R Notebook
Análise FOMO - Evania
#Carregando biblioteca
library(lavaan)library(readxl)
data <- read_excel("/Volumes/Documentos/Dropbox (Profissional)/Universo/Colaborações Cristina/Coloboracões Evania/pesquisa/Arquivo_Final.xlsx")
View(data)dta<-datadta_CFA<-dtaModelo 1 FOMO - RMSEA alto
# specify the model
CFA.model <-'
exclusao_rel =~ FOMO1 + FOMO2 + FOMO3 + FOMO4 + FOMO5
exclusao_info =~ FOMO6 + FOMO7 + FOMO8 + FOMO9+ FOMO10
'# fit the model
fit <- cfa(model=CFA.model,data=dta_CFA,ordered=TRUE,estimator="ULSMV")# display summary output
summary(fit, fit.measures=TRUE,standardized=T, rsquare=T)lavaan 0.6-7 ended normally after 24 iterations
Estimator ULS
Optimization method NLMINB
Number of free parameters 51
Number of observations 519
Model Test User Model:
Standard Robust
Test Statistic 47.383 256.756
Degrees of freedom 34 34
P-value (Unknown) NA 0.000
Scaling correction factor 0.195
Shift parameter 13.588
simple second-order correction
Model Test Baseline Model:
Test statistic 10700.188 9124.903
Degrees of freedom 45 45
P-value NA 0.000
Scaling correction factor 1.177
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.999 0.975
Tucker-Lewis Index (TLI) 0.998 0.968
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.028 0.112
90 Percent confidence interval - lower 0.000 0.100
90 Percent confidence interval - upper 0.045 0.126
P-value RMSEA <= 0.05 0.986 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
exclusao_rel =~
FOMO1 1.000 0.901 0.901
FOMO2 0.986 0.019 51.397 0.000 0.888 0.888
FOMO3 1.008 0.019 52.143 0.000 0.908 0.908
FOMO4 0.888 0.022 40.697 0.000 0.801 0.801
FOMO5 0.919 0.024 38.254 0.000 0.829 0.829
exclusao_info =~
FOMO6 1.000 0.882 0.882
FOMO7 1.119 0.021 54.177 0.000 0.987 0.987
FOMO8 1.062 0.022 48.823 0.000 0.936 0.936
FOMO9 0.987 0.022 43.890 0.000 0.871 0.871
FOMO10 1.029 0.022 46.418 0.000 0.907 0.907
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
exclusao_rel ~~
exclusao_info 0.561 0.025 22.150 0.000 0.705 0.705
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.FOMO1 0.000 0.000 0.000
.FOMO2 0.000 0.000 0.000
.FOMO3 0.000 0.000 0.000
.FOMO4 0.000 0.000 0.000
.FOMO5 0.000 0.000 0.000
.FOMO6 0.000 0.000 0.000
.FOMO7 0.000 0.000 0.000
.FOMO8 0.000 0.000 0.000
.FOMO9 0.000 "verbose" 0.000 0.000
.FOMO10 0.000 0.000 0.000
exclusao_rel 0.000 0.000 0.000
exclusao_info 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
FOMO1|t1 -0.875 0.063 -13.790 0.000 -0.875 -0.875
FOMO1|t2 -0.007 0.055 -0.132 0.895 -0.007 -0.007
FOMO1|t3 0.484 0.057 8.427 0.000 0.484 0.484
FOMO1|t4 1.452 0.082 17.634 0.000 1.452 1.452
FOMO2|t1 -1.060 0.068 -15.607 0.000 -1.060 -1.060
FOMO2|t2 0.041 0.055 0.745 0.456 0.041 0.041
FOMO2|t3 0.452 0.057 7.908 0.000 0.452 0.452
FOMO2|t4 1.573 0.089 17.753 0.000 1.573 1.573
FOMO3|t1 -1.140 0.070 -16.223 0.000 -1.140 -1.140
FOMO3|t2 0.022 0.055 0.395 0.693 0.022 0.022
FOMO3|t3 0.352 0.056 6.258 0.000 0.352 0.352
FOMO3|t4 1.682 0.095 17.665 0.000 1.682 1.682
FOMO4|t1 -0.716 0.061 -11.831 0.000 -0.716 -0.716
FOMO4|t2 0.286 0.056 5.124 0.000 0.286 0.286
FOMO4|t3 0.813 0.062 13.067 0.000 0.813 0.813
FOMO4|t4 1.644 0.093 17.717 0.000 1.644 1.644
FOMO5|t1 -0.979 0.066 -14.875 0.000 -0.979 -0.979
FOMO5|t2 -0.022 0.055 -0.395 0.693 -0.022 -0.022
FOMO5|t3 0.312 0.056 5.560 0.000 0.312 0.312
FOMO5|t4 1.663 0.094 17.694 0.000 1.663 1.663
FOMO6|t1 -1.399 0.080 -17.507 0.000 -1.399 -1.399
FOMO6|t2 -0.614 0.059 -10.399 0.000 -0.614 -0.614
FOMO6|t3 -0.271 0.056 -4.862 0.000 -0.271 -0.271
FOMO6|t4 0.941 0.065 14.494 0.000 0.941 0.941
FOMO7|t1 -1.510 0.085 -17.720 0.000 -1.510 -1.510
FOMO7|t2 -0.691 0.060 -11.497 0.000 -0.691 -0.691
FOMO7|t3 -0.415 0.057 -7.301 0.000 -0.415 -0.415
FOMO7|t4 0.868 0.063 13.711 0.000 0.868 0.868
FOMO8|t1 -1.724 0.098 -17.587 0.000 -1.724 -1.724
FOMO8|t2 -0.861 0.063 -13.631 0.000 -0.861 -0.861
FOMO8|t3 -0.479 0.057 -8.340 0.000 -0.479 -0.479
FOMO8|t4 0.833 0.063 13.310 0.000 0.833 0.833
FOMO9|t1 -1.466 0.083 -17.660 0.000 -1.466 -1.466
FOMO9|t2 -0.625 0.059 -10.568 0.000 -0.625 -0.625
FOMO9|t3 -0.227 0.056 -4.075 0.000 -0.227 -0.227
FOMO9|t4 1.027 0.067 15.319 0.000 1.027 1.027
FOMO10|t1 -1.541 0.087 -17.744 0.000 -1.541 -1.541
FOMO10|t2 -0.691 0.060 -11.497 0.000 -0.691 -0.691
FOMO10|t3 -0.389 0.057 -6.867 0.000 -0.389 -0.389
FOMO10|t4 1.077 0.068 15.748 0.000 1.077 1.077
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.FOMO1 0.188 0.188 0.188
.FOMO2 0.211 0.211 0.211
.FOMO3 0.175 0.175 0.175
.FOMO4 0.359 0.359 0.359
.FOMO5 0.313 0.313 0.313
.FOMO6 0.222 0.222 0.222
.FOMO7 0.026 0.026 0.026
.FOMO8 0.123 0.123 0.123
.FOMO9 0.242 0.242 0.242
.FOMO10 0.177 0.177 0.177
exclusao_rel 0.812 0.025 33.051 0.000 1.000 1.000
exclusao_info 0.778 0.026 30.230 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
FOMO1 1.000 1.000 1.000
FOMO2 1.000 1.000 1.000
FOMO3 1.000 1.000 1.000
FOMO4 1.000 1.000 1.000
FOMO5 1.000 1.000 1.000
FOMO6 1.000 1.000 1.000
FOMO7 1.000 1.000 1.000
FOMO8 1.000 1.000 1.000
FOMO9 1.000 1.000 1.000
FOMO10 1.000 1.000 1.000
R-Square:
Estimate
FOMO1 0.812
FOMO2 0.789
FOMO3 0.825
FOMO4 0.641
FOMO5 0.687
FOMO6 0.778
FOMO7 0.974
FOMO8 0.877
FOMO9 0.758
FOMO10 0.823modificationindices(object = fit,sort. = T)Modelo FOMO - após indice de modificação
# specify the model
CFA.model <-'
exclusao_rel =~ FOMO1 + FOMO2 + FOMO3 + FOMO4 + FOMO5
exclusao_info =~ FOMO6 + FOMO7 + FOMO8 + FOMO9+ FOMO10
FOMO2 ~~ FOMO3
'# fit the model
fit <- cfa(model=CFA.model,data=dta_CFA,ordered=TRUE,estimator="ULSMV")# display summary output
summary(fit, fit.measures=TRUE,standardized=T, rsquare=T)modificationindices(object = fit,sort. = T)library(semTools)
semTools::reliability(fit)library(semMediation)Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table discriminantValidityTable(fit)For constructs with categorical indicators, the alpha and the average variance extracted are calculated from polychoric (polyserial) correlations, not from Pearson correlations.library(semPlot)Registered S3 methods overwritten by 'lme4':
method from
cooks.distance.influence.merMod car
influence.merMod car
dfbeta.influence.merMod car
dfbetas.influence.merMod car
Registered S3 methods overwritten by 'huge':
method from
plot.sim BDgraph
print.sim BDgraphsemPaths(fit,what="std", residuals = F, thresholds =F, intercepts = F,edge.label.cex =1)
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