0.137 0.021 6.562 0.000 0.137 0.101
## Viv_PrInf|t1 -4.158 0.167 -24.859 0.000 -4.158 -2.589
## Viv_PrInf|t2 -3.066 0.121 -25.293 0.000 -3.066 -1.909
## Viv_PrInf|t3 -2.219 0.089 -24.798 0.000 -2.219 -1.381
## Viv_PrInf|t4 0.300 0.027 11.177 0.000 0.300 0.187
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Viv_AdmT 0.916 0.223 4.097 0.000 0.916 0.291
## .Viv_EvInt 1.310 0.231 5.662 0.000 1.310 0.428
## .Viv_DiF 1.509 0.311 4.858 0.000 1.509 0.408
## .Viv_Pla 1.172 0.313 3.737 0.000 1.172 0.339
## .Viv_HabRC 1.395 0.371 3.758 0.000 1.395 0.464
## .Viv_HabR 1.048 0.186 5.636 0.000 1.048 0.570
## .Viv_PrInf 1.075 0.218 4.923 0.000 1.075 0.417
## Viv_Soft 1.014 0.102 9.931 0.000 1.000 1.000
## Viv_Hard 0.720 0.079 9.134 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Viv_AdmT 0.564 0.564 1.000
## Viv_EvInt 0.572 0.572 1.000
## Viv_DiF 0.520 0.520 1.000
## Viv_Pla 0.538 0.538 1.000
## Viv_HabRC 0.577 0.577 1.000
## Viv_HabR 0.738 0.738 1.000
## Viv_PrInf 0.623 0.623 1.000
##
## R-Square:
## Estimate
## Viv_AdmT 0.709
## Viv_EvInt 0.572
## Viv_DiF 0.592
## Viv_Pla 0.661
## Viv_HabRC 0.536
## Viv_HabR 0.430
## Viv_PrInf 0.583lavaan::fitMeasures(invariance$fit.means,c("chisq.scaled","df.scaled","pvalue.scaled","srmr","cfi.scaled","tli.scaled","rmsea.scaled","rmsea.ci.lower.scaled","rmsea.ci.upper.scaled"))## chisq.scaled df.scaled pvalue.scaled
## 207.916 122.000 0.000
## srmr cfi.scaled tli.scaled
## 0.021 0.998 0.999
## rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.019 0.015 0.024modificationindices(invariance$fit.means, sort.=T,maximum.number = 10)## lhs op rhs block group level mi epc sepc.lv sepc.all sepc.nox
## 189 Viv_Hard ~1 3 3 1 191.37 -0.439 -0.402 -0.402 -0.402
## 125 Viv_Soft ~1 2 2 1 101.90 0.123 0.124 0.124 0.124
## 186 Viv_HabR ~1 3 3 1 101.56 -0.594 -0.594 -0.358 -0.358
## 249 Viv_HabR ~1 4 4 1 100.59 0.472 0.472 0.348 0.348
## 252 Viv_Hard ~1 4 4 1 62.57 0.204 0.241 0.241 0.241
## 126 Viv_Hard ~1 2 2 1 59.30 0.112 0.119 0.119 0.119
## 187 Viv_PrInf ~1 3 3 1 54.35 -0.512 -0.512 -0.267 -0.267
## 62 Viv_Soft ~1 1 1 1 41.88 -0.084 -0.084 -0.084 -0.084
## 120 Viv_DiF ~1 2 2 1 39.11 0.218 0.218 0.125 0.125
## 63 Viv_Hard ~1 1 1 1 38.72 -0.097 -0.097 -0.097 -0.097semTools::reliability(invariance$fit.means)## For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord". Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.## $`2`
## Viv_Soft Viv_Hard
## alpha 0.8310 0.7855
## alpha.ord 0.8974 0.8670
## omega 0.8206 0.7201
## omega2 0.8206 0.7201
## omega3 0.8189 0.7197
## avevar 0.6758 0.6438
##
## $`3`
## Viv_Soft Viv_Hard
## alpha 0.8319 0.7612
## alpha.ord 0.9014 0.8536
## omega 0.8163 0.6886
## omega2 0.8163 0.6886
## omega3 0.8114 0.6882
## avevar 0.6816 0.6069
##
## $`1`
## Viv_Soft Viv_Hard
## alpha 0.8446 0.7938
## alpha.ord 0.9133 0.8695
## omega 0.8599 0.7368
## omega2 0.8599 0.7368
## omega3 0.8584 0.7339
## avevar 0.7187 0.6659
##
## $`4`
## Viv_Soft Viv_Hard
## alpha 0.8247 0.7078
## alpha.ord 0.8860 0.8249
## omega 0.7842 0.6129
## omega2 0.7842 0.6129
## omega3 0.7828 0.6136
## avevar 0.6327 0.52595.1.1.8.5 Checking Latent Differences
partial<-partialInvarianceCat(invariance,type="means",return.fit = F)
partial## $estimates
## poolest mean:2 mean:3 mean:1 mean:4 std:2 std:3 std:1
## Viv_Soft~1 0 0 0.1181 -0.04287 -0.006166 0 0.1157 -0.0420
## Viv_Hard~1 0 0 0.1201 -0.30166 0.276174 0 0.1217 -0.3057
## std:4 diff_std:3 vs. 2 diff_std:1 vs. 2 diff_std:4 vs. 2
## Viv_Soft~1 -0.006041 0.1157 -0.0420 -0.006041
## Viv_Hard~1 0.279875 0.1217 -0.3057 0.279875
##
## $results
## free.chi free.df free.p free.cfi fix.chi fix.df fix.p
## Viv_Soft~1 6.648 3 0.08400006 -0.001778 7.446 3 0.058954598
## Viv_Hard~1 23.021 3 0.00003998 -0.005042 26.562 3 0.000007273
## fix.cfi wald.chi wald.df wald.p
## Viv_Soft~1 -0.001778 NA NA NA
## Viv_Hard~1 -0.005042 NA NA NA5.1.2 Predicting Latent as Factor Scores
#data_predict <- predict(fit)
#data <- cbind(data,data_predict)
write.csv(data,"data_CFA_cleyton.csv")
#names(data)