Modul 5 Analisis Multivariat
Nasliyatul Intani (24031554097) Aryanti Indah Lestari (24031554196)
2026-05-14
DESKRIPSI DATA
Dataset yang digunakan dalam penelitian ini dapat diakses melalu url berikut: https://data.mendeley.com/datasets/6jmv43nffk/2 Dataset ini merupakan data mengenai hubungan antara Teacher Self-Concept (TSC) dan Teacher Efficacy (TE) terhadap tingkat burnout guru. Burnout pada penelitian ini terdiri atas tiga konstruk, yaitu Emotional Exhaustion (EE), Depersonalization (DE), dan Reduced Personal Accomplishment (RPA). Data diperoleh dari survei terhadap guru di Indonesia dan dipublikasikan dalam jurnal Data in Brief oleh Prasojo dkk. (2020). Dataset dapat diakses melalui repository Mendeley Data.
Dataset terdiri dari 876 responden guru dari tiga kota di Indonesia, yaitu Jambi, Bandung, dan Yogyakarta. Instrumen penelitian menggunakan skala Likert 5 poin dengan rentang nilai 1–5. Variabel Teacher Self-Concept terdiri dari 5 indikator (TSC1–TSC5), Teacher Efficacy terdiri dari 5 indikator (TE1–TE5), Emotional Exhaustion terdiri dari 5 indikator (EE1–EE5), Depersonalization terdiri dari 3 indikator (DE1–DE3), dan Reduced Personal Accomplishment terdiri dari 5 indikator (RPA1–RPA5).
DESKRIPSI TUGAS
Penelitian ini menggunakan metode Structural Equation Modeling berbasis Covariance (SEM-CB) untuk menganalisis hubungan antara Teacher Self Concept, Teacher Efficacy, Emotional Exhaustion, Depersonalization, dan Reduced Personal Accomplishment. Tahapan penelitian meliputi eksplorasi data, preprocessing, uji asumsi (normalitas multivariat, multikolinearitas, dan KMO), Confirmatory Factor Analysis (CFA), serta analisis model struktural SEM. Hasil uji normalitas menunjukkan data tidak berdistribusi normal multivariat, sehingga estimasi parameter menggunakan metode Maximum Likelihood Robust (MLR) agar hasil estimasi tetap robust terhadap pelanggaran asumsi normalitas. Evaluasi model dilakukan menggunakan indeks kelayakan model seperti CFI, TLI, RMSEA, dan SRMR, serta pengujian validitas dan reliabilitas konstruk melalui nilai factor loading, Construct Reliability (CR), dan Average Variance Extracted (AVE).
install.packages("readxl")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("psych")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("car")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("lavaan")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("semPlot")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)library(readxl)
library(psych)
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitlibrary(lavaan)## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covlibrary(semPlot)## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod cardata <- read_excel("2. Response.xlsx")ANALYSIS DATA EKSPLORATIF
head(data)## # A tibble: 6 × 23
## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3 TE4 TE5 EE1 EE2 EE3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 4 4 4 4 4 4 4 4 4 4 4 4
## 2 4 4 4 4 4 4 4 4 4 4 4 4 4
## 3 4 4 5 4 5 5 4 4 5 4 4 5 5
## 4 4 4 5 4 5 5 4 4 5 4 4 5 5
## 5 4 5 3 4 4 4 4 4 4 4 4 4 4
## 6 4 5 3 4 4 4 4 4 4 4 4 4 4
## # ℹ 10 more variables: EE4 <dbl>, EE5 <dbl>, DE1 <dbl>, DE2 <dbl>, DE3 <dbl>,
## # RPA1 <dbl>, RPA2 <dbl>, RPA3 <dbl>, RPA4 <dbl>, RPA5 <dbl>str(data)## tibble [876 × 23] (S3: tbl_df/tbl/data.frame)
## $ TSC1: num [1:876] 4 4 4 4 4 4 1 1 2 2 ...
## $ TSC2: num [1:876] 4 4 4 4 5 5 4 4 2 2 ...
## $ TSC3: num [1:876] 4 4 5 5 3 3 4 4 2 2 ...
## $ TSC4: num [1:876] 4 4 4 4 4 4 4 4 2 2 ...
## $ TSC5: num [1:876] 4 4 5 5 4 4 4 4 2 2 ...
## $ TE1 : num [1:876] 4 4 5 5 4 4 3 3 1 1 ...
## $ TE2 : num [1:876] 4 4 4 4 4 4 3 3 2 2 ...
## $ TE3 : num [1:876] 4 4 4 4 4 4 3 3 1 1 ...
## $ TE4 : num [1:876] 4 4 5 5 4 4 3 3 1 1 ...
## $ TE5 : num [1:876] 4 4 4 4 4 4 3 3 1 1 ...
## $ EE1 : num [1:876] 4 4 4 4 4 4 4 4 1 1 ...
## $ EE2 : num [1:876] 4 4 5 5 4 4 4 4 1 1 ...
## $ EE3 : num [1:876] 4 4 5 5 4 4 4 4 1 1 ...
## $ EE4 : num [1:876] 4 4 4 4 4 4 4 4 1 1 ...
## $ EE5 : num [1:876] 4 4 5 5 4 4 3 3 1 1 ...
## $ DE1 : num [1:876] 4 4 5 5 4 4 1 1 3 3 ...
## $ DE2 : num [1:876] 4 4 5 5 4 4 1 1 2 2 ...
## $ DE3 : num [1:876] 4 4 5 5 4 4 4 4 2 2 ...
## $ RPA1: num [1:876] 4 4 5 5 4 4 3 3 2 2 ...
## $ RPA2: num [1:876] 4 4 4 4 4 4 3 3 1 2 ...
## $ RPA3: num [1:876] 4 4 5 5 4 4 3 4 2 1 ...
## $ RPA4: num [1:876] 4 4 4 4 4 4 4 3 2 2 ...
## $ RPA5: num [1:876] 4 4 5 5 4 4 4 4 1 1 ...options(repr.plot.width = 16, repr.plot.height = 4)
n_cols <- ncol(data)
par(mfrow = c(1, min(4, n_cols)), mar = c(4,4,2,1))
for(col in colnames(data)){
if(is.numeric(data[[col]])){
hist(
data[[col]],
main = paste("Histogram of", col),
xlab = col,
col = "lightblue",
border = "black"
)
} else {
counts <- table(data[[col]])
barplot(
counts,
main = paste("Barplot of", col),
xlab = col,
col = "lightgreen",
border = "black",
las = 2
)
}
}
par(mfrow = c(1,1))
boxplot(
data,
main = "Boxplot Semua Variabel",
col = "skyblue",
las = 2
)
Preprocessing
sum(is.na(data))## [1] 0colMeans(is.na(data)) * 100## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3 TE4 TE5 EE1 EE2 EE3 EE4 EE5 DE1
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DE2 DE3 RPA1 RPA2 RPA3 RPA4 RPA5
## 0 0 0 0 0 0 0duplikat <- data[duplicated(data), ]
duplikat## # A tibble: 3 × 23
## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3 TE4 TE5 EE1 EE2 EE3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 4 4 4 4 4 4 4 4 4 4 4 4
## 2 4 4 5 4 5 5 4 4 5 4 4 5 5
## 3 4 5 3 4 4 4 4 4 4 4 4 4 4
## # ℹ 10 more variables: EE4 <dbl>, EE5 <dbl>, DE1 <dbl>, DE2 <dbl>, DE3 <dbl>,
## # RPA1 <dbl>, RPA2 <dbl>, RPA3 <dbl>, RPA4 <dbl>, RPA5 <dbl>Statistika Deskriptif
summary(data)## TSC1 TSC2 TSC3 TSC4
## Min. :1.000 Min. :2.000 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.653 Mean :3.809 Mean :3.732 Mean :3.709
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## TSC5 TE1 TE2 TE3
## Min. :2.000 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.822 Mean :4.061 Mean :4.043 Mean :4.121
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## TE4 TE5 EE1 EE2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.105 Mean :3.902 Mean :3.812 Mean :3.727
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## EE3 EE4 EE5 DE1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.878 Mean :3.687 Mean :3.987 Mean :3.925
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## DE2 DE3 RPA1 RPA2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.596 Mean :3.817 Mean :3.933 Mean :3.941
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## RPA3 RPA4 RPA5
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000
## Mean :3.884 Mean :3.869 Mean :3.842
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000describe(data)## vars n mean sd median trimmed mad min max range skew kurtosis se
## TSC1 1 876 3.65 0.68 4 3.62 0.00 1 5 4 -0.09 0.06 0.02
## TSC2 2 876 3.81 0.64 4 3.78 0.00 2 5 3 -0.07 -0.14 0.02
## TSC3 3 876 3.73 0.64 4 3.71 0.00 2 5 3 -0.17 -0.02 0.02
## TSC4 4 876 3.71 0.67 4 3.67 0.00 2 5 3 -0.03 -0.25 0.02
## TSC5 5 876 3.82 0.65 4 3.79 0.00 2 5 3 -0.10 -0.13 0.02
## TE1 6 876 4.06 0.71 4 4.10 0.00 1 5 4 -0.47 0.38 0.02
## TE2 7 876 4.04 0.70 4 4.07 0.00 2 5 3 -0.22 -0.45 0.02
## TE3 8 876 4.12 0.71 4 4.17 0.00 1 5 4 -0.72 1.60 0.02
## TE4 9 876 4.11 0.69 4 4.15 0.00 1 5 4 -0.47 0.51 0.02
## TE5 10 876 3.90 0.75 4 3.92 0.00 1 5 4 -0.41 0.16 0.03
## EE1 11 876 3.81 0.76 4 3.81 0.00 1 5 4 -0.35 0.23 0.03
## EE2 12 876 3.73 0.85 4 3.75 1.48 1 5 4 -0.37 0.12 0.03
## EE3 13 876 3.88 0.83 4 3.91 1.48 1 5 4 -0.31 -0.40 0.03
## EE4 14 876 3.69 0.80 4 3.67 1.48 1 5 4 -0.03 -0.41 0.03
## EE5 15 876 3.99 0.81 4 4.03 1.48 1 5 4 -0.43 -0.27 0.03
## DE1 16 876 3.92 0.68 4 3.93 0.00 1 5 4 -0.53 1.25 0.02
## DE2 17 876 3.60 0.68 4 3.58 1.48 1 5 4 -0.22 0.64 0.02
## DE3 18 876 3.82 0.70 4 3.79 0.00 1 5 4 -0.14 0.01 0.02
## RPA1 19 876 3.93 0.83 4 3.97 1.48 1 5 4 -0.59 0.50 0.03
## RPA2 20 876 3.94 0.80 4 3.99 0.00 1 5 4 -0.79 1.22 0.03
## RPA3 21 876 3.88 0.79 4 3.91 0.00 1 5 4 -0.59 0.75 0.03
## RPA4 22 876 3.87 0.76 4 3.89 0.00 1 5 4 -0.48 0.33 0.03
## RPA5 23 876 3.84 0.79 4 3.86 0.00 1 5 4 -0.53 0.67 0.03Uji Asumsi
1. Normalitas Multivariat
mardia(data)
## Call: mardia(x = data)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 876 num.vars = 23
## b1p = 51.31 skew = 7491.94 with probability <= 0
## small sample skew = 7519.74 with probability <= 0
## b2p = 659.26 kurtosis = 36.77 with probability <= 02. Uji Multikolinearitas
data_manifest <- data[, c(
"TSC1","TSC2","TSC3","TSC4","TSC5",
"TE1","TE2","TE3","TE4","TE5",
"EE1","EE2","EE3","EE4","EE5",
"DE1","DE2","DE3",
"RPA1","RPA2","RPA3","RPA4","RPA5"
)]
data_manifest[] <- lapply(
data_manifest,
function(x) as.numeric(as.character(x))
)
# Determinan matriks kovarians
cov_matrix <- cov(
data_manifest,
use = "complete.obs"
)
det(cov_matrix)## [1] 4.25847e-12# VIF
data_manifest_clean <- na.omit(data_manifest)
model_vif <- lm(
EE1 ~
TSC1 + TSC2 + TSC3 + TSC4 + TSC5 +
TE1 + TE2 + TE3 + TE4 + TE5,
data = data_manifest_clean
)
vif(model_vif)## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3
## 1.603007 1.691369 1.571140 1.473345 1.698497 2.242678 1.954055 2.404414
## TE4 TE5
## 2.848394 1.6609943. Uji Kecukupan Sampel (Uji KMO)
r <- cor(data)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.94
## MSA for each item =
## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3 TE4 TE5 EE1 EE2 EE3 EE4 EE5 DE1
## 0.96 0.96 0.95 0.94 0.96 0.93 0.96 0.94 0.94 0.96 0.95 0.94 0.95 0.94 0.97 0.87
## DE2 DE3 RPA1 RPA2 RPA3 RPA4 RPA5
## 0.86 0.92 0.91 0.91 0.95 0.94 0.96CONFIRMATORY FACTOR ANALYSIS (CFA)
model_cfa_tsc <- '
TeacherSelfConcept =~
TSC1 + TSC2 + TSC3 + TSC4 + TSC5
'
model_cfa_te <- '
TeacherEfficacy =~
TE1 + TE2 + TE3 + TE4 + TE5
'
model_cfa_ee <- '
EmotionalExhaustion =~
EE1 + EE2 + EE3 + EE4 + EE5
'
model_cfa_de <- '
Depersonalization =~
DE1 + DE2 + DE3
'
model_cfa_rpa <- '
ReducedPersonalAccomplishment =~
RPA1 + RPA2 + RPA3 + RPA4 + RPA5
'
fit_tsc <- cfa(
model_cfa_tsc,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
fit_te <- cfa(
model_cfa_te,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
fit_ee <- cfa(
model_cfa_ee,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
fit_de <- cfa(
model_cfa_de,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
fit_rpa <- cfa(
model_cfa_rpa,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
summary(fit_tsc, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-21 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 8.171 6.611
## Degrees of freedom 5 5
## P-value (Chi-square) 0.147 0.251
## Scaling correction factor 1.236
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1215.817 998.361
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.218
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 0.998
## Tucker-Lewis Index (TLI) 0.995 0.997
##
## Robust Comparative Fit Index (CFI) 0.998
## Robust Tucker-Lewis Index (TLI) 0.997
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3772.404 -3772.404
## Scaling correction factor 1.078
## for the MLR correction
## Loglikelihood unrestricted model (H1) -3768.318 -3768.318
## Scaling correction factor 1.130
## for the MLR correction
##
## Akaike (AIC) 7564.808 7564.808
## Bayesian (BIC) 7612.561 7612.561
## Sample-size adjusted Bayesian (SABIC) 7580.804 7580.804
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.027 0.019
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.059 0.050
## P-value H_0: RMSEA <= 0.050 0.867 0.948
## P-value H_0: RMSEA >= 0.080 0.002 0.000
##
## Robust RMSEA 0.021
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.060
## P-value H_0: Robust RMSEA <= 0.050 0.874
## P-value H_0: Robust RMSEA >= 0.080 0.003
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.014 0.014
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TeacherSelfConcept =~
## TSC1 0.444 0.022 20.441 0.000 0.444 0.649
## TSC2 0.447 0.021 21.471 0.000 0.447 0.700
## TSC3 0.431 0.023 18.937 0.000 0.431 0.673
## TSC4 0.424 0.024 17.636 0.000 0.424 0.630
## TSC5 0.460 0.022 21.334 0.000 0.460 0.706
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.271 0.021 13.024 0.000 0.271 0.578
## .TSC2 0.208 0.015 14.180 0.000 0.208 0.510
## .TSC3 0.223 0.013 16.920 0.000 0.223 0.546
## .TSC4 0.273 0.017 15.725 0.000 0.273 0.603
## .TSC5 0.213 0.014 15.127 0.000 0.213 0.501
## TeachrSlfCncpt 1.000 1.000 1.000summary(fit_te, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-21 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 41.889 31.664
## Degrees of freedom 5 5
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.323
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2161.813 1692.357
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.277
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.983 0.984
## Tucker-Lewis Index (TLI) 0.966 0.968
##
## Robust Comparative Fit Index (CFI) 0.984
## Robust Tucker-Lewis Index (TLI) 0.967
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3661.736 -3661.736
## Scaling correction factor 1.228
## for the MLR correction
## Loglikelihood unrestricted model (H1) -3640.791 -3640.791
## Scaling correction factor 1.260
## for the MLR correction
##
## Akaike (AIC) 7343.471 7343.471
## Bayesian (BIC) 7391.225 7391.225
## Sample-size adjusted Bayesian (SABIC) 7359.467 7359.467
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092 0.078
## 90 Percent confidence interval - lower 0.067 0.056
## 90 Percent confidence interval - upper 0.118 0.101
## P-value H_0: RMSEA <= 0.050 0.003 0.018
## P-value H_0: RMSEA >= 0.080 0.798 0.474
##
## Robust RMSEA 0.090
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.121
## P-value H_0: Robust RMSEA <= 0.050 0.012
## P-value H_0: Robust RMSEA >= 0.080 0.735
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.023 0.023
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TeacherEfficacy =~
## TE1 0.554 0.025 22.424 0.000 0.554 0.778
## TE2 0.505 0.020 24.949 0.000 0.505 0.724
## TE3 0.573 0.027 21.109 0.000 0.573 0.813
## TE4 0.595 0.023 26.124 0.000 0.595 0.863
## TE5 0.479 0.028 17.260 0.000 0.479 0.636
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TE1 0.200 0.014 13.829 0.000 0.200 0.394
## .TE2 0.232 0.015 15.828 0.000 0.232 0.476
## .TE3 0.169 0.014 12.363 0.000 0.169 0.340
## .TE4 0.121 0.010 11.791 0.000 0.121 0.255
## .TE5 0.338 0.021 15.919 0.000 0.338 0.596
## TeacherEfficcy 1.000 1.000 1.000summary(fit_ee, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-21 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 67.496 47.218
## Degrees of freedom 5 5
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.429
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2112.226 1372.421
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.539
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.970 0.969
## Tucker-Lewis Index (TLI) 0.941 0.938
##
## Robust Comparative Fit Index (CFI) 0.971
## Robust Tucker-Lewis Index (TLI) 0.942
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4255.102 -4255.102
## Scaling correction factor 1.290
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4221.354 -4221.354
## Scaling correction factor 1.336
## for the MLR correction
##
## Akaike (AIC) 8530.204 8530.204
## Bayesian (BIC) 8577.957 8577.957
## Sample-size adjusted Bayesian (SABIC) 8546.200 8546.200
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.119 0.098
## 90 Percent confidence interval - lower 0.095 0.078
## 90 Percent confidence interval - upper 0.146 0.120
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.996 0.928
##
## Robust RMSEA 0.117
## 90 Percent confidence interval - lower 0.088
## 90 Percent confidence interval - upper 0.149
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.981
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.029 0.029
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EmotionalExhaustion =~
## EE1 0.562 0.025 22.821 0.000 0.562 0.741
## EE2 0.667 0.024 27.377 0.000 0.667 0.786
## EE3 0.654 0.024 27.632 0.000 0.654 0.790
## EE4 0.625 0.025 25.493 0.000 0.625 0.786
## EE5 0.585 0.027 21.317 0.000 0.585 0.722
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .EE1 0.259 0.019 13.831 0.000 0.259 0.451
## .EE2 0.274 0.021 12.844 0.000 0.274 0.382
## .EE3 0.257 0.019 13.317 0.000 0.257 0.376
## .EE4 0.242 0.021 11.442 0.000 0.242 0.382
## .EE5 0.314 0.023 13.539 0.000 0.314 0.478
## EmotionlExhstn 1.000 1.000 1.000summary(fit_de, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-21 ended normally after 12 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 553.051 459.938
## Degrees of freedom 3 3
## P-value 0.000 0.000
## Scaling correction factor 1.202
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2459.442 -2459.442
## Loglikelihood unrestricted model (H1) -2459.442 -2459.442
##
## Akaike (AIC) 4930.884 4930.884
## Bayesian (BIC) 4959.536 4959.536
## Sample-size adjusted Bayesian (SABIC) 4940.481 4940.481
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 NA
## 90 Percent confidence interval - lower 0.000 NA
## 90 Percent confidence interval - upper 0.000 NA
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Depersonalization =~
## DE1 0.458 0.032 14.351 0.000 0.458 0.678
## DE2 0.464 0.032 14.648 0.000 0.464 0.681
## DE3 0.500 0.031 16.099 0.000 0.500 0.717
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DE1 0.248 0.018 13.624 0.000 0.248 0.541
## .DE2 0.249 0.017 14.928 0.000 0.249 0.537
## .DE3 0.237 0.025 9.484 0.000 0.237 0.486
## Depersonaliztn 1.000 1.000 1.000summary(fit_rpa, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-21 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 119.445 70.302
## Degrees of freedom 5 5
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.699
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1927.234 941.139
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 2.048
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.940 0.930
## Tucker-Lewis Index (TLI) 0.881 0.860
##
## Robust Comparative Fit Index (CFI) 0.942
## Robust Tucker-Lewis Index (TLI) 0.884
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4305.671 -4305.671
## Scaling correction factor 1.874
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4245.948 -4245.948
## Scaling correction factor 1.816
## for the MLR correction
##
## Akaike (AIC) 8631.341 8631.341
## Bayesian (BIC) 8679.095 8679.095
## Sample-size adjusted Bayesian (SABIC) 8647.337 8647.337
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.162 0.122
## 90 Percent confidence interval - lower 0.137 0.103
## 90 Percent confidence interval - upper 0.187 0.142
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.159
## 90 Percent confidence interval - lower 0.127
## 90 Percent confidence interval - upper 0.193
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050 0.050
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## ReducedPersonalAccomplishment =~
## RPA1 0.701 0.029 23.958 0.000 0.701
## RPA2 0.703 0.030 23.144 0.000 0.703
## RPA3 0.583 0.033 17.721 0.000 0.583
## RPA4 0.459 0.033 13.948 0.000 0.459
## RPA5 0.417 0.035 11.760 0.000 0.417
## Std.all
##
## 0.841
## 0.874
## 0.739
## 0.601
## 0.531
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .RPA1 0.204 0.018 11.408 0.000 0.204 0.293
## .RPA2 0.153 0.016 9.528 0.000 0.153 0.236
## .RPA3 0.282 0.034 8.391 0.000 0.282 0.453
## .RPA4 0.374 0.031 11.998 0.000 0.374 0.639
## .RPA5 0.443 0.032 13.953 0.000 0.443 0.718
## RdcdPrsnlAccmp 1.000 1.000 1.000lavInspect(fit_tsc, "options")$estimator## [1] "ML"Visualisasi
semPaths(
object = fit_tsc,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(
lat = "lightblue",
man = "lightgreen"
)
)
semPaths(
object = fit_te,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(
lat = "lightblue",
man = "lightgreen"
)
)
semPaths(
object = fit_ee,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(
lat = "lightblue",
man = "lightgreen"
)
)
semPaths(
object = fit_de,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(
lat = "lightblue",
man = "lightgreen"
)
)
semPaths(
object = fit_rpa,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7,
edge.label.cex = 1.2,
label.cex = 1.3,
color = list(
lat = "lightblue",
man = "lightgreen"
)
)
Ringkasan Fit Indeks CFA
get_cr_ave <- function(fit_model){
std_loadings <- inspect(
fit_model,
"std"
)$lambda
loadings <- std_loadings[
std_loadings != 0
]
CR <- (sum(loadings)^2) /
(
(sum(loadings)^2) +
sum(1 - loadings^2)
)
AVE <- sum(loadings^2) / length(loadings)
return(c(CR = CR, AVE = AVE))
}
#CR dan AVE tiap konstruk
cr_ave_tsc <- get_cr_ave(fit_tsc)
cr_ave_te <- get_cr_ave(fit_te)
cr_ave_ee <- get_cr_ave(fit_ee)
cr_ave_de <- get_cr_ave(fit_de)
cr_ave_rpa <- get_cr_ave(fit_rpa)
cfa_fits <- data.frame(
Konstruk = c(
"TeacherSelfConcept",
"TeacherEfficacy",
"EmotionalExhaustion",
"Depersonalization",
"ReducedPersonalAccomplishment"
),
CFI = c(
fitMeasures(fit_tsc, "cfi"),
fitMeasures(fit_te, "cfi"),
fitMeasures(fit_ee, "cfi"),
fitMeasures(fit_de, "cfi"),
fitMeasures(fit_rpa, "cfi")
),
RMSEA = c(
fitMeasures(fit_tsc, "rmsea"),
fitMeasures(fit_te, "rmsea"),
fitMeasures(fit_ee, "rmsea"),
fitMeasures(fit_de, "rmsea"),
fitMeasures(fit_rpa, "rmsea")
),
SRMR = c(
fitMeasures(fit_tsc, "srmr"),
fitMeasures(fit_te, "srmr"),
fitMeasures(fit_ee, "srmr"),
fitMeasures(fit_de, "srmr"),
fitMeasures(fit_rpa, "srmr")
),
TLI = c(
fitMeasures(fit_tsc, "tli"),
fitMeasures(fit_te, "tli"),
fitMeasures(fit_ee, "tli"),
fitMeasures(fit_de, "tli"),
fitMeasures(fit_rpa, "tli")
),
CR = c(
cr_ave_tsc["CR"],
cr_ave_te["CR"],
cr_ave_ee["CR"],
cr_ave_de["CR"],
cr_ave_rpa["CR"]
),
AVE = c(
cr_ave_tsc["AVE"],
cr_ave_te["AVE"],
cr_ave_ee["AVE"],
cr_ave_de["AVE"],
cr_ave_rpa["AVE"]
)
)
print(cfa_fits)## Konstruk CFI RMSEA SRMR TLI
## 1 TeacherSelfConcept 0.9973702 0.02690697 1.362183e-02 0.9947404
## 2 TeacherEfficacy 0.9828566 0.09177285 2.278549e-02 0.9657131
## 3 EmotionalExhaustion 0.9702714 0.11945108 2.931875e-02 0.9405427
## 4 Depersonalization 1.0000000 0.00000000 1.231263e-08 1.0000000
## 5 ReducedPersonalAccomplishment 0.9403071 0.16164485 4.994111e-02 0.8806142
## CR AVE
## 1 0.8047031 0.4522464
## 2 0.8759030 0.5878660
## 3 0.8762210 0.5863799
## 4 0.7336059 0.4787741
## 5 0.8459721 0.5319149STRUCTURAL EQUATION MODELING (SEM)
model_sem <- '
TeacherSelfConcept =~
TSC1 + TSC2 + TSC3 + TSC4 + TSC5
TeacherEfficacy =~
TE1 + TE2 + TE3 + TE4 + TE5
EmotionalExhaustion =~
EE1 + EE2 + EE3 + EE4 + EE5
Depersonalization =~
DE1 + DE2 + DE3
ReducedPersonalAccomplishment =~
RPA1 + RPA2 + RPA3 + RPA4 + RPA5
EmotionalExhaustion ~ TeacherSelfConcept + TeacherEfficacy
Depersonalization ~ TeacherSelfConcept + TeacherEfficacy
ReducedPersonalAccomplishment ~ TeacherSelfConcept + TeacherEfficacy
'
fit_sem <- sem(
model_sem,
data = data,
std.lv = TRUE,
estimator = "MLR"
)
summary(
fit_sem,
fit.measures = TRUE,
standardized = TRUE
)## lavaan 0.6-21 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 876
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 863.884 770.948
## Degrees of freedom 220 220
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.121
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 10422.842 9011.931
## Degrees of freedom 253 253
## P-value 0.000 0.000
## Scaling correction factor 1.157
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.937 0.937
## Tucker-Lewis Index (TLI) 0.927 0.928
##
## Robust Comparative Fit Index (CFI) 0.939
## Robust Tucker-Lewis Index (TLI) 0.930
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17541.444 -17541.444
## Scaling correction factor 1.292
## for the MLR correction
## Loglikelihood unrestricted model (H1) -17109.502 -17109.502
## Scaling correction factor 1.155
## for the MLR correction
##
## Akaike (AIC) 35194.889 35194.889
## Bayesian (BIC) 35462.309 35462.309
## Sample-size adjusted Bayesian (SABIC) 35284.466 35284.466
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058 0.053
## 90 Percent confidence interval - lower 0.054 0.050
## 90 Percent confidence interval - upper 0.062 0.057
## P-value H_0: RMSEA <= 0.050 0.001 0.069
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.057
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.061
## P-value H_0: Robust RMSEA <= 0.050 0.006
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040 0.040
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## TeacherSelfConcept =~
## TSC1 0.452 0.021 21.751 0.000 0.452
## TSC2 0.449 0.020 22.339 0.000 0.449
## TSC3 0.424 0.022 18.856 0.000 0.424
## TSC4 0.417 0.023 18.023 0.000 0.417
## TSC5 0.464 0.021 22.479 0.000 0.464
## TeacherEfficacy =~
## TE1 0.548 0.025 22.142 0.000 0.548
## TE2 0.507 0.020 25.763 0.000 0.507
## TE3 0.566 0.027 20.966 0.000 0.566
## TE4 0.595 0.022 26.503 0.000 0.595
## TE5 0.495 0.027 18.254 0.000 0.495
## EmotionalExhaustion =~
## EE1 0.315 0.018 17.867 0.000 0.567
## EE2 0.358 0.020 17.866 0.000 0.644
## EE3 0.363 0.018 19.772 0.000 0.654
## EE4 0.337 0.020 17.146 0.000 0.606
## EE5 0.345 0.016 20.892 0.000 0.620
## Depersonalization =~
## DE1 0.353 0.027 13.217 0.000 0.454
## DE2 0.346 0.026 13.102 0.000 0.445
## DE3 0.406 0.025 16.176 0.000 0.522
## ReducedPersonalAccomplishment =~
## RPA1 0.491 0.028 17.743 0.000 0.699
## RPA2 0.481 0.027 17.679 0.000 0.685
## RPA3 0.416 0.022 18.947 0.000 0.593
## RPA4 0.333 0.020 16.239 0.000 0.474
## RPA5 0.302 0.023 12.909 0.000 0.431
## Std.all
##
## 0.660
## 0.703
## 0.663
## 0.620
## 0.711
##
## 0.771
## 0.727
## 0.804
## 0.864
## 0.657
##
## 0.748
## 0.759
## 0.791
## 0.762
## 0.766
##
## 0.670
## 0.653
## 0.748
##
## 0.839
## 0.852
## 0.752
## 0.620
## 0.548
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv
## EmotionalExhaustion ~
## TeachrSlfCncpt 0.980 0.110 8.881 0.000 0.544
## TeacherEfficcy 0.656 0.093 7.065 0.000 0.364
## Depersonalization ~
## TeachrSlfCncpt 0.512 0.089 5.737 0.000 0.398
## TeacherEfficcy 0.374 0.072 5.212 0.000 0.290
## ReducedPersonalAccomplishment ~
## TeachrSlfCncpt 0.591 0.074 8.001 0.000 0.415
## TeacherEfficcy 0.522 0.079 6.637 0.000 0.366
## Std.all
##
## 0.544
## 0.364
##
## 0.398
## 0.290
##
## 0.415
## 0.366
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TeacherSelfConcept ~~
## TeacherEfficcy 0.662 0.031 21.143 0.000 0.662 0.662
## .EmotionalExhaustion ~~
## .Depersonaliztn -0.070 0.067 -1.049 0.294 -0.070 -0.070
## .RdcdPrsnlAccmp 0.371 0.049 7.523 0.000 0.371 0.371
## .Depersonalization ~~
## .RdcdPrsnlAccmp -0.123 0.054 -2.280 0.023 -0.123 -0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.264 0.017 15.168 0.000 0.264 0.564
## .TSC2 0.206 0.013 15.372 0.000 0.206 0.506
## .TSC3 0.229 0.012 18.611 0.000 0.229 0.560
## .TSC4 0.279 0.016 17.228 0.000 0.279 0.616
## .TSC5 0.210 0.012 16.894 0.000 0.210 0.494
## .TE1 0.206 0.014 14.660 0.000 0.206 0.406
## .TE2 0.229 0.014 16.845 0.000 0.229 0.471
## .TE3 0.176 0.013 13.842 0.000 0.176 0.354
## .TE4 0.121 0.010 12.630 0.000 0.121 0.254
## .TE5 0.323 0.020 16.049 0.000 0.323 0.569
## .EE1 0.254 0.018 14.238 0.000 0.254 0.441
## .EE2 0.305 0.024 12.858 0.000 0.305 0.424
## .EE3 0.257 0.018 14.553 0.000 0.257 0.375
## .EE4 0.265 0.019 14.038 0.000 0.265 0.419
## .EE5 0.271 0.019 14.258 0.000 0.271 0.413
## .DE1 0.252 0.017 14.538 0.000 0.252 0.550
## .DE2 0.267 0.015 17.674 0.000 0.267 0.574
## .DE3 0.215 0.022 9.839 0.000 0.215 0.441
## .RPA1 0.206 0.017 12.373 0.000 0.206 0.297
## .RPA2 0.178 0.016 11.248 0.000 0.178 0.275
## .RPA3 0.270 0.030 8.929 0.000 0.270 0.434
## .RPA4 0.360 0.030 12.047 0.000 0.360 0.615
## .RPA5 0.431 0.032 13.593 0.000 0.431 0.699
## TeachrSlfCncpt 1.000 1.000 1.000
## TeacherEfficcy 1.000 1.000 1.000
## .EmotionlExhstn 1.000 0.308 0.308
## .Depersonaliztn 1.000 0.604 0.604
## .RdcdPrsnlAccmp 1.000 0.493 0.493Visualisasi
semPaths(
object = fit_sem,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 7,
sizeLat = 7,
color = list(
lat = "lightblue",
man = "lightgreen"
),
edge.label.cex = 1.2,
label.cex = 1.3
)