Install dan load package yang diperlukan
library(readxl)
library(dplyr)
##
## 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)
library(tidyr)
library(DataExplorer)
library(lavaan)
## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.
library(semPlot)
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:lavaan':
##
## cor2cov
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(MVN)
##
## Attaching package: 'MVN'
## The following object is masked from 'package:psych':
##
## mardia
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
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>
names(data)
## [1] "TSC1" "TSC2" "TSC3" "TSC4" "TSC5" "TE1" "TE2" "TE3" "TE4" "TE5"
## [11] "EE1" "EE2" "EE3" "EE4" "EE5" "DE1" "DE2" "DE3" "RPA1" "RPA2"
## [21] "RPA3" "RPA4" "RPA5"
Missing value
sum(is.na(data))
## [1] 0
p <- ncol(data)
Duplicate data
duplikat <- data[duplicated(data), ]
print(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>
sum(duplicated(data))
## [1] 3
data_clean <- data %>% distinct()
data_clean <- data[!duplicated(data), ]
EDA
Statistik deskriptif
summary(data_clean)
## TSC1 TSC2 TSC3 TSC4 TSC5
## Min. :1.000 Min. :2.000 Min. :2.000 Min. :2.000 Min. :2.00
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00
## Median :4.000 Median :4.000 Median :4.000 Median :4.000 Median :4.00
## Mean :3.652 Mean :3.808 Mean :3.731 Mean :3.708 Mean :3.82
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## TE1 TE2 TE3 TE4 TE5
## Min. :1.00 Min. :2.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.06 Mean :4.044 Mean :4.121 Mean :4.104 Mean :3.901
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## EE1 EE2 EE3 EE4
## 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.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.811 Mean :3.725 Mean :3.876 Mean :3.686
## 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
## EE5 DE1 DE2 DE3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.986 Mean :3.923 Mean :3.593 Mean :3.816
## 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
## RPA1 RPA2 RPA3 RPA4 RPA5
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.00 Median :4.000 Median :4.000 Median :4.000
## Mean :3.931 Mean :3.94 Mean :3.882 Mean :3.868 Mean :3.841
## 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
Struktur data
str(data_clean)
## tibble [873 × 23] (S3: tbl_df/tbl/data.frame)
## $ TSC1: num [1:873] 4 4 4 1 1 2 2 2 2 2 ...
## $ TSC2: num [1:873] 4 4 5 4 4 2 2 2 2 2 ...
## $ TSC3: num [1:873] 4 5 3 4 4 2 2 2 2 2 ...
## $ TSC4: num [1:873] 4 4 4 4 4 2 2 2 2 4 ...
## $ TSC5: num [1:873] 4 5 4 4 4 2 2 2 2 2 ...
## $ TE1 : num [1:873] 4 5 4 3 3 1 1 2 2 2 ...
## $ TE2 : num [1:873] 4 4 4 3 3 2 2 2 2 2 ...
## $ TE3 : num [1:873] 4 4 4 3 3 1 1 3 3 2 ...
## $ TE4 : num [1:873] 4 5 4 3 3 1 1 2 2 2 ...
## $ TE5 : num [1:873] 4 4 4 3 3 1 1 2 2 2 ...
## $ EE1 : num [1:873] 4 4 4 4 4 1 1 3 3 2 ...
## $ EE2 : num [1:873] 4 5 4 4 4 1 1 2 2 2 ...
## $ EE3 : num [1:873] 4 5 4 4 4 1 1 2 2 3 ...
## $ EE4 : num [1:873] 4 4 4 4 4 1 1 2 2 3 ...
## $ EE5 : num [1:873] 4 5 4 3 3 1 1 2 2 2 ...
## $ DE1 : num [1:873] 4 5 4 1 1 3 3 2 2 2 ...
## $ DE2 : num [1:873] 4 5 4 1 1 2 2 1 1 3 ...
## $ DE3 : num [1:873] 4 5 4 4 4 2 2 2 2 2 ...
## $ RPA1: num [1:873] 4 5 4 3 3 2 2 1 1 4 ...
## $ RPA2: num [1:873] 4 4 4 3 3 1 2 1 1 4 ...
## $ RPA3: num [1:873] 4 5 4 3 4 2 1 1 2 2 ...
## $ RPA4: num [1:873] 4 4 4 4 3 2 2 2 1 4 ...
## $ RPA5: num [1:873] 4 5 4 4 4 1 1 2 2 4 ...
Distribusi untuk semua variabel numerik
plot_histogram(data_clean)
Korelasi antar variabel numerik
plot_correlation(data_clean, type = "continuous")
Boxplot per kolom
boxplot(numeric_data,
main = "Boxplot Semua Variabel Numerik (Base R)",
col = "skyblue",
las = 2, # Rotasi label sumbu x
outline = TRUE)
Uji Asumsi SEM
Uji Normalitas Multivariat
mardia(numeric_data)
## Test Statistic p.value
## 1 Mardia Skewness 7480.71917 0.000000e+00
## 2 Mardia Kurtosis 36.98154 2.267527e-299
Uji Multikolinearitas
vif_results <- data.frame(
Indikator = character(),
VIF = numeric(),
stringsAsFactors = FALSE
)
for (var in colnames(numeric_data)) {
predictors <- setdiff(colnames(numeric_data), var)
formula <- as.formula(paste(var, "~", paste(predictors, collapse = " + ")))
model <- lm(formula, data = numeric_data)
r2 <- summary(model)$r.squared
vif_val <- 1 / (1 - r2)
vif_results <- rbind(vif_results, data.frame(Indikator = var, VIF = round(vif_val, 4)))
}
print(vif_results)
## Indikator VIF
## 1 TSC1 1.6799
## 2 TSC2 1.7831
## 3 TSC3 1.6771
## 4 TSC4 1.5790
## 5 TSC5 1.8071
## 6 TE1 2.3015
## 7 TE2 2.0259
## 8 TE3 2.5286
## 9 TE4 3.1218
## 10 TE5 1.8096
## 11 EE1 2.2230
## 12 EE2 2.3546
## 13 EE3 2.4301
## 14 EE4 2.3608
## 15 EE5 2.2943
## 16 DE1 1.6019
## 17 DE2 1.5905
## 18 DE3 1.7023
## 19 RPA1 2.8888
## 20 RPA2 2.9097
## 21 RPA3 2.1238
## 22 RPA4 1.7219
## 23 RPA5 1.4854
Uji Validitas dan Reliabilitas
Uji validitas konstruk
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)
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-19 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 873
##
## Model Test User Model:
##
## Test statistic 863.501
## Degrees of freedom 220
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 10384.937
## Degrees of freedom 253
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.936
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17497.912
## Loglikelihood unrestricted model (H1) -17066.162
##
## Akaike (AIC) 35107.825
## Bayesian (BIC) 35375.053
## Sample-size adjusted Bayesian (SABIC) 35197.210
##
## 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 0.041
##
## 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 0.994 0.057 17.462 0.000 0.450 0.705
## TSC3 0.937 0.056 16.636 0.000 0.424 0.664
## TSC4 0.924 0.059 15.707 0.000 0.418 0.620
## TSC5 1.023 0.058 17.570 0.000 0.463 0.710
## TE =~
## TE1 1.000 0.549 0.770
## TE2 0.927 0.042 21.999 0.000 0.509 0.728
## TE3 1.036 0.042 24.693 0.000 0.568 0.805
## TE4 1.086 0.041 26.704 0.000 0.596 0.863
## TE5 0.904 0.046 19.589 0.000 0.496 0.657
## EE =~
## EE1 1.000 0.569 0.748
## EE2 1.132 0.051 22.317 0.000 0.644 0.759
## EE3 1.151 0.049 23.319 0.000 0.655 0.790
## EE4 1.068 0.048 22.436 0.000 0.607 0.763
## EE5 1.092 0.048 22.527 0.000 0.621 0.765
## DE =~
## DE1 1.000 0.453 0.669
## DE2 0.978 0.066 14.735 0.000 0.443 0.651
## DE3 1.150 0.074 15.630 0.000 0.521 0.747
## RPA =~
## RPA1 1.000 0.700 0.838
## RPA2 0.982 0.034 29.189 0.000 0.687 0.853
## RPA3 0.847 0.034 24.698 0.000 0.593 0.751
## RPA4 0.679 0.035 19.280 0.000 0.475 0.620
## RPA5 0.615 0.037 16.599 0.000 0.430 0.547
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TSC ~~
## TE 0.164 0.014 11.927 0.000 0.661 0.661
## EE 0.202 0.016 12.786 0.000 0.785 0.785
## DE 0.121 0.012 10.069 0.000 0.588 0.588
## RPA 0.208 0.017 12.132 0.000 0.656 0.656
## TE ~~
## EE 0.226 0.017 13.432 0.000 0.724 0.724
## DE 0.138 0.013 10.396 0.000 0.554 0.554
## RPA 0.246 0.019 13.049 0.000 0.640 0.640
## EE ~~
## DE 0.126 0.013 9.531 0.000 0.491 0.491
## RPA 0.292 0.021 13.927 0.000 0.735 0.735
## DE ~~
## RPA 0.120 0.015 8.028 0.000 0.379 0.379
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.265 0.015 18.117 0.000 0.265 0.565
## .TSC2 0.205 0.012 17.324 0.000 0.205 0.503
## .TSC3 0.228 0.013 18.056 0.000 0.228 0.559
## .TSC4 0.280 0.015 18.655 0.000 0.280 0.616
## .TSC5 0.210 0.012 17.207 0.000 0.210 0.495
## .TE1 0.206 0.012 17.565 0.000 0.206 0.407
## .TE2 0.230 0.013 18.335 0.000 0.230 0.470
## .TE3 0.176 0.011 16.674 0.000 0.176 0.353
## .TE4 0.121 0.009 14.173 0.000 0.121 0.254
## .TE5 0.324 0.017 19.179 0.000 0.324 0.568
## .EE1 0.254 0.014 17.965 0.000 0.254 0.440
## .EE2 0.305 0.017 17.768 0.000 0.305 0.424
## .EE3 0.257 0.015 17.049 0.000 0.257 0.375
## .EE4 0.266 0.015 17.693 0.000 0.266 0.419
## .EE5 0.272 0.015 17.634 0.000 0.272 0.414
## .DE1 0.253 0.016 15.516 0.000 0.253 0.552
## .DE2 0.267 0.017 16.057 0.000 0.267 0.576
## .DE3 0.216 0.017 12.600 0.000 0.216 0.442
## .RPA1 0.207 0.014 14.826 0.000 0.207 0.297
## .RPA2 0.177 0.013 14.088 0.000 0.177 0.273
## .RPA3 0.271 0.015 17.631 0.000 0.271 0.435
## .RPA4 0.361 0.019 19.345 0.000 0.361 0.616
## .RPA5 0.432 0.022 19.837 0.000 0.432 0.700
## TSC 0.205 0.020 10.260 0.000 1.000 1.000
## TE 0.301 0.023 13.040 0.000 1.000 1.000
## EE 0.323 0.026 12.512 0.000 1.000 1.000
## DE 0.205 0.021 9.639 0.000 1.000 1.000
## RPA 0.490 0.033 14.698 0.000 1.000 1.000
Uji reliabilitas konstruk
TSC_items <- numeric_data %>% select(TSC1, TSC2, TSC3, TSC4, TSC5)
TE_items <- numeric_data %>% select(TE1, TE2, TE3, TE4, TE5)
EE_items <- numeric_data %>% select(EE1, EE2, EE3, EE4, EE5)
DE_items <- numeric_data %>% select(DE1, DE2, DE3)
RPA_items <- numeric_data %>% select(RPA1, RPA2, RPA3, RPA4, RPA5)
# Hitung Cronbach's Alpha
alpha(TSC_items)
##
## Reliability analysis
## Call: alpha(x = TSC_items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.77 0.45 4.1 0.011 3.7 0.49 0.45
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.8 0.82
## Duhachek 0.78 0.8 0.82
##
## 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 0.00066 0.46
## TSC2 0.76 0.76 0.70 0.44 3.2 0.013 0.00117 0.44
## TSC3 0.77 0.77 0.71 0.45 3.3 0.013 0.00131 0.45
## TSC4 0.78 0.78 0.73 0.47 3.5 0.012 0.00068 0.47
## TSC5 0.76 0.76 0.70 0.44 3.1 0.013 0.00123 0.44
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TSC1 873 0.74 0.74 0.63 0.57 3.7 0.69
## TSC2 873 0.76 0.77 0.68 0.61 3.8 0.64
## TSC3 873 0.75 0.75 0.66 0.59 3.7 0.64
## TSC4 873 0.73 0.73 0.62 0.55 3.7 0.67
## TSC5 873 0.77 0.77 0.69 0.62 3.8 0.65
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## TSC1 0 0.03 0.38 0.51 0.09 0
## TSC2 0 0.01 0.28 0.59 0.11 0
## TSC3 0 0.02 0.31 0.58 0.08 0
## TSC4 0 0.02 0.35 0.53 0.10 0
## TSC5 0 0.01 0.28 0.58 0.12 0
alpha(TE_items)
##
## Reliability analysis
## Call: alpha(x = TE_items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.86 0.58 6.9 0.0069 4 0.58 0.59
##
## 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
## TE1 0.84 0.85 0.81 0.58 5.5 0.0086 0.0057 0.58
## TE2 0.85 0.85 0.82 0.59 5.8 0.0084 0.0100 0.59
## TE3 0.83 0.83 0.80 0.56 5.0 0.0094 0.0087 0.57
## TE4 0.82 0.83 0.79 0.54 4.8 0.0098 0.0055 0.56
## TE5 0.87 0.87 0.84 0.63 6.9 0.0071 0.0027 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TE1 873 0.81 0.82 0.76 0.70 4.1 0.71
## TE2 873 0.80 0.80 0.73 0.68 4.0 0.70
## TE3 873 0.85 0.85 0.81 0.75 4.1 0.71
## TE4 873 0.87 0.87 0.84 0.78 4.1 0.69
## TE5 873 0.75 0.74 0.63 0.59 3.9 0.76
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## TE1 0.00 0.01 0.17 0.55 0.26 0
## TE2 0.00 0.01 0.20 0.54 0.26 0
## TE3 0.01 0.00 0.14 0.56 0.29 0
## TE4 0.00 0.01 0.15 0.56 0.28 0
## TE5 0.00 0.03 0.23 0.53 0.20 0
alpha(EE_items)
##
## Reliability analysis
## Call: alpha(x = EE_items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.86 0.59 7.1 0.0067 3.8 0.66 0.59
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.88 0.89
## Duhachek 0.86 0.88 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 0.0031 0.61
## EE2 0.85 0.85 0.81 0.58 5.5 0.0085 0.0011 0.56
## EE3 0.84 0.84 0.81 0.58 5.4 0.0087 0.0028 0.56
## EE4 0.85 0.85 0.81 0.58 5.5 0.0086 0.0022 0.59
## EE5 0.86 0.86 0.82 0.60 6.0 0.0078 0.0016 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## EE1 873 0.80 0.81 0.74 0.69 3.8 0.76
## EE2 873 0.83 0.83 0.77 0.72 3.7 0.85
## EE3 873 0.84 0.83 0.78 0.73 3.9 0.83
## EE4 873 0.83 0.83 0.77 0.72 3.7 0.80
## EE5 873 0.79 0.79 0.72 0.67 4.0 0.81
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## EE1 0.00 0.03 0.28 0.52 0.17 0
## EE2 0.01 0.05 0.32 0.44 0.18 0
## EE3 0.00 0.04 0.27 0.44 0.24 0
## EE4 0.00 0.05 0.37 0.42 0.16 0
## EE5 0.00 0.03 0.23 0.45 0.28 0
alpha(DE_items)
##
## Reliability analysis
## Call: alpha(x = DE_items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.73 0.65 0.48 2.7 0.016 3.8 0.55 0.48
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.7 0.73 0.76
## Duhachek 0.7 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## DE1 0.65 0.65 0.49 0.49 1.9 0.023 NA 0.49
## DE2 0.65 0.65 0.48 0.48 1.9 0.024 NA 0.48
## DE3 0.63 0.63 0.46 0.46 1.7 0.025 NA 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## DE1 873 0.80 0.80 0.64 0.55 3.9 0.68
## DE2 873 0.80 0.80 0.64 0.55 3.6 0.68
## DE3 873 0.82 0.81 0.66 0.57 3.8 0.70
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## DE1 0.00 0.01 0.20 0.61 0.17 0
## DE2 0.01 0.02 0.41 0.49 0.07 0
## DE3 0.00 0.01 0.30 0.54 0.15 0
alpha(RPA_items)
##
## Reliability analysis
## Call: alpha(x = RPA_items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.84 0.83 0.52 5.4 0.0083 3.9 0.63 0.48
##
## 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
## RPA1 0.80 0.80 0.76 0.50 4.0 0.0111 0.0061 0.48
## RPA2 0.79 0.79 0.75 0.48 3.7 0.0118 0.0075 0.44
## RPA3 0.80 0.80 0.78 0.50 3.9 0.0112 0.0187 0.44
## RPA4 0.83 0.83 0.81 0.55 4.8 0.0095 0.0194 0.53
## RPA5 0.85 0.85 0.83 0.58 5.6 0.0084 0.0127 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## RPA1 873 0.83 0.82 0.79 0.70 3.9 0.83
## RPA2 873 0.85 0.85 0.83 0.75 3.9 0.81
## RPA3 873 0.82 0.82 0.77 0.71 3.9 0.79
## RPA4 873 0.74 0.75 0.64 0.59 3.9 0.77
## RPA5 873 0.69 0.69 0.55 0.52 3.8 0.79
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## RPA1 0.01 0.02 0.24 0.46 0.26 0
## RPA2 0.01 0.03 0.20 0.53 0.23 0
## RPA3 0.01 0.03 0.24 0.52 0.20 0
## RPA4 0.00 0.04 0.23 0.54 0.19 0
## RPA5 0.01 0.03 0.26 0.52 0.18 0
Modeling SEM
model_sem <- '
# Measurement Model
TSC =~ TSC1 + TSC2 + TSC3 + TSC4 + TSC5
TE =~ TE1 + TE2 + TE3 + TE5
EE =~ EE1 + EE2 + EE3 + EE4
DE =~ DE1 + DE2 + DE3
RPA =~ RPA1 + RPA2 + RPA3 + RPA4
# Structural Model
TE ~ TSC
EE ~ TSC + TE
DE ~ TSC + TE
RPA ~ TSC + TE
'
# Estimasi model SEM
fit_sem <- sem(model_sem, data = numeric_data, estimator = "MLR")
# Ringkasan hasil SEM
summary(fit_sem, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-19 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 50
##
## Number of observations 873
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 594.150 540.628
## Degrees of freedom 160 160
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.099
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 8328.798 7296.934
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.141
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.947 0.946
## Tucker-Lewis Index (TLI) 0.937 0.936
##
## Robust Comparative Fit Index (CFI) 0.948
## Robust Tucker-Lewis Index (TLI) 0.939
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15392.920 -15392.920
## Scaling correction factor 1.276
## for the MLR correction
## Loglikelihood unrestricted model (H1) -15095.845 -15095.845
## Scaling correction factor 1.141
## for the MLR correction
##
## Akaike (AIC) 30885.841 30885.841
## Bayesian (BIC) 31124.437 31124.437
## Sample-size adjusted Bayesian (SABIC) 30965.649 30965.649
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056 0.052
## 90 Percent confidence interval - lower 0.051 0.048
## 90 Percent confidence interval - upper 0.061 0.057
## P-value H_0: RMSEA <= 0.050 0.024 0.211
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.055
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.060
## P-value H_0: Robust RMSEA <= 0.050 0.061
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039 0.039
##
## 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
## TSC =~
## TSC1 1.000 0.454 0.662
## TSC2 0.985 0.047 20.977 0.000 0.447 0.700
## TSC3 0.935 0.053 17.780 0.000 0.424 0.665
## TSC4 0.922 0.057 16.049 0.000 0.419 0.621
## TSC5 1.021 0.048 21.191 0.000 0.463 0.711
## TE =~
## TE1 1.000 0.527 0.739
## TE2 1.007 0.044 22.781 0.000 0.530 0.759
## TE3 1.077 0.045 23.762 0.000 0.567 0.803
## TE5 0.950 0.051 18.529 0.000 0.500 0.663
## EE =~
## EE1 1.000 0.565 0.743
## EE2 1.197 0.048 24.793 0.000 0.676 0.797
## EE3 1.147 0.055 21.035 0.000 0.648 0.782
## EE4 1.104 0.052 21.169 0.000 0.623 0.783
## DE =~
## DE1 1.000 0.453 0.670
## DE2 0.981 0.069 14.155 0.000 0.445 0.653
## DE3 1.147 0.099 11.570 0.000 0.520 0.745
## RPA =~
## RPA1 1.000 0.710 0.851
## RPA2 0.977 0.029 33.627 0.000 0.694 0.861
## RPA3 0.824 0.048 17.025 0.000 0.585 0.742
## RPA4 0.650 0.051 12.750 0.000 0.462 0.603
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TE ~
## TSC 0.772 0.060 12.910 0.000 0.665 0.665
## EE ~
## TSC 0.669 0.073 9.165 0.000 0.538 0.538
## TE 0.364 0.060 6.026 0.000 0.339 0.339
## DE ~
## TSC 0.417 0.064 6.561 0.000 0.418 0.418
## TE 0.221 0.054 4.111 0.000 0.257 0.257
## RPA ~
## TSC 0.646 0.080 8.083 0.000 0.413 0.413
## TE 0.482 0.069 6.954 0.000 0.357 0.357
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .EE ~~
## .DE -0.009 0.008 -1.112 0.266 -0.076 -0.076
## .RPA 0.050 0.010 4.880 0.000 0.293 0.293
## .DE ~~
## .RPA -0.022 0.010 -2.209 0.027 -0.123 -0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .TSC1 0.264 0.017 15.185 0.000 0.264 0.562
## .TSC2 0.208 0.014 15.205 0.000 0.208 0.509
## .TSC3 0.227 0.012 18.469 0.000 0.227 0.558
## .TSC4 0.279 0.016 17.179 0.000 0.279 0.614
## .TSC5 0.210 0.012 16.862 0.000 0.210 0.495
## .TE1 0.230 0.015 15.425 0.000 0.230 0.453
## .TE2 0.207 0.014 15.121 0.000 0.207 0.424
## .TE3 0.177 0.013 13.569 0.000 0.177 0.355
## .TE5 0.320 0.021 15.450 0.000 0.320 0.561
## .EE1 0.258 0.019 13.743 0.000 0.258 0.448
## .EE2 0.262 0.020 12.979 0.000 0.262 0.365
## .EE3 0.266 0.019 13.969 0.000 0.266 0.388
## .EE4 0.246 0.019 12.713 0.000 0.246 0.388
## .DE1 0.253 0.017 14.461 0.000 0.253 0.552
## .DE2 0.266 0.015 17.390 0.000 0.266 0.573
## .DE3 0.217 0.022 9.841 0.000 0.217 0.445
## .RPA1 0.192 0.016 11.798 0.000 0.192 0.276
## .RPA2 0.168 0.016 10.802 0.000 0.168 0.259
## .RPA3 0.279 0.031 8.951 0.000 0.279 0.449
## .RPA4 0.373 0.031 11.840 0.000 0.373 0.636
## TSC 0.206 0.019 10.971 0.000 1.000 1.000
## .TE 0.155 0.016 9.439 0.000 0.557 0.557
## .EE 0.113 0.013 9.002 0.000 0.353 0.353
## .DE 0.127 0.019 6.509 0.000 0.617 0.617
## .RPA 0.255 0.028 9.190 0.000 0.505 0.505
semPaths(
fit_sem,
what = "std",
layout = "spring",
edge.label.cex = 0.9,
sizeMan = 4,
sizeLat = 5,
nCharNodes = 0,
residuals = FALSE,
optimizeLatRes = TRUE,
edge.color = "black",
asize = 3
)
