Install dan load package yang diperlukan
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.4.3
library(DataExplorer)
## Warning: package 'DataExplorer' was built under R version 4.4.3
library(lavaan)
## 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.
library(semPlot)
## Warning: package 'semPlot' was built under R version 4.4.3
library(psych)
## Warning: package 'psych' was built under R version 4.4.3
##
## Attaching package: 'psych'
## The following object is masked from 'package:lavaan':
##
## cor2cov
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
Load Data
data <- read_excel("C:/Users/eliza/Downloads/2. Response(1).xlsx")
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>
Cek Missing Value
# Cek jumlah missing value per kolom
missing_counts <- colSums(is.na(data))
# Tampilkan hanya kolom yang punya missing value
missing_counts[missing_counts > 0]
## named numeric(0)
Handling Outliers
# Mengambil data numerik dari data asli
numeric_data <- data %>% dplyr::select(where(is.numeric))
# Simpan data asli untuk perbandingan (jika perlu)
numeric_data_raw <- numeric_data
# Fungsi Z-score clipping untuk outlier
zscore_clip <- function(x, z_thresh = 3) {
z <- scale(x)
x[z < -z_thresh] <- mean(x[z >= -z_thresh & z <= z_thresh], na.rm = TRUE)
x[z > z_thresh] <- mean(x[z >= -z_thresh & z <= z_thresh], na.rm = TRUE)
return(x)
}
# Terapkan clipping outlier (tanpa membuat NA)
numeric_data_clean <- numeric_data %>%
mutate(across(everything(), zscore_clip))
# Imputasi dengan median jika masih ada NA (cadangan, bisa dilepas kalau tidak perlu)
safe_median <- function(x) {
if (all(is.na(x))) return(0)
median(x, na.rm = TRUE)
}
numeric_data_imputed <- numeric_data_clean %>%
mutate(across(everything(), ~ ifelse(is.na(.), safe_median(.), .)))
# Cek missing value sebelum dan sesudah imputasi
cat("Missing setelah z-score clipping:\n")
## Missing setelah z-score clipping:
print(colSums(is.na(numeric_data_clean)))
## 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 0
cat("\nMissing setelah imputasi:\n")
##
## Missing setelah imputasi:
print(colSums(is.na(numeric_data_imputed)))
## 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 0
Uji Reliabilitas dengan Cronbach’s Alpha
# List variabel per konstruk
konstruk_list <- list(
TSC = c("TSC1", "TSC2", "TSC3", "TSC4", "TSC5"),
TE = c("TE1", "TE2", "TE3", "TE4", "TE5"),
EE = c("EE1", "EE2", "EE3", "EE4", "EE5"),
DE = c("DE1", "DE2", "DE3"),
RPA = c("RPA1", "RPA2", "RPA3", "RPA4", "RPA5")
)
# Uji alpha untuk setiap konstruk
for (nama_konstruk in names(konstruk_list)) {
indikator <- konstruk_list[[nama_konstruk]]
sub_data <- numeric_data_imputed[, indikator]
cat("\n Reliabilitas untuk konstruk:", nama_konstruk, "\n")
print(alpha(sub_data))
}
##
## Reliabilitas untuk konstruk: TSC
##
## Reliability analysis
## Call: alpha(x = sub_data)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.77 0.46 4.2 0.01 3.7 0.49 0.45
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.81 0.83
## Duhachek 0.79 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## TSC1 0.77 0.77 0.72 0.46 3.4 0.013 0.00061 0.46
## TSC2 0.76 0.76 0.71 0.45 3.2 0.013 0.00120 0.45
## TSC3 0.77 0.77 0.72 0.46 3.4 0.013 0.00118 0.45
## TSC4 0.78 0.78 0.73 0.47 3.6 0.012 0.00053 0.47
## TSC5 0.76 0.76 0.71 0.44 3.2 0.013 0.00094 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TSC1 876 0.75 0.74 0.65 0.58 3.7 0.67
## TSC2 876 0.76 0.77 0.68 0.61 3.8 0.64
## TSC3 876 0.75 0.75 0.66 0.59 3.7 0.64
## TSC4 876 0.73 0.73 0.62 0.56 3.7 0.67
## TSC5 876 0.77 0.77 0.69 0.62 3.8 0.65
##
## Non missing response frequency for each item
## 2 3 3.65903890160183 4 5 miss
## TSC1 0.03 0.38 0 0.51 0.09 0
## TSC2 0.01 0.28 0 0.59 0.12 0
## TSC3 0.02 0.31 0 0.58 0.09 0
## TSC4 0.02 0.35 0 0.53 0.10 0
## TSC5 0.01 0.28 0 0.58 0.13 0
##
## Reliabilitas untuk konstruk: TE
##
## Reliability analysis
## Call: alpha(x = sub_data)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.83 0.53 5.7 0.0082 4.1 0.54 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.85 0.86
## Duhachek 0.83 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## TE1 0.81 0.82 0.78 0.53 4.5 0.0104 0.0051 0.51
## TE2 0.81 0.82 0.78 0.53 4.5 0.0104 0.0081 0.51
## TE3 0.81 0.81 0.77 0.51 4.2 0.0108 0.0060 0.51
## TE4 0.80 0.80 0.76 0.50 4.1 0.0111 0.0043 0.51
## TE5 0.85 0.85 0.81 0.58 5.5 0.0085 0.0021 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TE1 876 0.80 0.79 0.73 0.66 4.1 0.70
## TE2 876 0.79 0.79 0.72 0.66 4.0 0.70
## TE3 876 0.81 0.82 0.76 0.70 4.2 0.64
## TE4 876 0.83 0.83 0.79 0.72 4.1 0.64
## TE5 876 0.73 0.72 0.60 0.55 3.9 0.74
##
## Non missing response frequency for each item
## 2 3 3.90846681922197 4 4.0675057208238 4.13163972286374
## TE1 0.01 0.17 0 0.55 0 0.00
## TE2 0.01 0.20 0 0.54 0 0.00
## TE3 0.00 0.14 0 0.56 0 0.00
## TE4 0.00 0.15 0 0.56 0 0.01
## TE5 0.03 0.23 0 0.54 0 0.00
## 4.15242494226328 5 miss
## TE1 0.00 0.26 0
## TE2 0.00 0.26 0
## TE3 0.01 0.29 0
## TE4 0.00 0.28 0
## TE5 0.00 0.20 0
##
## Reliabilitas untuk konstruk: EE
##
## Reliability analysis
## Call: alpha(x = sub_data)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.85 0.58 6.9 0.0068 3.8 0.64 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.87 0.89
## Duhachek 0.86 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## EE1 0.85 0.85 0.82 0.59 5.8 0.0081 0.0017 0.60
## EE2 0.84 0.84 0.80 0.57 5.2 0.0088 0.0012 0.55
## EE3 0.84 0.84 0.81 0.57 5.4 0.0087 0.0019 0.55
## EE4 0.84 0.84 0.81 0.58 5.4 0.0086 0.0011 0.58
## EE5 0.85 0.85 0.82 0.59 5.8 0.0081 0.0018 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## EE1 876 0.79 0.80 0.72 0.67 3.8 0.73
## EE2 876 0.83 0.83 0.78 0.73 3.8 0.80
## EE3 876 0.83 0.82 0.77 0.72 3.9 0.82
## EE4 876 0.82 0.82 0.76 0.71 3.7 0.79
## EE5 876 0.80 0.80 0.72 0.68 4.0 0.80
##
## Non missing response frequency for each item
## 2 3 3.69336384439359 3.75866050808314 3.82454128440367
## EE1 0.03 0.28 0 0.00 0
## EE2 0.05 0.32 0 0.01 0
## EE3 0.04 0.27 0 0.00 0
## EE4 0.05 0.37 0 0.00 0
## EE5 0.03 0.23 0 0.00 0
## 3.88443935926773 3.99427917620137 4 5 miss
## EE1 0 0 0.52 0.17 0
## EE2 0 0 0.44 0.18 0
## EE3 0 0 0.45 0.24 0
## EE4 0 0 0.42 0.16 0
## EE5 0 0 0.45 0.28 0
##
## Reliabilitas untuk konstruk: DE
##
## Reliability analysis
## Call: alpha(x = sub_data)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.71 0.62 0.45 2.4 0.017 3.8 0.52 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.67 0.71 0.74
## Duhachek 0.67 0.71 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## DE1 0.63 0.63 0.46 0.46 1.7 0.025 NA 0.46
## DE2 0.65 0.65 0.48 0.48 1.8 0.024 NA 0.48
## DE3 0.57 0.57 0.40 0.40 1.3 0.029 NA 0.40
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## DE1 876 0.78 0.79 0.61 0.51 3.9 0.65
## DE2 876 0.78 0.78 0.60 0.50 3.6 0.65
## DE3 876 0.82 0.81 0.67 0.56 3.8 0.69
##
## Non missing response frequency for each item
## 2 3 3.61379310344828 3.82379862700229 3.93807339449541 4 5 miss
## DE1 0.01 0.20 0.00 0 0 0.61 0.17 0
## DE2 0.02 0.41 0.01 0 0 0.49 0.07 0
## DE3 0.01 0.30 0.00 0 0 0.54 0.15 0
##
## Reliabilitas untuk konstruk: RPA
##
## Reliability analysis
## Call: alpha(x = sub_data)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.82 0.5 5 0.0089 3.9 0.58 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.83 0.85
## Duhachek 0.82 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## RPA1 0.78 0.78 0.74 0.48 3.7 0.0119 0.0051 0.47
## RPA2 0.78 0.78 0.74 0.47 3.5 0.0122 0.0078 0.44
## RPA3 0.78 0.78 0.76 0.47 3.6 0.0122 0.0165 0.43
## RPA4 0.81 0.81 0.79 0.52 4.4 0.0103 0.0203 0.52
## RPA5 0.84 0.84 0.81 0.56 5.1 0.0091 0.0105 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## RPA1 876 0.81 0.81 0.78 0.69 4.0 0.77
## RPA2 876 0.82 0.82 0.79 0.71 4.0 0.73
## RPA3 876 0.82 0.82 0.76 0.70 3.9 0.74
## RPA4 876 0.74 0.74 0.63 0.58 3.9 0.75
## RPA5 876 0.68 0.68 0.54 0.50 3.9 0.74
##
## Non missing response frequency for each item
## 2 3 3.86866359447005 3.87857961053837 3.91013824884793
## RPA1 0.02 0.24 0.00 0 0.00
## RPA2 0.03 0.20 0.00 0 0.00
## RPA3 0.03 0.24 0.00 0 0.01
## RPA4 0.04 0.23 0.00 0 0.00
## RPA5 0.03 0.26 0.01 0 0.00
## 3.96651270207852 3.97803468208092 4 5 miss
## RPA1 0.01 0.00 0.46 0.26 0
## RPA2 0.00 0.01 0.53 0.23 0
## RPA3 0.00 0.00 0.52 0.20 0
## RPA4 0.00 0.00 0.54 0.19 0
## RPA5 0.00 0.00 0.52 0.18 0
colSums(is.na(numeric_data_clean))
## 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 0
Uji Validitas dengan EFA
# Uji KMO
KMO(numeric_data_clean)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = numeric_data_clean)
## Overall MSA = 0.94
## MSA for each item =
## TSC1 TSC2 TSC3 TSC4 TSC5 TE1 TE2 TE3 TE4 TE5 EE1 EE2 EE3 EE4 EE5 DE1
## 0.95 0.96 0.94 0.94 0.96 0.94 0.95 0.92 0.92 0.96 0.95 0.94 0.95 0.93 0.98 0.87
## DE2 DE3 RPA1 RPA2 RPA3 RPA4 RPA5
## 0.85 0.90 0.92 0.91 0.95 0.96 0.95
# Menentukan jumlah faktor jika kamu sudah tahu (misalnya 3 faktor)
efa_result <- fa(numeric_data_clean, nfactors = 3, rotate = "varimax")
# Menampilkan hasil loading faktor
print(efa_result$loadings, cutoff = 0.3)
##
## Loadings:
## MR1 MR3 MR2
## TSC1 0.405 0.454
## TSC2 0.419 0.472
## TSC3 0.359 0.543
## TSC4 0.382 0.452
## TSC5 0.435 0.449
## TE1 0.658
## TE2 0.611
## TE3 0.701
## TE4 0.738
## TE5 0.342 0.449
## EE1 0.541 0.360
## EE2 0.670
## EE3 0.613
## EE4 0.548 0.317
## EE5 0.577 0.362
## DE1 0.555
## DE2 0.505
## DE3 0.593
## RPA1 0.725
## RPA2 0.702
## RPA3 0.666
## RPA4 0.534
## RPA5 0.457
##
## MR1 MR3 MR2
## SS loadings 4.953 2.932 2.787
## Proportion Var 0.215 0.127 0.121
## Cumulative Var 0.215 0.343 0.464
# Parallel Analysis (menentukan jumlah faktor optimal)
fa.parallel(numeric_data_clean, fa = "fa")
## Parallel analysis suggests that the number of factors = 6 and the number of components = NA
Modeling dengan SEM
# Spesifikasi model SEM berdasarkan teori dan hasil EFA
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 0.493
# Menjalankan SEM
fit <- sem(model_sem, data = numeric_data_clean)
# Ringkasan hasil SEM
summary(fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-19 ended normally after 64 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 876
##
## Model Test User Model:
##
## Test statistic 837.560
## Degrees of freedom 220
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 9660.461
## Degrees of freedom 253
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.934
## Tucker-Lewis Index (TLI) 0.925
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17203.104
## Loglikelihood unrestricted model (H1) -16784.323
##
## Akaike (AIC) 34518.207
## Bayesian (BIC) 34785.628
## Sample-size adjusted Bayesian (SABIC) 34607.784
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.057
## 90 Percent confidence interval - lower 0.053
## 90 Percent confidence interval - upper 0.061
## P-value H_0: RMSEA <= 0.050 0.004
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## 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 0.451 0.671
## TSC2 0.994 0.056 17.664 0.000 0.449 0.703
## TSC3 0.946 0.056 16.927 0.000 0.427 0.668
## TSC4 0.926 0.058 15.915 0.000 0.418 0.621
## TSC5 1.027 0.058 17.831 0.000 0.463 0.711
## TE =~
## TE1 1.000 0.516 0.740
## TE2 0.982 0.048 20.577 0.000 0.506 0.726
## TE3 0.941 0.044 21.526 0.000 0.485 0.759
## TE4 1.000 0.044 22.755 0.000 0.516 0.804
## TE5 0.909 0.051 17.877 0.000 0.469 0.633
## EE =~
## EE1 1.000 0.543 0.739
## EE2 1.146 0.050 22.717 0.000 0.622 0.782
## EE3 1.165 0.052 22.495 0.000 0.632 0.774
## EE4 1.086 0.050 21.765 0.000 0.589 0.750
## EE5 1.120 0.051 22.120 0.000 0.608 0.762
## DE =~
## DE1 1.000 0.414 0.640
## DE2 0.935 0.070 13.339 0.000 0.387 0.599
## DE3 1.265 0.087 14.550 0.000 0.524 0.765
## RPA =~
## RPA1 1.000 0.636 0.823
## RPA2 0.930 0.036 26.040 0.000 0.591 0.806
## RPA3 0.880 0.036 24.118 0.000 0.559 0.758
## RPA4 0.730 0.039 18.933 0.000 0.464 0.622
## RPA5 0.621 0.039 15.921 0.000 0.395 0.536
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EE ~
## TSC 0.624 0.057 11.032 0.000 0.519 0.519
## TE 0.414 0.044 9.356 0.000 0.394 0.394
## DE ~
## TSC 0.411 0.056 7.333 0.000 0.448 0.448
## TE 0.173 0.044 3.945 0.000 0.216 0.216
## RPA ~
## TSC 0.589 0.069 8.586 0.000 0.418 0.418
## TE 0.452 0.057 7.985 0.000 0.367 0.367
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC ~~
## TE 0.146 0.013 11.449 0.000 0.627 0.627
## .EE ~~
## .DE -0.006 0.006 -1.097 0.273 -0.063 -0.063
## .RPA 0.061 0.008 7.386 0.000 0.441 0.441
## .DE ~~
## .RPA -0.014 0.008 -1.751 0.080 -0.093 -0.093
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.249 0.014 17.913 0.000 0.249 0.550
## .TSC2 0.206 0.012 17.323 0.000 0.206 0.506
## .TSC3 0.227 0.013 17.969 0.000 0.227 0.554
## .TSC4 0.278 0.015 18.624 0.000 0.278 0.614
## .TSC5 0.210 0.012 17.148 0.000 0.210 0.494
## .TE1 0.219 0.013 17.371 0.000 0.219 0.452
## .TE2 0.230 0.013 17.664 0.000 0.230 0.473
## .TE3 0.173 0.010 16.932 0.000 0.173 0.424
## .TE4 0.146 0.009 15.561 0.000 0.146 0.354
## .TE5 0.329 0.017 18.986 0.000 0.329 0.599
## .EE1 0.245 0.013 18.129 0.000 0.245 0.454
## .EE2 0.246 0.014 17.259 0.000 0.246 0.389
## .EE3 0.267 0.015 17.432 0.000 0.267 0.400
## .EE4 0.269 0.015 17.928 0.000 0.269 0.437
## .EE5 0.267 0.015 17.700 0.000 0.267 0.419
## .DE1 0.247 0.016 15.862 0.000 0.247 0.590
## .DE2 0.268 0.016 16.931 0.000 0.268 0.641
## .DE3 0.194 0.018 10.972 0.000 0.194 0.415
## .RPA1 0.192 0.013 14.922 0.000 0.192 0.322
## .RPA2 0.188 0.012 15.624 0.000 0.188 0.350
## .RPA3 0.232 0.014 17.110 0.000 0.232 0.426
## .RPA4 0.341 0.018 19.167 0.000 0.341 0.613
## .RPA5 0.387 0.020 19.809 0.000 0.387 0.713
## TSC 0.204 0.019 10.481 0.000 1.000 1.000
## TE 0.266 0.022 12.173 0.000 1.000 1.000
## .EE 0.094 0.010 9.738 0.000 0.320 0.320
## .DE 0.108 0.013 8.322 0.000 0.632 0.632
## .RPA 0.201 0.017 12.176 0.000 0.498 0.498
CFA
# Model CFA tanpa structural path
model_cfa <- '
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
'
fit_cfa <- cfa(model_cfa, data = numeric_data_imputed)
# Ringkasan hasil CFA
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-19 ended normally after 68 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 876
##
## Model Test User Model:
##
## Test statistic 837.560
## Degrees of freedom 220
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 9660.461
## Degrees of freedom 253
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.934
## Tucker-Lewis Index (TLI) 0.925
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17203.104
## Loglikelihood unrestricted model (H1) -16784.323
##
## Akaike (AIC) 34518.207
## Bayesian (BIC) 34785.628
## Sample-size adjusted Bayesian (SABIC) 34607.784
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.057
## 90 Percent confidence interval - lower 0.053
## 90 Percent confidence interval - upper 0.061
## P-value H_0: RMSEA <= 0.050 0.004
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## 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 0.451 0.671
## TSC2 0.994 0.056 17.664 0.000 0.449 0.703
## TSC3 0.946 0.056 16.927 0.000 0.427 0.668
## TSC4 0.926 0.058 15.915 0.000 0.418 0.621
## TSC5 1.027 0.058 17.831 0.000 0.463 0.711
## TE =~
## TE1 1.000 0.516 0.740
## TE2 0.982 0.048 20.577 0.000 0.506 0.726
## TE3 0.941 0.044 21.526 0.000 0.485 0.759
## TE4 1.000 0.044 22.755 0.000 0.516 0.804
## TE5 0.909 0.051 17.877 0.000 0.469 0.633
## EE =~
## EE1 1.000 0.543 0.739
## EE2 1.146 0.050 22.717 0.000 0.622 0.782
## EE3 1.165 0.052 22.495 0.000 0.632 0.774
## EE4 1.086 0.050 21.765 0.000 0.589 0.750
## EE5 1.120 0.051 22.120 0.000 0.608 0.762
## DE =~
## DE1 1.000 0.414 0.640
## DE2 0.935 0.070 13.339 0.000 0.387 0.599
## DE3 1.265 0.087 14.550 0.000 0.524 0.765
## RPA =~
## RPA1 1.000 0.636 0.823
## RPA2 0.930 0.036 26.040 0.000 0.591 0.806
## RPA3 0.880 0.036 24.118 0.000 0.559 0.758
## RPA4 0.730 0.039 18.933 0.000 0.464 0.622
## RPA5 0.621 0.039 15.921 0.000 0.395 0.536
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC ~~
## TE 0.146 0.013 11.449 0.000 0.627 0.627
## EE 0.188 0.015 12.706 0.000 0.766 0.766
## DE 0.109 0.011 9.805 0.000 0.583 0.583
## RPA 0.186 0.015 12.049 0.000 0.648 0.648
## TE ~~
## EE 0.201 0.015 13.024 0.000 0.719 0.719
## DE 0.106 0.011 9.278 0.000 0.496 0.496
## RPA 0.206 0.016 12.503 0.000 0.629 0.629
## EE ~~
## DE 0.105 0.012 8.999 0.000 0.469 0.469
## RPA 0.262 0.019 13.976 0.000 0.760 0.760
## DE ~~
## RPA 0.098 0.013 7.724 0.000 0.374 0.374
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.249 0.014 17.913 0.000 0.249 0.550
## .TSC2 0.206 0.012 17.323 0.000 0.206 0.506
## .TSC3 0.227 0.013 17.969 0.000 0.227 0.554
## .TSC4 0.278 0.015 18.624 0.000 0.278 0.614
## .TSC5 0.210 0.012 17.148 0.000 0.210 0.494
## .TE1 0.219 0.013 17.371 0.000 0.219 0.452
## .TE2 0.230 0.013 17.664 0.000 0.230 0.473
## .TE3 0.173 0.010 16.932 0.000 0.173 0.424
## .TE4 0.146 0.009 15.561 0.000 0.146 0.354
## .TE5 0.329 0.017 18.986 0.000 0.329 0.599
## .EE1 0.245 0.013 18.129 0.000 0.245 0.454
## .EE2 0.246 0.014 17.259 0.000 0.246 0.389
## .EE3 0.267 0.015 17.432 0.000 0.267 0.400
## .EE4 0.269 0.015 17.928 0.000 0.269 0.437
## .EE5 0.267 0.015 17.700 0.000 0.267 0.419
## .DE1 0.247 0.016 15.862 0.000 0.247 0.590
## .DE2 0.268 0.016 16.931 0.000 0.268 0.641
## .DE3 0.194 0.018 10.972 0.000 0.194 0.415
## .RPA1 0.192 0.013 14.922 0.000 0.192 0.322
## .RPA2 0.188 0.012 15.624 0.000 0.188 0.350
## .RPA3 0.232 0.014 17.110 0.000 0.232 0.426
## .RPA4 0.341 0.018 19.167 0.000 0.341 0.613
## .RPA5 0.387 0.020 19.809 0.000 0.387 0.713
## TSC 0.204 0.019 10.481 0.000 1.000 1.000
## TE 0.266 0.022 12.174 0.000 1.000 1.000
## EE 0.294 0.024 12.307 0.000 1.000 1.000
## DE 0.172 0.019 8.996 0.000 1.000 1.000
## RPA 0.404 0.028 14.214 0.000 1.000 1.000
semPaths(fit_cfa,
what = "std",
layout = "spring",
edge.label.cex = 0.9,
sizeMan = 4,
sizeLat = 5,
nCharNodes = 0,
residuals = FALSE,
optimizeLatRes = TRUE,
edge.color = "black")
