HLQ_CFA_Puola.R
ivanropovik
Mon Feb 6 10:35:42 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 == 2) %>% 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] "0.273%"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 5data <- 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
## 25 104 402 110
##
## $HL2
## x
## 1 2 3 4
## 16 49 353 223
##
## $HL3
## x
## 1 2 3 4
## 30 148 334 129
##
## $HL4
## x
## 1 2 3 4
## 8 64 276 293
##
## $HL5
## x
## 1 2 3 4
## 29 103 312 197
##
## $HL6
## x
## 1 2 3 4
## 28 95 315 203
##
## $HL7
## x
## 1 2 3 4
## 31 98 334 178
##
## $HL8
## x
## 1 2 3 4
## 30 75 326 210
##
## $HL9
## x
## 1 2 3 4
## 21 70 355 195
##
## $HL10
## x
## 1 2 3 4
## 11 71 313 246
##
## $HL11
## x
## 1 2 3 4
## 12 82 322 225
##
## $HL12
## x
## 1 2 3 4
## 19 90 354 178
##
## $HL13
## x
## 1 2 3 4
## 30 168 311 132
##
## $HL14
## x
## 1 2 3 4
## 31 120 303 187
##
## $HL15
## x
## 1 2 3 4
## 16 100 369 156
##
## $HL16
## x
## 1 2 3 4
## 27 115 345 154
##
## $HL17
## x
## 1 2 3 4
## 20 47 274 300
##
## $HL18
## x
## 1 2 3 4
## 13 58 318 252
##
## $HL19
## x
## 1 2 3 4
## 15 105 318 203
##
## $HL20
## x
## 1 2 3 4
## 23 62 356 200
##
## $HL21
## x
## 1 2 3 4
## 24 101 354 162
##
## $HL22
## x
## 1 2 3 4
## 20 68 295 258
##
## $HL23
## x
## 1 2 3 4
## 29 93 337 182
##
## $HL24
## x
## 1 2 3 4
## 19 83 346 193Finálne dáta
#View(data)Deskriptívna analýza
Deskriptívne štatistiky
describe(data, na.rm = TRUE, skew = TRUE, ranges = FALSE)## vars n mean sd skew kurtosis se
## HL1 1 641 2.93 0.70 -0.60 0.76 0.03
## HL2 2 641 3.22 0.69 -0.78 1.01 0.03
## HL3 3 641 2.88 0.78 -0.38 -0.17 0.03
## HL4 4 641 3.33 0.71 -0.78 0.15 0.03
## HL5 5 641 3.06 0.80 -0.62 -0.01 0.03
## HL6 6 641 3.08 0.80 -0.66 0.10 0.03
## HL7 7 641 3.03 0.79 -0.64 0.17 0.03
## HL8 8 641 3.12 0.79 -0.78 0.45 0.03
## HL9 9 641 3.13 0.73 -0.71 0.67 0.03
## HL10 10 641 3.24 0.71 -0.67 0.20 0.03
## HL11 11 641 3.19 0.72 -0.59 0.09 0.03
## HL12 12 641 3.08 0.73 -0.58 0.34 0.03
## HL13 13 641 2.85 0.80 -0.28 -0.40 0.03
## HL14 14 641 3.01 0.82 -0.54 -0.22 0.03
## HL15 15 641 3.04 0.71 -0.48 0.30 0.03
## HL16 16 641 2.98 0.77 -0.52 0.09 0.03
## HL17 17 641 3.33 0.75 -1.07 1.04 0.03
## HL18 18 641 3.26 0.70 -0.76 0.56 0.03
## HL19 19 641 3.11 0.75 -0.51 -0.14 0.03
## HL20 20 641 3.14 0.73 -0.78 0.85 0.03
## HL21 21 641 3.02 0.75 -0.57 0.27 0.03
## HL22 22 641 3.23 0.76 -0.84 0.46 0.03
## HL23 23 641 3.05 0.78 -0.66 0.25 0.03
## HL24 24 641 3.11 0.73 -0.63 0.38 0.03Veľkosť vzorky
nrow(data)## [1] 641Redukcia počtu kategórií v prípade nízkej frekvencie odpoveďovej kategórie
(odhad polychorickej kovariančnej matice predpokladá absenciu buniek HLa x HLb s nulovou frekvenciou)
data$HL2 <- ifelse(data$HL2 == 1, yes = 2, no = data$HL2)
data$HL4 <- ifelse(data$HL4 == 1, yes = 2, no = data$HL4)
data$HL10 <- ifelse(data$HL10 == 1, yes = 2, no = data$HL10)
data$HL11 <- ifelse(data$HL11 == 1, yes = 2, no = data$HL11)
data$HL15 <- ifelse(data$HL15 == 1, yes = 2, no = data$HL15)
data$HL18 <- ifelse(data$HL18 == 1, yes = 2, no = data$HL18)
data$HL19 <- ifelse(data$HL19 == 1, yes = 2, no = data$HL19)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 0.58 0.51 0.45 0.47 0.35 0.41 0.43 0.40 0.47 0.42 0.46 0.41 0.41
## HL2 0.58 1.00 0.50 0.50 0.48 0.40 0.47 0.43 0.45 0.47 0.41 0.48 0.42 0.34
## HL3 0.51 0.50 1.00 0.48 0.52 0.30 0.47 0.53 0.35 0.40 0.42 0.42 0.54 0.35
## HL4 0.45 0.50 0.48 1.00 0.49 0.39 0.41 0.46 0.44 0.59 0.44 0.47 0.35 0.35
## HL5 0.47 0.48 0.52 0.49 1.00 0.35 0.49 0.51 0.43 0.46 0.51 0.51 0.44 0.36
## HL6 0.35 0.40 0.30 0.39 0.35 1.00 0.34 0.33 0.35 0.39 0.28 0.28 0.24 0.33
## HL7 0.41 0.47 0.47 0.41 0.49 0.34 1.00 0.43 0.45 0.43 0.47 0.48 0.39 0.38
## HL8 0.43 0.43 0.53 0.46 0.51 0.33 0.43 1.00 0.41 0.43 0.40 0.42 0.46 0.34
## HL9 0.40 0.45 0.35 0.44 0.43 0.35 0.45 0.41 1.00 0.56 0.58 0.52 0.37 0.38
## HL10 0.47 0.47 0.40 0.59 0.46 0.39 0.43 0.43 0.56 1.00 0.57 0.55 0.46 0.41
## HL11 0.42 0.41 0.42 0.44 0.51 0.28 0.47 0.40 0.58 0.57 1.00 0.62 0.48 0.40
## HL12 0.46 0.48 0.42 0.47 0.51 0.28 0.48 0.42 0.52 0.55 0.62 1.00 0.55 0.50
## HL13 0.41 0.42 0.54 0.35 0.44 0.24 0.39 0.46 0.37 0.46 0.48 0.55 1.00 0.51
## HL14 0.41 0.34 0.35 0.35 0.36 0.33 0.38 0.34 0.38 0.41 0.40 0.50 0.51 1.00
## HL15 0.44 0.43 0.45 0.40 0.49 0.31 0.35 0.48 0.47 0.60 0.52 0.50 0.54 0.53
## HL16 0.45 0.39 0.39 0.36 0.45 0.25 0.44 0.39 0.45 0.48 0.51 0.63 0.53 0.47
## HL17 0.44 0.48 0.36 0.40 0.40 0.39 0.35 0.31 0.58 0.54 0.48 0.44 0.35 0.42
## HL18 0.49 0.49 0.42 0.44 0.48 0.35 0.44 0.45 0.53 0.55 0.53 0.45 0.43 0.42
## HL19 0.40 0.33 0.39 0.32 0.40 0.21 0.43 0.36 0.49 0.46 0.53 0.53 0.39 0.40
## HL20 0.48 0.41 0.49 0.44 0.47 0.36 0.50 0.45 0.50 0.54 0.57 0.57 0.46 0.43
## HL21 0.46 0.41 0.57 0.41 0.51 0.33 0.44 0.44 0.40 0.50 0.48 0.55 0.51 0.43
## HL22 0.38 0.40 0.38 0.32 0.40 0.27 0.37 0.35 0.47 0.41 0.47 0.44 0.31 0.47
## HL23 0.36 0.37 0.39 0.39 0.41 0.36 0.43 0.32 0.46 0.49 0.53 0.45 0.37 0.45
## HL24 0.45 0.50 0.52 0.46 0.52 0.38 0.51 0.42 0.48 0.50 0.57 0.56 0.47 0.45
## HL15 HL16 HL17 HL18 HL19 HL20 HL21 HL22 HL23 HL24
## HL1 0.44 0.45 0.44 0.49 0.40 0.48 0.46 0.38 0.36 0.45
## HL2 0.43 0.39 0.48 0.49 0.33 0.41 0.41 0.40 0.37 0.50
## HL3 0.45 0.39 0.36 0.42 0.39 0.49 0.57 0.38 0.39 0.52
## HL4 0.40 0.36 0.40 0.44 0.32 0.44 0.41 0.32 0.39 0.46
## HL5 0.49 0.45 0.40 0.48 0.40 0.47 0.51 0.40 0.41 0.52
## HL6 0.31 0.25 0.39 0.35 0.21 0.36 0.33 0.27 0.36 0.38
## HL7 0.35 0.44 0.35 0.44 0.43 0.50 0.44 0.37 0.43 0.51
## HL8 0.48 0.39 0.31 0.45 0.36 0.45 0.44 0.35 0.32 0.42
## HL9 0.47 0.45 0.58 0.53 0.49 0.50 0.40 0.47 0.46 0.48
## HL10 0.60 0.48 0.54 0.55 0.46 0.54 0.50 0.41 0.49 0.50
## HL11 0.52 0.51 0.48 0.53 0.53 0.57 0.48 0.47 0.53 0.57
## HL12 0.50 0.63 0.44 0.45 0.53 0.57 0.55 0.44 0.45 0.56
## HL13 0.54 0.53 0.35 0.43 0.39 0.46 0.51 0.31 0.37 0.47
## HL14 0.53 0.47 0.42 0.42 0.40 0.43 0.43 0.47 0.45 0.45
## HL15 1.00 0.51 0.48 0.56 0.46 0.56 0.48 0.47 0.50 0.53
## HL16 0.51 1.00 0.40 0.48 0.46 0.46 0.58 0.36 0.49 0.53
## HL17 0.48 0.40 1.00 0.67 0.49 0.48 0.43 0.52 0.50 0.46
## HL18 0.56 0.48 0.67 1.00 0.54 0.62 0.44 0.52 0.55 0.57
## HL19 0.46 0.46 0.49 0.54 1.00 0.59 0.47 0.43 0.52 0.48
## HL20 0.56 0.46 0.48 0.62 0.59 1.00 0.61 0.49 0.49 0.58
## HL21 0.48 0.58 0.43 0.44 0.47 0.61 1.00 0.46 0.50 0.53
## HL22 0.47 0.36 0.52 0.52 0.43 0.49 0.46 1.00 0.59 0.56
## HL23 0.50 0.49 0.50 0.55 0.52 0.49 0.50 0.59 1.00 0.63
## HL24 0.53 0.53 0.46 0.57 0.48 0.58 0.53 0.56 0.63 1.00Priemerná korelácia
polychoric.cor.low <- polychoric.cor$rho[lower.tri(polychoric.cor$rho)]
mean(abs(polychoric.cor.low))## [1] 0.4518515Výpočet polychorickej kovariančnej matice
SDs <- describe(data, na.rm = TRUE)$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 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 HL12 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 HL24 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
## 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 HL13 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 HL3 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 HL3 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 HL20 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 ok.
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] 1Výsledky
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] 4.92282497 0.17460064 0.02517518 -0.04464059 -0.07796020Štvrtá 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 39 iterations
##
## Number of observations 641
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 605.302 494.843
## Degrees of freedom 242 102
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.223
## 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) 0.643 0.027 24.269 0.000 0.643 0.643
## HL8 (b) 0.610 0.027 22.408 0.000 0.610 0.610
## HL14 (c) 0.610 0.027 22.398 0.000 0.610 0.610
## HL18 (d) 0.742 0.022 33.438 0.000 0.742 0.742
## prac_know =~
## HL2 (f) 0.693 0.027 25.363 0.000 0.693 0.693
## HL4 (g) 0.671 0.030 22.723 0.000 0.671 0.671
## HL6 (h) 0.510 0.031 16.188 0.000 0.510 0.510
## HL10 (i) 0.778 0.023 34.478 0.000 0.778 0.778
## HL17 (j) 0.725 0.024 30.145 0.000 0.725 0.725
## crit_think =~
## HL7 (k) 0.651 0.025 26.088 0.000 0.651 0.651
## HL12 (l) 0.766 0.020 38.057 0.000 0.766 0.766
## HL16 (m) 0.707 0.021 34.320 0.000 0.707 0.707
## HL21 (n) 0.732 0.021 34.483 0.000 0.732 0.732
## HL24 (o) 0.778 0.022 35.597 0.000 0.778 0.778
## self_aware =~
## HL5 (p) 0.676 0.023 29.214 0.000 0.676 0.676
## HL11 (q) 0.734 0.021 35.070 0.000 0.734 0.734
## HL15 (r) 0.716 0.024 29.548 0.000 0.716 0.716
## HL19 (s) 0.662 0.025 26.157 0.000 0.662 0.662
## HL22 (t) 0.646 0.026 24.681 0.000 0.646 0.646
## citizenship =~
## HL3 (u) 0.646 0.025 25.976 0.000 0.646 0.646
## HL9 (v) 0.663 0.024 27.402 0.000 0.663 0.663
## HL13 (x) 0.635 0.024 26.289 0.000 0.635 0.635
## HL20 (y) 0.733 0.020 36.440 0.000 0.733 0.733
## HL23 (z) 0.675 0.023 29.447 0.000 0.675 0.675
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## theor_know ~~
## prac_know 1.003 0.021 47.740 0.000 1.003 1.003
## crit_think 0.947 0.017 54.349 0.000 0.947 0.947
## self_aware 1.019 0.019 52.764 0.000 1.019 1.019
## citizenship 1.049 0.018 59.138 0.000 1.049 1.049
## prac_know ~~
## crit_think 0.857 0.021 41.558 0.000 0.857 0.857
## self_aware 0.912 0.022 41.788 0.000 0.912 0.912
## citizenship 0.949 0.019 50.759 0.000 0.949 0.949
## crit_think ~~
## self_aware 0.976 0.015 62.999 0.000 0.976 0.976
## citizenship 1.026 0.011 89.978 0.000 1.026 1.026
## self_aware ~~
## citizenship 1.059 0.015 72.333 0.000 1.059 1.059
##
## 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 -1.762 0.091 -19.442 0.000 -1.762 -1.762
## HL1|t2 -0.837 0.056 -14.844 0.000 -0.837 -0.837
## HL1|t3 0.948 0.059 16.191 0.000 0.948 0.948
## HL8|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL8|t2 -0.979 0.059 -16.533 0.000 -0.979 -0.979
## HL8|t3 0.447 0.051 8.691 0.000 0.447 0.447
## HL14|t1 -1.661 0.084 -19.670 0.000 -1.661 -1.661
## HL14|t2 -0.721 0.055 -13.220 0.000 -0.721 -0.721
## HL14|t3 0.548 0.052 10.475 0.000 0.548 0.548
## HL18|t1 -1.222 0.066 -18.622 0.000 -1.222 -1.222
## HL18|t2 0.271 0.050 5.401 0.000 0.271 0.271
## HL2|t1 -1.274 0.067 -18.923 0.000 -1.274 -1.274
## HL2|t2 0.391 0.051 7.676 0.000 0.391 0.391
## HL4|t1 -1.214 0.065 -18.569 0.000 -1.214 -1.214
## HL4|t2 0.108 0.050 2.170 0.030 0.108 0.108
## HL6|t1 -1.709 0.087 -19.581 0.000 -1.709 -1.709
## HL6|t2 -0.871 0.057 -15.276 0.000 -0.871 -0.871
## HL6|t3 0.477 0.052 9.236 0.000 0.477 0.477
## HL10|t1 -1.136 0.063 -18.004 0.000 -1.136 -1.136
## HL10|t2 0.296 0.050 5.872 0.000 0.296 0.296
## HL17|t1 -1.863 0.098 -19.059 0.000 -1.863 -1.863
## HL17|t2 -1.256 0.067 -18.826 0.000 -1.256 -1.256
## HL17|t3 0.080 0.050 1.618 0.106 0.080 0.080
## HL7|t1 -1.661 0.084 -19.670 0.000 -1.661 -1.661
## HL7|t2 -0.837 0.056 -14.844 0.000 -0.837 -0.837
## HL7|t3 0.590 0.053 11.168 0.000 0.590 0.590
## HL12|t1 -1.886 0.100 -18.953 0.000 -1.886 -1.886
## HL12|t2 -0.954 0.059 -16.260 0.000 -0.954 -0.954
## HL12|t3 0.590 0.053 11.168 0.000 0.590 0.590
## HL16|t1 -1.727 0.088 -19.541 0.000 -1.727 -1.727
## HL16|t2 -0.767 0.055 -13.891 0.000 -0.767 -0.767
## HL16|t3 0.705 0.054 12.994 0.000 0.705 0.705
## HL21|t1 -1.781 0.092 -19.382 0.000 -1.781 -1.781
## HL21|t2 -0.860 0.057 -15.133 0.000 -0.860 -0.860
## HL21|t3 0.666 0.054 12.390 0.000 0.666 0.666
## HL24|t1 -1.886 0.100 -18.953 0.000 -1.886 -1.886
## HL24|t2 -0.998 0.060 -16.735 0.000 -0.998 -0.998
## HL24|t3 0.521 0.052 10.011 0.000 0.521 0.521
## HL5|t1 -1.693 0.086 -19.616 0.000 -1.693 -1.693
## HL5|t2 -0.821 0.056 -14.626 0.000 -0.821 -0.821
## HL5|t3 0.503 0.052 9.701 0.000 0.503 0.503
## HL11|t1 -1.051 0.061 -17.260 0.000 -1.051 -1.051
## HL11|t2 0.383 0.051 7.519 0.000 0.383 0.383
## HL15|t1 -0.912 0.058 -15.773 0.000 -0.912 -0.912
## HL15|t2 0.696 0.054 12.844 0.000 0.696 0.696
## HL19|t1 -0.888 0.057 -15.490 0.000 -0.888 -0.888
## HL19|t2 0.477 0.052 9.236 0.000 0.477 0.477
## HL22|t1 -1.863 0.098 -19.059 0.000 -1.863 -1.863
## HL22|t2 -1.093 0.062 -17.640 0.000 -1.093 -1.093
## HL22|t3 0.247 0.050 4.929 0.000 0.247 0.247
## HL3|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL3|t2 -0.590 0.053 -11.168 0.000 -0.590 -0.590
## HL3|t3 0.837 0.056 14.844 0.000 0.837 0.837
## HL9|t1 -1.842 0.096 -19.154 0.000 -1.842 -1.842
## HL9|t2 -1.072 0.061 -17.452 0.000 -1.072 -1.072
## HL9|t3 0.512 0.052 9.856 0.000 0.512 0.512
## HL13|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL13|t2 -0.499 0.052 -9.624 0.000 -0.499 -0.499
## HL13|t3 0.821 0.056 14.626 0.000 0.821 0.821
## HL20|t1 -1.801 0.093 -19.314 0.000 -1.801 -1.801
## HL20|t2 -1.114 0.063 -17.824 0.000 -1.114 -1.114
## HL20|t3 0.490 0.052 9.469 0.000 0.490 0.490
## HL23|t1 -1.693 0.086 -19.616 0.000 -1.693 -1.693
## HL23|t2 -0.877 0.057 -15.348 0.000 -0.877 -0.877
## HL23|t3 0.571 0.053 10.861 0.000 0.571 0.571
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.586 0.586 0.586
## .HL8 0.628 0.628 0.628
## .HL14 0.628 0.628 0.628
## .HL18 0.450 0.450 0.450
## .HL2 0.520 0.520 0.520
## .HL4 0.550 0.550 0.550
## .HL6 0.740 0.740 0.740
## .HL10 0.395 0.395 0.395
## .HL17 0.474 0.474 0.474
## .HL7 0.577 0.577 0.577
## .HL12 0.413 0.413 0.413
## .HL16 0.500 0.500 0.500
## .HL21 0.465 0.465 0.465
## .HL24 0.394 0.394 0.394
## .HL5 0.543 0.543 0.543
## .HL11 0.462 0.462 0.462
## .HL15 0.488 0.488 0.488
## .HL19 0.562 0.562 0.562
## .HL22 0.583 0.583 0.583
## .HL3 0.582 0.582 0.582
## .HL9 0.560 0.560 0.560
## .HL13 0.597 0.597 0.597
## .HL20 0.463 0.463 0.463
## .HL23 0.544 0.544 0.544
## 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 0.414
## HL8 0.372
## HL14 0.372
## HL18 0.550
## HL2 0.480
## HL4 0.450
## HL6 0.260
## HL10 0.605
## HL17 0.526
## HL7 0.423
## HL12 0.587
## HL16 0.500
## HL21 0.535
## HL24 0.606
## HL5 0.457
## HL11 0.538
## HL15 0.512
## HL19 0.438
## HL22 0.417
## HL3 0.418
## HL9 0.440
## HL13 0.403
## HL20 0.537
## HL23 0.456Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model)## npar fmin
## 99.000 0.472
## chisq df
## 605.302 242.000
## pvalue chisq.scaled
## 0.000 494.843
## df.scaled pvalue.scaled
## 102.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.223 51141.700
## baseline.df baseline.pvalue
## 276.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 3526.125 19.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 14.504
## cfi tli
## 0.993 0.992
## nnfi rfi
## 0.992 0.987
## nfi pnfi
## 0.988 0.866
## ifi rni
## 0.993 0.993
## cfi.scaled tli.scaled
## 0.888 0.979
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.979 NA
## rfi.scaled nfi.scaled
## 0.974 0.860
## ifi.scaled rni.scaled
## 0.860 0.992
## rni.robust rmsea
## NA 0.048
## rmsea.ci.lower rmsea.ci.upper
## 0.044 0.053
## rmsea.pvalue rmsea.scaled
## 0.697 0.078
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.071 0.084
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.047
## rmr_nomean srmr
## 0.048 0.047
## srmr_bentler srmr_bentler_nomean
## 0.047 0.048
## srmr_bollen srmr_bollen_nomean
## 0.047 0.048
## srmr_mplus srmr_mplus_nomean
## 0.047 0.048
## cn_05 cn_01
## 296.297 314.074
## gfi agfi
## 0.991 0.987
## pgfi mfi
## 0.703 0.753Diagram
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 0.039 0.000
## HL14 0.014 -0.037 0.000
## HL18 0.014 -0.003 -0.034 0.000
## HL2 0.131 0.011 -0.080 -0.026 0.000
## HL4 0.012 0.052 -0.063 -0.054 0.031 0.000
## HL6 0.022 0.021 0.020 -0.031 0.046 0.043 0.000
## HL10 -0.029 -0.044 -0.066 -0.030 -0.067 0.066 -0.011 0.000
## HL17 -0.024 -0.133 -0.023 0.128 -0.018 -0.084 0.023 -0.025 0.000
## HL7 0.015 0.051 0.005 -0.019 0.083 0.037 0.053 -0.005 -0.057 0.000
## HL12 -0.004 -0.025 0.057 -0.084 0.028 0.025 -0.051 0.039 -0.032 -0.014
## HL16 0.021 -0.023 0.061 -0.018 -0.028 -0.045 -0.059 0.006 -0.039 -0.024
## HL21 0.019 0.018 0.005 -0.070 -0.027 -0.015 0.010 0.010 -0.024 -0.039
## HL24 -0.022 -0.030 0.000 0.027 0.038 0.012 0.038 -0.023 -0.025 -0.001
## HL5 0.024 0.089 -0.065 -0.035 0.048 0.080 0.036 -0.021 -0.048 0.058
## HL11 -0.061 -0.052 -0.053 -0.023 -0.056 -0.011 -0.063 0.050 -0.002 0.002
## HL15 -0.030 0.034 0.083 0.015 -0.022 -0.040 -0.023 0.090 0.005 -0.102
## HL19 -0.032 -0.048 -0.007 0.040 -0.091 -0.080 -0.093 -0.014 0.051 0.012
## HL22 -0.045 -0.053 0.073 0.033 -0.011 -0.075 -0.034 -0.051 0.093 -0.040
## HL3 0.076 0.117 -0.063 -0.080 0.074 0.070 -0.010 -0.073 -0.082 0.034
## HL9 -0.048 -0.017 -0.041 0.017 0.016 0.021 0.028 0.071 0.124 0.012
## HL13 -0.023 0.055 0.106 -0.068 0.002 -0.056 -0.070 -0.010 -0.091 -0.037
## HL20 -0.010 -0.021 -0.041 0.048 -0.070 -0.024 0.008 0.002 -0.020 0.013
## HL23 -0.097 -0.116 0.018 0.022 -0.077 -0.035 0.034 -0.009 0.032 -0.022
## HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22 HL3
## HL1
## HL8
## HL14
## HL18
## HL2
## HL4
## HL6
## HL10
## HL17
## HL7
## HL12 0.000
## HL16 0.090 0.000
## HL21 -0.013 0.058 0.000
## HL24 -0.041 -0.023 -0.036 0.000
## HL5 0.003 -0.017 0.028 0.009 0.000
## HL11 0.069 0.007 -0.043 0.009 0.019 0.000
## HL15 -0.040 0.011 -0.029 -0.015 0.002 -0.002 0.000
## HL19 0.032 0.005 0.001 -0.022 -0.047 0.046 -0.014 0.000
## HL22 -0.046 -0.084 0.001 0.067 -0.033 -0.007 0.006 0.005 0.000
## HL3 -0.091 -0.078 0.084 0.002 0.056 -0.079 -0.037 -0.065 -0.066 0.000
## HL9 -0.002 -0.034 -0.102 -0.044 -0.042 0.060 -0.032 0.021 0.012 -0.078
## HL13 0.047 0.071 0.033 -0.038 -0.017 -0.009 0.055 -0.054 -0.127 0.125
## HL20 -0.002 -0.067 0.057 -0.002 -0.053 0.001 -0.001 0.075 -0.013 0.020
## HL23 -0.077 -0.003 -0.003 0.093 -0.075 0.003 -0.013 0.045 0.128 -0.046
## 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 -0.047 0.000
## HL20 0.013 -0.004 0.000
## HL23 0.016 -0.061 -0.005 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 ">.1" "." "." "." "Diag" "." "." "." "." "."
## HL4 "." "." "." "." "." "Diag" "." "." "." "."
## HL6 "." "." "." "." "." "." "Diag" "." "." "."
## HL10 "." "." "." "." "." "." "." "Diag" "." "."
## HL17 "." "." "." ">.1" "." "." "." "." "Diag" "."
## HL7 "." "." "." "." "." "." "." "." "." "Diag"
## HL12 "." "." "." "." "." "." "." "." "." "."
## HL16 "." "." "." "." "." "." "." "." "." "."
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL5 "." "." "." "." "." "." "." "." "." "."
## HL11 "." "." "." "." "." "." "." "." "." "."
## HL15 "." "." "." "." "." "." "." "." "." "."
## HL19 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL3 "." ">.1" "." "." "." "." "." "." "." "."
## HL9 "." "." "." "." "." "." "." "." ">.1" "."
## HL13 "." "." ">.1" "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL12 HL16 HL21 HL24 HL5 HL11 HL15 HL19 HL22 HL3
## HL1 "." "." "." "." "." "." "." "." "." "."
## HL8 "." "." "." "." "." "." "." "." "." ">.1"
## HL14 "." "." "." "." "." "." "." "." "." "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL2 "." "." "." "." "." "." "." "." "." "."
## HL4 "." "." "." "." "." "." "." "." "." "."
## HL6 "." "." "." "." "." "." "." "." "." "."
## HL10 "." "." "." "." "." "." "." "." "." "."
## HL17 "." "." "." "." "." "." "." "." "." "."
## HL7 "." "." "." "." "." "." "." "." "." "."
## HL12 "Diag" "." "." "." "." "." "." "." "." "."
## HL16 "." "Diag" "." "." "." "." "." "." "." "."
## HL21 "." "." "Diag" "." "." "." "." "." "." "."
## HL24 "." "." "." "Diag" "." "." "." "." "." "."
## HL5 "." "." "." "." "Diag" "." "." "." "." "."
## HL11 "." "." "." "." "." "Diag" "." "." "." "."
## HL15 "." "." "." "." "." "." "Diag" "." "." "."
## HL19 "." "." "." "." "." "." "." "Diag" "." "."
## HL22 "." "." "." "." "." "." "." "." "Diag" "."
## HL3 "." "." "." "." "." "." "." "." "." "Diag"
## HL9 "." "." "." "." "." "." "." "." "." "."
## HL13 "." "." "." "." "." "." "." "." "." ">.1"
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." ">.1" "."
## HL9 HL13 HL20 HL23
## HL1 "." "." "." "."
## HL8 "." "." "." "."
## HL14 "." ">.1" "." "."
## HL18 "." "." "." "."
## HL2 "." "." "." "."
## HL4 "." "." "." "."
## HL6 "." "." "." "."
## HL10 "." "." "." "."
## HL17 ">.1" "." "." "."
## HL7 "." "." "." "."
## HL12 "." "." "." "."
## HL16 "." "." "." "."
## HL21 "." "." "." "."
## HL24 "." "." "." "."
## HL5 "." "." "." "."
## HL11 "." "." "." "."
## 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 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 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 HL21 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 HL14 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 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 HL16 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
## 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
## HL14Počet buniek HLa x HLb s nulovou frekvenciou je ok.
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 14 iterations
##
## Number of observations 641
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 684.883 552.535
## Degrees of freedom 252 105
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.240
## 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) 0.650 0.025 25.583 0.000 0.650 0.650
## HL2 (b) 0.653 0.026 25.099 0.000 0.653 0.653
## HL3 (c) 0.664 0.025 26.901 0.000 0.664 0.664
## HL4 (d) 0.633 0.028 22.306 0.000 0.633 0.633
## HL5 (e) 0.678 0.023 30.084 0.000 0.678 0.678
## HL6 (f) 0.480 0.031 15.644 0.000 0.480 0.480
## HL7 (g) 0.635 0.025 25.833 0.000 0.635 0.635
## HL8 (h) 0.617 0.026 23.524 0.000 0.617 0.617
## HL9 (i) 0.682 0.024 28.558 0.000 0.682 0.682
## HL10 (j) 0.731 0.022 33.735 0.000 0.731 0.731
## HL11 (k) 0.735 0.021 35.258 0.000 0.735 0.735
## HL12 (l) 0.747 0.020 36.649 0.000 0.747 0.747
## HL13 (m) 0.653 0.024 27.082 0.000 0.653 0.653
## HL14 (n) 0.617 0.026 23.613 0.000 0.617 0.617
## HL15 (o) 0.718 0.024 30.412 0.000 0.718 0.718
## HL16 (p) 0.691 0.021 32.318 0.000 0.691 0.691
## HL17 (q) 0.683 0.023 29.324 0.000 0.683 0.683
## HL18 (r) 0.751 0.021 36.381 0.000 0.751 0.751
## HL19 (s) 0.663 0.025 26.540 0.000 0.663 0.663
## HL20 (t) 0.755 0.019 39.295 0.000 0.755 0.755
## HL21 (u) 0.714 0.021 33.654 0.000 0.714 0.714
## HL22 (v) 0.648 0.025 25.620 0.000 0.648 0.648
## HL23 (x) 0.695 0.022 31.030 0.000 0.695 0.695
## HL24 (y) 0.759 0.022 34.944 0.000 0.759 0.759
##
## 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 -1.762 0.091 -19.442 0.000 -1.762 -1.762
## HL1|t2 -0.837 0.056 -14.844 0.000 -0.837 -0.837
## HL1|t3 0.948 0.059 16.191 0.000 0.948 0.948
## HL2|t1 -1.274 0.067 -18.923 0.000 -1.274 -1.274
## HL2|t2 0.391 0.051 7.676 0.000 0.391 0.391
## HL3|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL3|t2 -0.590 0.053 -11.168 0.000 -0.590 -0.590
## HL3|t3 0.837 0.056 14.844 0.000 0.837 0.837
## HL4|t1 -1.214 0.065 -18.569 0.000 -1.214 -1.214
## HL4|t2 0.108 0.050 2.170 0.030 0.108 0.108
## HL5|t1 -1.693 0.086 -19.616 0.000 -1.693 -1.693
## HL5|t2 -0.821 0.056 -14.626 0.000 -0.821 -0.821
## HL5|t3 0.503 0.052 9.701 0.000 0.503 0.503
## HL6|t1 -1.709 0.087 -19.581 0.000 -1.709 -1.709
## HL6|t2 -0.871 0.057 -15.276 0.000 -0.871 -0.871
## HL6|t3 0.477 0.052 9.236 0.000 0.477 0.477
## HL7|t1 -1.661 0.084 -19.670 0.000 -1.661 -1.661
## HL7|t2 -0.837 0.056 -14.844 0.000 -0.837 -0.837
## HL7|t3 0.590 0.053 11.168 0.000 0.590 0.590
## HL8|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL8|t2 -0.979 0.059 -16.533 0.000 -0.979 -0.979
## HL8|t3 0.447 0.051 8.691 0.000 0.447 0.447
## HL9|t1 -1.842 0.096 -19.154 0.000 -1.842 -1.842
## HL9|t2 -1.072 0.061 -17.452 0.000 -1.072 -1.072
## HL9|t3 0.512 0.052 9.856 0.000 0.512 0.512
## HL10|t1 -1.136 0.063 -18.004 0.000 -1.136 -1.136
## HL10|t2 0.296 0.050 5.872 0.000 0.296 0.296
## HL11|t1 -1.051 0.061 -17.260 0.000 -1.051 -1.051
## HL11|t2 0.383 0.051 7.519 0.000 0.383 0.383
## HL12|t1 -1.886 0.100 -18.953 0.000 -1.886 -1.886
## HL12|t2 -0.954 0.059 -16.260 0.000 -0.954 -0.954
## HL12|t3 0.590 0.053 11.168 0.000 0.590 0.590
## HL13|t1 -1.677 0.085 -19.646 0.000 -1.677 -1.677
## HL13|t2 -0.499 0.052 -9.624 0.000 -0.499 -0.499
## HL13|t3 0.821 0.056 14.626 0.000 0.821 0.821
## HL14|t1 -1.661 0.084 -19.670 0.000 -1.661 -1.661
## HL14|t2 -0.721 0.055 -13.220 0.000 -0.721 -0.721
## HL14|t3 0.548 0.052 10.475 0.000 0.548 0.548
## HL15|t1 -0.912 0.058 -15.773 0.000 -0.912 -0.912
## HL15|t2 0.696 0.054 12.844 0.000 0.696 0.696
## HL16|t1 -1.727 0.088 -19.541 0.000 -1.727 -1.727
## HL16|t2 -0.767 0.055 -13.891 0.000 -0.767 -0.767
## HL16|t3 0.705 0.054 12.994 0.000 0.705 0.705
## HL17|t1 -1.863 0.098 -19.059 0.000 -1.863 -1.863
## HL17|t2 -1.256 0.067 -18.826 0.000 -1.256 -1.256
## HL17|t3 0.080 0.050 1.618 0.106 0.080 0.080
## HL18|t1 -1.222 0.066 -18.622 0.000 -1.222 -1.222
## HL18|t2 0.271 0.050 5.401 0.000 0.271 0.271
## HL19|t1 -0.888 0.057 -15.490 0.000 -0.888 -0.888
## HL19|t2 0.477 0.052 9.236 0.000 0.477 0.477
## HL20|t1 -1.801 0.093 -19.314 0.000 -1.801 -1.801
## HL20|t2 -1.114 0.063 -17.824 0.000 -1.114 -1.114
## HL20|t3 0.490 0.052 9.469 0.000 0.490 0.490
## HL21|t1 -1.781 0.092 -19.382 0.000 -1.781 -1.781
## HL21|t2 -0.860 0.057 -15.133 0.000 -0.860 -0.860
## HL21|t3 0.666 0.054 12.390 0.000 0.666 0.666
## HL22|t1 -1.863 0.098 -19.059 0.000 -1.863 -1.863
## HL22|t2 -1.093 0.062 -17.640 0.000 -1.093 -1.093
## HL22|t3 0.247 0.050 4.929 0.000 0.247 0.247
## HL23|t1 -1.693 0.086 -19.616 0.000 -1.693 -1.693
## HL23|t2 -0.877 0.057 -15.348 0.000 -0.877 -0.877
## HL23|t3 0.571 0.053 10.861 0.000 0.571 0.571
## HL24|t1 -1.886 0.100 -18.953 0.000 -1.886 -1.886
## HL24|t2 -0.998 0.060 -16.735 0.000 -0.998 -0.998
## HL24|t3 0.521 0.052 10.011 0.000 0.521 0.521
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HL1 0.578 0.578 0.578
## .HL2 0.573 0.573 0.573
## .HL3 0.559 0.559 0.559
## .HL4 0.599 0.599 0.599
## .HL5 0.540 0.540 0.540
## .HL6 0.769 0.769 0.769
## .HL7 0.597 0.597 0.597
## .HL8 0.619 0.619 0.619
## .HL9 0.535 0.535 0.535
## .HL10 0.466 0.466 0.466
## .HL11 0.459 0.459 0.459
## .HL12 0.441 0.441 0.441
## .HL13 0.573 0.573 0.573
## .HL14 0.619 0.619 0.619
## .HL15 0.485 0.485 0.485
## .HL16 0.523 0.523 0.523
## .HL17 0.534 0.534 0.534
## .HL18 0.436 0.436 0.436
## .HL19 0.560 0.560 0.560
## .HL20 0.430 0.430 0.430
## .HL21 0.490 0.490 0.490
## .HL22 0.581 0.581 0.581
## .HL23 0.518 0.518 0.518
## .HL24 0.423 0.423 0.423
## 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 0.422
## HL2 0.427
## HL3 0.441
## HL4 0.401
## HL5 0.460
## HL6 0.231
## HL7 0.403
## HL8 0.381
## HL9 0.465
## HL10 0.534
## HL11 0.541
## HL12 0.559
## HL13 0.427
## HL14 0.381
## HL15 0.515
## HL16 0.477
## HL17 0.466
## HL18 0.564
## HL19 0.440
## HL20 0.570
## HL21 0.510
## HL22 0.419
## HL23 0.482
## HL24 0.577Priemerny faktorovy naboj
mean(inspect(fitted.model2,what="std")$lambda)## [1] 0.6771869Test modelu, indexy blízkej zhody modelu a dát
Treba si všímať .scaled indexy
fitMeasures(fitted.model2)## npar fmin
## 89.000 0.534
## chisq df
## 684.883 252.000
## pvalue chisq.scaled
## 0.000 552.535
## df.scaled pvalue.scaled
## 105.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.240 51141.700
## baseline.df baseline.pvalue
## 276.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 3526.125 19.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 14.504
## cfi tli
## 0.991 0.991
## nnfi rfi
## 0.991 0.985
## nfi pnfi
## 0.987 0.901
## ifi rni
## 0.991 0.991
## cfi.scaled tli.scaled
## 0.872 0.977
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.977 NA
## rfi.scaled nfi.scaled
## 0.972 0.843
## ifi.scaled rni.scaled
## 0.843 0.991
## rni.robust rmsea
## NA 0.052
## rmsea.ci.lower rmsea.ci.upper
## 0.047 0.056
## rmsea.pvalue rmsea.scaled
## 0.256 0.082
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.076 0.088
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.049
## rmr_nomean srmr
## 0.051 0.049
## srmr_bentler srmr_bentler_nomean
## 0.049 0.051
## srmr_bollen srmr_bollen_nomean
## 0.049 0.051
## srmr_mplus srmr_mplus_nomean
## 0.049 0.051
## cn_05 cn_01
## 272.022 288.019
## gfi agfi
## 0.990 0.986
## pgfi mfi
## 0.731 0.713Chi^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] 9.876887e-09Diagram
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 0.153 0.000
## HL3 0.081 0.066 0.000
## HL4 0.034 0.083 0.061 0.000
## HL5 0.026 0.033 0.068 0.064 0.000
## HL6 0.039 0.086 -0.017 0.081 0.025 0.000
## HL7 -0.001 0.054 0.044 0.009 0.057 0.033 0.000
## HL8 0.031 0.032 0.121 0.072 0.091 0.036 0.035 0.000
## HL9 -0.044 0.007 -0.102 0.012 -0.029 0.021 0.022 -0.013 0.000
## HL10 -0.002 -0.006 -0.081 0.125 -0.037 0.035 -0.036 -0.019 0.062 0.000
## HL11 -0.059 -0.073 -0.065 -0.027 0.016 -0.075 0.001 -0.050 0.074 0.034
## HL12 -0.023 -0.005 -0.080 -0.008 0.001 -0.076 0.010 -0.044 0.009 0.003
## HL13 -0.018 -0.007 0.102 -0.066 -0.005 -0.077 -0.028 0.059 -0.071 -0.019
## HL14 0.006 -0.059 -0.059 -0.043 -0.064 0.035 -0.010 -0.045 -0.037 -0.041
## HL15 -0.028 -0.038 -0.024 -0.056 -0.001 -0.035 -0.103 0.036 -0.018 0.074
## HL16 0.004 -0.059 -0.067 -0.076 -0.018 -0.082 -0.002 -0.041 -0.024 -0.027
## HL17 0.000 0.039 -0.091 -0.030 -0.063 0.064 -0.086 -0.111 0.115 0.041
## HL18 0.003 -0.001 -0.075 -0.030 -0.033 -0.012 -0.038 -0.014 0.021 0.000
## HL19 -0.030 -0.106 -0.053 -0.095 -0.049 -0.104 0.011 -0.046 0.034 -0.029
## HL20 -0.006 -0.081 -0.008 -0.036 -0.040 0.000 0.023 -0.018 -0.015 -0.009
## HL21 0.000 -0.059 0.095 -0.046 0.026 -0.014 -0.016 0.000 -0.092 -0.024
## HL22 -0.043 -0.026 -0.054 -0.090 -0.036 -0.045 -0.041 -0.052 0.024 -0.066
## HL23 -0.093 -0.087 -0.071 -0.046 -0.062 0.027 -0.012 -0.112 -0.010 -0.019
## HL24 -0.042 0.004 0.014 -0.022 0.008 0.013 0.023 -0.049 -0.033 -0.059
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1
## HL2
## HL3
## HL4
## HL5
## HL6
## HL7
## HL8
## HL9
## HL10
## HL11 0.000
## HL12 0.069 0.000
## HL13 0.004 0.058 0.000
## HL14 -0.051 0.039 0.109 0.000
## HL15 -0.005 -0.041 0.068 0.085 0.000
## HL16 0.006 0.116 0.081 0.044 0.010 0.000
## HL17 -0.019 -0.066 -0.100 -0.001 -0.012 -0.071 0.000
## HL18 -0.021 -0.107 -0.064 -0.044 0.016 -0.039 0.155 0.000
## HL19 0.044 0.032 -0.042 -0.005 -0.017 0.004 0.036 0.041 0.000
## HL20 0.016 0.010 -0.031 -0.037 0.013 -0.056 -0.031 0.051 0.088 0.000
## HL21 -0.044 0.013 0.043 -0.012 -0.030 0.083 -0.057 -0.093 -0.001 0.068
## HL22 -0.010 -0.047 -0.115 0.075 0.004 -0.085 0.078 0.035 0.002 0.000
## HL23 0.017 -0.066 -0.086 0.021 0.001 0.007 0.022 0.026 0.058 -0.035
## HL24 0.008 -0.012 -0.027 -0.018 -0.016 0.003 -0.059 0.004 -0.023 0.011
## 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 0.000 0.000
## HL23 0.007 0.140 0.000
## HL24 -0.009 0.066 0.105 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 ">.1" "Diag" "." "." "." "." "." "." "." "."
## HL3 "." "." "Diag" "." "." "." "." ">.1" "." "."
## HL4 "." "." "." "Diag" "." "." "." "." "." ">.1"
## HL5 "." "." "." "." "Diag" "." "." "." "." "."
## HL6 "." "." "." "." "." "Diag" "." "." "." "."
## HL7 "." "." "." "." "." "." "Diag" "." "." "."
## HL8 "." "." ">.1" "." "." "." "." "Diag" "." "."
## HL9 "." "." "." "." "." "." "." "." "Diag" "."
## HL10 "." "." "." ">.1" "." "." "." "." "." "Diag"
## HL11 "." "." "." "." "." "." "." "." "." "."
## HL12 "." "." "." "." "." "." "." "." "." "."
## HL13 "." "." ">.1" "." "." "." "." "." "." "."
## HL14 "." "." "." "." "." "." "." "." "." "."
## HL15 "." "." "." "." "." "." "." "." "." "."
## HL16 "." "." "." "." "." "." "." "." "." "."
## HL17 "." "." "." "." "." "." "." "." ">.1" "."
## HL18 "." "." "." "." "." "." "." "." "." "."
## HL19 "." "." "." "." "." "." "." "." "." "."
## HL20 "." "." "." "." "." "." "." "." "." "."
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## HL11 HL12 HL13 HL14 HL15 HL16 HL17 HL18 HL19 HL20
## HL1 "." "." "." "." "." "." "." "." "." "."
## HL2 "." "." "." "." "." "." "." "." "." "."
## HL3 "." "." ">.1" "." "." "." "." "." "." "."
## HL4 "." "." "." "." "." "." "." "." "." "."
## HL5 "." "." "." "." "." "." "." "." "." "."
## HL6 "." "." "." "." "." "." "." "." "." "."
## HL7 "." "." "." "." "." "." "." "." "." "."
## HL8 "." "." "." "." "." "." "." "." "." "."
## HL9 "." "." "." "." "." "." ">.1" "." "." "."
## HL10 "." "." "." "." "." "." "." "." "." "."
## HL11 "Diag" "." "." "." "." "." "." "." "." "."
## HL12 "." "Diag" "." "." "." ">.1" "." "." "." "."
## HL13 "." "." "Diag" ">.1" "." "." "." "." "." "."
## HL14 "." "." ">.1" "Diag" "." "." "." "." "." "."
## HL15 "." "." "." "." "Diag" "." "." "." "." "."
## HL16 "." ">.1" "." "." "." "Diag" "." "." "." "."
## HL17 "." "." "." "." "." "." "Diag" ">.1" "." "."
## HL18 "." "." "." "." "." "." ">.1" "Diag" "." "."
## HL19 "." "." "." "." "." "." "." "." "Diag" "."
## HL20 "." "." "." "." "." "." "." "." "." "Diag"
## HL21 "." "." "." "." "." "." "." "." "." "."
## HL22 "." "." "." "." "." "." "." "." "." "."
## HL23 "." "." "." "." "." "." "." "." "." "."
## HL24 "." "." "." "." "." "." "." "." "." "."
## 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 "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