Analisis Faktor - Faktor Psikologis yang Mempengaruhi Burnout pada Guru dengan Pendekatan SEM dan Pemodelan
Kelompok 3 - Analisis Multivariat 2023F
2025-05-18
Anggota Kelompok 3 :
- Elizabeth Hanov (23031554055)
- Aghnia Alya Amarilla (23031554102)
- Nashita Erha Fitri (23031554116)
Packages
packages <- c("psych", "GPArotation", "lavaan", "semPlot", "MASS", "ggplot2", "tidyverse", "cluster", "factoextra", "corrplot")
install.packages(setdiff(packages, rownames(installed.packages())))
lapply(packages, library, character.only = TRUE)## Warning: package 'psych' was built under R version 4.4.3## Warning: package 'GPArotation' was built under R version 4.4.3##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, varimin## Warning: package 'lavaan' was built under R version 4.4.3## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2cov## Warning: package 'semPlot' was built under R version 4.4.3## Warning: package 'MASS' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
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## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks MASS::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors## Warning: package 'cluster' was built under R version 4.4.3## Warning: package 'factoextra' was built under R version 4.4.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## Warning: package 'corrplot' was built under R version 4.4.3## corrplot 0.95 loaded## [[1]]
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Load Data
{data <- readxl::read_excel("C:/Users/asus/Downloads/2. Response.xlsx", sheet = "Form Responses 1")}Exploratory Data Analysis
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. :5.000str(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 ...2. Uji Validitas dan Kesesuaian Konstruk
EFA (Exploratory Factor Analysis)
KMO Test
KMO_result <- KMO(cor(data)) print(KMO_result)## Kaiser-Meyer-Olkin factor adequacy ## Call: KMO(r = cor(data)) ## 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 0.96
Bartlett’s test
bartlett_test <- cortest.bartlett(cor(data), n = nrow(data)) print(bartlett_test)## $chisq ## [1] 10309.81 ## ## $p.value ## [1] 0 ## ## $df ## [1] 253
Scree plot for determining number of factors
fa.parallel(data, fa = "fa", n.iter = 100)
## Parallel analysis suggests that the number of factors = 5 and the number of components = NAEFA with 5 factors (as teori)
efa_result <- fa(data, nfactors = 5, rotate = "varimax")
print(efa_result)## Factor Analysis using method = minres
## Call: fa(r = data, nfactors = 5, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR3 MR4 MR5 MR1 MR2 h2 u2 com
## TSC1 0.22 0.23 0.52 0.15 0.19 0.43 0.57 2.3
## TSC2 0.22 0.19 0.57 0.22 0.16 0.48 0.52 2.1
## TSC3 0.14 0.12 0.58 0.22 0.22 0.47 0.53 1.8
## TSC4 0.10 0.19 0.51 0.22 0.18 0.39 0.61 2.1
## TSC5 0.25 0.23 0.60 0.18 0.11 0.52 0.48 2.0
## TE1 0.69 0.19 0.19 0.15 0.16 0.60 0.40 1.5
## TE2 0.62 0.22 0.20 0.19 0.15 0.53 0.47 1.8
## TE3 0.73 0.25 0.17 0.17 0.11 0.67 0.33 1.5
## TE4 0.75 0.26 0.20 0.18 0.23 0.75 0.25 1.8
## TE5 0.46 0.22 0.21 0.28 0.21 0.43 0.57 3.2
## EE1 0.33 0.29 0.23 0.54 0.14 0.56 0.44 2.9
## EE2 0.16 0.27 0.30 0.68 0.09 0.67 0.33 1.9
## EE3 0.27 0.33 0.32 0.56 0.10 0.61 0.39 2.9
## EE4 0.23 0.23 0.30 0.65 0.11 0.63 0.37 2.0
## EE5 0.37 0.37 0.32 0.40 0.16 0.57 0.43 4.2
## DE1 0.22 0.07 0.22 -0.04 0.60 0.46 0.54 1.6
## DE2 0.07 0.11 0.12 0.14 0.70 0.54 0.46 1.2
## DE3 0.21 0.11 0.20 0.16 0.62 0.50 0.50 1.7
## RPA1 0.24 0.70 0.30 0.17 0.03 0.67 0.33 1.7
## RPA2 0.22 0.78 0.24 0.13 0.04 0.73 0.27 1.4
## RPA3 0.19 0.68 0.19 0.21 0.10 0.59 0.41 1.6
## RPA4 0.19 0.54 0.13 0.23 0.16 0.42 0.58 2.0
## RPA5 0.19 0.47 0.09 0.21 0.16 0.33 0.67 2.1
##
## MR3 MR4 MR5 MR1 MR2
## SS loadings 3.08 3.02 2.49 2.29 1.68
## Proportion Var 0.13 0.13 0.11 0.10 0.07
## Cumulative Var 0.13 0.27 0.37 0.47 0.55
## Proportion Explained 0.25 0.24 0.20 0.18 0.13
## Cumulative Proportion 0.25 0.49 0.68 0.87 1.00
##
## Mean item complexity = 2.1
## Test of the hypothesis that 5 factors are sufficient.
##
## df null model = 253 with the objective function = 11.9 with Chi Square = 10309.81
## df of the model are 148 and the objective function was 0.64
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 876 with the empirical chi square 233.97 with prob < 8.5e-06
## The total n.obs was 876 with Likelihood Chi Square = 555.41 with prob < 1.8e-48
##
## Tucker Lewis Index of factoring reliability = 0.93
## RMSEA index = 0.056 and the 90 % confidence intervals are 0.051 0.061
## BIC = -447.34
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR3 MR4 MR5 MR1 MR2
## Correlation of (regression) scores with factors 0.89 0.89 0.80 0.84 0.83
## Multiple R square of scores with factors 0.80 0.78 0.64 0.71 0.68
## Minimum correlation of possible factor scores 0.59 0.57 0.29 0.42 0.37fa.diagram(efa_result)
3. MDS (Perceptual Mapping)
dist_matrix <- dist(scale(data))
mds_fit <- cmdscale(dist_matrix, eig = TRUE, k = 2)
plot(mds_fit$points, type = "n", main = "MDS Plot (2D)")
text(mds_fit$points, labels = colnames(data), col = "blue", cex = 0.9)
4. SEM Model Specification
Konstruk sesuai dengan variabel TSC, TE, EE, DE, RPA
model_sem <- '
TSC =~ TSC1 + TSC2 + TSC3 + TSC4 + TSC5
TE =~ TE1 + TE2 + TE3 + TE4 + TE5
EE =~ EE1 + EE2 + EE3 + EE4 + EE5
DE =~ DE1 + DE2 + DE3
RPA =~ RPA1 + RPA2 + RPA3 + RPA4 + RPA5
# Structural paths (misalnya TSC & TE mempengaruhi burnout: EE, DE, RPA)
EE ~ TSC + TE
DE ~ TSC + TE
RPA ~ TSC + TE
'
fit_sem <- sem(model_sem, data = data)
summary(fit_sem, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-19 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 876
##
## Model Test User Model:
##
## Test statistic 863.884
## Degrees of freedom 220
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 10422.842
## Degrees of freedom 253
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.937
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17541.444
## Loglikelihood unrestricted model (H1) -17109.502
##
## Akaike (AIC) 35194.889
## Bayesian (BIC) 35462.309
## Sample-size adjusted Bayesian (SABIC) 35284.466
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.062
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC =~
## TSC1 1.000 0.452 0.660
## TSC2 0.993 0.057 17.461 0.000 0.449 0.703
## TSC3 0.938 0.056 16.654 0.000 0.424 0.663
## TSC4 0.923 0.059 15.739 0.000 0.417 0.620
## TSC5 1.026 0.058 17.631 0.000 0.464 0.711
## TE =~
## TE1 1.000 0.548 0.771
## TE2 0.925 0.042 22.024 0.000 0.507 0.727
## TE3 1.033 0.042 24.713 0.000 0.566 0.804
## TE4 1.086 0.041 26.770 0.000 0.595 0.864
## TE5 0.903 0.046 19.618 0.000 0.495 0.657
## EE =~
## EE1 1.000 0.567 0.748
## EE2 1.135 0.051 22.355 0.000 0.644 0.759
## EE3 1.154 0.049 23.358 0.000 0.654 0.791
## EE4 1.069 0.048 22.451 0.000 0.606 0.762
## EE5 1.094 0.048 22.564 0.000 0.620 0.766
## DE =~
## DE1 1.000 0.454 0.670
## DE2 0.981 0.066 14.838 0.000 0.445 0.653
## DE3 1.150 0.073 15.733 0.000 0.522 0.748
## RPA =~
## RPA1 1.000 0.699 0.839
## RPA2 0.980 0.034 29.186 0.000 0.685 0.852
## RPA3 0.848 0.034 24.764 0.000 0.593 0.752
## RPA4 0.678 0.035 19.312 0.000 0.474 0.620
## RPA5 0.616 0.037 16.661 0.000 0.431 0.548
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EE ~
## TSC 0.683 0.062 11.074 0.000 0.544 0.544
## TE 0.377 0.043 8.672 0.000 0.364 0.364
## DE ~
## TSC 0.400 0.061 6.598 0.000 0.398 0.398
## TE 0.240 0.046 5.227 0.000 0.290 0.290
## RPA ~
## TSC 0.642 0.077 8.323 0.000 0.415 0.415
## TE 0.467 0.059 7.943 0.000 0.366 0.366
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC ~~
## TE 0.164 0.014 11.953 0.000 0.662 0.662
## .EE ~~
## .DE -0.008 0.006 -1.217 0.224 -0.070 -0.070
## .RPA 0.057 0.009 6.453 0.000 0.371 0.371
## .DE ~~
## .RPA -0.021 0.009 -2.374 0.018 -0.123 -0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.264 0.015 18.146 0.000 0.264 0.564
## .TSC2 0.206 0.012 17.395 0.000 0.206 0.506
## .TSC3 0.229 0.013 18.102 0.000 0.229 0.560
## .TSC4 0.279 0.015 18.689 0.000 0.279 0.616
## .TSC5 0.210 0.012 17.214 0.000 0.210 0.494
## .TE1 0.206 0.012 17.583 0.000 0.206 0.406
## .TE2 0.229 0.012 18.373 0.000 0.229 0.471
## .TE3 0.176 0.011 16.728 0.000 0.176 0.354
## .TE4 0.121 0.009 14.178 0.000 0.121 0.254
## .TE5 0.323 0.017 19.213 0.000 0.323 0.569
## .EE1 0.254 0.014 18.009 0.000 0.254 0.441
## .EE2 0.305 0.017 17.795 0.000 0.305 0.424
## .EE3 0.257 0.015 17.071 0.000 0.257 0.375
## .EE4 0.265 0.015 17.734 0.000 0.265 0.419
## .EE5 0.271 0.015 17.661 0.000 0.271 0.413
## .DE1 0.252 0.016 15.549 0.000 0.252 0.550
## .DE2 0.267 0.017 16.066 0.000 0.267 0.574
## .DE3 0.215 0.017 12.626 0.000 0.215 0.441
## .RPA1 0.206 0.014 14.844 0.000 0.206 0.297
## .RPA2 0.178 0.013 14.170 0.000 0.178 0.275
## .RPA3 0.270 0.015 17.644 0.000 0.270 0.434
## .RPA4 0.360 0.019 19.376 0.000 0.360 0.615
## .RPA5 0.431 0.022 19.864 0.000 0.431 0.699
## TSC 0.204 0.020 10.283 0.000 1.000 1.000
## TE 0.301 0.023 13.070 0.000 1.000 1.000
## .EE 0.099 0.010 9.785 0.000 0.308 0.308
## .DE 0.124 0.014 8.796 0.000 0.604 0.604
## .RPA 0.241 0.019 12.734 0.000 0.493 0.493Plot SEM
semPaths(fit_sem, "std", layout = "tree", whatLabels = "std", edge.label.cex = 0.9, sizeMan = 5)
5. CFA for Model Refinement
fit_cfa <- cfa(model_sem, data = data)
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-19 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 876
##
## Model Test User Model:
##
## Test statistic 863.884
## Degrees of freedom 220
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 10422.842
## Degrees of freedom 253
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.937
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17541.444
## Loglikelihood unrestricted model (H1) -17109.502
##
## Akaike (AIC) 35194.889
## Bayesian (BIC) 35462.309
## Sample-size adjusted Bayesian (SABIC) 35284.466
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.062
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC =~
## TSC1 1.000 0.452 0.660
## TSC2 0.993 0.057 17.461 0.000 0.449 0.703
## TSC3 0.938 0.056 16.654 0.000 0.424 0.663
## TSC4 0.923 0.059 15.739 0.000 0.417 0.620
## TSC5 1.026 0.058 17.631 0.000 0.464 0.711
## TE =~
## TE1 1.000 0.548 0.771
## TE2 0.925 0.042 22.024 0.000 0.507 0.727
## TE3 1.033 0.042 24.713 0.000 0.566 0.804
## TE4 1.086 0.041 26.770 0.000 0.595 0.864
## TE5 0.903 0.046 19.618 0.000 0.495 0.657
## EE =~
## EE1 1.000 0.567 0.748
## EE2 1.135 0.051 22.355 0.000 0.644 0.759
## EE3 1.154 0.049 23.358 0.000 0.654 0.791
## EE4 1.069 0.048 22.451 0.000 0.606 0.762
## EE5 1.094 0.048 22.564 0.000 0.620 0.766
## DE =~
## DE1 1.000 0.454 0.670
## DE2 0.981 0.066 14.838 0.000 0.445 0.653
## DE3 1.150 0.073 15.733 0.000 0.522 0.748
## RPA =~
## RPA1 1.000 0.699 0.839
## RPA2 0.980 0.034 29.186 0.000 0.685 0.852
## RPA3 0.848 0.034 24.764 0.000 0.593 0.752
## RPA4 0.678 0.035 19.312 0.000 0.474 0.620
## RPA5 0.616 0.037 16.661 0.000 0.431 0.548
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EE ~
## TSC 0.683 0.062 11.074 0.000 0.544 0.544
## TE 0.377 0.043 8.672 0.000 0.364 0.364
## DE ~
## TSC 0.400 0.061 6.598 0.000 0.398 0.398
## TE 0.240 0.046 5.227 0.000 0.290 0.290
## RPA ~
## TSC 0.642 0.077 8.323 0.000 0.415 0.415
## TE 0.467 0.059 7.943 0.000 0.366 0.366
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC ~~
## TE 0.164 0.014 11.953 0.000 0.662 0.662
## .EE ~~
## .DE -0.008 0.006 -1.217 0.224 -0.070 -0.070
## .RPA 0.057 0.009 6.453 0.000 0.371 0.371
## .DE ~~
## .RPA -0.021 0.009 -2.374 0.018 -0.123 -0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.264 0.015 18.146 0.000 0.264 0.564
## .TSC2 0.206 0.012 17.395 0.000 0.206 0.506
## .TSC3 0.229 0.013 18.102 0.000 0.229 0.560
## .TSC4 0.279 0.015 18.689 0.000 0.279 0.616
## .TSC5 0.210 0.012 17.214 0.000 0.210 0.494
## .TE1 0.206 0.012 17.583 0.000 0.206 0.406
## .TE2 0.229 0.012 18.373 0.000 0.229 0.471
## .TE3 0.176 0.011 16.728 0.000 0.176 0.354
## .TE4 0.121 0.009 14.178 0.000 0.121 0.254
## .TE5 0.323 0.017 19.213 0.000 0.323 0.569
## .EE1 0.254 0.014 18.009 0.000 0.254 0.441
## .EE2 0.305 0.017 17.795 0.000 0.305 0.424
## .EE3 0.257 0.015 17.071 0.000 0.257 0.375
## .EE4 0.265 0.015 17.734 0.000 0.265 0.419
## .EE5 0.271 0.015 17.661 0.000 0.271 0.413
## .DE1 0.252 0.016 15.549 0.000 0.252 0.550
## .DE2 0.267 0.017 16.066 0.000 0.267 0.574
## .DE3 0.215 0.017 12.626 0.000 0.215 0.441
## .RPA1 0.206 0.014 14.844 0.000 0.206 0.297
## .RPA2 0.178 0.013 14.170 0.000 0.178 0.275
## .RPA3 0.270 0.015 17.644 0.000 0.270 0.434
## .RPA4 0.360 0.019 19.376 0.000 0.360 0.615
## .RPA5 0.431 0.022 19.864 0.000 0.431 0.699
## TSC 0.204 0.020 10.283 0.000 1.000 1.000
## TE 0.301 0.023 13.070 0.000 1.000 1.000
## .EE 0.099 0.010 9.785 0.000 0.308 0.308
## .DE 0.124 0.014 8.796 0.000 0.604 0.604
## .RPA 0.241 0.019 12.734 0.000 0.493 0.493Plot CFA
semPaths(fit_cfa, "std", layout = "tree", whatLabels = "std", edge.label.cex = 0.9, sizeMan = 5)
6. Interpretasi Hasil
Evaluasi Model Fit
fitMeasures(fit_sem, c("chisq", "df", "pvalue", "cfi", "rmsea", "tli"))## chisq df pvalue cfi rmsea tli
## 863.884 220.000 0.000 0.937 0.058 0.927Korelasi antar konstruk
cor_matrix <- cor(data)
corrplot(cor_matrix, method = "circle", tl.cex = 0.6)