HLQ_CFA_Suomi.R
ivanropovik
Mon Feb 6 10:34:22 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 == 1) %>% select(HL1:HL25)
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] "0.23%"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", "HL25"), m = 1)## -- Imputation 1 --
##
## 1 2 3 4 5 6 7 8 9 10 11data <- 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", "HL25")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", "HL25")],
function(x){table(x, useNA = "ifany")})## $HL1
## x
## 1 2 3 4
## 1 3 80 90
##
## $HL2
## x
## 1 2 3 4
## 1 9 61 103
##
## $HL3
## x
## 1 2 3 4
## 7 20 83 64
##
## $HL4
## x
## 1 2 3 4
## 2 13 65 94
##
## $HL5
## x
## 1 2 3 4
## 2 8 76 88
##
## $HL6
## x
## 1 2 3 4
## 4 15 57 98
##
## $HL7
## x
## 1 2 3 4
## 2 13 81 78
##
## $HL8
## x
## 1 2 3 4
## 2 27 83 62
##
## $HL9
## x
## 1 2 3 4
## 2 20 82 70
##
## $HL10
## x
## 1 2 3 4
## 1 13 74 86
##
## $HL11
## x
## 1 2 3 4
## 1 11 81 81
##
## $HL12
## x
## 1 2 3 4
## 1 17 91 65
##
## $HL13
## x
## 1 2 3 4
## 11 41 76 46
##
## $HL14
## x
## 1 2 3 4
## 1 7 38 128
##
## $HL15
## x
## 1 2 3 4
## 2 15 63 94
##
## $HL16
## x
## 1 2 3 4
## 2 19 84 69
##
## $HL17
## x
## 1 2 3 4
## 1 10 50 113
##
## $HL18
## x
## 1 2 3 4
## 1 11 63 99
##
## $HL19
## x
## 1 2 3 4
## 4 23 85 62
##
## $HL20
## x
## 1 2 3 4
## 3 15 82 74
##
## $HL21
## x
## 1 2 3 4
## 2 17 84 71
##
## $HL22
## x
## 2 3 4
## 16 69 89
##
## $HL23
## x
## 1 2 3 4
## 1 20 82 71
##
## $HL24
## x
## 2 3 4
## 13 80 81
##
## $HL25
## x
## 2 3 4
## 13 59 102Finálne dáta
#View(data)Deskriptívna analýza
Deskriptívne štatistiky
describe(data, na.rm = TRUE, skew = TRUE, ranges = FALSE)## vars n mean sd skew kurtosis se
## HL1 1 174 3.49 0.57 -0.71 0.57 0.04
## HL2 2 174 3.53 0.62 -1.10 0.79 0.05
## HL3 3 174 3.17 0.79 -0.81 0.37 0.06
## HL4 4 174 3.44 0.68 -1.03 0.65 0.05
## HL5 5 174 3.44 0.64 -0.95 0.99 0.05
## HL6 6 174 3.43 0.75 -1.21 0.95 0.06
## HL7 7 174 3.35 0.67 -0.77 0.41 0.05
## HL8 8 174 3.18 0.73 -0.46 -0.42 0.06
## HL9 9 174 3.26 0.70 -0.61 -0.09 0.05
## HL10 10 174 3.41 0.65 -0.77 0.05 0.05
## HL11 11 174 3.39 0.63 -0.67 0.10 0.05
## HL12 12 174 3.26 0.65 -0.45 -0.19 0.05
## HL13 13 174 2.90 0.86 -0.40 -0.55 0.07
## HL14 14 174 3.68 0.58 -1.82 3.10 0.04
## HL15 15 174 3.43 0.70 -1.01 0.47 0.05
## HL16 16 174 3.26 0.70 -0.61 -0.02 0.05
## HL17 17 174 3.58 0.63 -1.34 1.26 0.05
## HL18 18 174 3.49 0.64 -1.02 0.49 0.05
## HL19 19 174 3.18 0.74 -0.63 0.07 0.06
## HL20 20 174 3.30 0.70 -0.79 0.49 0.05
## HL21 21 174 3.29 0.69 -0.65 0.11 0.05
## HL22 22 174 3.42 0.66 -0.68 -0.60 0.05
## HL23 23 174 3.28 0.69 -0.53 -0.38 0.05
## HL24 24 174 3.39 0.62 -0.51 -0.67 0.05
## HL25 25 174 3.51 0.63 -0.92 -0.23 0.05Veľkosť vzorky
nrow(data)## [1] 174Redukcia počtu kategórií v prípade nízkej frekvencie odpoveďovej kategórie
(odhad polychorickej kovariančnej matice predpokladá absenciu buniek HLa x HLb s nulovou frekvenciou)
data$HL1 <- ifelse(data$HL1 == 1, yes = 2, no = data$HL1)
data$HL1 <- ifelse(data$HL1 == 2, yes = 3, no = data$HL1)
data$HL2 <- ifelse(data$HL2 == 1, yes = 2, no = data$HL2)
data$HL2 <- ifelse(data$HL2 == 2, yes = 3, 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$HL14)
data$HL14 <- ifelse(data$HL14 == 2, yes = 3, no = data$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)
data$HL25 <- ifelse(data$HL25 == 1, yes = 2, no = data$HL25)Matica polychorických korelácií
polychoric.cor <- polychoric(data, correct = FALSE, smooth = TRUE,
global = FALSE, na.rm = TRUE)## Warning in cor.smooth(mat): Matrix was not positive definite, smoothing was
## doneround(polychoric.cor$rho, 2)## HL1 HL2 HL3 HL4 HL5 HL6 HL7 HL8 HL9 HL10 HL11 HL12 HL13 HL14
## HL1 1.00 0.69 0.37 0.48 0.55 0.37 0.37 0.49 0.52 0.53 0.58 0.54 0.48 0.34
## HL2 0.69 1.00 0.59 0.44 0.66 0.53 0.23 0.36 0.42 0.54 0.51 0.54 0.38 0.35
## HL3 0.37 0.59 1.00 0.58 0.62 0.49 0.31 0.62 0.56 0.57 0.62 0.65 0.51 0.53
## HL4 0.48 0.44 0.58 1.00 0.75 0.56 0.44 0.55 0.64 0.67 0.47 0.62 0.39 0.50
## HL5 0.55 0.66 0.62 0.75 1.00 0.63 0.37 0.58 0.58 0.61 0.57 0.55 0.41 0.41
## HL6 0.37 0.53 0.49 0.56 0.63 1.00 0.20 0.45 0.41 0.46 0.56 0.62 0.38 0.43
## HL7 0.37 0.23 0.31 0.44 0.37 0.20 1.00 0.49 0.50 0.46 0.42 0.59 0.48 0.44
## HL8 0.49 0.36 0.62 0.55 0.58 0.45 0.49 1.00 0.77 0.55 0.62 0.64 0.63 0.43
## HL9 0.52 0.42 0.56 0.64 0.58 0.41 0.50 0.77 1.00 0.64 0.59 0.66 0.63 0.52
## HL10 0.53 0.54 0.57 0.67 0.61 0.46 0.46 0.55 0.64 1.00 0.66 0.67 0.50 0.57
## HL11 0.58 0.51 0.62 0.47 0.57 0.56 0.42 0.62 0.59 0.66 1.00 0.71 0.50 0.56
## HL12 0.54 0.54 0.65 0.62 0.55 0.62 0.59 0.64 0.66 0.67 0.71 1.00 0.67 0.56
## HL13 0.48 0.38 0.51 0.39 0.41 0.38 0.48 0.63 0.63 0.50 0.50 0.67 1.00 0.44
## HL14 0.34 0.35 0.53 0.50 0.41 0.43 0.44 0.43 0.52 0.57 0.56 0.56 0.44 1.00
## HL15 0.42 0.39 0.58 0.48 0.53 0.43 0.52 0.57 0.52 0.53 0.60 0.57 0.67 0.75
## HL16 0.53 0.50 0.50 0.42 0.52 0.37 0.51 0.54 0.53 0.61 0.52 0.66 0.70 0.51
## HL17 0.47 0.57 0.44 0.40 0.50 0.57 0.26 0.49 0.46 0.59 0.55 0.52 0.48 0.59
## HL18 0.48 0.54 0.56 0.43 0.47 0.43 0.48 0.65 0.54 0.64 0.57 0.63 0.44 0.57
## HL19 0.41 0.34 0.57 0.40 0.53 0.35 0.53 0.58 0.67 0.54 0.46 0.65 0.61 0.45
## HL20 0.53 0.54 0.60 0.54 0.66 0.50 0.52 0.68 0.74 0.68 0.62 0.68 0.56 0.61
## HL21 0.53 0.50 0.55 0.52 0.67 0.51 0.55 0.65 0.64 0.57 0.59 0.71 0.69 0.56
## HL22 0.38 0.26 0.48 0.54 0.51 0.42 0.51 0.62 0.56 0.51 0.50 0.57 0.56 0.55
## HL23 0.44 0.46 0.49 0.53 0.56 0.45 0.40 0.58 0.59 0.58 0.56 0.65 0.63 0.52
## HL24 0.60 0.40 0.58 0.62 0.63 0.40 0.53 0.73 0.69 0.62 0.59 0.64 0.58 0.66
## HL25 0.35 0.30 0.53 0.63 0.59 0.42 0.50 0.64 0.65 0.53 0.44 0.60 0.53 0.79
## HL15 HL16 HL17 HL18 HL19 HL20 HL21 HL22 HL23 HL24 HL25
## HL1 0.42 0.53 0.47 0.48 0.41 0.53 0.53 0.38 0.44 0.60 0.35
## HL2 0.39 0.50 0.57 0.54 0.34 0.54 0.50 0.26 0.46 0.40 0.30
## HL3 0.58 0.50 0.44 0.56 0.57 0.60 0.55 0.48 0.49 0.58 0.53
## HL4 0.48 0.42 0.40 0.43 0.40 0.54 0.52 0.54 0.53 0.62 0.63
## HL5 0.53 0.52 0.50 0.47 0.53 0.66 0.67 0.51 0.56 0.63 0.59
## HL6 0.43 0.37 0.57 0.43 0.35 0.50 0.51 0.42 0.45 0.40 0.42
## HL7 0.52 0.51 0.26 0.48 0.53 0.52 0.55 0.51 0.40 0.53 0.50
## HL8 0.57 0.54 0.49 0.65 0.58 0.68 0.65 0.62 0.58 0.73 0.64
## HL9 0.52 0.53 0.46 0.54 0.67 0.74 0.64 0.56 0.59 0.69 0.65
## HL10 0.53 0.61 0.59 0.64 0.54 0.68 0.57 0.51 0.58 0.62 0.53
## HL11 0.60 0.52 0.55 0.57 0.46 0.62 0.59 0.50 0.56 0.59 0.44
## HL12 0.57 0.66 0.52 0.63 0.65 0.68 0.71 0.57 0.65 0.64 0.60
## HL13 0.67 0.70 0.48 0.44 0.61 0.56 0.69 0.56 0.63 0.58 0.53
## HL14 0.75 0.51 0.59 0.57 0.45 0.61 0.56 0.55 0.52 0.66 0.79
## HL15 1.00 0.67 0.56 0.50 0.49 0.59 0.65 0.52 0.59 0.65 0.66
## HL16 0.67 1.00 0.62 0.63 0.60 0.67 0.68 0.47 0.58 0.60 0.54
## HL17 0.56 0.62 1.00 0.62 0.41 0.63 0.47 0.45 0.52 0.54 0.55
## HL18 0.50 0.63 0.62 1.00 0.55 0.69 0.55 0.53 0.51 0.69 0.55
## HL19 0.49 0.60 0.41 0.55 1.00 0.69 0.70 0.55 0.68 0.70 0.56
## HL20 0.59 0.67 0.63 0.69 0.69 1.00 0.82 0.73 0.73 0.73 0.68
## HL21 0.65 0.68 0.47 0.55 0.70 0.82 1.00 0.70 0.71 0.69 0.60
## HL22 0.52 0.47 0.45 0.53 0.55 0.73 0.70 1.00 0.76 0.60 0.66
## HL23 0.59 0.58 0.52 0.51 0.68 0.73 0.71 0.76 1.00 0.72 0.66
## HL24 0.65 0.60 0.54 0.69 0.70 0.73 0.69 0.60 0.72 1.00 0.81
## HL25 0.66 0.54 0.55 0.55 0.56 0.68 0.60 0.66 0.66 0.81 1.00Priemerná korelácia
polychoric.cor.low <- polychoric.cor$rho[lower.tri(polychoric.cor$rho)]
mean(abs(polychoric.cor.low))## [1] 0.5495773Výpočet 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 + e*HL25
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", "HL25"))## 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 HL25 x
## HL8## 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
## HL8## 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
## HL8## 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 HL22 x
## HL8## 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
## HL8## 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
## HL8## 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 HL12 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
## HL25## 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 HL12 x
## HL25## 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
## HL25## 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
## HL25## 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
## HL25## 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
## HL4## 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 HL12 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 HL24 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 HL20 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 HL12 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 HL13 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 HL16 x
## HL12## 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 HL21 x
## HL12## 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
## HL12## 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
## HL12## 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 HL22 x
## HL12## 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
## HL12## 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
## HL21## 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 HL22 x
## HL21## 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
## HL21## 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
## HL21## 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
## 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 HL13 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 HL20 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
## 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 HL13 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 HL13 x
## HL19## 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
## HL22## 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
## HL22## 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
## HL22## 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
## HL13## 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]]$df
alfa <- .05
n <- nrow(data)
rmsea0 <- .05 # RMSEA za predpokladu H0
rmseaa <- .08 # RMSEA za predpokladu H1
ncp0 <- (n-1)*df*rmsea0**2 ;
ncpa <-(n-1)*df*rmseaa**2 ;
if(rmsea0 < rmseaa) {
cval <- qchisq(1-alfa,df=df,ncp=ncp0)
sila.rmsea <- 1 - pchisq(cval,df=df,ncp=ncpa)
} else {
cval <- qchisq(alfa,df=df,ncp=ncp0)
sila.rmsea <- pchisq(cval,df=df,ncp=ncpa)
}
rm(ncp0, ncpa, cval)
print(round(sila.rmsea,10))## [1] 0.9994319Výsledky
Kovariančná matica je non-positive definite z dôvodu, že viaceré z definovaných latentných premenných su kolineárne (de facto identické). Štyri z korelácií v rámci štrukturálneho modelu sú väčšie ako 1.
eigen(inspect(fitted.model, "cov.lv") )$values## [1] 4.93376296 0.14667059 0.02932833 -0.02102687 -0.08873501Štvrtá a piata eigenvalue majú negatívnu ale relatívne nízku hodnotu, výsledky testu modelu sú ako-tak 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 49 iterations
##
## Number of observations 174
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 333.517 123.899
## Degrees of freedom 265 51
## P-value (Chi-square) 0.003 0.000
## Scaling correction factor 2.692
## 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 0.061 10.637 0.000 0.653 0.653
## HL8 (b) 0.815 0.034 23.930 0.000 0.815 0.815
## HL14 (c) 0.755 0.058 13.012 0.000 0.755 0.755
## HL18 (d) 0.757 0.041 18.439 0.000 0.757 0.757
## HL25 (e) 0.812 0.035 23.049 0.000 0.812 0.812
## prac_know =~
## HL2 (f) 0.679 0.063 10.845 0.000 0.679 0.679
## HL4 (g) 0.771 0.040 19.121 0.000 0.771 0.771
## HL6 (h) 0.659 0.056 11.744 0.000 0.659 0.659
## HL10 (i) 0.829 0.033 24.758 0.000 0.829 0.829
## HL17 (j) 0.729 0.051 14.414 0.000 0.729 0.729
## crit_think =~
## HL7 (k) 0.605 0.053 11.461 0.000 0.605 0.605
## HL12 (l) 0.835 0.031 26.634 0.000 0.835 0.835
## HL16 (m) 0.765 0.040 19.230 0.000 0.765 0.765
## HL21 (n) 0.849 0.025 33.371 0.000 0.849 0.849
## HL24 (o) 0.853 0.025 33.779 0.000 0.853 0.853
## self_aware =~
## HL5 (p) 0.734 0.046 15.862 0.000 0.734 0.734
## HL11 (q) 0.723 0.044 16.457 0.000 0.723 0.723
## HL15 (r) 0.729 0.042 17.266 0.000 0.729 0.729
## HL19 (s) 0.729 0.038 19.347 0.000 0.729 0.729
## HL22 (t) 0.728 0.041 17.651 0.000 0.728 0.728
## citizenship =~
## HL3 (u) 0.717 0.042 16.971 0.000 0.717 0.717
## HL9 (v) 0.806 0.033 24.338 0.000 0.806 0.806
## HL13 (x) 0.734 0.042 17.341 0.000 0.734 0.734
## HL20 (y) 0.873 0.025 34.241 0.000 0.873 0.873
## HL23 (z) 0.796 0.035 22.938 0.000 0.796 0.796
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## theor_know ~~
## prac_know 0.916 0.032 28.595 0.000 0.916 0.916
## crit_think 0.987 0.022 45.602 0.000 0.987 0.987
## self_aware 1.011 0.021 47.057 0.000 1.011 1.011
## citizenship 0.989 0.022 44.847 0.000 0.989 0.989
## prac_know ~~
## crit_think 0.887 0.034 26.384 0.000 0.887 0.887
## self_aware 0.985 0.031 31.378 0.000 0.985 0.985
## citizenship 0.909 0.030 30.490 0.000 0.909 0.909
## crit_think ~~
## self_aware 1.048 0.018 57.067 0.000 1.048 1.048
## citizenship 1.022 0.018 57.915 0.000 1.022 1.022
## self_aware ~~
## citizenship 1.072 0.019 56.762 0.000 1.072 1.072
##
## 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
## .HL25 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 -0.043 0.095 -0.454 0.650 -0.043 -0.043
## HL8|t1 -0.967 0.113 -8.531 0.000 -0.967 -0.967
## HL8|t2 0.368 0.098 3.771 0.000 0.368 0.368
## HL14|t1 -0.630 0.102 -6.146 0.000 -0.630 -0.630
## HL18|t1 -1.484 0.145 -10.222 0.000 -1.484 -1.484
## HL18|t2 -0.174 0.096 -1.813 0.070 -0.174 -0.174
## HL25|t1 -1.442 0.142 -10.178 0.000 -1.442 -1.442
## HL25|t2 -0.218 0.096 -2.266 0.023 -0.218 -0.218
## HL2|t1 -0.233 0.096 -2.417 0.016 -0.233 -0.233
## HL4|t1 -1.364 0.136 -10.056 0.000 -1.364 -1.364
## HL4|t2 -0.101 0.095 -1.058 0.290 -0.101 -0.101
## HL6|t1 -1.231 0.127 -9.709 0.000 -1.231 -1.231
## HL6|t2 -0.159 0.096 -1.662 0.096 -0.159 -0.159
## HL10|t1 -1.402 0.139 -10.122 0.000 -1.402 -1.402
## HL10|t2 0.014 0.095 0.151 0.880 0.014 0.014
## HL17|t1 -1.528 0.149 -10.250 0.000 -1.528 -1.528
## HL17|t2 -0.384 0.098 -3.921 0.000 -0.384 -0.384
## HL7|t1 -1.364 0.136 -10.056 0.000 -1.364 -1.364
## HL7|t2 0.130 0.096 1.360 0.174 0.130 0.130
## HL12|t1 -1.262 0.129 -9.806 0.000 -1.262 -1.262
## HL12|t2 0.322 0.097 3.320 0.001 0.322 0.322
## HL16|t1 -1.172 0.123 -9.501 0.000 -1.172 -1.172
## HL16|t2 0.262 0.096 2.718 0.007 0.262 0.262
## HL21|t1 -1.231 0.127 -9.709 0.000 -1.231 -1.231
## HL21|t2 0.233 0.096 2.417 0.016 0.233 0.233
## HL24|t1 -1.442 0.142 -10.178 0.000 -1.442 -1.442
## HL24|t2 0.087 0.095 0.907 0.364 0.087 0.087
## HL5|t1 -1.576 0.154 -10.259 0.000 -1.576 -1.576
## HL5|t2 -0.014 0.095 -0.151 0.880 -0.014 -0.014
## HL11|t1 -1.484 0.145 -10.222 0.000 -1.484 -1.484
## HL11|t2 0.087 0.095 0.907 0.364 0.087 0.087
## HL15|t1 -1.295 0.131 -9.896 0.000 -1.295 -1.295
## HL15|t2 -0.101 0.095 -1.058 0.290 -0.101 -0.101
## HL19|t1 -1.014 0.115 -8.788 0.000 -1.014 -1.014
## HL19|t2 0.368 0.098 3.771 0.000 0.368 0.368
## HL22|t1 -1.329 0.133 -9.980 0.000 -1.329 -1.329
## HL22|t2 -0.029 0.095 -0.302 0.762 -0.029 -0.029
## HL3|t1 -1.014 0.115 -8.788 0.000 -1.014 -1.014
## HL3|t2 0.338 0.097 3.471 0.001 0.338 0.338
## HL9|t1 -1.143 0.122 -9.390 0.000 -1.143 -1.143
## HL9|t2 0.247 0.096 2.568 0.010 0.247 0.247
## HL13|t1 -0.528 0.100 -5.263 0.000 -0.528 -0.528
## HL13|t2 0.630 0.102 6.146 0.000 0.630 0.630
## HL20|t1 -1.262 0.129 -9.806 0.000 -1.262 -1.262
## HL20|t2 0.188 0.096 1.964 0.049 0.188 0.188
## HL23|t1 -1.172 0.123 -9.501 0.000 -1.172 -1.172
## HL23|t2 0.233 0.096 2.417 0.016 0.233 0.233
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.574 0.574 0.574
## .HL8 0.335 0.335 0.335
## .HL14 0.431 0.431 0.431
## .HL18 0.426 0.426 0.426
## .HL25 0.341 0.341 0.341
## .HL2 0.538 0.538 0.538
## .HL4 0.406 0.406 0.406
## .HL6 0.566 0.566 0.566
## .HL10 0.313 0.313 0.313
## .HL17 0.468 0.468 0.468
## .HL7 0.634 0.634 0.634
## .HL12 0.304 0.304 0.304
## .HL16 0.415 0.415 0.415
## .HL21 0.279 0.279 0.279
## .HL24 0.273 0.273 0.273
## .HL5 0.461 0.461 0.461
## .HL11 0.477 0.477 0.477
## .HL15 0.468 0.468 0.468
## .HL19 0.468 0.468 0.468
## .HL22 0.470 0.470 0.470
## .HL3 0.485 0.485 0.485
## .HL9 0.351 0.351 0.351
## .HL13 0.461 0.461 0.461
## .HL20 0.238 0.238 0.238
## .HL23 0.367 0.367 0.367
## 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
## HL25 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 0.426
## HL8 0.665
## HL14 0.569
## HL18 0.574
## HL25 0.659
## HL2 0.462
## HL4 0.594
## HL6 0.434
## HL10 0.687
## HL17 0.532
## HL7 0.366
## HL12 0.696
## HL16 0.585
## HL21 0.721
## HL24 0.727
## HL5 0.539
## HL11 0.523
## HL15 0.532
## HL19 0.532
## HL22 0.530
## HL3 0.515
## HL9 0.649
## HL13 0.539
## HL20 0.762
## HL23 0.633Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model)## npar fmin
## 82.000 0.958
## chisq df
## 333.517 265.000
## pvalue chisq.scaled
## 0.003 123.899
## df.scaled pvalue.scaled
## 51.000 0.000
## chisq.scaling.factor baseline.chisq
## 2.692 24290.950
## baseline.df baseline.pvalue
## 300.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 1140.130 14.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 21.305
## cfi tli
## 0.997 0.997
## nnfi rfi
## 0.997 0.984
## nfi pnfi
## 0.986 0.871
## ifi rni
## 0.997 0.997
## cfi.scaled tli.scaled
## 0.935 0.982
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.982 NA
## rfi.scaled nfi.scaled
## 0.970 0.891
## ifi.scaled rni.scaled
## 0.891 0.997
## rni.robust rmsea
## NA 0.039
## rmsea.ci.lower rmsea.ci.upper
## 0.024 0.051
## rmsea.pvalue rmsea.scaled
## 0.933 0.091
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.079 0.103
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.069
## rmr_nomean srmr
## 0.072 0.069
## srmr_bentler srmr_bentler_nomean
## 0.069 0.072
## srmr_bollen srmr_bollen_nomean
## 0.069 0.072
## srmr_mplus srmr_mplus_nomean
## 0.069 0.072
## cn_05 cn_01
## 158.674 167.755
## gfi agfi
## 0.987 0.984
## pgfi mfi
## 0.754 0.820Diagram
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 HL25 HL2 HL4 HL6 HL10 HL17
## HL1 0.000
## HL8 -0.040 0.000
## HL14 -0.155 -0.183 0.000
## HL18 -0.016 0.035 -0.003 0.000
## HL25 -0.183 -0.020 0.175 -0.063 0.000
## HL2 0.291 -0.143 -0.112 0.072 -0.204 0.000
## HL4 0.023 -0.024 -0.033 -0.102 0.060 -0.081 0.000
## HL6 -0.021 -0.044 -0.028 -0.032 -0.074 0.087 0.050 0.000
## HL10 0.031 -0.072 -0.004 0.069 -0.091 -0.017 0.029 -0.088 0.000
## HL17 0.030 -0.058 0.086 0.113 0.004 0.083 -0.163 0.088 -0.019 0.000
## HL7 -0.022 0.005 -0.012 0.030 0.013 -0.132 0.027 -0.155 0.012 -0.129
## HL12 0.003 -0.027 -0.058 0.006 -0.071 0.039 0.048 0.135 0.060 -0.024
## HL16 0.037 -0.077 -0.058 0.061 -0.073 0.042 -0.100 -0.073 0.051 0.126
## HL21 -0.018 -0.037 -0.078 -0.081 -0.076 -0.009 -0.059 0.012 -0.056 -0.083
## HL24 0.058 0.047 0.024 0.051 0.129 -0.120 0.035 -0.093 -0.004 -0.012
## HL5 0.069 -0.027 -0.152 -0.095 -0.013 0.176 0.193 0.159 0.008 -0.032
## HL11 0.103 0.022 0.015 0.020 -0.150 0.025 -0.079 0.093 0.075 0.031
## HL15 -0.055 -0.033 0.197 -0.052 0.063 -0.095 -0.077 -0.041 -0.063 0.036
## HL19 -0.066 -0.021 -0.100 -0.005 -0.043 -0.147 -0.150 -0.121 -0.051 -0.108
## HL22 -0.095 0.023 0.000 -0.028 0.065 -0.231 -0.012 -0.056 -0.082 -0.073
## HL3 -0.096 0.044 -0.010 0.022 -0.040 0.158 0.084 0.064 0.029 -0.033
## HL9 -0.001 0.123 -0.081 -0.063 0.000 -0.070 0.078 -0.076 0.034 -0.078
## HL13 0.008 0.040 -0.109 -0.112 -0.063 -0.073 -0.123 -0.059 -0.050 -0.009
## HL20 -0.031 -0.024 -0.044 0.035 -0.023 0.003 -0.074 -0.027 0.025 0.056
## HL23 -0.080 -0.055 -0.072 -0.087 0.027 -0.019 -0.023 -0.026 -0.014 -0.012
## HL7 HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22
## HL1
## HL8
## HL14
## HL18
## HL25
## HL2
## HL4
## HL6
## HL10
## HL17
## HL7 0.000
## HL12 0.088 0.000
## HL16 0.048 0.022 0.000
## HL21 0.033 0.003 0.035 0.000
## HL24 0.012 -0.067 -0.056 -0.031 0.000
## HL5 -0.097 -0.086 -0.066 0.018 -0.025 0.000
## HL11 -0.037 0.081 -0.062 -0.048 -0.055 0.046 0.000
## HL15 0.061 -0.072 0.081 0.002 -0.007 -0.003 0.074 0.000
## HL19 0.074 0.011 0.012 0.050 0.048 0.000 -0.071 -0.040 0.000
## HL22 0.046 -0.063 -0.113 0.057 -0.051 -0.020 -0.026 -0.012 0.014 0.000
## HL3 -0.131 0.036 -0.063 -0.068 -0.042 0.053 0.065 0.019 0.013 -0.079
## HL9 0.006 -0.024 -0.103 -0.061 -0.009 -0.052 -0.034 -0.113 0.039 -0.067
## HL13 0.027 0.046 0.125 0.054 -0.063 -0.172 -0.072 0.093 0.033 -0.010
## HL20 -0.018 -0.064 -0.009 0.061 -0.028 -0.024 -0.058 -0.092 0.010 0.045
## HL23 -0.092 -0.023 -0.039 0.019 0.030 -0.071 -0.052 -0.023 0.063 0.147
## HL3 HL9 HL13 HL20 HL23
## HL1
## HL8
## HL14
## HL18
## HL25
## HL2
## HL4
## HL6
## HL10
## HL17
## HL7
## HL12
## HL16
## HL21
## HL24
## HL5
## HL11
## HL15
## HL19
## HL22
## HL3 0.000
## HL9 -0.015 0.000
## HL13 -0.015 0.035 0.000
## HL20 -0.024 0.042 -0.082 0.000
## HL23 -0.081 -0.053 0.051 0.035 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 = 25Ak máme v matici (p(p+1)/2 - p) = 300 elementov, tak
(p*(p+1)/2 - p)*.05## [1] 15z 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 HL25 HL2 HL4 HL6 HL10 HL17
## HL1 "Diag" "." "." "." "." ">.1" "." "." "." "."
## HL8 "." "Diag" "." "." "." "." "." "." "." "."
## HL14 "." "." "Diag" "." ">.1" "." "." "." "." "."
## HL18 "." "." "." "Diag" "." "." "." "." "." ">.1"
## HL25 "." "." ">.1" "." "Diag" "." "." "." "." "."
## HL2 ">.1" "." "." "." "." "Diag" "." "." "." "."
## HL4 "." "." "." "." "." "." "Diag" "." "." "."
## HL6 "." "." "." "." "." "." "." "Diag" "." "."
## HL10 "." "." "." "." "." "." "." "." "Diag" "."
## HL17 "." "." "." ">.1" "." "." "." "." "." "Diag"
## HL7 "." "." "." "." "." "." "." "." "." "."
## HL12 "." "." "." "." "." "." "." ">.1" "." "."
## HL16 "." "." "." "." "." "." "." "." "." ">.1"
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." ">.1" "." "." "." "." "."
## HL5 "." "." "." "." "." ">.1" ">.1" ">.1" "." "."
## HL11 ">.1" "." "." "." "." "." "." "." "." "."
## HL15 "." "." ">.1" "." "." "." "." "." "." "."
## HL19 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL3 "." "." "." "." "." ">.1" "." "." "." "."
## HL9 "." ">.1" "." "." "." "." "." "." "." "."
## HL13 "." "." "." "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL7 HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22
## HL1 "." "." "." "." "." "." ">.1" "." "." "."
## HL8 "." "." "." "." "." "." "." "." "." "."
## HL14 "." "." "." "." "." "." "." ">.1" "." "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL25 "." "." "." "." ">.1" "." "." "." "." "."
## HL2 "." "." "." "." "." ">.1" "." "." "." "."
## HL4 "." "." "." "." "." ">.1" "." "." "." "."
## HL6 "." ">.1" "." "." "." ">.1" "." "." "." "."
## HL10 "." "." "." "." "." "." "." "." "." "."
## HL17 "." "." ">.1" "." "." "." "." "." "." "."
## HL7 "Diag" "." "." "." "." "." "." "." "." "."
## HL12 "." "Diag" "." "." "." "." "." "." "." "."
## HL16 "." "." "Diag" "." "." "." "." "." "." "."
## HL21 "." "." "." "Diag" "." "." "." "." "." "."
## HL24 "." "." "." "." "Diag" "." "." "." "." "."
## HL5 "." "." "." "." "." "Diag" "." "." "." "."
## HL11 "." "." "." "." "." "." "Diag" "." "." "."
## HL15 "." "." "." "." "." "." "." "Diag" "." "."
## HL19 "." "." "." "." "." "." "." "." "Diag" "."
## HL22 "." "." "." "." "." "." "." "." "." "Diag"
## HL3 "." "." "." "." "." "." "." "." "." "."
## HL9 "." "." "." "." "." "." "." "." "." "."
## HL13 "." "." ">.1" "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." ">.1"
## HL3 HL9 HL13 HL20 HL23
## HL1 "." "." "." "." "."
## HL8 "." ">.1" "." "." "."
## HL14 "." "." "." "." "."
## HL18 "." "." "." "." "."
## HL25 "." "." "." "." "."
## HL2 ">.1" "." "." "." "."
## HL4 "." "." "." "." "."
## HL6 "." "." "." "." "."
## HL10 "." "." "." "." "."
## HL17 "." "." "." "." "."
## HL7 "." "." "." "." "."
## HL12 "." "." "." "." "."
## HL16 "." "." ">.1" "." "."
## HL21 "." "." "." "." "."
## HL24 "." "." "." "." "."
## HL5 "." "." "." "." "."
## HL11 "." "." "." "." "."
## HL15 "." "." "." "." "."
## HL19 "." "." "." "." "."
## HL22 "." "." "." "." ">.1"
## HL3 "Diag" "." "." "." "."
## HL9 "." "Diag" "." "." "."
## HL13 "." "." "Diag" "." "."
## HL20 "." "." "." "Diag" "."
## HL23 "." "." "." "." "Diag"Odhad reliability
rel.HLQ.collumns <- full.data %>% select(HL1:HL25, 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", "HL25")], na.rm = TRUE)$total$std.alpha # pre theor_know## [1] 0.7119647alpha(rel.HLQ.na.rm[,c("HL2", "HL4", "HL6", "HL10", "HL17")], na.rm = TRUE)$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", "HL25")], 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$Rtheor_know <- rowMeans(rel.HLQ.na.rm[,c("RHL1", "RHL8", "RHL14", "RHL18", "RHL25")], na.rm = TRUE)
#rel.HLQ.na.rm$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] 0.1306571ICCs$prac_know$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 + z*HL25
'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", "HL25"))## 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
## 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 HL12 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 HL24 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 HL5 x
## HL4## 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 HL25 x
## HL5## 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 HL8 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 HL12 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 HL13 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 HL9 x
## HL8## 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
## HL8## 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 HL22 x
## HL8## 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
## HL8## 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 HL25 x
## HL8## 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 HL12 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 HL24 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 HL16 x
## HL12## 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
## HL12## 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
## HL12## 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 HL21 x
## HL12## 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 HL22 x
## HL12## 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
## HL12## 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 HL25 x
## HL12## 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
## 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 HL19 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 HL21 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 HL22 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 HL23 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 HL24 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 HL25 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 HL20 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 HL25 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 HL21 x
## HL20## 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 HL22 x
## HL20## 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
## HL20## 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 HL22 x
## HL21## 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
## HL21## 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
## HL22## 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 HL25 x
## HL24Počet 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 17 iterations
##
## Number of observations 174
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 355.106 130.313
## Degrees of freedom 275 52
## P-value (Chi-square) 0.001 0.000
## Scaling correction factor 2.725
## 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) 0.644 0.059 10.839 0.000 0.644 0.644
## HL2 (b) 0.637 0.060 10.536 0.000 0.637 0.637
## HL3 (c) 0.722 0.041 17.432 0.000 0.722 0.722
## HL4 (d) 0.721 0.044 16.507 0.000 0.721 0.721
## HL5 (e) 0.762 0.045 17.008 0.000 0.762 0.762
## HL6 (f) 0.618 0.057 10.818 0.000 0.618 0.618
## HL7 (g) 0.604 0.053 11.345 0.000 0.604 0.604
## HL8 (h) 0.803 0.032 25.024 0.000 0.803 0.803
## HL9 (i) 0.812 0.032 24.987 0.000 0.812 0.812
## HL10 (j) 0.773 0.035 21.822 0.000 0.773 0.773
## HL11 (k) 0.750 0.042 17.649 0.000 0.750 0.750
## HL12 (l) 0.834 0.030 27.479 0.000 0.834 0.834
## HL13 (m) 0.740 0.042 17.514 0.000 0.740 0.740
## HL14 (n) 0.745 0.058 12.806 0.000 0.745 0.745
## HL15 (o) 0.755 0.041 18.351 0.000 0.755 0.755
## HL16 (p) 0.764 0.039 19.464 0.000 0.764 0.764
## HL17 (q) 0.683 0.050 13.591 0.000 0.683 0.683
## HL18 (r) 0.747 0.040 18.476 0.000 0.747 0.747
## HL19 (s) 0.757 0.036 20.926 0.000 0.757 0.757
## HL20 (t) 0.881 0.025 35.916 0.000 0.881 0.881
## HL21 (u) 0.850 0.024 35.343 0.000 0.850 0.850
## HL22 (v) 0.755 0.039 19.174 0.000 0.755 0.755
## HL23 (x) 0.802 0.035 23.094 0.000 0.802 0.802
## HL24 (y) 0.853 0.025 34.249 0.000 0.853 0.853
## HL25 (z) 0.800 0.036 22.527 0.000 0.800 0.800
##
## 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
## .HL25 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 -0.043 0.095 -0.454 0.650 -0.043 -0.043
## HL2|t1 -0.233 0.096 -2.417 0.016 -0.233 -0.233
## HL3|t1 -1.014 0.115 -8.788 0.000 -1.014 -1.014
## HL3|t2 0.338 0.097 3.471 0.001 0.338 0.338
## HL4|t1 -1.364 0.136 -10.056 0.000 -1.364 -1.364
## HL4|t2 -0.101 0.095 -1.058 0.290 -0.101 -0.101
## HL5|t1 -1.576 0.154 -10.259 0.000 -1.576 -1.576
## HL5|t2 -0.014 0.095 -0.151 0.880 -0.014 -0.014
## HL6|t1 -1.231 0.127 -9.709 0.000 -1.231 -1.231
## HL6|t2 -0.159 0.096 -1.662 0.096 -0.159 -0.159
## HL7|t1 -1.364 0.136 -10.056 0.000 -1.364 -1.364
## HL7|t2 0.130 0.096 1.360 0.174 0.130 0.130
## HL8|t1 -0.967 0.113 -8.531 0.000 -0.967 -0.967
## HL8|t2 0.368 0.098 3.771 0.000 0.368 0.368
## HL9|t1 -1.143 0.122 -9.390 0.000 -1.143 -1.143
## HL9|t2 0.247 0.096 2.568 0.010 0.247 0.247
## HL10|t1 -1.402 0.139 -10.122 0.000 -1.402 -1.402
## HL10|t2 0.014 0.095 0.151 0.880 0.014 0.014
## HL11|t1 -1.484 0.145 -10.222 0.000 -1.484 -1.484
## HL11|t2 0.087 0.095 0.907 0.364 0.087 0.087
## HL12|t1 -1.262 0.129 -9.806 0.000 -1.262 -1.262
## HL12|t2 0.322 0.097 3.320 0.001 0.322 0.322
## HL13|t1 -0.528 0.100 -5.263 0.000 -0.528 -0.528
## HL13|t2 0.630 0.102 6.146 0.000 0.630 0.630
## HL14|t1 -0.630 0.102 -6.146 0.000 -0.630 -0.630
## HL15|t1 -1.295 0.131 -9.896 0.000 -1.295 -1.295
## HL15|t2 -0.101 0.095 -1.058 0.290 -0.101 -0.101
## HL16|t1 -1.172 0.123 -9.501 0.000 -1.172 -1.172
## HL16|t2 0.262 0.096 2.718 0.007 0.262 0.262
## HL17|t1 -1.528 0.149 -10.250 0.000 -1.528 -1.528
## HL17|t2 -0.384 0.098 -3.921 0.000 -0.384 -0.384
## HL18|t1 -1.484 0.145 -10.222 0.000 -1.484 -1.484
## HL18|t2 -0.174 0.096 -1.813 0.070 -0.174 -0.174
## HL19|t1 -1.014 0.115 -8.788 0.000 -1.014 -1.014
## HL19|t2 0.368 0.098 3.771 0.000 0.368 0.368
## HL20|t1 -1.262 0.129 -9.806 0.000 -1.262 -1.262
## HL20|t2 0.188 0.096 1.964 0.049 0.188 0.188
## HL21|t1 -1.231 0.127 -9.709 0.000 -1.231 -1.231
## HL21|t2 0.233 0.096 2.417 0.016 0.233 0.233
## HL22|t1 -1.329 0.133 -9.980 0.000 -1.329 -1.329
## HL22|t2 -0.029 0.095 -0.302 0.762 -0.029 -0.029
## HL23|t1 -1.172 0.123 -9.501 0.000 -1.172 -1.172
## HL23|t2 0.233 0.096 2.417 0.016 0.233 0.233
## HL24|t1 -1.442 0.142 -10.178 0.000 -1.442 -1.442
## HL24|t2 0.087 0.095 0.907 0.364 0.087 0.087
## HL25|t1 -1.442 0.142 -10.178 0.000 -1.442 -1.442
## HL25|t2 -0.218 0.096 -2.266 0.023 -0.218 -0.218
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.585 0.585 0.585
## .HL2 0.595 0.595 0.595
## .HL3 0.478 0.478 0.478
## .HL4 0.480 0.480 0.480
## .HL5 0.420 0.420 0.420
## .HL6 0.618 0.618 0.618
## .HL7 0.635 0.635 0.635
## .HL8 0.355 0.355 0.355
## .HL9 0.341 0.341 0.341
## .HL10 0.402 0.402 0.402
## .HL11 0.438 0.438 0.438
## .HL12 0.304 0.304 0.304
## .HL13 0.452 0.452 0.452
## .HL14 0.445 0.445 0.445
## .HL15 0.430 0.430 0.430
## .HL16 0.416 0.416 0.416
## .HL17 0.534 0.534 0.534
## .HL18 0.442 0.442 0.442
## .HL19 0.427 0.427 0.427
## .HL20 0.224 0.224 0.224
## .HL21 0.278 0.278 0.278
## .HL22 0.430 0.430 0.430
## .HL23 0.356 0.356 0.356
## .HL24 0.273 0.273 0.273
## .HL25 0.360 0.360 0.360
## 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
## HL25 1.000 1.000 1.000
##
## R-Square:
## Estimate
## HL1 0.415
## HL2 0.405
## HL3 0.522
## HL4 0.520
## HL5 0.580
## HL6 0.382
## HL7 0.365
## HL8 0.645
## HL9 0.659
## HL10 0.598
## HL11 0.562
## HL12 0.696
## HL13 0.548
## HL14 0.555
## HL15 0.570
## HL16 0.584
## HL17 0.466
## HL18 0.558
## HL19 0.573
## HL20 0.776
## HL21 0.722
## HL22 0.570
## HL23 0.644
## HL24 0.727
## HL25 0.640Priemerny faktorovy naboj
mean(inspect(fitted.model2,what="std")$lambda)## [1] 0.752462Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model2)## npar fmin
## 72.000 1.020
## chisq df
## 355.106 275.000
## pvalue chisq.scaled
## 0.001 130.313
## df.scaled pvalue.scaled
## 52.000 0.000
## chisq.scaling.factor baseline.chisq
## 2.725 24290.950
## baseline.df baseline.pvalue
## 300.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 1140.130 14.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 21.305
## cfi tli
## 0.997 0.996
## nnfi rfi
## 0.996 0.984
## nfi pnfi
## 0.985 0.903
## ifi rni
## 0.997 0.997
## cfi.scaled tli.scaled
## 0.930 0.981
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.981 NA
## rfi.scaled nfi.scaled
## 0.969 0.886
## ifi.scaled rni.scaled
## 0.886 0.997
## rni.robust rmsea
## NA 0.041
## rmsea.ci.lower rmsea.ci.upper
## 0.027 0.053
## rmsea.pvalue rmsea.scaled
## 0.890 0.093
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.081 0.105
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.071
## rmr_nomean srmr
## 0.074 0.071
## srmr_bentler srmr_bentler_nomean
## 0.071 0.074
## srmr_bollen srmr_bollen_nomean
## 0.071 0.074
## srmr_mplus srmr_mplus_nomean
## 0.071 0.074
## cn_05 cn_01
## 154.305 162.977
## gfi agfi
## 0.987 0.983
## pgfi mfi
## 0.782 0.793Chi^2 test rozdielov medzi modelmi - p-hodnota
pchisq((fitted.model2@test[[2]]$stat - fitted.model@test[[2]]$stat),
(fitted.model2@test[[1]]$df - fitted.model@test[[1]]$df),
lower.tail = FALSE)## [1] 0.779334Diagram
semPaths(fitted.model2, style = "mx", layout = "circle",
edge.label.cex = 0.5, sizeLat = 5, nCharNodes = 0,
nDigits = 2, "Standardized",
intercepts = FALSE, residuals = TRUE, exoVar = FALSE,
fade = TRUE, groups = "latents", pastel = TRUE)
Test modelu indikuje prítomnosť chybnej špecifikácie modelu. Popri indexoch blízkej zhody je preto potrebné analyzovať lokálne zdroje chybnej špecifikácie na základe matice reziduálnych korelácií
Matica reziduálnych korelácií
residuals.m2 <- residuals(fitted.model2, type = "cor" )$cor
residuals.m2## HL1 HL2 HL3 HL4 HL5 HL6 HL7 HL8 HL9 HL10
## HL1 0.000
## HL2 0.287 0.000
## HL3 -0.098 0.140 0.000
## HL4 0.020 -0.017 0.066 0.000
## HL5 0.063 0.182 0.068 0.201 0.000
## HL6 -0.026 0.141 0.047 0.113 0.165 0.000
## HL7 -0.021 -0.152 -0.124 0.006 -0.091 -0.174 0.000
## HL8 -0.025 -0.147 0.042 -0.028 -0.034 -0.049 0.007 0.000
## HL9 -0.004 -0.090 -0.024 0.058 -0.035 -0.095 0.014 0.121 0.000
## HL10 0.028 0.054 0.011 0.110 0.018 -0.020 -0.010 -0.075 0.014 0.000
## HL11 0.097 0.032 0.079 -0.071 0.006 0.099 -0.031 0.016 -0.018 0.086
## HL12 0.004 0.011 0.045 0.017 -0.080 0.107 0.089 -0.026 -0.014 0.028
## HL13 0.005 -0.091 -0.023 -0.143 -0.158 -0.077 0.034 0.038 0.026 -0.069
## HL14 -0.142 -0.117 -0.013 -0.037 -0.159 -0.032 -0.011 -0.166 -0.084 -0.008
## HL15 -0.060 -0.087 0.034 -0.068 -0.042 -0.035 0.067 -0.039 -0.096 -0.052
## HL16 0.038 0.017 -0.054 -0.128 -0.060 -0.098 0.049 -0.076 -0.093 0.022
## HL17 0.026 0.144 -0.051 -0.093 -0.025 0.146 -0.149 -0.061 -0.099 0.058
## HL18 -0.002 0.068 0.020 -0.106 -0.102 -0.036 0.032 0.053 -0.065 0.066
## HL19 -0.073 -0.141 0.027 -0.143 -0.041 -0.116 0.079 -0.028 0.054 -0.041
## HL20 -0.035 -0.019 -0.035 -0.098 -0.008 -0.049 -0.010 -0.028 0.031 0.001
## HL21 -0.018 -0.038 -0.060 -0.092 0.024 -0.017 0.033 -0.036 -0.051 -0.089
## HL22 -0.101 -0.224 -0.064 -0.004 -0.060 -0.050 0.052 0.017 -0.050 -0.072
## HL23 -0.083 -0.038 -0.090 -0.045 -0.055 -0.045 -0.085 -0.058 -0.064 -0.035
## HL24 0.058 -0.149 -0.033 0.004 -0.018 -0.122 0.013 0.049 0.001 -0.037
## HL25 -0.168 -0.208 -0.042 0.056 -0.020 -0.078 0.016 -0.001 -0.002 -0.093
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1
## HL2
## HL3
## HL4
## HL5
## HL6
## HL7
## HL8
## HL9
## HL10
## HL11 0.000
## HL12 0.088 0.000
## HL13 -0.057 0.055 0.000
## HL14 0.008 -0.058 -0.113 0.000
## HL15 0.035 -0.065 0.108 0.191 0.000
## HL16 -0.056 0.023 0.133 -0.058 0.088 0.000
## HL17 0.039 -0.054 -0.027 0.081 0.044 0.099 0.000
## HL18 0.013 0.007 -0.115 0.013 -0.058 0.062 0.109 0.000
## HL19 -0.111 0.017 0.047 -0.107 -0.080 0.017 -0.101 -0.012 0.000
## HL20 -0.041 -0.055 -0.093 -0.049 -0.075 0.000 0.032 0.031 0.026 0.000
## HL21 -0.042 0.002 0.062 -0.078 0.009 0.035 -0.114 -0.081 0.055 0.069
## HL22 -0.066 -0.056 0.005 -0.007 -0.051 -0.107 -0.065 -0.035 -0.027 0.061
## HL23 -0.037 -0.014 0.041 -0.076 -0.006 -0.030 -0.033 -0.091 0.077 0.022
## HL24 -0.048 -0.067 -0.055 0.024 0.001 -0.055 -0.042 0.052 0.053 -0.018
## HL25 -0.157 -0.070 -0.065 0.192 0.057 -0.072 0.000 -0.046 -0.050 -0.027
## HL21 HL22 HL23 HL24 HL25
## HL1
## HL2
## HL3
## HL4
## HL5
## HL6
## HL7
## HL8
## HL9
## HL10
## HL11
## HL12
## HL13
## HL14
## HL15
## HL16
## HL17
## HL18
## HL19
## HL20
## HL21 0.000
## HL22 0.063 0.000
## HL23 0.027 0.162 0.000
## HL24 -0.032 -0.044 0.038 0.000
## HL25 -0.076 0.058 0.024 0.131 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 = 25Ak máme v matici (p(p+1)/2 - p) = 300 elementov, tak
(p*(p+1)/2 - p)*.05## [1] 15z 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 ">.1" "Diag" ">.1" "." ">.1" ">.1" "." "." "." "."
## HL3 "." ">.1" "Diag" "." "." "." "." "." "." "."
## HL4 "." "." "." "Diag" ">.1" ">.1" "." "." "." ">.1"
## HL5 "." ">.1" "." ">.1" "Diag" ">.1" "." "." "." "."
## HL6 "." ">.1" "." ">.1" ">.1" "Diag" "." "." "." "."
## HL7 "." "." "." "." "." "." "Diag" "." "." "."
## HL8 "." "." "." "." "." "." "." "Diag" ">.1" "."
## HL9 "." "." "." "." "." "." "." ">.1" "Diag" "."
## HL10 "." "." "." ">.1" "." "." "." "." "." "Diag"
## HL11 "." "." "." "." "." "." "." "." "." "."
## HL12 "." "." "." "." "." ">.1" "." "." "." "."
## HL13 "." "." "." "." "." "." "." "." "." "."
## HL14 "." "." "." "." "." "." "." "." "." "."
## HL15 "." "." "." "." "." "." "." "." "." "."
## HL16 "." "." "." "." "." "." "." "." "." "."
## HL17 "." ">.1" "." "." "." ">.1" "." "." "." "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL19 "." "." "." "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL25 "." "." "." "." "." "." "." "." "." "."
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1 "." "." "." "." "." "." "." "." "." "."
## HL2 "." "." "." "." "." "." ">.1" "." "." "."
## HL3 "." "." "." "." "." "." "." "." "." "."
## HL4 "." "." "." "." "." "." "." "." "." "."
## HL5 "." "." "." "." "." "." "." "." "." "."
## HL6 "." ">.1" "." "." "." "." ">.1" "." "." "."
## HL7 "." "." "." "." "." "." "." "." "." "."
## HL8 "." "." "." "." "." "." "." "." "." "."
## HL9 "." "." "." "." "." "." "." "." "." "."
## HL10 "." "." "." "." "." "." "." "." "." "."
## HL11 "Diag" "." "." "." "." "." "." "." "." "."
## HL12 "." "Diag" "." "." "." "." "." "." "." "."
## HL13 "." "." "Diag" "." ">.1" ">.1" "." "." "." "."
## HL14 "." "." "." "Diag" ">.1" "." "." "." "." "."
## HL15 "." "." ">.1" ">.1" "Diag" "." "." "." "." "."
## HL16 "." "." ">.1" "." "." "Diag" "." "." "." "."
## HL17 "." "." "." "." "." "." "Diag" ">.1" "." "."
## HL18 "." "." "." "." "." "." ">.1" "Diag" "." "."
## HL19 "." "." "." "." "." "." "." "." "Diag" "."
## HL20 "." "." "." "." "." "." "." "." "." "Diag"
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL25 "." "." "." ">.1" "." "." "." "." "." "."
## HL21 HL22 HL23 HL24 HL25
## HL1 "." "." "." "." "."
## HL2 "." "." "." "." "."
## HL3 "." "." "." "." "."
## HL4 "." "." "." "." "."
## HL5 "." "." "." "." "."
## HL6 "." "." "." "." "."
## HL7 "." "." "." "." "."
## HL8 "." "." "." "." "."
## HL9 "." "." "." "." "."
## HL10 "." "." "." "." "."
## HL11 "." "." "." "." "."
## HL12 "." "." "." "." "."
## HL13 "." "." "." "." "."
## HL14 "." "." "." "." ">.1"
## HL15 "." "." "." "." "."
## HL16 "." "." "." "." "."
## HL17 "." "." "." "." "."
## HL18 "." "." "." "." "."
## HL19 "." "." "." "." "."
## HL20 "." "." "." "." "."
## HL21 "Diag" "." "." "." "."
## HL22 "." "Diag" ">.1" "." "."
## HL23 "." ">.1" "Diag" "." "."
## HL24 "." "." "." "Diag" ">.1"
## HL25 "." "." "." ">.1" "Diag"Odhad reliability
Odhad internej konzistencie - Cronbachova alfa
alpha(rel.HLQ.na.rm[,1:25])$total$std.alpha # pre jednofaktorovu (25 polozkovu) skalu## [1] 0.9310071Odhad stability v čase
Výpočet sumárneho skóre HLQ
rel.HLQ.na.rm$HLQ_sum <- rowMeans(rel.HLQ.na.rm[,c("HL1", "HL2", "HL3", "HL4", "HL5", "HL6", "HL7", "HL8", "HL9",
"HL10", "HL11", "HL12", "HL13", "HL14", "HL15", "HL16", "HL17",
"HL18", "HL19", "HL20", "HL21", "HL22", "HL23", "HL24", "HL25")], na.rm = TRUE)Výpočet sumárného skóre retestu RHLQ
#rel.HLQ.na.rm$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", "RHL25")], na.rm = TRUE)Test-retest korelácia
#with(rel.HLQ.na.rm, cor.test(HLQ_sum, RHLQ_sum))$estimate # pre jednofaktorovu (25 polozkovu) skalu