HLQ_CFA_Belg.R
ivanropovik
Mon Feb 6 10:36:38 2017
Potrebné R libraries
library(lavaan, quietly = TRUE, warn.conflicts = FALSE)## This is lavaan 0.5-22## lavaan is BETA software! Please report any bugs.library(semPlot, quietly = TRUE, warn.conflicts = FALSE)
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
library(psych, quietly = TRUE, warn.conflicts = FALSE)
library(ICC, quietly = TRUE, warn.conflicts = FALSE)
library(Amelia, quietly = TRUE, warn.conflicts = FALSE)## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.4, built: 2015-12-05)
## ## Copyright (C) 2005-2017 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##library(BaylorEdPsych, quietly = TRUE, warn.conflicts = FALSE)Príprava dát
Načítanie dátového súboru
#rm(list = ls())
setwd(dir = "/Users/ivanropovik/OneDrive/MANUSCRIPTS/2017 HQL Validizacna studia")
full.data <- read.csv(file = "data_4countries_raw.csv", header = TRUE, sep = ";")
#View(full.data)Selekcia premenných pre CFA nástroja
HLQ.collumns <- full.data %>% filter(cntr == 4) %>% select(HL1:HL24)
delete.na <- function(HLQ.collumns, n=NULL) {
HLQ.collumns[rowSums(is.na(HLQ.collumns)) <= n,]
}Vizualizácia chýbajucich dát po odstránení prázdnych riadkov
data.na.rm <- delete.na(HLQ.collumns, n = 10)
missmap(data.na.rm, rank.order = TRUE)
Percento chýbajúcich dát
paste(round(sum(is.na(data.na.rm))/prod(dim(data.na.rm))*100, 3), "%", sep = "")## [1] "1.975%"Imputácia chýbajúcich dát - bootstraped expected maximization
set.seed(123)
data_imput <- amelia(data.na.rm, ords = c("HL1", "HL2", "HL3", "HL4", "HL5",
"HL6", "HL7", "HL8", "HL9", "HL10",
"HL11", "HL12", "HL13", "HL14", "HL15",
"HL16", "HL17", "HL18", "HL19", "HL20",
"HL21", "HL22", "HL23", "HL24"), m = 1)## -- Imputation 1 --
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13data <- as.data.frame(data_imput$imputations)
names(data) <- c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9",
"HL10", "HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17",
"HL18", "HL19", "HL20", "HL21", "HL22", "HL23", "HL24")Frekvenčné tabuľky
lapply(data[,c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9",
"HL10", "HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17",
"HL18", "HL19", "HL20", "HL21", "HL22", "HL23", "HL24")],
function(x){table(x, useNA = "ifany")})## $HL1
## x
## 1 2 3 4
## 2 21 127 42
##
## $HL2
## x
## 1 2 3 4
## 4 22 114 52
##
## $HL3
## x
## 1 2 3 4
## 9 34 113 36
##
## $HL4
## x
## 1 2 3 4
## 8 30 102 52
##
## $HL5
## x
## 1 2 3 4
## 2 18 116 56
##
## $HL6
## x
## 1 2 3 4
## 3 24 89 76
##
## $HL7
## x
## 1 2 3 4
## 13 41 106 32
##
## $HL8
## x
## 1 2 3 4
## 12 44 106 30
##
## $HL9
## x
## 1 2 3 4
## 6 39 104 43
##
## $HL10
## x
## 1 2 3 4
## 4 36 122 30
##
## $HL11
## x
## 1 2 3 4
## 5 29 110 48
##
## $HL12
## x
## 1 2 3 4
## 5 48 110 29
##
## $HL13
## x
## 1 2 3 4
## 7 34 116 35
##
## $HL14
## x
## 1 2 3 4
## 8 57 85 42
##
## $HL15
## x
## 1 2 3 4
## 5 19 116 52
##
## $HL16
## x
## 1 2 3 4
## 4 50 115 23
##
## $HL17
## x
## 2 3 4
## 16 87 89
##
## $HL18
## x
## 2 3 4
## 17 85 90
##
## $HL19
## x
## 1 2 3 4
## 8 42 108 34
##
## $HL20
## x
## 1 2 3 4
## 8 38 115 31
##
## $HL21
## x
## 1 2 3 4
## 4 34 113 41
##
## $HL22
## x
## 1 2 3 4
## 7 28 105 52
##
## $HL23
## x
## 2 3 4
## 39 114 39
##
## $HL24
## x
## 1 2 3 4
## 6 29 124 33Finálne dáta
#View(data)Deskriptívna analýza
Deskriptívne štatistiky
describe(data, na.rm = TRUE, skew = TRUE, ranges = FALSE)## vars n mean sd skew kurtosis se
## HL1 1 192 3.09 0.60 -0.32 0.80 0.04
## HL2 2 192 3.11 0.68 -0.54 0.62 0.05
## HL3 3 192 2.92 0.74 -0.56 0.38 0.05
## HL4 4 192 3.03 0.77 -0.60 0.17 0.06
## HL5 5 192 3.18 0.63 -0.40 0.47 0.05
## HL6 6 192 3.24 0.73 -0.64 -0.06 0.05
## HL7 7 192 2.82 0.79 -0.50 0.01 0.06
## HL8 8 192 2.80 0.77 -0.46 0.00 0.06
## HL9 9 192 2.96 0.74 -0.39 -0.08 0.05
## HL10 10 192 2.93 0.65 -0.38 0.52 0.05
## HL11 11 192 3.05 0.71 -0.50 0.30 0.05
## HL12 12 192 2.85 0.70 -0.25 -0.02 0.05
## HL13 13 192 2.93 0.71 -0.52 0.47 0.05
## HL14 14 192 2.84 0.81 -0.17 -0.65 0.06
## HL15 15 192 3.12 0.68 -0.65 0.95 0.05
## HL16 16 192 2.82 0.66 -0.23 0.09 0.05
## HL17 17 192 3.38 0.64 -0.52 -0.67 0.05
## HL18 18 192 3.38 0.64 -0.54 -0.68 0.05
## HL19 19 192 2.88 0.74 -0.41 0.07 0.05
## HL20 20 192 2.88 0.72 -0.50 0.38 0.05
## HL21 21 192 2.99 0.69 -0.37 0.18 0.05
## HL22 22 192 3.05 0.75 -0.60 0.30 0.05
## HL23 23 192 3.00 0.64 0.00 -0.56 0.05
## HL24 24 192 2.96 0.67 -0.58 0.92 0.05Veľkosť vzorky
nrow(data)## [1] 192Redukcia počtu kategórií v prípade nízkej frekvencie odpoveďovej kategórie
(odhad polychorickej kovariančnej matice predpokladá absenciu buniek HLa x HLb s nulovou frekvenciou)
data$HL1 <- ifelse(data$HL1 == 1, yes = 2, no = data$HL1)
data$HL2 <- ifelse(data$HL2 == 1, yes = 2, no = data$HL2)
data$HL3 <- ifelse(data$HL3 == 1, yes = 2, no = data$HL3)
data$HL4 <- ifelse(data$HL4 == 1, yes = 2, no = data$HL4)
data$HL5 <- ifelse(data$HL5 == 1, yes = 2, no = data$HL5)
data$HL6 <- ifelse(data$HL6 == 1, yes = 2, no = data$HL6)
data$HL7 <- ifelse(data$HL7 == 1, yes = 2, no = data$HL7)
data$HL8 <- ifelse(data$HL8 == 1, yes = 2, no = data$HL8)
data$HL9 <- ifelse(data$HL9 == 1, yes = 2, no = data$HL9)
data$HL10 <- ifelse(data$HL10 == 1, yes = 2, no = data$HL10)
data$HL11 <- ifelse(data$HL11 == 1, yes = 2, no = data$HL11)
data$HL12 <- ifelse(data$HL12 == 1, yes = 2, no = data$HL12)
data$HL13 <- ifelse(data$HL13 == 1, yes = 2, no = data$HL13)
data$HL14 <- ifelse(data$HL14 == 1, yes = 2, no = data$HL14)
data$HL15 <- ifelse(data$HL15 == 1, yes = 2, no = data$HL15)
data$HL16 <- ifelse(data$HL16 == 1, yes = 2, no = data$HL16)
data$HL17 <- ifelse(data$HL17 == 1, yes = 2, no = data$HL17)
data$HL18 <- ifelse(data$HL18 == 1, yes = 2, no = data$HL18)
data$HL19 <- ifelse(data$HL19 == 1, yes = 2, no = data$HL19)
data$HL20 <- ifelse(data$HL20 == 1, yes = 2, no = data$HL20)
data$HL21 <- ifelse(data$HL21 == 1, yes = 2, no = data$HL21)
data$HL22 <- ifelse(data$HL22 == 1, yes = 2, no = data$HL22)
data$HL23 <- ifelse(data$HL23 == 1, yes = 2, no = data$HL23)
data$HL24 <- ifelse(data$HL24 == 1, yes = 2, no = data$HL24)Matica polychorických korelácií
polychoric.cor <- polychoric(data, correct = FALSE, smooth = TRUE,
global = FALSE, na.rm = TRUE)
round(polychoric.cor$rho, 2)## HL1 HL2 HL3 HL4 HL5 HL6 HL7 HL8 HL9 HL10 HL11 HL12 HL13 HL14
## HL1 1.00 0.45 0.39 0.59 0.45 0.23 0.38 0.38 0.40 0.46 0.39 0.26 0.40 0.26
## HL2 0.45 1.00 0.51 0.48 0.43 0.41 0.35 0.19 0.15 0.44 0.13 0.29 0.39 0.11
## HL3 0.39 0.51 1.00 0.44 0.44 0.28 0.25 0.30 0.29 0.32 0.18 0.29 0.52 0.35
## HL4 0.59 0.48 0.44 1.00 0.41 0.40 0.38 0.31 0.26 0.48 0.28 0.36 0.36 0.10
## HL5 0.45 0.43 0.44 0.41 1.00 0.42 0.34 0.39 0.35 0.43 0.23 0.22 0.34 0.22
## HL6 0.23 0.41 0.28 0.40 0.42 1.00 0.33 0.26 0.19 0.43 0.04 0.26 0.23 0.14
## HL7 0.38 0.35 0.25 0.38 0.34 0.33 1.00 0.44 0.35 0.41 0.41 0.42 0.37 0.16
## HL8 0.38 0.19 0.30 0.31 0.39 0.26 0.44 1.00 0.46 0.44 0.40 0.57 0.43 0.39
## HL9 0.40 0.15 0.29 0.26 0.35 0.19 0.35 0.46 1.00 0.45 0.44 0.33 0.35 0.48
## HL10 0.46 0.44 0.32 0.48 0.43 0.43 0.41 0.44 0.45 1.00 0.27 0.52 0.37 0.32
## HL11 0.39 0.13 0.18 0.28 0.23 0.04 0.41 0.40 0.44 0.27 1.00 0.41 0.38 0.24
## HL12 0.26 0.29 0.29 0.36 0.22 0.26 0.42 0.57 0.33 0.52 0.41 1.00 0.50 0.27
## HL13 0.40 0.39 0.52 0.36 0.34 0.23 0.37 0.43 0.35 0.37 0.38 0.50 1.00 0.33
## HL14 0.26 0.11 0.35 0.10 0.22 0.14 0.16 0.39 0.48 0.32 0.24 0.27 0.33 1.00
## HL15 0.40 0.36 0.39 0.32 0.36 0.25 0.32 0.34 0.47 0.42 0.25 0.20 0.38 0.47
## HL16 0.47 0.23 0.44 0.35 0.40 0.11 0.32 0.54 0.50 0.37 0.32 0.40 0.45 0.28
## HL17 0.45 0.36 0.28 0.37 0.12 0.27 0.30 0.24 0.31 0.38 0.19 0.17 0.21 0.31
## HL18 0.43 0.37 0.34 0.35 0.29 0.31 0.41 0.50 0.48 0.37 0.44 0.23 0.29 0.30
## HL19 0.42 0.33 0.23 0.35 0.27 0.05 0.45 0.42 0.35 0.40 0.28 0.36 0.33 0.35
## HL20 0.43 0.35 0.40 0.38 0.21 0.21 0.43 0.51 0.44 0.46 0.26 0.42 0.45 0.35
## HL21 0.25 0.32 0.34 0.34 0.45 0.22 0.36 0.44 0.33 0.37 0.31 0.40 0.52 0.26
## HL22 0.27 0.31 0.33 0.29 0.39 0.21 0.33 0.29 0.29 0.25 0.31 0.25 0.42 0.16
## HL23 0.46 0.38 0.37 0.36 0.33 0.27 0.34 0.34 0.52 0.38 0.44 0.32 0.44 0.38
## HL24 0.47 0.39 0.38 0.40 0.40 0.19 0.39 0.43 0.43 0.47 0.29 0.22 0.33 0.36
## HL15 HL16 HL17 HL18 HL19 HL20 HL21 HL22 HL23 HL24
## HL1 0.40 0.47 0.45 0.43 0.42 0.43 0.25 0.27 0.46 0.47
## HL2 0.36 0.23 0.36 0.37 0.33 0.35 0.32 0.31 0.38 0.39
## HL3 0.39 0.44 0.28 0.34 0.23 0.40 0.34 0.33 0.37 0.38
## HL4 0.32 0.35 0.37 0.35 0.35 0.38 0.34 0.29 0.36 0.40
## HL5 0.36 0.40 0.12 0.29 0.27 0.21 0.45 0.39 0.33 0.40
## HL6 0.25 0.11 0.27 0.31 0.05 0.21 0.22 0.21 0.27 0.19
## HL7 0.32 0.32 0.30 0.41 0.45 0.43 0.36 0.33 0.34 0.39
## HL8 0.34 0.54 0.24 0.50 0.42 0.51 0.44 0.29 0.34 0.43
## HL9 0.47 0.50 0.31 0.48 0.35 0.44 0.33 0.29 0.52 0.43
## HL10 0.42 0.37 0.38 0.37 0.40 0.46 0.37 0.25 0.38 0.47
## HL11 0.25 0.32 0.19 0.44 0.28 0.26 0.31 0.31 0.44 0.29
## HL12 0.20 0.40 0.17 0.23 0.36 0.42 0.40 0.25 0.32 0.22
## HL13 0.38 0.45 0.21 0.29 0.33 0.45 0.52 0.42 0.44 0.33
## HL14 0.47 0.28 0.31 0.30 0.35 0.35 0.26 0.16 0.38 0.36
## HL15 1.00 0.48 0.48 0.54 0.34 0.52 0.49 0.25 0.34 0.41
## HL16 0.48 1.00 0.39 0.41 0.37 0.49 0.40 0.13 0.35 0.48
## HL17 0.48 0.39 1.00 0.57 0.18 0.24 0.23 0.30 0.45 0.41
## HL18 0.54 0.41 0.57 1.00 0.31 0.42 0.39 0.42 0.51 0.58
## HL19 0.34 0.37 0.18 0.31 1.00 0.47 0.47 0.35 0.39 0.58
## HL20 0.52 0.49 0.24 0.42 0.47 1.00 0.50 0.42 0.39 0.53
## HL21 0.49 0.40 0.23 0.39 0.47 0.50 1.00 0.41 0.29 0.48
## HL22 0.25 0.13 0.30 0.42 0.35 0.42 0.41 1.00 0.47 0.32
## HL23 0.34 0.35 0.45 0.51 0.39 0.39 0.29 0.47 1.00 0.57
## HL24 0.41 0.48 0.41 0.58 0.58 0.53 0.48 0.32 0.57 1.00Priemerná korelácia
polychoric.cor.low <- polychoric.cor$rho[lower.tri(polychoric.cor$rho)]
mean(abs(polychoric.cor.low))## [1] 0.3578521Výpočet polychorickej kovariančnej matice
SDs <- describe(data, na.rm = TRUE)$sd
polychoric.cov <- cor2cov(R = polychoric.cor$rho, sds = SDs)Definovanie, test a estimácia CFA modelu
Špecifikácia 5-faktorového modelu merania
model <- '
theor_know =~ a*HL1 + b*HL8 + c*HL14 + d*HL18
prac_know =~ f*HL2 + g*HL4 + h*HL6 + i*HL10 + j*HL17
crit_think =~ k*HL7 + l*HL12 + m*HL16 + n*HL21 + o*HL24
self_aware =~ p*HL5 + q*HL11 + r*HL15 + s*HL19 + t*HL22
citizenship =~ u*HL3 + v*HL9 + x*HL13 + y*HL20 + z*HL23
'Estimácia a test modelu, podľa Muthén, 1984
fitted.model <- cfa(model = model, data = data, meanstructure = TRUE, std.lv = TRUE, mimic = "Mplus",
estimator = "WLSMVS", test = "Satterthwaite", orthogonal = FALSE, bootstrap = 5000,
ordered = c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9", "HL10",
"HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17", "HL18", "HL19",
"HL20", "HL21", "HL22", "HL23", "HL24"))## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL2 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL4 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL10 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL16 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL14## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL10 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL7 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL16 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL11 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL9 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL20 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL23 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL3 x
## HL2## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL10## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL13 x
## HL17## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL23 x
## HL17## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL7## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL16## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL19 x
## HL24## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL3 x
## HL5## Warning in lav_object_post_check(lavobject): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use inspect(fit,"cov.lv") to investigate.Počet buniek HLa x HLb s nulovou frekvenciou je tak na hrane. Majú malú vzorku a tým pádom nízke frekvencie v rámci odpoveďových kategórií.
Analýza štatistickej sily pre test blízkej zhody (na základe RMSEA distribúcie)
Štatistická sila pre detekciu chybného modelu (RMSEA > .08)
df <- fitted.model@test[[1]]$df
alfa <- .05
n <- nrow(data)
rmsea0 <- .05 # RMSEA za predpokladu H0
rmseaa <- .08 # RMSEA za predpokladu H1
ncp0 <- (n-1)*df*rmsea0**2 ;
ncpa <-(n-1)*df*rmseaa**2 ;
if(rmsea0 < rmseaa) {
cval <- qchisq(1-alfa,df=df,ncp=ncp0)
sila.rmsea <- 1 - pchisq(cval,df=df,ncp=ncpa)
} else {
cval <- qchisq(alfa,df=df,ncp=ncp0)
sila.rmsea <- pchisq(cval,df=df,ncp=ncpa)
}
rm(ncp0, ncpa, cval)
print(round(sila.rmsea,10))## [1] 0.9996262Výsledky
Kovariančná matica je non-positive definite z dôvodu, že viaceré z definovaných latentných premenných su kolineárne (de facto identické). Päť z korelácií v rámci štrukturálneho modelu sú väčšie ako 1.
eigen(inspect(fitted.model, "cov.lv") )$values## [1] 4.875304732 0.251617310 0.005002159 -0.038336690 -0.093587511Štvrtá a piata eigenvalue majú negatívnu ale nízku hodnotu, výsledky testu modelu sú interpretovateľné.
Test modelu, odhady voľných parametrov (faktorové náboje)
Stačí si všímať “Robust” test, Latent variable, Covariances a R-square. Intercepts, Thresholds, Intercepts (…) môžte kľudne ignorovať.
summary(fitted.model, standardized = TRUE, rsquare = TRUE)## lavaan (0.5-22) converged normally after 35 iterations
##
## Number of observations 192
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 321.160 147.697
## Degrees of freedom 242 68
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 2.174
## for the mean and variance adjusted correction (WLSMV)
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## theor_know =~
## HL1 (a) 0.653 0.054 12.017 0.000 0.653 0.653
## HL8 (b) 0.668 0.050 13.301 0.000 0.668 0.668
## HL14 (c) 0.484 0.061 7.883 0.000 0.484 0.484
## HL18 (d) 0.683 0.045 15.026 0.000 0.683 0.683
## prac_know =~
## HL2 (f) 0.635 0.056 11.379 0.000 0.635 0.635
## HL4 (g) 0.689 0.056 12.273 0.000 0.689 0.689
## HL6 (h) 0.479 0.070 6.839 0.000 0.479 0.479
## HL10 (i) 0.753 0.046 16.312 0.000 0.753 0.753
## HL17 (j) 0.612 0.064 9.565 0.000 0.612 0.612
## crit_think =~
## HL7 (k) 0.588 0.053 11.073 0.000 0.588 0.588
## HL12 (l) 0.576 0.057 10.171 0.000 0.576 0.576
## HL16 (m) 0.639 0.051 12.604 0.000 0.639 0.639
## HL21 (n) 0.628 0.051 12.222 0.000 0.628 0.628
## HL24 (o) 0.695 0.049 14.120 0.000 0.695 0.695
## self_aware =~
## HL5 (p) 0.550 0.062 8.814 0.000 0.550 0.550
## HL11 (q) 0.499 0.057 8.776 0.000 0.499 0.499
## HL15 (r) 0.622 0.047 13.158 0.000 0.622 0.622
## HL19 (s) 0.579 0.056 10.255 0.000 0.579 0.579
## HL22 (t) 0.502 0.062 8.064 0.000 0.502 0.502
## citizenship =~
## HL3 (u) 0.589 0.060 9.809 0.000 0.589 0.589
## HL9 (v) 0.645 0.047 13.731 0.000 0.645 0.645
## HL13 (x) 0.651 0.048 13.510 0.000 0.651 0.651
## HL20 (y) 0.698 0.048 14.504 0.000 0.698 0.698
## HL23 (z) 0.672 0.047 14.246 0.000 0.672 0.672
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## theor_know ~~
## prac_know 0.888 0.057 15.586 0.000 0.888 0.888
## crit_think 1.034 0.050 20.491 0.000 1.034 1.034
## self_aware 1.074 0.057 18.790 0.000 1.074 1.074
## citizenship 0.997 0.048 20.956 0.000 0.997 0.997
## prac_know ~~
## crit_think 0.852 0.055 15.614 0.000 0.852 0.852
## self_aware 0.865 0.066 13.011 0.000 0.865 0.865
## citizenship 0.815 0.053 15.274 0.000 0.815 0.815
## crit_think ~~
## self_aware 1.081 0.040 27.003 0.000 1.081 1.081
## citizenship 1.018 0.040 25.442 0.000 1.018 1.018
## self_aware ~~
## citizenship 1.035 0.042 24.546 0.000 1.035 1.035
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.000 0.000 0.000
## .HL8 0.000 0.000 0.000
## .HL14 0.000 0.000 0.000
## .HL18 0.000 0.000 0.000
## .HL2 0.000 0.000 0.000
## .HL4 0.000 0.000 0.000
## .HL6 0.000 0.000 0.000
## .HL10 0.000 0.000 0.000
## .HL17 0.000 0.000 0.000
## .HL7 0.000 0.000 0.000
## .HL12 0.000 0.000 0.000
## .HL16 0.000 0.000 0.000
## .HL21 0.000 0.000 0.000
## .HL24 0.000 0.000 0.000
## .HL5 0.000 0.000 0.000
## .HL11 0.000 0.000 0.000
## .HL15 0.000 0.000 0.000
## .HL19 0.000 0.000 0.000
## .HL22 0.000 0.000 0.000
## .HL3 0.000 0.000 0.000
## .HL9 0.000 0.000 0.000
## .HL13 0.000 0.000 0.000
## .HL20 0.000 0.000 0.000
## .HL23 0.000 0.000 0.000
## theor_know 0.000 0.000 0.000
## prac_know 0.000 0.000 0.000
## crit_think 0.000 0.000 0.000
## self_aware 0.000 0.000 0.000
## citizenship 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HL1|t1 -1.176 0.118 -10.000 0.000 -1.176 -1.176
## HL1|t2 0.776 0.101 7.660 0.000 0.776 0.776
## HL8|t1 -0.549 0.096 -5.724 0.000 -0.549 -0.549
## HL8|t2 1.010 0.110 9.209 0.000 1.010 1.010
## HL14|t1 -0.416 0.094 -4.449 0.000 -0.416 -0.416
## HL14|t2 0.776 0.101 7.660 0.000 0.776 0.776
## HL18|t1 -1.350 0.128 -10.535 0.000 -1.350 -1.350
## HL18|t2 0.078 0.091 0.864 0.388 0.078 0.078
## HL2|t1 -1.101 0.114 -9.677 0.000 -1.101 -1.101
## HL2|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL4|t1 -0.849 0.104 -8.194 0.000 -0.849 -0.849
## HL4|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL6|t1 -1.078 0.113 -9.563 0.000 -1.078 -1.078
## HL6|t2 0.264 0.092 2.876 0.004 0.264 0.264
## HL10|t1 -0.812 0.102 -7.929 0.000 -0.812 -0.812
## HL10|t2 1.010 0.110 9.209 0.000 1.010 1.010
## HL17|t1 -1.383 0.130 -10.602 0.000 -1.383 -1.383
## HL17|t2 0.092 0.091 1.008 0.314 0.092 0.092
## HL7|t1 -0.579 0.096 -6.005 0.000 -0.579 -0.579
## HL7|t2 0.967 0.108 8.964 0.000 0.967 0.967
## HL12|t1 -0.595 0.097 -6.145 0.000 -0.595 -0.595
## HL12|t2 1.032 0.111 9.329 0.000 1.032 1.032
## HL16|t1 -0.579 0.096 -6.005 0.000 -0.579 -0.579
## HL16|t2 1.176 0.118 10.000 0.000 1.176 1.176
## HL21|t1 -0.849 0.104 -8.194 0.000 -0.849 -0.849
## HL21|t2 0.794 0.102 7.795 0.000 0.794 0.794
## HL24|t1 -0.907 0.106 -8.584 0.000 -0.907 -0.907
## HL24|t2 0.947 0.107 8.838 0.000 0.947 0.947
## HL5|t1 -1.258 0.122 -10.291 0.000 -1.258 -1.258
## HL5|t2 0.549 0.096 5.724 0.000 0.549 0.549
## HL11|t1 -0.927 0.106 -8.712 0.000 -0.927 -0.927
## HL11|t2 0.674 0.099 6.841 0.000 0.674 0.674
## HL15|t1 -1.150 0.116 -9.896 0.000 -1.150 -1.150
## HL15|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL19|t1 -0.642 0.098 -6.564 0.000 -0.642 -0.642
## HL19|t2 0.927 0.106 8.712 0.000 0.927 0.927
## HL22|t1 -0.907 0.106 -8.584 0.000 -0.907 -0.907
## HL22|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL3|t1 -0.759 0.101 -7.525 0.000 -0.759 -0.759
## HL3|t2 0.887 0.105 8.455 0.000 0.887 0.887
## HL9|t1 -0.725 0.100 -7.253 0.000 -0.725 -0.725
## HL9|t2 0.759 0.101 7.525 0.000 0.759 0.759
## HL13|t1 -0.794 0.102 -7.795 0.000 -0.794 -0.794
## HL13|t2 0.907 0.106 8.584 0.000 0.907 0.907
## HL20|t1 -0.708 0.099 -7.116 0.000 -0.708 -0.708
## HL20|t2 0.988 0.109 9.087 0.000 0.988 0.988
## HL23|t1 -0.831 0.103 -8.062 0.000 -0.831 -0.831
## HL23|t2 0.831 0.103 8.062 0.000 0.831 0.831
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.573 0.573 0.573
## .HL8 0.554 0.554 0.554
## .HL14 0.766 0.766 0.766
## .HL18 0.534 0.534 0.534
## .HL2 0.597 0.597 0.597
## .HL4 0.525 0.525 0.525
## .HL6 0.771 0.771 0.771
## .HL10 0.432 0.432 0.432
## .HL17 0.626 0.626 0.626
## .HL7 0.654 0.654 0.654
## .HL12 0.668 0.668 0.668
## .HL16 0.592 0.592 0.592
## .HL21 0.605 0.605 0.605
## .HL24 0.517 0.517 0.517
## .HL5 0.698 0.698 0.698
## .HL11 0.751 0.751 0.751
## .HL15 0.613 0.613 0.613
## .HL19 0.665 0.665 0.665
## .HL22 0.748 0.748 0.748
## .HL3 0.653 0.653 0.653
## .HL9 0.584 0.584 0.584
## .HL13 0.576 0.576 0.576
## .HL20 0.513 0.513 0.513
## .HL23 0.548 0.548 0.548
## theor_know 1.000 1.000 1.000
## prac_know 1.000 1.000 1.000
## crit_think 1.000 1.000 1.000
## self_aware 1.000 1.000 1.000
## citizenship 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HL1 1.000 1.000 1.000
## HL8 1.000 1.000 1.000
## HL14 1.000 1.000 1.000
## HL18 1.000 1.000 1.000
## HL2 1.000 1.000 1.000
## HL4 1.000 1.000 1.000
## HL6 1.000 1.000 1.000
## HL10 1.000 1.000 1.000
## HL17 1.000 1.000 1.000
## HL7 1.000 1.000 1.000
## HL12 1.000 1.000 1.000
## HL16 1.000 1.000 1.000
## HL21 1.000 1.000 1.000
## HL24 1.000 1.000 1.000
## HL5 1.000 1.000 1.000
## HL11 1.000 1.000 1.000
## HL15 1.000 1.000 1.000
## HL19 1.000 1.000 1.000
## HL22 1.000 1.000 1.000
## HL3 1.000 1.000 1.000
## HL9 1.000 1.000 1.000
## HL13 1.000 1.000 1.000
## HL20 1.000 1.000 1.000
## HL23 1.000 1.000 1.000
##
## R-Square:
## Estimate
## HL1 0.427
## HL8 0.446
## HL14 0.234
## HL18 0.466
## HL2 0.403
## HL4 0.475
## HL6 0.229
## HL10 0.568
## HL17 0.374
## HL7 0.346
## HL12 0.332
## HL16 0.408
## HL21 0.395
## HL24 0.483
## HL5 0.302
## HL11 0.249
## HL15 0.387
## HL19 0.335
## HL22 0.252
## HL3 0.347
## HL9 0.416
## HL13 0.424
## HL20 0.487
## HL23 0.452Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model)## npar fmin
## 82.000 0.836
## chisq df
## 321.160 242.000
## pvalue chisq.scaled
## 0.000 147.697
## df.scaled pvalue.scaled
## 68.000 0.000
## chisq.scaling.factor baseline.chisq
## 2.174 7324.149
## baseline.df baseline.pvalue
## 276.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 746.920 28.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 9.806
## cfi tli
## 0.989 0.987
## nnfi rfi
## 0.987 0.950
## nfi pnfi
## 0.956 0.838
## ifi rni
## 0.989 0.989
## cfi.scaled tli.scaled
## 0.889 0.954
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.954 NA
## rfi.scaled nfi.scaled
## 0.919 0.802
## ifi.scaled rni.scaled
## 0.802 0.989
## rni.robust rmsea
## NA 0.041
## rmsea.ci.lower rmsea.ci.upper
## 0.028 0.053
## rmsea.pvalue rmsea.scaled
## 0.884 0.078
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.067 0.090
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.075
## rmr_nomean srmr
## 0.078 0.075
## srmr_bentler srmr_bentler_nomean
## 0.075 0.078
## srmr_bollen srmr_bollen_nomean
## 0.075 0.078
## srmr_mplus srmr_mplus_nomean
## 0.075 0.078
## cn_05 cn_01
## 167.098 177.097
## gfi agfi
## 0.969 0.958
## pgfi mfi
## 0.724 0.813Diagram
semPaths(fitted.model, style = "mx", layout = "circle",
edge.label.cex = 0.5, sizeLat = 5, nCharNodes = 0,
nDigits = 2, "Standardized", intercepts = FALSE,
residuals = TRUE, exoVar = FALSE, fade = TRUE,
groups = "latents", pastel = TRUE)
Test modelu indikuje prítomnosť chybnej špecifikácie modelu. Popri indexoch blízkej zhody je preto potrebné analyzovať lokálne zdroje chybnej špecifikácie na základe matice reziduálnych korelácií
Matica reziduálnych korelácií
residuals <- residuals(fitted.model, type = "cor")$cor
residuals## HL1 HL8 HL14 HL18 HL2 HL4 HL6 HL10 HL17 HL7
## HL1 0.000
## HL8 -0.051 0.000
## HL14 -0.054 0.068 0.000
## HL18 -0.019 0.049 -0.032 0.000
## HL2 0.084 -0.190 -0.166 -0.013 0.000
## HL4 0.194 -0.103 -0.195 -0.072 0.043 0.000
## HL6 -0.048 -0.024 -0.063 0.024 0.104 0.070 0.000
## HL10 0.020 -0.002 -0.008 -0.091 -0.042 -0.041 0.071 0.000
## HL17 0.100 -0.120 0.051 0.199 -0.029 -0.055 -0.024 -0.084 0.000
## HL7 -0.020 0.031 -0.139 -0.001 0.036 0.040 0.092 0.035 -0.009 0.000
## HL12 -0.126 0.172 -0.019 -0.174 -0.025 0.018 0.022 0.146 -0.133 0.082
## HL16 0.043 0.097 -0.036 -0.042 -0.114 -0.023 -0.150 -0.040 0.056 -0.058
## HL21 -0.172 0.010 -0.057 -0.050 -0.015 -0.027 -0.041 -0.038 -0.094 -0.008
## HL24 -0.003 -0.050 0.015 0.089 0.011 -0.013 -0.093 0.021 0.051 -0.023
## HL5 0.062 -0.007 -0.066 -0.112 0.130 0.082 0.191 0.068 -0.174 -0.011
## HL11 0.038 0.040 -0.016 0.070 -0.141 -0.017 -0.168 -0.056 -0.070 0.090
## HL15 -0.038 -0.103 0.151 0.083 0.015 -0.054 -0.008 0.010 0.148 -0.078
## HL19 0.017 0.008 0.052 -0.111 0.009 0.008 -0.191 0.025 -0.125 0.086
## HL22 -0.083 -0.069 -0.101 0.054 0.037 -0.014 -0.002 -0.080 0.031 0.007
## HL3 0.004 -0.094 0.067 -0.060 0.210 0.109 0.054 -0.040 -0.014 -0.100
## HL9 -0.022 0.033 0.172 0.040 -0.187 -0.104 -0.057 0.056 -0.010 -0.039
## HL13 -0.024 -0.003 0.021 -0.154 0.049 -0.006 -0.029 -0.025 -0.115 -0.020
## HL20 -0.029 0.043 0.010 -0.052 -0.007 -0.009 -0.063 0.028 -0.108 0.014
## HL23 0.024 -0.103 0.051 0.054 0.035 -0.015 0.010 -0.031 0.117 -0.058
## HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22 HL3
## HL1
## HL8
## HL14
## HL18
## HL2
## HL4
## HL6
## HL10
## HL17
## HL7
## HL12 0.000
## HL16 0.036 0.000
## HL21 0.043 -0.003 0.000
## HL24 -0.178 0.040 0.045 0.000
## HL5 -0.123 0.020 0.082 -0.012 0.000
## HL11 0.097 -0.021 -0.027 -0.080 -0.045 0.000
## HL15 -0.191 0.051 0.071 -0.057 0.020 -0.061 0.000
## HL19 -0.001 -0.030 0.074 0.149 -0.046 -0.007 -0.022 0.000
## HL22 -0.062 -0.219 0.068 -0.056 0.117 0.064 -0.058 0.059 0.000
## HL3 -0.053 0.053 -0.039 -0.037 0.106 -0.121 0.008 -0.126 0.027 0.000
## HL9 -0.050 0.084 -0.081 -0.031 -0.020 0.111 0.050 -0.036 -0.043 -0.093
## HL13 0.121 0.028 0.103 -0.127 -0.035 0.040 -0.044 -0.063 0.078 0.135
## HL20 0.015 0.038 0.057 0.033 -0.187 -0.098 0.069 0.050 0.053 -0.012
## HL23 -0.072 -0.084 -0.141 0.096 -0.055 0.092 -0.093 -0.012 0.125 -0.026
## HL9 HL13 HL20 HL23
## HL1
## HL8
## HL14
## HL18
## HL2
## HL4
## HL6
## HL10
## HL17
## HL7
## HL12
## HL16
## HL21
## HL24
## HL5
## HL11
## HL15
## HL19
## HL22
## HL3
## HL9 0.000
## HL13 -0.066 0.000
## HL20 -0.006 0.000 0.000
## HL23 0.089 0.000 -0.075 0.000Pre prehladnejšiu vizualizáciu, matica reziduí s vyznačenými reziduálnymi hodnotami > .1 (štandardizované z-reziduá je možné odhadnúť iba v prípade použitia estimátora z rodiny maximum likelihood. Arbitrárna hodnota .1 preto, lebo neumožní produkt dvoch nábojov > .3)
Počet premenných
p = 24Ak máme v matici (p(p+1)/2 - p) = 300 elementov, tak
(p*(p+1)/2 - p)*.05## [1] 13.8z nich môže byť signifikantných na hladine alfa = .05
HRUBÁ APROXIMÁCIA - približne tolko elementov môže byť > .1 Diag = diagonála, >.1 = reziduálna hodnota vyššia ako .1
ifelse(residuals == 0, "Diag", ifelse(residuals > .1, ">.1", "."))## HL1 HL8 HL14 HL18 HL2 HL4 HL6 HL10 HL17 HL7
## HL1 "Diag" "." "." "." "." ">.1" "." "." "." "."
## HL8 "." "Diag" "." "." "." "." "." "." "." "."
## HL14 "." "." "Diag" "." "." "." "." "." "." "."
## HL18 "." "." "." "Diag" "." "." "." "." ">.1" "."
## HL2 "." "." "." "." "Diag" "." ">.1" "." "." "."
## HL4 ">.1" "." "." "." "." "Diag" "." "." "." "."
## HL6 "." "." "." "." ">.1" "." "Diag" "." "." "."
## HL10 "." "." "." "." "." "." "." "Diag" "." "."
## HL17 "." "." "." ">.1" "." "." "." "." "Diag" "."
## HL7 "." "." "." "." "." "." "." "." "." "Diag"
## HL12 "." ">.1" "." "." "." "." "." ">.1" "." "."
## HL16 "." "." "." "." "." "." "." "." "." "."
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL5 "." "." "." "." ">.1" "." ">.1" "." "." "."
## HL11 "." "." "." "." "." "." "." "." "." "."
## HL15 "." "." ">.1" "." "." "." "." "." ">.1" "."
## HL19 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL3 "." "." "." "." ">.1" ">.1" "." "." "." "."
## HL9 "." "." ">.1" "." "." "." "." "." "." "."
## HL13 "." "." "." "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." ">.1" "."
## HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22 HL3
## HL1 "." "." "." "." "." "." "." "." "." "."
## HL8 ">.1" "." "." "." "." "." "." "." "." "."
## HL14 "." "." "." "." "." "." ">.1" "." "." "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL2 "." "." "." "." ">.1" "." "." "." "." ">.1"
## HL4 "." "." "." "." "." "." "." "." "." ">.1"
## HL6 "." "." "." "." ">.1" "." "." "." "." "."
## HL10 ">.1" "." "." "." "." "." "." "." "." "."
## HL17 "." "." "." "." "." "." ">.1" "." "." "."
## HL7 "." "." "." "." "." "." "." "." "." "."
## HL12 "Diag" "." "." "." "." "." "." "." "." "."
## HL16 "." "Diag" "." "." "." "." "." "." "." "."
## HL21 "." "." "Diag" "." "." "." "." "." "." "."
## HL24 "." "." "." "Diag" "." "." "." ">.1" "." "."
## HL5 "." "." "." "." "Diag" "." "." "." ">.1" ">.1"
## HL11 "." "." "." "." "." "Diag" "." "." "." "."
## HL15 "." "." "." "." "." "." "Diag" "." "." "."
## HL19 "." "." "." ">.1" "." "." "." "Diag" "." "."
## HL22 "." "." "." "." ">.1" "." "." "." "Diag" "."
## HL3 "." "." "." "." ">.1" "." "." "." "." "Diag"
## HL9 "." "." "." "." "." ">.1" "." "." "." "."
## HL13 ">.1" "." ">.1" "." "." "." "." "." "." ">.1"
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." ">.1" "."
## HL9 HL13 HL20 HL23
## HL1 "." "." "." "."
## HL8 "." "." "." "."
## HL14 ">.1" "." "." "."
## HL18 "." "." "." "."
## HL2 "." "." "." "."
## HL4 "." "." "." "."
## HL6 "." "." "." "."
## HL10 "." "." "." "."
## HL17 "." "." "." ">.1"
## HL7 "." "." "." "."
## HL12 "." ">.1" "." "."
## HL16 "." "." "." "."
## HL21 "." ">.1" "." "."
## HL24 "." "." "." "."
## HL5 "." "." "." "."
## HL11 ">.1" "." "." "."
## HL15 "." "." "." "."
## HL19 "." "." "." "."
## HL22 "." "." "." ">.1"
## HL3 "." ">.1" "." "."
## HL9 "Diag" "." "." "."
## HL13 "." "Diag" "." "."
## HL20 "." "." "Diag" "."
## HL23 "." "." "." "Diag"Odhad reliability
rel.HLQ.collumns <- full.data %>% select(HL1:HL24, ID3)
delete.na <- function(rel.HLQ.collumns, n=NULL) {
rel.HLQ.collumns[rowSums(is.na(rel.HLQ.collumns)) <= n,]
}
rel.HLQ.na.rm <- delete.na(rel.HLQ.collumns, n = 10)Odhad internej konzistencie - Cronbachova alfa
alpha(rel.HLQ.na.rm[,c("HL1", "HL8", "HL14", "HL18")], na.rm = TRUE)$total$std.alpha # pre theor_know## [1] 0.664335alpha(rel.HLQ.na.rm[,c("HL2", "HL4", "HL6", "HL10", "HL17")], na.rm = TRUE)$total$std.alpha # pre prac_know## [1] 0.7288641alpha(rel.HLQ.na.rm[,c("HL7", "HL12", "HL16", "HL21", "HL24")], na.rm = TRUE)$total$std.alpha # pre crit_think## [1] 0.7867088alpha(rel.HLQ.na.rm[,c("HL5", "HL11", "HL15", "HL19", "HL22")], na.rm = TRUE)$total$std.alpha # pre self_aware## [1] 0.7189408alpha(rel.HLQ.na.rm[,c("HL3", "HL9", "HL13", "HL20", "HL23")], na.rm = TRUE)$total$std.alpha # pre citizenship## [1] 0.7546782Odhad stability v čase
Výpočet sumárneho skóre každej z 5 dimenzií HL
rel.HLQ.na.rm$theor_know <- rowMeans(rel.HLQ.na.rm[,c("HL1", "HL8", "HL14", "HL18")], na.rm = TRUE)
rel.HLQ.na.rm$prac_know <- rowMeans(rel.HLQ.na.rm[,c("HL2", "HL4", "HL6", "HL10", "HL17")], na.rm = TRUE)
rel.HLQ.na.rm$crit_think <- rowMeans(rel.HLQ.na.rm[,c("HL7", "HL12", "HL16", "HL21", "HL24")], na.rm = TRUE)
rel.HLQ.na.rm$self_aware <- rowMeans(rel.HLQ.na.rm[,c("HL5", "HL11", "HL15", "HL19", "HL22")], na.rm = TRUE)
rel.HLQ.na.rm$citizenship <- rowMeans(rel.HLQ.na.rm[,c("HL3", "HL9", "HL13", "HL20", "HL23")], na.rm = TRUE)Výpočet sumárneho skóre každej z 5 dimenzií HL - RETEST
#rel.HLQ.na.rm$Rtheor_know <- rowMeans(rel.HLQ.na.rm[,c("RHL1", "RHL8", "RHL14", "RHL18")], na.rm = TRUE)
#rel.HLQ.na.rm$Rprac_know <- rowMeans(rel.HLQ.na.rm[,c("RHL2", "RHL4", "RHL6", "RHL10", "RHL17")], na.rm = TRUE)
#rel.HLQ.na.rm$Rcrit_think <- rowMeans(rel.HLQ.na.rm[,c("RHL7", "RHL12", "RHL16", "RHL21", "RHL24")], na.rm = TRUE)
#rel.HLQ.na.rm$Rself_aware <- rowMeans(rel.HLQ.na.rm[,c("RHL5", "RHL11", "RHL15", "RHL19", "RHL22")], na.rm = TRUE)
#rel.HLQ.na.rm$Rcitizenship <- rowMeans(rel.HLQ.na.rm[,c("RHL3", "RHL9", "RHL13", "RHL20", "RHL23")], na.rm = TRUE)Test-retest korelácia
#with(rel.HLQ.na.rm, cor.test(theor_know, Rtheor_know))$estimate # pre theor_know
#with(rel.HLQ.na.rm, cor.test(crit_think, Rcrit_think))$estimate # pre crit_think
##with(rel.HLQ.na.rm, cor.test(prac_know, Rprac_know))$estimate # pre prac_know
#with(rel.HLQ.na.rm, cor.test(self_aware, Rself_aware))$estimate # pre self_aware
#with(rel.HLQ.na.rm, cor.test(citizenship, Rcitizenship))$estimate # pre citizenshipIntra-class korelácie pre 5 dimenzií HL
Overenie prítomnosti hierarchickej štruktúry v dátach, ktorá mohla vznikúť použitým spôsobom vzorkovania populácie (cluster sampling) Cluster = školská trieda (premenná ID3)
ICCs <- (lapply(rel.HLQ.na.rm[,c("theor_know", "prac_know", "crit_think", "self_aware", "citizenship")],
function(x){ICCest(ID3, x, rel.HLQ.na.rm)}))## NAs removed from rows:
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297## Warning in ICCest(ID3, x, rel.HLQ.na.rm):## Warning in ICCest(ID3, x, rel.HLQ.na.rm): 'x' has been coerced to a factor## NAs removed from rows:
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297## Warning in ICCest(ID3, x, rel.HLQ.na.rm):
## Warning in ICCest(ID3, x, rel.HLQ.na.rm): 'x' has been coerced to a factor## NAs removed from rows:
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297## Warning in ICCest(ID3, x, rel.HLQ.na.rm):
## Warning in ICCest(ID3, x, rel.HLQ.na.rm): 'x' has been coerced to a factor## NAs removed from rows:
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297## Warning in ICCest(ID3, x, rel.HLQ.na.rm):
## Warning in ICCest(ID3, x, rel.HLQ.na.rm): 'x' has been coerced to a factor## NAs removed from rows:
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297## Warning in ICCest(ID3, x, rel.HLQ.na.rm):
## Warning in ICCest(ID3, x, rel.HLQ.na.rm): 'x' has been coerced to a factorICCs$theor_know$ICC # ICC pre theor_know## [1] 0.1361273ICCs$prac_know$ICC # ICC pre prac_know## [1] 0.06576667ICCs$crit_think$ICC # ICC pre crit_think## [1] 0.1114863ICCs$self_aware$ICC # ICC pre self_aware## [1] 0.07610024ICCs$citizenship$ICC # ICC pre citizenship## [1] 0.05677021Intra-class korelácie už nie sú zanedbateľnéá (viď ICC pre theor_know a crit_think). Dáta majú už mierne hierarchickú štruktúru, čo bez použitia multi-level techník skresľuje odhady.
Alternatívny model
Špecifikácia jednofaktorového modelu
model2 <- '
HLQ =~ a*HL1 + b*HL2 + c*HL3 + d*HL4 + e*HL5 + f*HL6 + g*HL7 + h*HL8 + i*HL9 +
j*HL10 + k*HL11 + l*HL12 + m*HL13 + n*HL14 + o*HL15 + p*HL16 + q*HL17 + r*HL18 +
s*HL19 + t*HL20 + u*HL21 + v*HL22 + x*HL23 + y*HL24
'Estimácia a test jednofaktorového modelu, podľa Muthén, 1984
fitted.model2 <- cfa(model = model2, data = data, meanstructure = TRUE, std.lv = TRUE, mimic = "Mplus",
estimator = "WLSMVS", test = "Satterthwaite", bootstrap = 5000,
ordered = c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9", "HL10",
"HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17", "HL18", "HL19",
"HL20", "HL21", "HL22", "HL23", "HL24"))## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL2 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL4 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL10 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL16 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL1## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL3 x
## HL2## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL5 x
## HL3## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL7## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL7## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL9## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL15 x
## HL10## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL10## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL11## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL13## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL17 x
## HL14## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL16 x
## HL15## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL15## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL16## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL18 x
## HL17## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL23 x
## HL17## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL20 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL23 x
## HL18## Warning in pc_cor_TS(fit.y1 = UNI[[i]], fit.y2 = UNI[[j]], method =
## optim.method, : lavaan WARNING: empty cell(s) in bivariate table of HL24 x
## HL19Počet buniek HLa x HLb s nulovou frekvenciou je tak na hrane. Majú malú vzorku a tým pádom nízke frekvencie v rámci odpoveďových kategórií.
Test modelu, odhady voľných parametrov (faktorové náboje)
Stačí si všímať “Robust” test, Latent variable, Covariances a R-square. Intercepts, Thresholds, Intercepts (…) môžte kľudne ignorovať.
summary(fitted.model2, standardized = TRUE, rsquare = TRUE)## lavaan (0.5-22) converged normally after 15 iterations
##
## Number of observations 192
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 342.956 153.181
## Degrees of freedom 252 69
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 2.239
## for the mean and variance adjusted correction (WLSMV)
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HLQ =~
## HL1 (a) 0.662 0.050 13.200 0.000 0.662 0.662
## HL2 (b) 0.566 0.058 9.776 0.000 0.566 0.566
## HL3 (c) 0.583 0.058 10.057 0.000 0.583 0.583
## HL4 (d) 0.611 0.054 11.228 0.000 0.611 0.611
## HL5 (e) 0.568 0.060 9.401 0.000 0.568 0.568
## HL6 (f) 0.425 0.067 6.305 0.000 0.425 0.425
## HL7 (g) 0.594 0.052 11.430 0.000 0.594 0.594
## HL8 (h) 0.677 0.047 14.543 0.000 0.677 0.677
## HL9 (i) 0.636 0.047 13.439 0.000 0.636 0.636
## HL10 (j) 0.666 0.043 15.461 0.000 0.666 0.666
## HL11 (k) 0.513 0.056 9.145 0.000 0.513 0.513
## HL12 (l) 0.582 0.056 10.413 0.000 0.582 0.582
## HL13 (m) 0.642 0.050 12.975 0.000 0.642 0.642
## HL14 (n) 0.490 0.061 8.082 0.000 0.490 0.490
## HL15 (o) 0.642 0.046 13.816 0.000 0.642 0.642
## HL16 (p) 0.646 0.051 12.584 0.000 0.646 0.646
## HL17 (q) 0.546 0.057 9.511 0.000 0.546 0.546
## HL18 (r) 0.692 0.044 15.579 0.000 0.692 0.692
## HL19 (s) 0.596 0.055 10.751 0.000 0.596 0.596
## HL20 (t) 0.689 0.046 15.043 0.000 0.689 0.689
## HL21 (u) 0.635 0.050 12.711 0.000 0.635 0.635
## HL22 (v) 0.518 0.062 8.360 0.000 0.518 0.518
## HL23 (x) 0.665 0.046 14.590 0.000 0.665 0.665
## HL24 (y) 0.704 0.047 14.918 0.000 0.704 0.704
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.000 0.000 0.000
## .HL2 0.000 0.000 0.000
## .HL3 0.000 0.000 0.000
## .HL4 0.000 0.000 0.000
## .HL5 0.000 0.000 0.000
## .HL6 0.000 0.000 0.000
## .HL7 0.000 0.000 0.000
## .HL8 0.000 0.000 0.000
## .HL9 0.000 0.000 0.000
## .HL10 0.000 0.000 0.000
## .HL11 0.000 0.000 0.000
## .HL12 0.000 0.000 0.000
## .HL13 0.000 0.000 0.000
## .HL14 0.000 0.000 0.000
## .HL15 0.000 0.000 0.000
## .HL16 0.000 0.000 0.000
## .HL17 0.000 0.000 0.000
## .HL18 0.000 0.000 0.000
## .HL19 0.000 0.000 0.000
## .HL20 0.000 0.000 0.000
## .HL21 0.000 0.000 0.000
## .HL22 0.000 0.000 0.000
## .HL23 0.000 0.000 0.000
## .HL24 0.000 0.000 0.000
## HLQ 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HL1|t1 -1.176 0.118 -10.000 0.000 -1.176 -1.176
## HL1|t2 0.776 0.101 7.660 0.000 0.776 0.776
## HL2|t1 -1.101 0.114 -9.677 0.000 -1.101 -1.101
## HL2|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL3|t1 -0.759 0.101 -7.525 0.000 -0.759 -0.759
## HL3|t2 0.887 0.105 8.455 0.000 0.887 0.887
## HL4|t1 -0.849 0.104 -8.194 0.000 -0.849 -0.849
## HL4|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL5|t1 -1.258 0.122 -10.291 0.000 -1.258 -1.258
## HL5|t2 0.549 0.096 5.724 0.000 0.549 0.549
## HL6|t1 -1.078 0.113 -9.563 0.000 -1.078 -1.078
## HL6|t2 0.264 0.092 2.876 0.004 0.264 0.264
## HL7|t1 -0.579 0.096 -6.005 0.000 -0.579 -0.579
## HL7|t2 0.967 0.108 8.964 0.000 0.967 0.967
## HL8|t1 -0.549 0.096 -5.724 0.000 -0.549 -0.549
## HL8|t2 1.010 0.110 9.209 0.000 1.010 1.010
## HL9|t1 -0.725 0.100 -7.253 0.000 -0.725 -0.725
## HL9|t2 0.759 0.101 7.525 0.000 0.759 0.759
## HL10|t1 -0.812 0.102 -7.929 0.000 -0.812 -0.812
## HL10|t2 1.010 0.110 9.209 0.000 1.010 1.010
## HL11|t1 -0.927 0.106 -8.712 0.000 -0.927 -0.927
## HL11|t2 0.674 0.099 6.841 0.000 0.674 0.674
## HL12|t1 -0.595 0.097 -6.145 0.000 -0.595 -0.595
## HL12|t2 1.032 0.111 9.329 0.000 1.032 1.032
## HL13|t1 -0.794 0.102 -7.795 0.000 -0.794 -0.794
## HL13|t2 0.907 0.106 8.584 0.000 0.907 0.907
## HL14|t1 -0.416 0.094 -4.449 0.000 -0.416 -0.416
## HL14|t2 0.776 0.101 7.660 0.000 0.776 0.776
## HL15|t1 -1.150 0.116 -9.896 0.000 -1.150 -1.150
## HL15|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL16|t1 -0.579 0.096 -6.005 0.000 -0.579 -0.579
## HL16|t2 1.176 0.118 10.000 0.000 1.176 1.176
## HL17|t1 -1.383 0.130 -10.602 0.000 -1.383 -1.383
## HL17|t2 0.092 0.091 1.008 0.314 0.092 0.092
## HL18|t1 -1.350 0.128 -10.535 0.000 -1.350 -1.350
## HL18|t2 0.078 0.091 0.864 0.388 0.078 0.078
## HL19|t1 -0.642 0.098 -6.564 0.000 -0.642 -0.642
## HL19|t2 0.927 0.106 8.712 0.000 0.927 0.927
## HL20|t1 -0.708 0.099 -7.116 0.000 -0.708 -0.708
## HL20|t2 0.988 0.109 9.087 0.000 0.988 0.988
## HL21|t1 -0.849 0.104 -8.194 0.000 -0.849 -0.849
## HL21|t2 0.794 0.102 7.795 0.000 0.794 0.794
## HL22|t1 -0.907 0.106 -8.584 0.000 -0.907 -0.907
## HL22|t2 0.610 0.097 6.285 0.000 0.610 0.610
## HL23|t1 -0.831 0.103 -8.062 0.000 -0.831 -0.831
## HL23|t2 0.831 0.103 8.062 0.000 0.831 0.831
## HL24|t1 -0.907 0.106 -8.584 0.000 -0.907 -0.907
## HL24|t2 0.947 0.107 8.838 0.000 0.947 0.947
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.562 0.562 0.562
## .HL2 0.680 0.680 0.680
## .HL3 0.660 0.660 0.660
## .HL4 0.626 0.626 0.626
## .HL5 0.678 0.678 0.678
## .HL6 0.819 0.819 0.819
## .HL7 0.647 0.647 0.647
## .HL8 0.542 0.542 0.542
## .HL9 0.595 0.595 0.595
## .HL10 0.556 0.556 0.556
## .HL11 0.737 0.737 0.737
## .HL12 0.661 0.661 0.661
## .HL13 0.587 0.587 0.587
## .HL14 0.760 0.760 0.760
## .HL15 0.588 0.588 0.588
## .HL16 0.583 0.583 0.583
## .HL17 0.702 0.702 0.702
## .HL18 0.521 0.521 0.521
## .HL19 0.645 0.645 0.645
## .HL20 0.525 0.525 0.525
## .HL21 0.596 0.596 0.596
## .HL22 0.732 0.732 0.732
## .HL23 0.558 0.558 0.558
## .HL24 0.504 0.504 0.504
## HLQ 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## HL1 1.000 1.000 1.000
## HL2 1.000 1.000 1.000
## HL3 1.000 1.000 1.000
## HL4 1.000 1.000 1.000
## HL5 1.000 1.000 1.000
## HL6 1.000 1.000 1.000
## HL7 1.000 1.000 1.000
## HL8 1.000 1.000 1.000
## HL9 1.000 1.000 1.000
## HL10 1.000 1.000 1.000
## HL11 1.000 1.000 1.000
## HL12 1.000 1.000 1.000
## HL13 1.000 1.000 1.000
## HL14 1.000 1.000 1.000
## HL15 1.000 1.000 1.000
## HL16 1.000 1.000 1.000
## HL17 1.000 1.000 1.000
## HL18 1.000 1.000 1.000
## HL19 1.000 1.000 1.000
## HL20 1.000 1.000 1.000
## HL21 1.000 1.000 1.000
## HL22 1.000 1.000 1.000
## HL23 1.000 1.000 1.000
## HL24 1.000 1.000 1.000
##
## R-Square:
## Estimate
## HL1 0.438
## HL2 0.320
## HL3 0.340
## HL4 0.374
## HL5 0.322
## HL6 0.181
## HL7 0.353
## HL8 0.458
## HL9 0.405
## HL10 0.444
## HL11 0.263
## HL12 0.339
## HL13 0.413
## HL14 0.240
## HL15 0.412
## HL16 0.417
## HL17 0.298
## HL18 0.479
## HL19 0.355
## HL20 0.475
## HL21 0.404
## HL22 0.268
## HL23 0.442
## HL24 0.496Priemerny faktorovy naboj
mean(inspect(fitted.model2,what="std")$lambda)## [1] 0.6062187Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model2)## npar fmin
## 72.000 0.893
## chisq df
## 342.956 252.000
## pvalue chisq.scaled
## 0.000 153.181
## df.scaled pvalue.scaled
## 69.000 0.000
## chisq.scaling.factor baseline.chisq
## 2.239 7324.149
## baseline.df baseline.pvalue
## 276.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 746.920 28.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 9.806
## cfi tli
## 0.987 0.986
## nnfi rfi
## 0.986 0.949
## nfi pnfi
## 0.953 0.870
## ifi rni
## 0.987 0.987
## cfi.scaled tli.scaled
## 0.883 0.952
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.952 NA
## rfi.scaled nfi.scaled
## 0.917 0.795
## ifi.scaled rni.scaled
## 0.795 0.988
## rni.robust rmsea
## NA 0.043
## rmsea.ci.lower rmsea.ci.upper
## 0.031 0.055
## rmsea.pvalue rmsea.scaled
## 0.825 0.080
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.069 0.091
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.077
## rmr_nomean srmr
## 0.080 0.077
## srmr_bentler srmr_bentler_nomean
## 0.077 0.080
## srmr_bollen srmr_bollen_nomean
## 0.077 0.080
## srmr_mplus srmr_mplus_nomean
## 0.077 0.080
## cn_05 cn_01
## 162.523 172.057
## gfi agfi
## 0.967 0.957
## pgfi mfi
## 0.752 0.788Chi^2 test rozdielov medzi modelmi - p-hodnota
pchisq((fitted.model2@test[[2]]$stat - fitted.model@test[[2]]$stat),
(fitted.model2@test[[1]]$df - fitted.model@test[[1]]$df),
lower.tail = FALSE)## [1] 0.8565412Diagram
semPaths(fitted.model2, style = "mx", layout = "circle",
edge.label.cex = 0.5, sizeLat = 5, nCharNodes = 0,
nDigits = 2, "Standardized",
intercepts = FALSE, residuals = TRUE, exoVar = FALSE,
fade = TRUE, groups = "latents", pastel = TRUE)
Test modelu indikuje prítomnosť chybnej špecifikácie modelu. Popri indexoch blízkej zhody je preto potrebné analyzovať lokálne zdroje chybnej špecifikácie na základe matice reziduálnych korelácií
Matica reziduálnych korelácií
residuals.m2 <- residuals(fitted.model2, type = "cor" )$cor
residuals.m2## HL1 HL2 HL3 HL4 HL5 HL6 HL7 HL8 HL9 HL10
## HL1 0.000
## HL2 0.078 0.000
## HL3 0.001 0.184 0.000
## HL4 0.189 0.134 0.083 0.000
## HL5 0.072 0.111 0.110 0.063 0.000
## HL6 -0.052 0.168 0.036 0.140 0.177 0.000
## HL7 -0.016 0.018 -0.094 0.022 0.001 0.079 0.000
## HL8 -0.063 -0.197 -0.097 -0.108 0.003 -0.028 0.035 0.000
## HL9 -0.023 -0.214 -0.084 -0.131 -0.014 -0.076 -0.031 0.031 0.000
## HL10 0.016 0.059 -0.067 0.071 0.048 0.149 0.017 -0.006 0.029 0.000
## HL11 0.049 -0.157 -0.116 -0.033 -0.062 -0.179 0.102 0.051 0.118 -0.072
## HL12 -0.123 -0.043 -0.048 0.001 -0.111 0.009 0.074 0.175 -0.043 0.128
## HL13 -0.025 0.022 0.144 -0.033 -0.030 -0.048 -0.011 -0.004 -0.055 -0.053
## HL14 -0.063 -0.171 0.065 -0.198 -0.059 -0.066 -0.136 0.059 0.172 -0.011
## HL15 -0.027 -0.007 0.013 -0.076 -0.003 -0.023 -0.064 -0.091 0.057 -0.012
## HL16 0.047 -0.133 0.059 -0.043 0.033 -0.164 -0.066 0.101 0.093 -0.060
## HL17 0.093 0.050 -0.039 0.033 -0.193 0.037 -0.026 -0.127 -0.036 0.013
## HL18 -0.031 -0.020 -0.063 -0.077 -0.102 0.020 0.003 0.036 0.039 -0.096
## HL19 0.029 -0.010 -0.121 -0.011 -0.066 -0.205 0.100 0.020 -0.028 0.005
## HL20 -0.031 -0.036 -0.003 -0.038 -0.181 -0.084 0.022 0.041 0.006 -0.003
## HL21 -0.169 -0.035 -0.032 -0.047 0.094 -0.055 -0.016 0.013 -0.072 -0.058
## HL22 -0.073 0.020 0.031 -0.031 0.099 -0.014 0.018 -0.060 -0.037 -0.098
## HL23 0.022 0.007 -0.018 -0.045 -0.051 -0.011 -0.051 -0.105 0.099 -0.061
## HL24 0.000 -0.011 -0.031 -0.035 0.002 -0.108 -0.032 -0.047 -0.022 -0.001
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1
## HL2
## HL3
## HL4
## HL5
## HL6
## HL7
## HL8
## HL9
## HL10
## HL11 0.000
## HL12 0.109 0.000
## HL13 0.047 0.129 0.000
## HL14 -0.008 -0.017 0.020 0.000
## HL15 -0.080 -0.178 -0.037 0.159 0.000
## HL16 -0.008 0.028 0.037 -0.033 0.066 0.000
## HL17 -0.086 -0.151 -0.141 0.046 0.126 0.037 0.000
## HL18 0.081 -0.171 -0.156 -0.041 0.095 -0.038 0.192 0.000
## HL19 -0.024 0.012 -0.055 0.061 -0.045 -0.015 -0.144 -0.098 0.000
## HL20 -0.091 0.023 0.011 0.009 0.076 0.047 -0.136 -0.054 0.058 0.000
## HL21 -0.014 0.035 0.111 -0.055 0.086 -0.012 -0.114 -0.046 0.088 0.066
## HL22 0.049 -0.051 0.084 -0.094 -0.079 -0.206 0.014 0.064 0.042 0.059
## HL23 0.098 -0.065 0.011 0.049 -0.088 -0.076 0.089 0.051 -0.005 -0.064
## HL24 -0.067 -0.188 -0.118 0.017 -0.042 0.029 0.029 0.093 0.164 0.042
## HL21 HL22 HL23 HL24
## HL1
## HL2
## HL3
## HL4
## HL5
## HL6
## HL7
## HL8
## HL9
## HL10
## HL11
## HL12
## HL13
## HL14
## HL15
## HL16
## HL17
## HL18
## HL19
## HL20
## HL21 0.000
## HL22 0.080 0.000
## HL23 -0.133 0.130 0.000
## HL24 0.035 -0.043 0.103 0.000Pre prehladnejšiu vizualizáciu, matica reziduí s vyznačenými reziduálnymi hodnotami > .1 (štandardizované z-reziduá je možné odhadnúť iba v prípade použitia estimátora z rodiny maximum likelihood. Arbitrárna hodnota .1 preto, lebo neumožní produkt dvoch nábojov > .3)
Počet premenných
p = 24Ak máme v matici (p(p+1)/2 - p) = 300 elementov, tak
(p*(p+1)/2 - p)*.05## [1] 13.8z nich môže byť signifikantných na hladine alfa = .05
HRUBÁ APROXIMÁCIA - približne toľko elementov môže byť > .1 Diag = diagonála, >.1 = reziduálna hodnota vyššia ako .1
ifelse(residuals.m2 == 0, "Diag", ifelse(residuals.m2 > .1, ">.1", "."))## HL1 HL2 HL3 HL4 HL5 HL6 HL7 HL8 HL9 HL10
## HL1 "Diag" "." "." ">.1" "." "." "." "." "." "."
## HL2 "." "Diag" ">.1" ">.1" ">.1" ">.1" "." "." "." "."
## HL3 "." ">.1" "Diag" "." ">.1" "." "." "." "." "."
## HL4 ">.1" ">.1" "." "Diag" "." ">.1" "." "." "." "."
## HL5 "." ">.1" ">.1" "." "Diag" ">.1" "." "." "." "."
## HL6 "." ">.1" "." ">.1" ">.1" "Diag" "." "." "." ">.1"
## HL7 "." "." "." "." "." "." "Diag" "." "." "."
## HL8 "." "." "." "." "." "." "." "Diag" "." "."
## HL9 "." "." "." "." "." "." "." "." "Diag" "."
## HL10 "." "." "." "." "." ">.1" "." "." "." "Diag"
## HL11 "." "." "." "." "." "." ">.1" "." ">.1" "."
## HL12 "." "." "." "." "." "." "." ">.1" "." ">.1"
## HL13 "." "." ">.1" "." "." "." "." "." "." "."
## HL14 "." "." "." "." "." "." "." "." ">.1" "."
## HL15 "." "." "." "." "." "." "." "." "." "."
## HL16 "." "." "." "." "." "." "." ">.1" "." "."
## HL17 "." "." "." "." "." "." "." "." "." "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL19 "." "." "." "." "." "." ">.1" "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1 "." "." "." "." "." "." "." "." "." "."
## HL2 "." "." "." "." "." "." "." "." "." "."
## HL3 "." "." ">.1" "." "." "." "." "." "." "."
## HL4 "." "." "." "." "." "." "." "." "." "."
## HL5 "." "." "." "." "." "." "." "." "." "."
## HL6 "." "." "." "." "." "." "." "." "." "."
## HL7 ">.1" "." "." "." "." "." "." "." ">.1" "."
## HL8 "." ">.1" "." "." "." ">.1" "." "." "." "."
## HL9 ">.1" "." "." ">.1" "." "." "." "." "." "."
## HL10 "." ">.1" "." "." "." "." "." "." "." "."
## HL11 "Diag" ">.1" "." "." "." "." "." "." "." "."
## HL12 ">.1" "Diag" ">.1" "." "." "." "." "." "." "."
## HL13 "." ">.1" "Diag" "." "." "." "." "." "." "."
## HL14 "." "." "." "Diag" ">.1" "." "." "." "." "."
## HL15 "." "." "." ">.1" "Diag" "." ">.1" "." "." "."
## HL16 "." "." "." "." "." "Diag" "." "." "." "."
## HL17 "." "." "." "." ">.1" "." "Diag" ">.1" "." "."
## HL18 "." "." "." "." "." "." ">.1" "Diag" "." "."
## HL19 "." "." "." "." "." "." "." "." "Diag" "."
## HL20 "." "." "." "." "." "." "." "." "." "Diag"
## HL21 "." "." ">.1" "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." ">.1" "."
## HL21 HL22 HL23 HL24
## HL1 "." "." "." "."
## HL2 "." "." "." "."
## HL3 "." "." "." "."
## HL4 "." "." "." "."
## HL5 "." "." "." "."
## HL6 "." "." "." "."
## HL7 "." "." "." "."
## HL8 "." "." "." "."
## HL9 "." "." "." "."
## HL10 "." "." "." "."
## HL11 "." "." "." "."
## HL12 "." "." "." "."
## HL13 ">.1" "." "." "."
## HL14 "." "." "." "."
## HL15 "." "." "." "."
## HL16 "." "." "." "."
## HL17 "." "." "." "."
## HL18 "." "." "." "."
## HL19 "." "." "." ">.1"
## HL20 "." "." "." "."
## HL21 "Diag" "." "." "."
## HL22 "." "Diag" ">.1" "."
## HL23 "." ">.1" "Diag" ">.1"
## HL24 "." "." ">.1" "Diag"Odhad reliability
Odhad internej konzistencie - Cronbachova alfa
alpha(rel.HLQ.na.rm[,1:24])$total$std.alpha # pre jednofaktorovu (24 polozkovu) skalu## [1] 0.9290116Odhad stability v čase
Výpočet sumárneho skóre HLQ
rel.HLQ.na.rm$HLQ_sum <- rowMeans(rel.HLQ.na.rm[,c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9",
"HL10", "HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17",
"HL18", "HL19", "HL20", "HL21", "HL22", "HL23", "HL24")], na.rm = TRUE)Výpočet sumárného skóre retestu RHLQ
#rel.HLQ.na.rm$RHLQ_sum <- rowMeans(rel.HLQ.na.rm[,c("RHL1", "RHL2", "RHL3", "RHL4", "RHL5", "RHL6", "RHL7", "RHL8", "RHL9",
# "RHL10", "RHL11", "RHL12", "RHL13", "RHL14", "RHL15", "RHL16", "RHL17",
# "RHL18", "RHL19", "RHL20", "RHL21", "RHL22", "RHL23", "RHL24")], na.rm = TRUE)Test-retest korelácia
#with(rel.HLQ.na.rm, cor.test(HLQ_sum, RHLQ_sum))$estimate # pre jednofaktorovu (24 polozkovu) skalu