Analisis PLS-SEM Quality Assurance pada Mahasiswa
Vincentius Putra Mahardika & Muhammad Nadhif Fathoni
2026-05-24
# install.packages("readxl")
# install.packages("dplyr")
# install.packages("psych")
# install.packages("seminr")
# install.packages("corrplot")
# install.packages("ggplot2")
# install.packages("car")
# install.packages("knitr")
# install.packages("tidyr")
# install.packages("patchwork")
# install.packages("DT")
library(readxl)
library(dplyr)
library(psych)
library(seminr)
library(corrplot)
library(ggplot2)
library(car)
library(knitr)
library(tidyr)
library(patchwork)
library(DT)1. Pendahuluan
Penelitian ini menggunakan dataset mengenai Quality Assurance pada mahasiswa universitas di Vietnam. Analisis dilakukan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan bantuan software R.
Dataset terdiri dari 1323 responden dan 64 variabel.
2. Import Dataset
## Rows: 1,323
## Columns: 64
## $ Year <chr> "2", "3", "5", "5", "4", "4", "4", "4", "3", "4", "5", "4", "4"…
## $ Gender <chr> "Male", "Female", "Female", "Female", "Female", "Female", "Fema…
## $ Age <dbl> 18, 20, 21, 22, 20, 20, 20, 20, 19, 21, 21, 20, 20, 23, 20, 22,…
## $ City <chr> "Hanoi", "Hue", "HCMC", "HCMC", "Hanoi", "Hanoi", "Hanoi", "Han…
## $ Q2.1 <dbl> 5, 3, 0, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 4, 5, 4, 4, 3, 4, 3, …
## $ Q2.2.1 <dbl> 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 2, 0, 2, 2, 0, 2, 0, 2, 2, …
## $ Q2.2.2 <dbl> 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q2.2.3 <dbl> 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q2.2.4 <dbl> 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q2.2.5 <dbl> 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q2.3.1 <dbl> 2, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 1, 2, …
## $ Q2.3.2 <dbl> 2, 2, 0, 0, 0, 2, 2, 1, 2, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0, 2, 2, …
## $ Q2.3.3 <dbl> 2, 2, 2, 0, 0, 2, 2, 1, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 0, 0, 2, …
## $ Q2.3.4 <dbl> 0, 2, 0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 1, 0, 2, 0, 0, 2, …
## $ Q2.4.1 <dbl> 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, …
## $ Q2.4.2 <dbl> 2, 2, 0, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 0, 0, 2, …
## $ Q2.4.3 <dbl> 2, 2, 0, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2, …
## $ Q2.4.4 <dbl> 1, 2, 0, 0, 0, 2, 2, 1, 1, 2, 1, 1, 1, 0, 2, 2, 0, 2, 0, 0, 2, …
## $ Q2.4.5 <dbl> 2, 2, 1, 0, 0, 2, 2, 1, 1, 2, 1, 0, 1, 0, 2, 2, 0, 2, 0, 0, 2, …
## $ Q2.5.1 <dbl> 5, 3, 4, 4, 3, 5, 3, 3, 5, 4, 4, 4, 4, 3, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.2 <dbl> 5, 3, 4, 4, 3, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.3 <dbl> 4, 3, 4, 4, 3, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.4 <dbl> 3, 3, 3, 4, 3, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.5 <dbl> 4, 3, 4, 4, 5, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.6 <dbl> 5, 3, 4, 4, 5, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.5.7 <dbl> 4, 3, 4, 4, 5, 5, 3, 3, 5, 4, 4, 4, 4, 2, 4, 5, 3, 4, 2, 3, 4, …
## $ Q2.6.1 <dbl> 4, 3, 3, 2, 3, 2, 3, 3, 4, 3, 3, 3, 4, 2, 5, 5, 3, 3, 4, 4, 4, …
## $ Q2.6.2 <dbl> 4, 3, 3, 3, 3, 2, 3, 3, 4, 2, 3, 3, 5, 1, 5, 4, 3, 3, 4, 3, 4, …
## $ Q2.6.3 <dbl> 5, 3, 3, 3, 3, 2, 3, 3, 4, 1, 4, 3, 4, 3, 5, 4, 3, 3, 4, 3, 4, …
## $ Q2.6.4 <dbl> 4, 3, 3, 3, 3, 2, 3, 3, 4, 0, 3, 3, 4, 4, 5, 4, 3, 3, 4, 3, 4, …
## $ Q2.6.5 <dbl> 3, 3, 3, 2, 3, 2, 3, 3, 4, 2, 3, 3, 5, 3, 5, 4, 3, 3, 4, 3, 4, …
## $ Q2.6.6 <dbl> 5, 3, 3, 2, 3, 2, 3, 3, 4, 0, 4, 3, 4, 3, 5, 4, 3, 3, 4, 2, 4, …
## $ Q2.6.7 <dbl> 4, 3, 3, 2, 3, 2, 3, 3, 4, 2, 3, 3, 5, 2, 5, 4, 3, 3, 4, 1, 4, …
## $ Q3.1.1 <dbl> 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, …
## $ Q3.1.2 <dbl> 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, …
## $ Q3.1.3 <dbl> 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, …
## $ Q3.1.4 <dbl> 2, 2, 2, 0, 0, 0, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, …
## $ Q3.1.5 <dbl> 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 0, 2, 2, 2, 2, …
## $ Q3.1.6 <dbl> 2, 2, 1, 0, 0, 0, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 0, 2, 2, 0, 2, …
## $ Q3.1.7 <dbl> 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, …
## $ Q3.2.1 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 0, 2, …
## $ Q3.2.2 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.2.3 <dbl> 2, 2, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.2.4 <dbl> 2, 2, 2, 1, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.2.5 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.2.6 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.2.7 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 0, 2, 0, 0, 2, …
## $ Q3.2.8 <dbl> 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.1 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.2 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.3 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.4 <dbl> 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.5 <dbl> 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, …
## $ Q3.3.6 <dbl> 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, …
## $ Q4.1.1 <dbl> 0, 5, 4, 3, 5, 4, 3, 3, 4, 4, 4, 4, 5, 4, 3, 5, 3, 4, 5, 3, 3, …
## $ Q4.1.2 <dbl> 0, 5, 3, 3, 5, 4, 3, 3, 4, 4, 4, 4, 5, 4, 3, 5, 3, 4, 5, 3, 3, …
## $ Q4.1.3 <dbl> 0, 5, 3, 3, 5, 4, 3, 3, 4, 4, 0, 4, 5, 4, 3, 5, 3, 4, 5, 3, 3, …
## $ Q4.1.4 <dbl> 0, 5, 2, 3, 5, 4, 1, 3, 3, 4, 0, 4, 5, 4, 3, 5, 3, 4, 5, 3, 3, …
## $ Q4.1.5 <dbl> 0, 5, 1, 3, 5, 4, 0, 3, 3, 4, 0, 3, 1, 4, 3, 5, 3, 3, 1, 3, 3, …
## $ Q4.1.6 <dbl> 3, 5, 1, 3, 5, 4, 3, 3, 3, 4, 0, 2, 3, 4, 3, 5, 3, 3, 1, 3, 3, …
## $ Q4.2.1 <dbl> 0, 4, 0, 3, 5, 4, 2, 3, 5, 4, 0, 3, 1, 3, 3, 4, 3, 3, 0, 2, 3, …
## $ Q4.2.2 <dbl> 0, 4, 2, 3, 5, 4, 2, 3, 5, 4, 0, 2, 3, 3, 3, 4, 3, 3, 0, 2, 3, …
## $ Q4.2.3 <dbl> 0, 4, 2, 3, 5, 4, 2, 3, 5, 4, 0, 2, 3, 3, 3, 4, 3, 3, 0, 3, 3, …
## $ Q4.2.4 <dbl> 3, 4, 2, 3, 5, 4, 2, 3, 5, 4, 0, 1, 3, 3, 3, 4, 3, 3, 0, 3, 3, …| Year | Gender | Age | City | Q2.1 | Q2.2.1 | Q2.2.2 | Q2.2.3 | Q2.2.4 | Q2.2.5 |
|---|---|---|---|---|---|---|---|---|---|
| 2 | Male | 18 | Hanoi | 5 | 0 | 0 | 0 | 0 | 0 |
| 3 | Female | 20 | Hue | 3 | 2 | 2 | 2 | 2 | 2 |
| 5 | Female | 21 | HCMC | 0 | 2 | 2 | 2 | 2 | 2 |
| 5 | Female | 22 | HCMC | 5 | 0 | 0 | 0 | 0 | 0 |
| 4 | Female | 20 | Hanoi | 5 | 0 | 0 | 0 | 0 | 0 |
| 4 | Female | 20 | Hanoi | 5 | 2 | 2 | 2 | 2 | 2 |
| 4 | Female | 20 | Hanoi | 5 | 2 | 2 | 2 | 2 | 2 |
| 4 | Female | 20 | Hanoi | 5 | 0 | 2 | 2 | 2 | 0 |
| 3 | Female | 19 | Vinh | 4 | 0 | 2 | 2 | 2 | 2 |
| 4 | Male | 21 | HCMC | 4 | 2 | 2 | 2 | 2 | 2 |
3. Pembersihan Data
missing_count <- colSums(is.na(data))
missing_count[missing_count > 0] |>
as.data.frame() |>
setNames("Jumlah NA") |>
kable(caption = "Variabel dengan Missing Value")| Jumlah NA | |
|---|---|
| Q2.1 | 1 |
## Jumlah observasi setelah hapus NA: 13224. Penentuan Konstruk Penelitian
Berdasarkan isi kuesioner, konstruk yang digunakan sebagai berikut:
construct_def <- data.frame(
Konstruk = c("Quality Policy (QP)",
"Quality Assurance Handbook (QAH)",
"Quality Assurance Unit (QAU)",
"Purpose of Quality Assurance (PQA)",
"Focus of Quality Assurance (FQA)",
"Quality Assurance Processes and Instruments (QAPI)",
"Student Support Services (SSS)",
"Graduate Employability (GE)",
"Evaluation Survey Participation (ESP)",
"Positive Change (PC)"),
Variabel = c("Q2.2.1 – Q2.2.5",
"Q2.3.1 – Q2.3.4",
"Q2.4.1 – Q2.4.5",
"Q2.5.1 – Q2.5.7",
"Q2.6.1 – Q2.6.7",
"Q3.1.1 – Q3.1.7",
"Q3.2.1 – Q3.2.8",
"Q3.3.1 – Q3.3.6",
"Q4.1.1 – Q4.1.6",
"Q4.2.1 – Q4.2.4"),
Jumlah = c(5, 4, 5, 7, 7, 7, 8, 6, 6, 4)
)
kable(construct_def, caption = "Tabel 1. Definisi Konstruk Penelitian")| Konstruk | Variabel | Jumlah |
|---|---|---|
| Quality Policy (QP) | Q2.2.1 – Q2.2.5 | 5 |
| Quality Assurance Handbook (QAH) | Q2.3.1 – Q2.3.4 | 4 |
| Quality Assurance Unit (QAU) | Q2.4.1 – Q2.4.5 | 5 |
| Purpose of Quality Assurance (PQA) | Q2.5.1 – Q2.5.7 | 7 |
| Focus of Quality Assurance (FQA) | Q2.6.1 – Q2.6.7 | 7 |
| Quality Assurance Processes and Instruments (QAPI) | Q3.1.1 – Q3.1.7 | 7 |
| Student Support Services (SSS) | Q3.2.1 – Q3.2.8 | 8 |
| Graduate Employability (GE) | Q3.3.1 – Q3.3.6 | 6 |
| Evaluation Survey Participation (ESP) | Q4.1.1 – Q4.1.6 | 6 |
| Positive Change (PC) | Q4.2.1 – Q4.2.4 | 4 |
5. Ekstraksi Variabel
6. Statistik Deskriptif
hasil_deskriptif <- describe(pls_data)
hasil_deskriptif |>
as.data.frame() |>
select(n, mean, sd, median, min, max, skew, kurtosis) |>
round(3) |>
kable(caption = "Tabel 2. Statistik Deskriptif Seluruh Indikator")| n | mean | sd | median | min | max | skew | kurtosis | |
|---|---|---|---|---|---|---|---|---|
| Q2.2.1 | 1322 | 1.716 | 0.681 | 2 | 0 | 2 | -2.049 | 2.304 |
| Q2.2.2 | 1322 | 1.641 | 0.750 | 2 | 0 | 2 | -1.669 | 0.864 |
| Q2.2.3 | 1322 | 1.626 | 0.762 | 2 | 0 | 2 | -1.600 | 0.641 |
| Q2.2.4 | 1322 | 1.662 | 0.730 | 2 | 0 | 2 | -1.762 | 1.204 |
| Q2.2.5 | 1322 | 1.450 | 0.864 | 2 | 0 | 2 | -1.005 | -0.903 |
| Q2.3.1 | 1322 | 1.369 | 0.868 | 2 | 0 | 2 | -0.787 | -1.212 |
| Q2.3.2 | 1322 | 1.228 | 0.871 | 2 | 0 | 2 | -0.457 | -1.530 |
| Q2.3.3 | 1322 | 1.222 | 0.918 | 2 | 0 | 2 | -0.453 | -1.660 |
| Q2.3.4 | 1322 | 1.071 | 0.950 | 1 | 0 | 2 | -0.142 | -1.877 |
| Q2.4.1 | 1322 | 1.632 | 0.753 | 2 | 0 | 2 | -1.629 | 0.751 |
| Q2.4.2 | 1322 | 1.635 | 0.749 | 2 | 0 | 2 | -1.641 | 0.803 |
| Q2.4.3 | 1322 | 1.592 | 0.788 | 2 | 0 | 2 | -1.464 | 0.219 |
| Q2.4.4 | 1322 | 1.433 | 0.767 | 2 | 0 | 2 | -0.911 | -0.712 |
| Q2.4.5 | 1322 | 1.547 | 0.776 | 2 | 0 | 2 | -1.292 | -0.097 |
| Q2.5.1 | 1322 | 3.404 | 1.345 | 4 | 0 | 5 | -1.371 | 1.398 |
| Q2.5.2 | 1322 | 3.468 | 1.338 | 4 | 0 | 5 | -1.375 | 1.480 |
| Q2.5.3 | 1322 | 3.480 | 1.330 | 4 | 0 | 5 | -1.376 | 1.503 |
| Q2.5.4 | 1322 | 3.400 | 1.359 | 4 | 0 | 5 | -1.340 | 1.251 |
| Q2.5.5 | 1322 | 3.393 | 1.350 | 4 | 0 | 5 | -1.312 | 1.169 |
| Q2.5.6 | 1322 | 3.451 | 1.355 | 4 | 0 | 5 | -1.349 | 1.331 |
| Q2.5.7 | 1322 | 3.411 | 1.371 | 4 | 0 | 5 | -1.355 | 1.232 |
| Q2.6.1 | 1322 | 3.460 | 1.186 | 4 | 0 | 5 | -1.282 | 2.166 |
| Q2.6.2 | 1322 | 3.281 | 1.254 | 3 | 0 | 5 | -1.059 | 1.158 |
| Q2.6.3 | 1322 | 3.243 | 1.220 | 3 | 0 | 5 | -1.069 | 1.292 |
| Q2.6.4 | 1322 | 3.175 | 1.277 | 3 | 0 | 5 | -1.060 | 0.981 |
| Q2.6.5 | 1322 | 3.158 | 1.271 | 3 | 0 | 5 | -1.032 | 0.963 |
| Q2.6.6 | 1322 | 3.227 | 1.285 | 3 | 0 | 5 | -0.939 | 0.731 |
| Q2.6.7 | 1322 | 3.079 | 1.387 | 3 | 0 | 5 | -0.857 | 0.219 |
| Q3.1.1 | 1322 | 1.825 | 0.540 | 2 | 0 | 2 | -2.909 | 6.769 |
| Q3.1.2 | 1322 | 1.701 | 0.655 | 2 | 0 | 2 | -1.941 | 2.126 |
| Q3.1.3 | 1322 | 1.535 | 0.827 | 2 | 0 | 2 | -1.263 | -0.341 |
| Q3.1.4 | 1322 | 1.562 | 0.805 | 2 | 0 | 2 | -1.356 | -0.078 |
| Q3.1.5 | 1322 | 1.641 | 0.741 | 2 | 0 | 2 | -1.667 | 0.902 |
| Q3.1.6 | 1322 | 1.627 | 0.753 | 2 | 0 | 2 | -1.605 | 0.694 |
| Q3.1.7 | 1322 | 1.781 | 0.597 | 2 | 0 | 2 | -2.487 | 4.435 |
| Q3.2.1 | 1322 | 1.852 | 0.499 | 2 | 0 | 2 | -3.235 | 8.844 |
| Q3.2.2 | 1322 | 1.844 | 0.501 | 2 | 0 | 2 | -3.122 | 8.250 |
| Q3.2.3 | 1322 | 1.734 | 0.645 | 2 | 0 | 2 | -2.148 | 2.851 |
| Q3.2.4 | 1322 | 1.821 | 0.526 | 2 | 0 | 2 | -2.847 | 6.634 |
| Q3.2.5 | 1322 | 1.862 | 0.482 | 2 | 0 | 2 | -3.389 | 9.894 |
| Q3.2.6 | 1322 | 1.746 | 0.624 | 2 | 0 | 2 | -2.221 | 3.247 |
| Q3.2.7 | 1322 | 1.697 | 0.662 | 2 | 0 | 2 | -1.924 | 2.032 |
| Q3.2.8 | 1322 | 1.578 | 0.761 | 2 | 0 | 2 | -1.403 | 0.194 |
| Q3.3.1 | 1322 | 1.608 | 0.768 | 2 | 0 | 2 | -1.528 | 0.444 |
| Q3.3.2 | 1322 | 1.567 | 0.794 | 2 | 0 | 2 | -1.373 | -0.001 |
| Q3.3.3 | 1322 | 1.557 | 0.808 | 2 | 0 | 2 | -1.337 | -0.128 |
| Q3.3.4 | 1322 | 1.525 | 0.828 | 2 | 0 | 2 | -1.231 | -0.407 |
| Q3.3.5 | 1322 | 1.525 | 0.826 | 2 | 0 | 2 | -1.230 | -0.401 |
| Q3.3.6 | 1322 | 1.721 | 0.672 | 2 | 0 | 2 | -2.075 | 2.442 |
| Q4.1.1 | 1322 | 3.258 | 1.389 | 4 | 0 | 5 | -1.121 | 0.686 |
| Q4.1.2 | 1322 | 3.315 | 1.363 | 4 | 0 | 5 | -1.104 | 0.722 |
| Q4.1.3 | 1322 | 3.282 | 1.396 | 4 | 0 | 5 | -1.091 | 0.585 |
| Q4.1.4 | 1322 | 3.111 | 1.426 | 3 | 0 | 5 | -0.954 | 0.173 |
| Q4.1.5 | 1322 | 2.955 | 1.447 | 3 | 0 | 5 | -0.783 | -0.236 |
| Q4.1.6 | 1322 | 3.051 | 1.373 | 3 | 0 | 5 | -0.881 | 0.173 |
| Q4.2.1 | 1322 | 2.856 | 1.429 | 3 | 0 | 5 | -0.798 | -0.153 |
| Q4.2.2 | 1322 | 2.772 | 1.427 | 3 | 0 | 5 | -0.685 | -0.356 |
| Q4.2.3 | 1322 | 2.868 | 1.443 | 3 | 0 | 5 | -0.778 | -0.225 |
| Q4.2.4 | 1322 | 2.855 | 1.419 | 3 | 0 | 5 | -0.694 | -0.276 |
7. Visualisasi Distribusi Data
construct_items <- list(
QP = c("Q2.2.1","Q2.2.2","Q2.2.3","Q2.2.4","Q2.2.5"),
QAH = c("Q2.3.1","Q2.3.2","Q2.3.3","Q2.3.4"),
QAU = c("Q2.4.1","Q2.4.2","Q2.4.3","Q2.4.4","Q2.4.5"),
PQA = c("Q2.5.1","Q2.5.2","Q2.5.3","Q2.5.4","Q2.5.5","Q2.5.6","Q2.5.7"),
FQA = c("Q2.6.1","Q2.6.2","Q2.6.3","Q2.6.4","Q2.6.5","Q2.6.6","Q2.6.7"),
QAPI = c("Q3.1.1","Q3.1.2","Q3.1.3","Q3.1.4","Q3.1.5","Q3.1.6","Q3.1.7"),
SSS = c("Q3.2.1","Q3.2.2","Q3.2.3","Q3.2.4","Q3.2.5","Q3.2.6","Q3.2.7","Q3.2.8"),
GE = c("Q3.3.1","Q3.3.2","Q3.3.3","Q3.3.4","Q3.3.5","Q3.3.6"),
ESP = c("Q4.1.1","Q4.1.2","Q4.1.3","Q4.1.4","Q4.1.5","Q4.1.6"),
PC = c("Q4.2.1","Q4.2.2","Q4.2.3","Q4.2.4")
)
construct_scores <- pls_data |>
mutate(across(everything(), as.numeric)) |>
bind_cols(
lapply(construct_items, function(items) {
rowMeans(select(pls_data, all_of(items)), na.rm = TRUE)
}) |>
bind_cols()
)construct_scores |>
select(all_of(names(construct_items))) |>
pivot_longer(everything(), names_to = "Konstruk", values_to = "Skor") |>
ggplot(aes(x = Konstruk, y = Skor, fill = Konstruk)) +
geom_boxplot(show.legend = FALSE) +
labs(title = "Distribusi Skor per Konstruk",
x = "Konstruk", y = "Skor") +
theme_minimal(base_size = 11) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot_list <- names(construct_items) |>
lapply(function(k) {
construct_scores |>
ggplot(aes(x = .data[[k]])) +
geom_histogram(bins = 20, fill = "steelblue", color = "white", alpha = 0.8) +
labs(title = k, x = "Skor", y = NULL) +
theme_minimal(base_size = 9)
})
wrap_plots(plot_list, ncol = 5)
8. Uji KMO
## Overall MSA: 0.947kmo_df <- data.frame(
Indikator = names(hasil_kmo$MSAi),
MSA = round(hasil_kmo$MSAi, 3)
)
kable(kmo_df, caption = "Tabel 3. Nilai MSA per Indikator")| Indikator | MSA | |
|---|---|---|
| Q2.2.1 | Q2.2.1 | 0.945 |
| Q2.2.2 | Q2.2.2 | 0.949 |
| Q2.2.3 | Q2.2.3 | 0.952 |
| Q2.2.4 | Q2.2.4 | 0.950 |
| Q2.2.5 | Q2.2.5 | 0.951 |
| Q2.3.1 | Q2.3.1 | 0.952 |
| Q2.3.2 | Q2.3.2 | 0.958 |
| Q2.3.3 | Q2.3.3 | 0.928 |
| Q2.3.4 | Q2.3.4 | 0.917 |
| Q2.4.1 | Q2.4.1 | 0.945 |
| Q2.4.2 | Q2.4.2 | 0.946 |
| Q2.4.3 | Q2.4.3 | 0.950 |
| Q2.4.4 | Q2.4.4 | 0.957 |
| Q2.4.5 | Q2.4.5 | 0.968 |
| Q2.5.1 | Q2.5.1 | 0.967 |
| Q2.5.2 | Q2.5.2 | 0.926 |
| Q2.5.3 | Q2.5.3 | 0.934 |
| Q2.5.4 | Q2.5.4 | 0.952 |
| Q2.5.5 | Q2.5.5 | 0.947 |
| Q2.5.6 | Q2.5.6 | 0.943 |
| Q2.5.7 | Q2.5.7 | 0.946 |
| Q2.6.1 | Q2.6.1 | 0.916 |
| Q2.6.2 | Q2.6.2 | 0.930 |
| Q2.6.3 | Q2.6.3 | 0.929 |
| Q2.6.4 | Q2.6.4 | 0.915 |
| Q2.6.5 | Q2.6.5 | 0.912 |
| Q2.6.6 | Q2.6.6 | 0.924 |
| Q2.6.7 | Q2.6.7 | 0.942 |
| Q3.1.1 | Q3.1.1 | 0.920 |
| Q3.1.2 | Q3.1.2 | 0.938 |
| Q3.1.3 | Q3.1.3 | 0.962 |
| Q3.1.4 | Q3.1.4 | 0.964 |
| Q3.1.5 | Q3.1.5 | 0.955 |
| Q3.1.6 | Q3.1.6 | 0.949 |
| Q3.1.7 | Q3.1.7 | 0.965 |
| Q3.2.1 | Q3.2.1 | 0.965 |
| Q3.2.2 | Q3.2.2 | 0.956 |
| Q3.2.3 | Q3.2.3 | 0.961 |
| Q3.2.4 | Q3.2.4 | 0.964 |
| Q3.2.5 | Q3.2.5 | 0.955 |
| Q3.2.6 | Q3.2.6 | 0.971 |
| Q3.2.7 | Q3.2.7 | 0.960 |
| Q3.2.8 | Q3.2.8 | 0.964 |
| Q3.3.1 | Q3.3.1 | 0.980 |
| Q3.3.2 | Q3.3.2 | 0.967 |
| Q3.3.3 | Q3.3.3 | 0.958 |
| Q3.3.4 | Q3.3.4 | 0.949 |
| Q3.3.5 | Q3.3.5 | 0.961 |
| Q3.3.6 | Q3.3.6 | 0.973 |
| Q4.1.1 | Q4.1.1 | 0.940 |
| Q4.1.2 | Q4.1.2 | 0.899 |
| Q4.1.3 | Q4.1.3 | 0.915 |
| Q4.1.4 | Q4.1.4 | 0.951 |
| Q4.1.5 | Q4.1.5 | 0.922 |
| Q4.1.6 | Q4.1.6 | 0.921 |
| Q4.2.1 | Q4.2.1 | 0.945 |
| Q4.2.2 | Q4.2.2 | 0.934 |
| Q4.2.3 | Q4.2.3 | 0.949 |
| Q4.2.4 | Q4.2.4 | 0.934 |
Interpretasi: Nilai Overall MSA sebesar 0.947 menunjukkan data sangat layak untuk analisis faktor (KMO > 0,8 = sangat baik).
9. Model PLS-SEM
Outer Model (Model Pengukuran)
measurement_model <- constructs(
composite("QP", multi_items("Q2.2.", 1:5)),
composite("QAH", multi_items("Q2.3.", 1:4)),
composite("QAU", multi_items("Q2.4.", 1:5)),
composite("PQA", multi_items("Q2.5.", 1:7)),
composite("FQA", multi_items("Q2.6.", 1:7)),
composite("QAPI", multi_items("Q3.1.", 1:7)),
composite("SSS", multi_items("Q3.2.", 1:8)),
composite("GE", multi_items("Q3.3.", 1:6)),
composite("ESP", multi_items("Q4.1.", 1:6)),
composite("PC", multi_items("Q4.2.", 1:4))
)Inner Model (Model Struktural)
10. Evaluasi Outer Model
Loading Factor
loadings_mat <- pls_model$outer_loadings
kable(round(loadings_mat, 3), caption = "Tabel 4. Outer Loadings")| QP | QAH | QAU | PQA | FQA | QAPI | SSS | GE | ESP | PC | |
|---|---|---|---|---|---|---|---|---|---|---|
| Q2.2.1 | 0.853 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.2.2 | 0.885 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.2.3 | 0.785 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.2.4 | 0.873 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.2.5 | 0.662 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.3.1 | 0.000 | 0.702 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.3.2 | 0.000 | 0.700 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.3.3 | 0.000 | 0.853 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.3.4 | 0.000 | 0.940 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.4.1 | 0.000 | 0.000 | 0.871 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.4.2 | 0.000 | 0.000 | 0.901 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.4.3 | 0.000 | 0.000 | 0.893 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.4.4 | 0.000 | 0.000 | 0.809 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.4.5 | 0.000 | 0.000 | 0.817 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.1 | 0.000 | 0.000 | 0.000 | 0.929 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.2 | 0.000 | 0.000 | 0.000 | 0.968 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.3 | 0.000 | 0.000 | 0.000 | 0.971 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.4 | 0.000 | 0.000 | 0.000 | 0.966 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.5 | 0.000 | 0.000 | 0.000 | 0.960 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.6 | 0.000 | 0.000 | 0.000 | 0.966 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.5.7 | 0.000 | 0.000 | 0.000 | 0.952 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.875 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.896 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.928 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.919 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.930 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.916 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q2.6.7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.889 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.704 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.743 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.769 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.808 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.830 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.834 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.1.7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.806 | 0.000 | 0.000 | 0.000 | 0.000 |
| Q3.2.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.775 | 0.000 | 0.000 | 0.000 |
| Q3.2.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.843 | 0.000 | 0.000 | 0.000 |
| Q3.2.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.777 | 0.000 | 0.000 | 0.000 |
| Q3.2.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.817 | 0.000 | 0.000 | 0.000 |
| Q3.2.5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.830 | 0.000 | 0.000 | 0.000 |
| Q3.2.6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.769 | 0.000 | 0.000 | 0.000 |
| Q3.2.7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.752 | 0.000 | 0.000 | 0.000 |
| Q3.2.8 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.713 | 0.000 | 0.000 | 0.000 |
| Q3.3.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.806 | 0.000 | 0.000 |
| Q3.3.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.868 | 0.000 | 0.000 |
| Q3.3.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.907 | 0.000 | 0.000 |
| Q3.3.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.910 | 0.000 | 0.000 |
| Q3.3.5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.892 | 0.000 | 0.000 |
| Q3.3.6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.789 | 0.000 | 0.000 |
| Q4.1.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.834 | 0.000 |
| Q4.1.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.888 | 0.000 |
| Q4.1.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.901 | 0.000 |
| Q4.1.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.916 | 0.000 |
| Q4.1.5 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.903 | 0.000 |
| Q4.1.6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.898 | 0.000 |
| Q4.2.1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.936 |
| Q4.2.2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.958 |
| Q4.2.3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.952 |
| Q4.2.4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.929 |
Interpretasi: Loading ≥ 0,70 → indikator ideal. Loading 0,50–0,70 → masih dapat dipertimbangkan. Loading < 0,50 → indikator dihapus.
Reliabilitas dan Validitas Konvergen
reliability <- summary(pls_model)$reliability
kable(round(reliability, 3), caption = "Tabel 5. Reliabilitas dan Validitas Konvergen")| alpha | rhoA | rhoC | AVE | |
|---|---|---|---|---|
| QP | 0.881 | 0.927 | 0.908 | 0.665 |
| QAH | 0.835 | 1.210 | 0.879 | 0.649 |
| QAU | 0.913 | 0.938 | 0.934 | 0.738 |
| PQA | 0.985 | 0.986 | 0.988 | 0.920 |
| FQA | 0.965 | 0.981 | 0.970 | 0.824 |
| QAPI | 0.896 | 0.899 | 0.919 | 0.618 |
| SSS | 0.911 | 0.912 | 0.928 | 0.617 |
| GE | 0.931 | 0.932 | 0.946 | 0.745 |
| ESP | 0.948 | 0.955 | 0.958 | 0.793 |
| PC | 0.959 | 0.960 | 0.970 | 0.891 |
Interpretasi: - Cronbach’s Alpha > 0,7 → Reliabel - rhoA > 0,7 → Reliabel - rhoC (Composite Reliability) > 0,7 → Reliabel - AVE > 0,5 → Validitas Konvergen terpenuhi
Validitas Diskriminan
Fornell-Larcker
fl_criteria <- summary(pls_model)$validity$fl_criteria
kable(round(fl_criteria, 3), caption = "Tabel 6. Fornell-Larcker Criterion")| QP | QAH | QAU | PQA | FQA | QAPI | SSS | GE | ESP | PC | |
|---|---|---|---|---|---|---|---|---|---|---|
| QP | 0.816 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| QAH | 0.407 | 0.805 | NA | NA | NA | NA | NA | NA | NA | NA |
| QAU | 0.571 | 0.502 | 0.859 | NA | NA | NA | NA | NA | NA | NA |
| PQA | 0.133 | -0.079 | 0.106 | 0.959 | NA | NA | NA | NA | NA | NA |
| FQA | 0.000 | 0.006 | 0.029 | -0.045 | 0.908 | NA | NA | NA | NA | NA |
| QAPI | 0.500 | 0.459 | 0.526 | 0.079 | -0.021 | 0.786 | NA | NA | NA | NA |
| SSS | 0.503 | 0.355 | 0.491 | 0.136 | -0.024 | 0.665 | 0.786 | NA | NA | NA |
| GE | 0.503 | 0.458 | 0.503 | 0.086 | -0.008 | 0.669 | 0.629 | 0.863 | NA | NA |
| ESP | 0.189 | 0.172 | 0.243 | 0.410 | -0.032 | 0.324 | 0.208 | 0.295 | 0.891 | NA |
| PC | 0.254 | 0.204 | 0.274 | 0.396 | -0.024 | 0.341 | 0.271 | 0.354 | 0.657 | 0.944 |
Interpretasi: Nilai diagonal (√AVE) harus lebih besar dibanding korelasi antar konstruk.
HTMT
htmt <- summary(pls_model)$validity$htmt
kable(round(htmt, 3), caption = "Tabel 7. Heterotrait-Monotrait Ratio (HTMT)")| QP | QAH | QAU | PQA | FQA | QAPI | SSS | GE | ESP | PC | |
|---|---|---|---|---|---|---|---|---|---|---|
| QP | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| QAH | 0.532 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| QAU | 0.644 | 0.601 | NA | NA | NA | NA | NA | NA | NA | NA |
| PQA | 0.125 | 0.068 | 0.106 | NA | NA | NA | NA | NA | NA | NA |
| FQA | 0.014 | 0.017 | 0.035 | 0.045 | NA | NA | NA | NA | NA | NA |
| QAPI | 0.574 | 0.543 | 0.585 | 0.086 | 0.026 | NA | NA | NA | NA | NA |
| SSS | 0.557 | 0.427 | 0.539 | 0.144 | 0.032 | 0.735 | NA | NA | NA | NA |
| GE | 0.568 | 0.520 | 0.547 | 0.090 | 0.025 | 0.732 | 0.684 | NA | NA | NA |
| ESP | 0.206 | 0.193 | 0.256 | 0.427 | 0.032 | 0.349 | 0.220 | 0.305 | NA | NA |
| PC | 0.282 | 0.218 | 0.294 | 0.408 | 0.025 | 0.366 | 0.289 | 0.375 | 0.683 | NA |
Interpretasi: HTMT < 0,9 → Validitas Diskriminan terpenuhi
11. Evaluasi Inner Model
R-Square
r_sq <- summary(pls_model)$paths
if ("R2" %in% colnames(r_sq)) {
r_sq <- r_sq[, "R2", drop = FALSE]
} else {
r_sq <- r_sq[, ncol(r_sq), drop = FALSE]
colnames(r_sq) <- "R2"
}
kable(round(r_sq, 3), caption = "Tabel 8. R-Square")| R2 | |
|---|---|
| R^2 | 0.431 |
| AdjR^2 | 0.431 |
| QP | NA |
| QAH | NA |
| QAU | NA |
| PQA | NA |
| FQA | NA |
| QAPI | NA |
| SSS | NA |
| GE | NA |
| ESP | 0.657 |
Interpretasi: Semakin besar R², semakin baik model menjelaskan variabel endogen.
12. Bootstrapping
13. Hasil Bootstrapping
##
## Results from Bootstrap resamples: 100
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## QP -> PQA 0.144 0.147 0.030 4.838 0.094
## QAH -> PQA -0.200 -0.203 0.043 -4.655 -0.263
## QAU -> PQA 0.125 0.126 0.030 4.093 0.061
## PQA -> FQA -0.045 -0.047 0.027 -1.643 -0.095
## FQA -> QAPI -0.021 -0.024 0.028 -0.763 -0.070
## QAPI -> SSS 0.665 0.667 0.030 22.375 0.607
## QAPI -> GE 0.669 0.670 0.023 29.182 0.625
## SSS -> ESP 0.037 0.034 0.035 1.054 -0.028
## GE -> ESP 0.271 0.275 0.034 8.002 0.204
## ESP -> PC 0.657 0.660 0.023 29.123 0.617
## 97.5% CI Bootstrap P Val
## QP -> PQA 0.198 0.000
## QAH -> PQA -0.151 0.020
## QAU -> PQA 0.184 0.000
## PQA -> FQA 0.023 0.120
## FQA -> QAPI 0.035 0.380
## QAPI -> SSS 0.708 0.000
## QAPI -> GE 0.713 0.000
## SSS -> ESP 0.097 0.400
## GE -> ESP 0.345 0.000
## ESP -> PC 0.700 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Q2.2.1 -> QP 0.330 0.340 0.044 7.471 0.278
## Q2.2.2 -> QP 0.332 0.335 0.043 7.713 0.278
## Q2.2.3 -> QP 0.191 0.186 0.039 4.851 0.083
## Q2.2.4 -> QP 0.267 0.262 0.037 7.247 0.191
## Q2.2.5 -> QP 0.062 0.054 0.065 0.958 -0.131
## Q2.3.1 -> QAH 0.175 0.129 0.188 0.933 -0.500
## Q2.3.2 -> QAH 0.150 0.113 0.140 1.073 -0.205
## Q2.3.3 -> QAH 0.274 0.256 0.093 2.946 0.066
## Q2.3.4 -> QAH 0.572 0.614 0.256 2.238 0.424
## Q2.4.1 -> QAU 0.232 0.224 0.030 7.811 0.181
## Q2.4.2 -> QAU 0.299 0.298 0.040 7.577 0.246
## Q2.4.3 -> QAU 0.257 0.256 0.032 7.980 0.189
## Q2.4.4 -> QAU 0.130 0.125 0.061 2.116 0.006
## Q2.4.5 -> QAU 0.237 0.251 0.042 5.579 0.177
## Q2.5.1 -> PQA 0.150 0.148 0.008 17.974 0.133
## Q2.5.2 -> PQA 0.154 0.154 0.005 28.984 0.145
## Q2.5.3 -> PQA 0.159 0.158 0.005 34.697 0.150
## Q2.5.4 -> PQA 0.152 0.153 0.006 25.429 0.141
## Q2.5.5 -> PQA 0.142 0.143 0.006 22.440 0.130
## Q2.5.6 -> PQA 0.149 0.151 0.005 32.726 0.142
## Q2.5.7 -> PQA 0.137 0.138 0.006 22.355 0.126
## Q2.6.1 -> FQA 0.134 0.136 0.109 1.225 -0.100
## Q2.6.2 -> FQA 0.160 0.152 0.056 2.844 -0.023
## Q2.6.3 -> FQA 0.212 0.200 0.059 3.604 0.097
## Q2.6.4 -> FQA 0.100 0.119 0.074 1.350 -0.021
## Q2.6.5 -> FQA 0.175 0.168 0.042 4.194 0.097
## Q2.6.6 -> FQA 0.163 0.158 0.057 2.853 0.026
## Q2.6.7 -> FQA 0.158 0.162 0.064 2.449 0.036
## Q3.1.1 -> QAPI 0.167 0.167 0.007 24.531 0.152
## Q3.1.2 -> QAPI 0.172 0.172 0.005 32.940 0.162
## Q3.1.3 -> QAPI 0.169 0.169 0.005 32.488 0.160
## Q3.1.4 -> QAPI 0.187 0.187 0.006 32.253 0.176
## Q3.1.5 -> QAPI 0.192 0.192 0.005 35.904 0.182
## Q3.1.6 -> QAPI 0.191 0.191 0.005 36.334 0.182
## Q3.1.7 -> QAPI 0.191 0.191 0.005 37.441 0.182
## Q3.2.1 -> SSS 0.153 0.153 0.006 23.698 0.140
## Q3.2.2 -> SSS 0.169 0.168 0.006 29.344 0.157
## Q3.2.3 -> SSS 0.172 0.171 0.008 22.716 0.158
## Q3.2.4 -> SSS 0.156 0.156 0.006 25.665 0.145
## Q3.2.5 -> SSS 0.160 0.160 0.005 30.161 0.149
## Q3.2.6 -> SSS 0.152 0.150 0.007 22.400 0.138
## Q3.2.7 -> SSS 0.155 0.154 0.007 22.490 0.143
## Q3.2.8 -> SSS 0.157 0.156 0.008 19.608 0.140
## Q3.3.1 -> GE 0.191 0.190 0.004 49.754 0.183
## Q3.3.2 -> GE 0.195 0.195 0.005 41.565 0.186
## Q3.3.3 -> GE 0.199 0.199 0.004 46.948 0.191
## Q3.3.4 -> GE 0.198 0.199 0.005 41.909 0.191
## Q3.3.5 -> GE 0.188 0.188 0.004 46.226 0.180
## Q3.3.6 -> GE 0.188 0.188 0.006 32.602 0.177
## Q4.1.1 -> ESP 0.156 0.155 0.006 28.036 0.142
## Q4.1.2 -> ESP 0.168 0.167 0.005 35.681 0.157
## Q4.1.3 -> ESP 0.177 0.177 0.004 44.243 0.170
## Q4.1.4 -> ESP 0.202 0.202 0.005 41.081 0.193
## Q4.1.5 -> ESP 0.214 0.215 0.006 35.307 0.204
## Q4.1.6 -> ESP 0.204 0.205 0.005 38.243 0.197
## Q4.2.1 -> PC 0.263 0.263 0.004 63.047 0.254
## Q4.2.2 -> PC 0.273 0.273 0.004 64.352 0.265
## Q4.2.3 -> PC 0.272 0.273 0.004 75.693 0.267
## Q4.2.4 -> PC 0.252 0.251 0.004 59.513 0.245
## 97.5% CI Bootstrap P Val
## Q2.2.1 -> QP 0.439 0.000
## Q2.2.2 -> QP 0.451 0.000
## Q2.2.3 -> QP 0.253 0.000
## Q2.2.4 -> QP 0.327 0.000
## Q2.2.5 -> QP 0.128 0.360
## Q2.3.1 -> QAH 0.345 0.220
## Q2.3.2 -> QAH 0.336 0.220
## Q2.3.3 -> QAH 0.429 0.020
## Q2.3.4 -> QAH 1.133 0.020
## Q2.4.1 -> QAU 0.272 0.000
## Q2.4.2 -> QAU 0.384 0.000
## Q2.4.3 -> QAU 0.325 0.000
## Q2.4.4 -> QAU 0.196 0.060
## Q2.4.5 -> QAU 0.361 0.000
## Q2.5.1 -> PQA 0.162 0.000
## Q2.5.2 -> PQA 0.164 0.000
## Q2.5.3 -> PQA 0.166 0.000
## Q2.5.4 -> PQA 0.163 0.000
## Q2.5.5 -> PQA 0.153 0.000
## Q2.5.6 -> PQA 0.159 0.000
## Q2.5.7 -> PQA 0.150 0.000
## Q2.6.1 -> FQA 0.316 0.140
## Q2.6.2 -> FQA 0.248 0.080
## Q2.6.3 -> FQA 0.328 0.040
## Q2.6.4 -> FQA 0.320 0.080
## Q2.6.5 -> FQA 0.271 0.000
## Q2.6.6 -> FQA 0.282 0.040
## Q2.6.7 -> FQA 0.321 0.020
## Q3.1.1 -> QAPI 0.179 0.000
## Q3.1.2 -> QAPI 0.181 0.000
## Q3.1.3 -> QAPI 0.179 0.000
## Q3.1.4 -> QAPI 0.198 0.000
## Q3.1.5 -> QAPI 0.202 0.000
## Q3.1.6 -> QAPI 0.201 0.000
## Q3.1.7 -> QAPI 0.200 0.000
## Q3.2.1 -> SSS 0.165 0.000
## Q3.2.2 -> SSS 0.179 0.000
## Q3.2.3 -> SSS 0.185 0.000
## Q3.2.4 -> SSS 0.167 0.000
## Q3.2.5 -> SSS 0.169 0.000
## Q3.2.6 -> SSS 0.164 0.000
## Q3.2.7 -> SSS 0.170 0.000
## Q3.2.8 -> SSS 0.171 0.000
## Q3.3.1 -> GE 0.198 0.000
## Q3.3.2 -> GE 0.203 0.000
## Q3.3.3 -> GE 0.206 0.000
## Q3.3.4 -> GE 0.209 0.000
## Q3.3.5 -> GE 0.194 0.000
## Q3.3.6 -> GE 0.199 0.000
## Q4.1.1 -> ESP 0.167 0.000
## Q4.1.2 -> ESP 0.177 0.000
## Q4.1.3 -> ESP 0.185 0.000
## Q4.1.4 -> ESP 0.214 0.000
## Q4.1.5 -> ESP 0.229 0.000
## Q4.1.6 -> ESP 0.216 0.000
## Q4.2.1 -> PC 0.271 0.000
## Q4.2.2 -> PC 0.282 0.000
## Q4.2.3 -> PC 0.279 0.000
## Q4.2.4 -> PC 0.259 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Q2.2.1 -> QP 0.853 0.856 0.018 48.089 0.827
## Q2.2.2 -> QP 0.885 0.884 0.012 70.806 0.864
## Q2.2.3 -> QP 0.785 0.781 0.031 25.671 0.716
## Q2.2.4 -> QP 0.873 0.866 0.020 43.791 0.815
## Q2.2.5 -> QP 0.662 0.655 0.049 13.455 0.545
## Q2.3.1 -> QAH 0.702 0.652 0.147 4.782 0.138
## Q2.3.2 -> QAH 0.700 0.658 0.104 6.713 0.394
## Q2.3.3 -> QAH 0.853 0.817 0.091 9.410 0.527
## Q2.3.4 -> QAH 0.940 0.924 0.132 7.135 0.854
## Q2.4.1 -> QAU 0.871 0.866 0.015 58.625 0.836
## Q2.4.2 -> QAU 0.901 0.899 0.013 71.378 0.869
## Q2.4.3 -> QAU 0.893 0.893 0.011 79.104 0.870
## Q2.4.4 -> QAU 0.809 0.808 0.030 26.997 0.741
## Q2.4.5 -> QAU 0.817 0.821 0.027 30.738 0.750
## Q2.5.1 -> PQA 0.929 0.928 0.010 92.265 0.909
## Q2.5.2 -> PQA 0.968 0.967 0.004 222.514 0.958
## Q2.5.3 -> PQA 0.971 0.971 0.004 227.864 0.962
## Q2.5.4 -> PQA 0.966 0.965 0.005 213.195 0.955
## Q2.5.5 -> PQA 0.960 0.959 0.005 174.686 0.947
## Q2.5.6 -> PQA 0.966 0.965 0.005 209.813 0.957
## Q2.5.7 -> PQA 0.952 0.952 0.006 150.841 0.938
## Q2.6.1 -> FQA 0.875 0.870 0.044 19.745 0.771
## Q2.6.2 -> FQA 0.896 0.889 0.032 28.142 0.816
## Q2.6.3 -> FQA 0.928 0.922 0.023 40.464 0.881
## Q2.6.4 -> FQA 0.919 0.917 0.020 46.540 0.865
## Q2.6.5 -> FQA 0.930 0.925 0.017 54.344 0.885
## Q2.6.6 -> FQA 0.916 0.911 0.018 50.725 0.864
## Q2.6.7 -> FQA 0.889 0.885 0.026 34.227 0.824
## Q3.1.1 -> QAPI 0.704 0.709 0.027 26.287 0.655
## Q3.1.2 -> QAPI 0.743 0.745 0.020 37.153 0.708
## Q3.1.3 -> QAPI 0.769 0.769 0.013 59.806 0.741
## Q3.1.4 -> QAPI 0.808 0.810 0.012 68.633 0.786
## Q3.1.5 -> QAPI 0.830 0.830 0.012 71.689 0.810
## Q3.1.6 -> QAPI 0.834 0.834 0.011 79.212 0.815
## Q3.1.7 -> QAPI 0.806 0.807 0.014 57.461 0.775
## Q3.2.1 -> SSS 0.775 0.779 0.026 29.282 0.719
## Q3.2.2 -> SSS 0.843 0.847 0.017 49.978 0.812
## Q3.2.3 -> SSS 0.777 0.781 0.016 49.508 0.750
## Q3.2.4 -> SSS 0.817 0.822 0.019 43.491 0.788
## Q3.2.5 -> SSS 0.830 0.833 0.021 38.730 0.781
## Q3.2.6 -> SSS 0.769 0.772 0.021 36.464 0.735
## Q3.2.7 -> SSS 0.752 0.753 0.017 45.004 0.722
## Q3.2.8 -> SSS 0.713 0.716 0.017 40.912 0.679
## Q3.3.1 -> GE 0.806 0.806 0.018 45.467 0.779
## Q3.3.2 -> GE 0.868 0.867 0.011 78.826 0.844
## Q3.3.3 -> GE 0.907 0.907 0.008 120.746 0.892
## Q3.3.4 -> GE 0.910 0.909 0.008 114.420 0.895
## Q3.3.5 -> GE 0.892 0.891 0.010 91.628 0.872
## Q3.3.6 -> GE 0.789 0.791 0.017 46.264 0.755
## Q4.1.1 -> ESP 0.834 0.833 0.014 57.898 0.808
## Q4.1.2 -> ESP 0.888 0.886 0.011 82.225 0.860
## Q4.1.3 -> ESP 0.901 0.901 0.010 86.415 0.881
## Q4.1.4 -> ESP 0.916 0.915 0.007 131.807 0.902
## Q4.1.5 -> ESP 0.903 0.903 0.008 116.299 0.889
## Q4.1.6 -> ESP 0.898 0.897 0.008 106.810 0.880
## Q4.2.1 -> PC 0.936 0.935 0.006 157.766 0.924
## Q4.2.2 -> PC 0.958 0.958 0.005 211.218 0.949
## Q4.2.3 -> PC 0.952 0.952 0.005 195.123 0.943
## Q4.2.4 -> PC 0.929 0.928 0.007 137.566 0.913
## 97.5% CI Bootstrap P Val
## Q2.2.1 -> QP 0.890 0.000
## Q2.2.2 -> QP 0.907 0.000
## Q2.2.3 -> QP 0.829 0.000
## Q2.2.4 -> QP 0.892 0.000
## Q2.2.5 -> QP 0.726 0.000
## Q2.3.1 -> QAH 0.781 0.000
## Q2.3.2 -> QAH 0.793 0.000
## Q2.3.3 -> QAH 0.892 0.000
## Q2.3.4 -> QAH 0.983 0.020
## Q2.4.1 -> QAU 0.892 0.000
## Q2.4.2 -> QAU 0.925 0.000
## Q2.4.3 -> QAU 0.914 0.000
## Q2.4.4 -> QAU 0.848 0.000
## Q2.4.5 -> QAU 0.865 0.000
## Q2.5.1 -> PQA 0.947 0.000
## Q2.5.2 -> PQA 0.974 0.000
## Q2.5.3 -> PQA 0.979 0.000
## Q2.5.4 -> PQA 0.974 0.000
## Q2.5.5 -> PQA 0.967 0.000
## Q2.5.6 -> PQA 0.974 0.000
## Q2.5.7 -> PQA 0.962 0.000
## Q2.6.1 -> FQA 0.924 0.000
## Q2.6.2 -> FQA 0.916 0.000
## Q2.6.3 -> FQA 0.943 0.000
## Q2.6.4 -> FQA 0.942 0.000
## Q2.6.5 -> FQA 0.945 0.000
## Q2.6.6 -> FQA 0.934 0.000
## Q2.6.7 -> FQA 0.926 0.000
## Q3.1.1 -> QAPI 0.755 0.000
## Q3.1.2 -> QAPI 0.783 0.000
## Q3.1.3 -> QAPI 0.792 0.000
## Q3.1.4 -> QAPI 0.830 0.000
## Q3.1.5 -> QAPI 0.853 0.000
## Q3.1.6 -> QAPI 0.854 0.000
## Q3.1.7 -> QAPI 0.832 0.000
## Q3.2.1 -> SSS 0.823 0.000
## Q3.2.2 -> SSS 0.879 0.000
## Q3.2.3 -> SSS 0.809 0.000
## Q3.2.4 -> SSS 0.857 0.000
## Q3.2.5 -> SSS 0.872 0.000
## Q3.2.6 -> SSS 0.811 0.000
## Q3.2.7 -> SSS 0.782 0.000
## Q3.2.8 -> SSS 0.745 0.000
## Q3.3.1 -> GE 0.841 0.000
## Q3.3.2 -> GE 0.885 0.000
## Q3.3.3 -> GE 0.922 0.000
## Q3.3.4 -> GE 0.922 0.000
## Q3.3.5 -> GE 0.907 0.000
## Q3.3.6 -> GE 0.818 0.000
## Q4.1.1 -> ESP 0.857 0.000
## Q4.1.2 -> ESP 0.907 0.000
## Q4.1.3 -> ESP 0.916 0.000
## Q4.1.4 -> ESP 0.926 0.000
## Q4.1.5 -> ESP 0.914 0.000
## Q4.1.6 -> ESP 0.911 0.000
## Q4.2.1 -> PC 0.946 0.000
## Q4.2.2 -> PC 0.965 0.000
## Q4.2.3 -> PC 0.960 0.000
## Q4.2.4 -> PC 0.941 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## QP -> QAH 0.532 0.531 0.029 0.470 0.580
## QP -> QAU 0.644 0.645 0.028 0.589 0.693
## QP -> PQA 0.125 0.130 0.027 0.079 0.177
## QP -> FQA 0.014 0.034 0.011 0.017 0.061
## QP -> QAPI 0.574 0.575 0.032 0.522 0.630
## QP -> SSS 0.557 0.561 0.033 0.501 0.617
## QP -> GE 0.568 0.573 0.030 0.516 0.633
## QP -> ESP 0.206 0.208 0.034 0.134 0.267
## QP -> PC 0.282 0.282 0.027 0.230 0.329
## QAH -> QAU 0.601 0.603 0.022 0.563 0.649
## QAH -> PQA 0.068 0.072 0.024 0.039 0.133
## QAH -> FQA 0.017 0.036 0.012 0.018 0.060
## QAH -> QAPI 0.543 0.545 0.024 0.497 0.586
## QAH -> SSS 0.427 0.427 0.026 0.382 0.473
## QAH -> GE 0.520 0.521 0.025 0.472 0.564
## QAH -> ESP 0.193 0.198 0.032 0.141 0.259
## QAH -> PC 0.218 0.224 0.029 0.164 0.279
## QAU -> PQA 0.106 0.110 0.031 0.046 0.160
## QAU -> FQA 0.035 0.045 0.017 0.022 0.080
## QAU -> QAPI 0.585 0.585 0.029 0.534 0.643
## QAU -> SSS 0.539 0.542 0.032 0.479 0.608
## QAU -> GE 0.547 0.549 0.031 0.491 0.606
## QAU -> ESP 0.256 0.256 0.030 0.200 0.301
## QAU -> PC 0.294 0.294 0.029 0.240 0.349
## PQA -> FQA 0.045 0.048 0.022 0.018 0.096
## PQA -> QAPI 0.086 0.090 0.025 0.052 0.140
## PQA -> SSS 0.144 0.146 0.027 0.092 0.191
## PQA -> GE 0.090 0.094 0.024 0.051 0.140
## PQA -> ESP 0.427 0.425 0.029 0.367 0.472
## PQA -> PC 0.408 0.414 0.029 0.365 0.469
## FQA -> QAPI 0.026 0.042 0.013 0.023 0.072
## FQA -> SSS 0.032 0.045 0.014 0.027 0.081
## FQA -> GE 0.025 0.037 0.011 0.023 0.065
## FQA -> ESP 0.032 0.039 0.018 0.017 0.080
## FQA -> PC 0.025 0.038 0.019 0.016 0.077
## QAPI -> SSS 0.735 0.735 0.031 0.670 0.780
## QAPI -> GE 0.732 0.732 0.025 0.686 0.779
## QAPI -> ESP 0.349 0.351 0.036 0.268 0.420
## QAPI -> PC 0.366 0.365 0.032 0.287 0.413
## SSS -> GE 0.684 0.687 0.022 0.645 0.726
## SSS -> ESP 0.220 0.220 0.032 0.162 0.275
## SSS -> PC 0.289 0.287 0.029 0.226 0.336
## GE -> ESP 0.305 0.306 0.031 0.243 0.370
## GE -> PC 0.375 0.373 0.032 0.309 0.428
## ESP -> PC 0.683 0.686 0.024 0.640 0.728
## Bootstrap P Val
## QP -> QAH 0.000
## QP -> QAU 0.000
## QP -> PQA 0.000
## QP -> FQA 0.000
## QP -> QAPI 0.000
## QP -> SSS 0.000
## QP -> GE 0.000
## QP -> ESP 0.000
## QP -> PC 0.000
## QAH -> QAU 0.000
## QAH -> PQA 0.000
## QAH -> FQA 0.000
## QAH -> QAPI 0.000
## QAH -> SSS 0.000
## QAH -> GE 0.000
## QAH -> ESP 0.000
## QAH -> PC 0.000
## QAU -> PQA 0.000
## QAU -> FQA 0.000
## QAU -> QAPI 0.000
## QAU -> SSS 0.000
## QAU -> GE 0.000
## QAU -> ESP 0.000
## QAU -> PC 0.000
## PQA -> FQA 0.000
## PQA -> QAPI 0.000
## PQA -> SSS 0.000
## PQA -> GE 0.000
## PQA -> ESP 0.000
## PQA -> PC 0.000
## FQA -> QAPI 0.000
## FQA -> SSS 0.000
## FQA -> GE 0.000
## FQA -> ESP 0.000
## FQA -> PC 0.000
## QAPI -> SSS 0.000
## QAPI -> GE 0.000
## QAPI -> ESP 0.000
## QAPI -> PC 0.000
## SSS -> GE 0.000
## SSS -> ESP 0.000
## SSS -> PC 0.000
## GE -> ESP 0.000
## GE -> PC 0.000
## ESP -> PC 0.000
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## QP -> PQA 0.144 0.147 0.030 0.094 0.198
## QP -> FQA -0.006 -0.007 0.004 -0.014 0.004
## QP -> QAPI 0.000 0.000 0.000 -0.000 0.001
## QP -> SSS 0.000 0.000 0.000 -0.000 0.000
## QP -> GE 0.000 0.000 0.000 -0.000 0.000
## QP -> ESP 0.000 0.000 0.000 -0.000 0.000
## QP -> PC 0.000 0.000 0.000 -0.000 0.000
## QAH -> PQA -0.200 -0.203 0.043 -0.263 -0.151
## QAH -> FQA 0.009 0.009 0.006 -0.006 0.019
## QAH -> QAPI -0.000 -0.000 0.000 -0.001 0.000
## QAH -> SSS -0.000 -0.000 0.000 -0.001 0.000
## QAH -> GE -0.000 -0.000 0.000 -0.001 0.000
## QAH -> ESP -0.000 -0.000 0.000 -0.000 0.000
## QAH -> PC -0.000 -0.000 0.000 -0.000 0.000
## QAU -> PQA 0.125 0.126 0.030 0.061 0.184
## QAU -> FQA -0.006 -0.006 0.004 -0.013 0.003
## QAU -> QAPI 0.000 0.000 0.000 -0.000 0.001
## QAU -> SSS 0.000 0.000 0.000 -0.000 0.000
## QAU -> GE 0.000 0.000 0.000 -0.000 0.000
## QAU -> ESP 0.000 0.000 0.000 -0.000 0.000
## QAU -> PC 0.000 0.000 0.000 -0.000 0.000
## PQA -> FQA -0.045 -0.047 0.027 -0.095 0.023
## PQA -> QAPI 0.001 0.001 0.002 -0.002 0.005
## PQA -> SSS 0.001 0.001 0.001 -0.001 0.003
## PQA -> GE 0.001 0.001 0.001 -0.001 0.003
## PQA -> ESP 0.000 0.000 0.000 -0.000 0.001
## PQA -> PC 0.000 0.000 0.000 -0.000 0.001
## FQA -> QAPI -0.021 -0.024 0.028 -0.070 0.035
## FQA -> SSS -0.014 -0.016 0.019 -0.046 0.024
## FQA -> GE -0.014 -0.016 0.019 -0.047 0.024
## FQA -> ESP -0.004 -0.005 0.006 -0.014 0.008
## FQA -> PC -0.003 -0.003 0.004 -0.010 0.005
## QAPI -> SSS 0.665 0.667 0.030 0.607 0.708
## QAPI -> GE 0.669 0.670 0.023 0.625 0.713
## QAPI -> ESP 0.206 0.208 0.024 0.161 0.251
## QAPI -> PC 0.136 0.137 0.017 0.105 0.168
## SSS -> ESP 0.037 0.034 0.035 -0.028 0.097
## SSS -> PC 0.024 0.023 0.023 -0.018 0.066
## GE -> ESP 0.271 0.275 0.034 0.204 0.345
## GE -> PC 0.178 0.182 0.023 0.134 0.228
## ESP -> PC 0.657 0.660 0.023 0.617 0.700Kriteria: - |T-statistik| > 1,96 → signifikan - p-value < 0,05 → signifikan
##
## Results from Bootstrap resamples: 100
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## QP -> PQA 0.144 0.147 0.030 4.838 0.094
## QAH -> PQA -0.200 -0.203 0.043 -4.655 -0.263
## QAU -> PQA 0.125 0.126 0.030 4.093 0.061
## PQA -> FQA -0.045 -0.047 0.027 -1.643 -0.095
## FQA -> QAPI -0.021 -0.024 0.028 -0.763 -0.070
## QAPI -> SSS 0.665 0.667 0.030 22.375 0.607
## QAPI -> GE 0.669 0.670 0.023 29.182 0.625
## SSS -> ESP 0.037 0.034 0.035 1.054 -0.028
## GE -> ESP 0.271 0.275 0.034 8.002 0.204
## ESP -> PC 0.657 0.660 0.023 29.123 0.617
## 97.5% CI Bootstrap P Val
## QP -> PQA 0.198 0.000
## QAH -> PQA -0.151 0.020
## QAU -> PQA 0.184 0.000
## PQA -> FQA 0.023 0.120
## FQA -> QAPI 0.035 0.380
## QAPI -> SSS 0.708 0.000
## QAPI -> GE 0.713 0.000
## SSS -> ESP 0.097 0.400
## GE -> ESP 0.345 0.000
## ESP -> PC 0.700 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Q2.2.1 -> QP 0.330 0.340 0.044 7.471 0.278
## Q2.2.2 -> QP 0.332 0.335 0.043 7.713 0.278
## Q2.2.3 -> QP 0.191 0.186 0.039 4.851 0.083
## Q2.2.4 -> QP 0.267 0.262 0.037 7.247 0.191
## Q2.2.5 -> QP 0.062 0.054 0.065 0.958 -0.131
## Q2.3.1 -> QAH 0.175 0.129 0.188 0.933 -0.500
## Q2.3.2 -> QAH 0.150 0.113 0.140 1.073 -0.205
## Q2.3.3 -> QAH 0.274 0.256 0.093 2.946 0.066
## Q2.3.4 -> QAH 0.572 0.614 0.256 2.238 0.424
## Q2.4.1 -> QAU 0.232 0.224 0.030 7.811 0.181
## Q2.4.2 -> QAU 0.299 0.298 0.040 7.577 0.246
## Q2.4.3 -> QAU 0.257 0.256 0.032 7.980 0.189
## Q2.4.4 -> QAU 0.130 0.125 0.061 2.116 0.006
## Q2.4.5 -> QAU 0.237 0.251 0.042 5.579 0.177
## Q2.5.1 -> PQA 0.150 0.148 0.008 17.974 0.133
## Q2.5.2 -> PQA 0.154 0.154 0.005 28.984 0.145
## Q2.5.3 -> PQA 0.159 0.158 0.005 34.697 0.150
## Q2.5.4 -> PQA 0.152 0.153 0.006 25.429 0.141
## Q2.5.5 -> PQA 0.142 0.143 0.006 22.440 0.130
## Q2.5.6 -> PQA 0.149 0.151 0.005 32.726 0.142
## Q2.5.7 -> PQA 0.137 0.138 0.006 22.355 0.126
## Q2.6.1 -> FQA 0.134 0.136 0.109 1.225 -0.100
## Q2.6.2 -> FQA 0.160 0.152 0.056 2.844 -0.023
## Q2.6.3 -> FQA 0.212 0.200 0.059 3.604 0.097
## Q2.6.4 -> FQA 0.100 0.119 0.074 1.350 -0.021
## Q2.6.5 -> FQA 0.175 0.168 0.042 4.194 0.097
## Q2.6.6 -> FQA 0.163 0.158 0.057 2.853 0.026
## Q2.6.7 -> FQA 0.158 0.162 0.064 2.449 0.036
## Q3.1.1 -> QAPI 0.167 0.167 0.007 24.531 0.152
## Q3.1.2 -> QAPI 0.172 0.172 0.005 32.940 0.162
## Q3.1.3 -> QAPI 0.169 0.169 0.005 32.488 0.160
## Q3.1.4 -> QAPI 0.187 0.187 0.006 32.253 0.176
## Q3.1.5 -> QAPI 0.192 0.192 0.005 35.904 0.182
## Q3.1.6 -> QAPI 0.191 0.191 0.005 36.334 0.182
## Q3.1.7 -> QAPI 0.191 0.191 0.005 37.441 0.182
## Q3.2.1 -> SSS 0.153 0.153 0.006 23.698 0.140
## Q3.2.2 -> SSS 0.169 0.168 0.006 29.344 0.157
## Q3.2.3 -> SSS 0.172 0.171 0.008 22.716 0.158
## Q3.2.4 -> SSS 0.156 0.156 0.006 25.665 0.145
## Q3.2.5 -> SSS 0.160 0.160 0.005 30.161 0.149
## Q3.2.6 -> SSS 0.152 0.150 0.007 22.400 0.138
## Q3.2.7 -> SSS 0.155 0.154 0.007 22.490 0.143
## Q3.2.8 -> SSS 0.157 0.156 0.008 19.608 0.140
## Q3.3.1 -> GE 0.191 0.190 0.004 49.754 0.183
## Q3.3.2 -> GE 0.195 0.195 0.005 41.565 0.186
## Q3.3.3 -> GE 0.199 0.199 0.004 46.948 0.191
## Q3.3.4 -> GE 0.198 0.199 0.005 41.909 0.191
## Q3.3.5 -> GE 0.188 0.188 0.004 46.226 0.180
## Q3.3.6 -> GE 0.188 0.188 0.006 32.602 0.177
## Q4.1.1 -> ESP 0.156 0.155 0.006 28.036 0.142
## Q4.1.2 -> ESP 0.168 0.167 0.005 35.681 0.157
## Q4.1.3 -> ESP 0.177 0.177 0.004 44.243 0.170
## Q4.1.4 -> ESP 0.202 0.202 0.005 41.081 0.193
## Q4.1.5 -> ESP 0.214 0.215 0.006 35.307 0.204
## Q4.1.6 -> ESP 0.204 0.205 0.005 38.243 0.197
## Q4.2.1 -> PC 0.263 0.263 0.004 63.047 0.254
## Q4.2.2 -> PC 0.273 0.273 0.004 64.352 0.265
## Q4.2.3 -> PC 0.272 0.273 0.004 75.693 0.267
## Q4.2.4 -> PC 0.252 0.251 0.004 59.513 0.245
## 97.5% CI Bootstrap P Val
## Q2.2.1 -> QP 0.439 0.000
## Q2.2.2 -> QP 0.451 0.000
## Q2.2.3 -> QP 0.253 0.000
## Q2.2.4 -> QP 0.327 0.000
## Q2.2.5 -> QP 0.128 0.360
## Q2.3.1 -> QAH 0.345 0.220
## Q2.3.2 -> QAH 0.336 0.220
## Q2.3.3 -> QAH 0.429 0.020
## Q2.3.4 -> QAH 1.133 0.020
## Q2.4.1 -> QAU 0.272 0.000
## Q2.4.2 -> QAU 0.384 0.000
## Q2.4.3 -> QAU 0.325 0.000
## Q2.4.4 -> QAU 0.196 0.060
## Q2.4.5 -> QAU 0.361 0.000
## Q2.5.1 -> PQA 0.162 0.000
## Q2.5.2 -> PQA 0.164 0.000
## Q2.5.3 -> PQA 0.166 0.000
## Q2.5.4 -> PQA 0.163 0.000
## Q2.5.5 -> PQA 0.153 0.000
## Q2.5.6 -> PQA 0.159 0.000
## Q2.5.7 -> PQA 0.150 0.000
## Q2.6.1 -> FQA 0.316 0.140
## Q2.6.2 -> FQA 0.248 0.080
## Q2.6.3 -> FQA 0.328 0.040
## Q2.6.4 -> FQA 0.320 0.080
## Q2.6.5 -> FQA 0.271 0.000
## Q2.6.6 -> FQA 0.282 0.040
## Q2.6.7 -> FQA 0.321 0.020
## Q3.1.1 -> QAPI 0.179 0.000
## Q3.1.2 -> QAPI 0.181 0.000
## Q3.1.3 -> QAPI 0.179 0.000
## Q3.1.4 -> QAPI 0.198 0.000
## Q3.1.5 -> QAPI 0.202 0.000
## Q3.1.6 -> QAPI 0.201 0.000
## Q3.1.7 -> QAPI 0.200 0.000
## Q3.2.1 -> SSS 0.165 0.000
## Q3.2.2 -> SSS 0.179 0.000
## Q3.2.3 -> SSS 0.185 0.000
## Q3.2.4 -> SSS 0.167 0.000
## Q3.2.5 -> SSS 0.169 0.000
## Q3.2.6 -> SSS 0.164 0.000
## Q3.2.7 -> SSS 0.170 0.000
## Q3.2.8 -> SSS 0.171 0.000
## Q3.3.1 -> GE 0.198 0.000
## Q3.3.2 -> GE 0.203 0.000
## Q3.3.3 -> GE 0.206 0.000
## Q3.3.4 -> GE 0.209 0.000
## Q3.3.5 -> GE 0.194 0.000
## Q3.3.6 -> GE 0.199 0.000
## Q4.1.1 -> ESP 0.167 0.000
## Q4.1.2 -> ESP 0.177 0.000
## Q4.1.3 -> ESP 0.185 0.000
## Q4.1.4 -> ESP 0.214 0.000
## Q4.1.5 -> ESP 0.229 0.000
## Q4.1.6 -> ESP 0.216 0.000
## Q4.2.1 -> PC 0.271 0.000
## Q4.2.2 -> PC 0.282 0.000
## Q4.2.3 -> PC 0.279 0.000
## Q4.2.4 -> PC 0.259 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Q2.2.1 -> QP 0.853 0.856 0.018 48.089 0.827
## Q2.2.2 -> QP 0.885 0.884 0.012 70.806 0.864
## Q2.2.3 -> QP 0.785 0.781 0.031 25.671 0.716
## Q2.2.4 -> QP 0.873 0.866 0.020 43.791 0.815
## Q2.2.5 -> QP 0.662 0.655 0.049 13.455 0.545
## Q2.3.1 -> QAH 0.702 0.652 0.147 4.782 0.138
## Q2.3.2 -> QAH 0.700 0.658 0.104 6.713 0.394
## Q2.3.3 -> QAH 0.853 0.817 0.091 9.410 0.527
## Q2.3.4 -> QAH 0.940 0.924 0.132 7.135 0.854
## Q2.4.1 -> QAU 0.871 0.866 0.015 58.625 0.836
## Q2.4.2 -> QAU 0.901 0.899 0.013 71.378 0.869
## Q2.4.3 -> QAU 0.893 0.893 0.011 79.104 0.870
## Q2.4.4 -> QAU 0.809 0.808 0.030 26.997 0.741
## Q2.4.5 -> QAU 0.817 0.821 0.027 30.738 0.750
## Q2.5.1 -> PQA 0.929 0.928 0.010 92.265 0.909
## Q2.5.2 -> PQA 0.968 0.967 0.004 222.514 0.958
## Q2.5.3 -> PQA 0.971 0.971 0.004 227.864 0.962
## Q2.5.4 -> PQA 0.966 0.965 0.005 213.195 0.955
## Q2.5.5 -> PQA 0.960 0.959 0.005 174.686 0.947
## Q2.5.6 -> PQA 0.966 0.965 0.005 209.813 0.957
## Q2.5.7 -> PQA 0.952 0.952 0.006 150.841 0.938
## Q2.6.1 -> FQA 0.875 0.870 0.044 19.745 0.771
## Q2.6.2 -> FQA 0.896 0.889 0.032 28.142 0.816
## Q2.6.3 -> FQA 0.928 0.922 0.023 40.464 0.881
## Q2.6.4 -> FQA 0.919 0.917 0.020 46.540 0.865
## Q2.6.5 -> FQA 0.930 0.925 0.017 54.344 0.885
## Q2.6.6 -> FQA 0.916 0.911 0.018 50.725 0.864
## Q2.6.7 -> FQA 0.889 0.885 0.026 34.227 0.824
## Q3.1.1 -> QAPI 0.704 0.709 0.027 26.287 0.655
## Q3.1.2 -> QAPI 0.743 0.745 0.020 37.153 0.708
## Q3.1.3 -> QAPI 0.769 0.769 0.013 59.806 0.741
## Q3.1.4 -> QAPI 0.808 0.810 0.012 68.633 0.786
## Q3.1.5 -> QAPI 0.830 0.830 0.012 71.689 0.810
## Q3.1.6 -> QAPI 0.834 0.834 0.011 79.212 0.815
## Q3.1.7 -> QAPI 0.806 0.807 0.014 57.461 0.775
## Q3.2.1 -> SSS 0.775 0.779 0.026 29.282 0.719
## Q3.2.2 -> SSS 0.843 0.847 0.017 49.978 0.812
## Q3.2.3 -> SSS 0.777 0.781 0.016 49.508 0.750
## Q3.2.4 -> SSS 0.817 0.822 0.019 43.491 0.788
## Q3.2.5 -> SSS 0.830 0.833 0.021 38.730 0.781
## Q3.2.6 -> SSS 0.769 0.772 0.021 36.464 0.735
## Q3.2.7 -> SSS 0.752 0.753 0.017 45.004 0.722
## Q3.2.8 -> SSS 0.713 0.716 0.017 40.912 0.679
## Q3.3.1 -> GE 0.806 0.806 0.018 45.467 0.779
## Q3.3.2 -> GE 0.868 0.867 0.011 78.826 0.844
## Q3.3.3 -> GE 0.907 0.907 0.008 120.746 0.892
## Q3.3.4 -> GE 0.910 0.909 0.008 114.420 0.895
## Q3.3.5 -> GE 0.892 0.891 0.010 91.628 0.872
## Q3.3.6 -> GE 0.789 0.791 0.017 46.264 0.755
## Q4.1.1 -> ESP 0.834 0.833 0.014 57.898 0.808
## Q4.1.2 -> ESP 0.888 0.886 0.011 82.225 0.860
## Q4.1.3 -> ESP 0.901 0.901 0.010 86.415 0.881
## Q4.1.4 -> ESP 0.916 0.915 0.007 131.807 0.902
## Q4.1.5 -> ESP 0.903 0.903 0.008 116.299 0.889
## Q4.1.6 -> ESP 0.898 0.897 0.008 106.810 0.880
## Q4.2.1 -> PC 0.936 0.935 0.006 157.766 0.924
## Q4.2.2 -> PC 0.958 0.958 0.005 211.218 0.949
## Q4.2.3 -> PC 0.952 0.952 0.005 195.123 0.943
## Q4.2.4 -> PC 0.929 0.928 0.007 137.566 0.913
## 97.5% CI Bootstrap P Val
## Q2.2.1 -> QP 0.890 0.000
## Q2.2.2 -> QP 0.907 0.000
## Q2.2.3 -> QP 0.829 0.000
## Q2.2.4 -> QP 0.892 0.000
## Q2.2.5 -> QP 0.726 0.000
## Q2.3.1 -> QAH 0.781 0.000
## Q2.3.2 -> QAH 0.793 0.000
## Q2.3.3 -> QAH 0.892 0.000
## Q2.3.4 -> QAH 0.983 0.020
## Q2.4.1 -> QAU 0.892 0.000
## Q2.4.2 -> QAU 0.925 0.000
## Q2.4.3 -> QAU 0.914 0.000
## Q2.4.4 -> QAU 0.848 0.000
## Q2.4.5 -> QAU 0.865 0.000
## Q2.5.1 -> PQA 0.947 0.000
## Q2.5.2 -> PQA 0.974 0.000
## Q2.5.3 -> PQA 0.979 0.000
## Q2.5.4 -> PQA 0.974 0.000
## Q2.5.5 -> PQA 0.967 0.000
## Q2.5.6 -> PQA 0.974 0.000
## Q2.5.7 -> PQA 0.962 0.000
## Q2.6.1 -> FQA 0.924 0.000
## Q2.6.2 -> FQA 0.916 0.000
## Q2.6.3 -> FQA 0.943 0.000
## Q2.6.4 -> FQA 0.942 0.000
## Q2.6.5 -> FQA 0.945 0.000
## Q2.6.6 -> FQA 0.934 0.000
## Q2.6.7 -> FQA 0.926 0.000
## Q3.1.1 -> QAPI 0.755 0.000
## Q3.1.2 -> QAPI 0.783 0.000
## Q3.1.3 -> QAPI 0.792 0.000
## Q3.1.4 -> QAPI 0.830 0.000
## Q3.1.5 -> QAPI 0.853 0.000
## Q3.1.6 -> QAPI 0.854 0.000
## Q3.1.7 -> QAPI 0.832 0.000
## Q3.2.1 -> SSS 0.823 0.000
## Q3.2.2 -> SSS 0.879 0.000
## Q3.2.3 -> SSS 0.809 0.000
## Q3.2.4 -> SSS 0.857 0.000
## Q3.2.5 -> SSS 0.872 0.000
## Q3.2.6 -> SSS 0.811 0.000
## Q3.2.7 -> SSS 0.782 0.000
## Q3.2.8 -> SSS 0.745 0.000
## Q3.3.1 -> GE 0.841 0.000
## Q3.3.2 -> GE 0.885 0.000
## Q3.3.3 -> GE 0.922 0.000
## Q3.3.4 -> GE 0.922 0.000
## Q3.3.5 -> GE 0.907 0.000
## Q3.3.6 -> GE 0.818 0.000
## Q4.1.1 -> ESP 0.857 0.000
## Q4.1.2 -> ESP 0.907 0.000
## Q4.1.3 -> ESP 0.916 0.000
## Q4.1.4 -> ESP 0.926 0.000
## Q4.1.5 -> ESP 0.914 0.000
## Q4.1.6 -> ESP 0.911 0.000
## Q4.2.1 -> PC 0.946 0.000
## Q4.2.2 -> PC 0.965 0.000
## Q4.2.3 -> PC 0.960 0.000
## Q4.2.4 -> PC 0.941 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## QP -> QAH 0.532 0.531 0.029 0.470 0.580
## QP -> QAU 0.644 0.645 0.028 0.589 0.693
## QP -> PQA 0.125 0.130 0.027 0.079 0.177
## QP -> FQA 0.014 0.034 0.011 0.017 0.061
## QP -> QAPI 0.574 0.575 0.032 0.522 0.630
## QP -> SSS 0.557 0.561 0.033 0.501 0.617
## QP -> GE 0.568 0.573 0.030 0.516 0.633
## QP -> ESP 0.206 0.208 0.034 0.134 0.267
## QP -> PC 0.282 0.282 0.027 0.230 0.329
## QAH -> QAU 0.601 0.603 0.022 0.563 0.649
## QAH -> PQA 0.068 0.072 0.024 0.039 0.133
## QAH -> FQA 0.017 0.036 0.012 0.018 0.060
## QAH -> QAPI 0.543 0.545 0.024 0.497 0.586
## QAH -> SSS 0.427 0.427 0.026 0.382 0.473
## QAH -> GE 0.520 0.521 0.025 0.472 0.564
## QAH -> ESP 0.193 0.198 0.032 0.141 0.259
## QAH -> PC 0.218 0.224 0.029 0.164 0.279
## QAU -> PQA 0.106 0.110 0.031 0.046 0.160
## QAU -> FQA 0.035 0.045 0.017 0.022 0.080
## QAU -> QAPI 0.585 0.585 0.029 0.534 0.643
## QAU -> SSS 0.539 0.542 0.032 0.479 0.608
## QAU -> GE 0.547 0.549 0.031 0.491 0.606
## QAU -> ESP 0.256 0.256 0.030 0.200 0.301
## QAU -> PC 0.294 0.294 0.029 0.240 0.349
## PQA -> FQA 0.045 0.048 0.022 0.018 0.096
## PQA -> QAPI 0.086 0.090 0.025 0.052 0.140
## PQA -> SSS 0.144 0.146 0.027 0.092 0.191
## PQA -> GE 0.090 0.094 0.024 0.051 0.140
## PQA -> ESP 0.427 0.425 0.029 0.367 0.472
## PQA -> PC 0.408 0.414 0.029 0.365 0.469
## FQA -> QAPI 0.026 0.042 0.013 0.023 0.072
## FQA -> SSS 0.032 0.045 0.014 0.027 0.081
## FQA -> GE 0.025 0.037 0.011 0.023 0.065
## FQA -> ESP 0.032 0.039 0.018 0.017 0.080
## FQA -> PC 0.025 0.038 0.019 0.016 0.077
## QAPI -> SSS 0.735 0.735 0.031 0.670 0.780
## QAPI -> GE 0.732 0.732 0.025 0.686 0.779
## QAPI -> ESP 0.349 0.351 0.036 0.268 0.420
## QAPI -> PC 0.366 0.365 0.032 0.287 0.413
## SSS -> GE 0.684 0.687 0.022 0.645 0.726
## SSS -> ESP 0.220 0.220 0.032 0.162 0.275
## SSS -> PC 0.289 0.287 0.029 0.226 0.336
## GE -> ESP 0.305 0.306 0.031 0.243 0.370
## GE -> PC 0.375 0.373 0.032 0.309 0.428
## ESP -> PC 0.683 0.686 0.024 0.640 0.728
## Bootstrap P Val
## QP -> QAH 0.000
## QP -> QAU 0.000
## QP -> PQA 0.000
## QP -> FQA 0.000
## QP -> QAPI 0.000
## QP -> SSS 0.000
## QP -> GE 0.000
## QP -> ESP 0.000
## QP -> PC 0.000
## QAH -> QAU 0.000
## QAH -> PQA 0.000
## QAH -> FQA 0.000
## QAH -> QAPI 0.000
## QAH -> SSS 0.000
## QAH -> GE 0.000
## QAH -> ESP 0.000
## QAH -> PC 0.000
## QAU -> PQA 0.000
## QAU -> FQA 0.000
## QAU -> QAPI 0.000
## QAU -> SSS 0.000
## QAU -> GE 0.000
## QAU -> ESP 0.000
## QAU -> PC 0.000
## PQA -> FQA 0.000
## PQA -> QAPI 0.000
## PQA -> SSS 0.000
## PQA -> GE 0.000
## PQA -> ESP 0.000
## PQA -> PC 0.000
## FQA -> QAPI 0.000
## FQA -> SSS 0.000
## FQA -> GE 0.000
## FQA -> ESP 0.000
## FQA -> PC 0.000
## QAPI -> SSS 0.000
## QAPI -> GE 0.000
## QAPI -> ESP 0.000
## QAPI -> PC 0.000
## SSS -> GE 0.000
## SSS -> ESP 0.000
## SSS -> PC 0.000
## GE -> ESP 0.000
## GE -> PC 0.000
## ESP -> PC 0.000
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## QP -> PQA 0.144 0.147 0.030 0.094 0.198
## QP -> FQA -0.006 -0.007 0.004 -0.014 0.004
## QP -> QAPI 0.000 0.000 0.000 -0.000 0.001
## QP -> SSS 0.000 0.000 0.000 -0.000 0.000
## QP -> GE 0.000 0.000 0.000 -0.000 0.000
## QP -> ESP 0.000 0.000 0.000 -0.000 0.000
## QP -> PC 0.000 0.000 0.000 -0.000 0.000
## QAH -> PQA -0.200 -0.203 0.043 -0.263 -0.151
## QAH -> FQA 0.009 0.009 0.006 -0.006 0.019
## QAH -> QAPI -0.000 -0.000 0.000 -0.001 0.000
## QAH -> SSS -0.000 -0.000 0.000 -0.001 0.000
## QAH -> GE -0.000 -0.000 0.000 -0.001 0.000
## QAH -> ESP -0.000 -0.000 0.000 -0.000 0.000
## QAH -> PC -0.000 -0.000 0.000 -0.000 0.000
## QAU -> PQA 0.125 0.126 0.030 0.061 0.184
## QAU -> FQA -0.006 -0.006 0.004 -0.013 0.003
## QAU -> QAPI 0.000 0.000 0.000 -0.000 0.001
## QAU -> SSS 0.000 0.000 0.000 -0.000 0.000
## QAU -> GE 0.000 0.000 0.000 -0.000 0.000
## QAU -> ESP 0.000 0.000 0.000 -0.000 0.000
## QAU -> PC 0.000 0.000 0.000 -0.000 0.000
## PQA -> FQA -0.045 -0.047 0.027 -0.095 0.023
## PQA -> QAPI 0.001 0.001 0.002 -0.002 0.005
## PQA -> SSS 0.001 0.001 0.001 -0.001 0.003
## PQA -> GE 0.001 0.001 0.001 -0.001 0.003
## PQA -> ESP 0.000 0.000 0.000 -0.000 0.001
## PQA -> PC 0.000 0.000 0.000 -0.000 0.001
## FQA -> QAPI -0.021 -0.024 0.028 -0.070 0.035
## FQA -> SSS -0.014 -0.016 0.019 -0.046 0.024
## FQA -> GE -0.014 -0.016 0.019 -0.047 0.024
## FQA -> ESP -0.004 -0.005 0.006 -0.014 0.008
## FQA -> PC -0.003 -0.003 0.004 -0.010 0.005
## QAPI -> SSS 0.665 0.667 0.030 0.607 0.708
## QAPI -> GE 0.669 0.670 0.023 0.625 0.713
## QAPI -> ESP 0.206 0.208 0.024 0.161 0.251
## QAPI -> PC 0.136 0.137 0.017 0.105 0.168
## SSS -> ESP 0.037 0.034 0.035 -0.028 0.097
## SSS -> PC 0.024 0.023 0.023 -0.018 0.066
## GE -> ESP 0.271 0.275 0.034 0.204 0.345
## GE -> PC 0.178 0.182 0.023 0.134 0.228
## ESP -> PC 0.657 0.660 0.023 0.617 0.70015. Interpretasi Hasil
Outer Model
Hasil menunjukkan sebagian besar indikator memiliki loading factor di atas 0,7. Beberapa indikator berada di bawah batas ideal (~0,66) namun masih dapat dipertimbangkan. Validitas konvergen tetap terpenuhi secara umum.
Nilai Cronbach’s Alpha, Composite Reliability, dan rhoA seluruh konstruk berada di atas 0,7 sehingga reliabel. Nilai AVE seluruhnya di atas 0,5 → validitas konvergen terpenuhi.
Uji validitas diskriminan (Fornell-Larcker dan HTMT) menunjukkan hasil baik: akar AVE lebih besar dari korelasi antar konstruk dan HTMT dalam batas dapat diterima.
Inner Model
# Ringkasan hubungan signifikan berdasarkan hasil bootstrapping
cat("Hubungan signifikan (p < 0.05): QP -> PQA, QAH -> PQA, QAU -> PQA, QAPI -> SSS, QAPI -> GE, GE -> ESP, ESP -> PC\n")## Hubungan signifikan (p < 0.05): QP -> PQA, QAH -> PQA, QAU -> PQA, QAPI -> SSS, QAPI -> GE, GE -> ESP, ESP -> PC## Hubungan tidak signifikan (p >= 0.05): PQA -> FQA, FQA -> QAPI, SSS -> ESPHasil bootstrapping menunjukkan: - Signifikan: QP → PQA (+), QAH → PQA (−), QAU → PQA (+), QAPI → SSS (+), QAPI → GE (+), GE → ESP (+), ESP → PC (+) - Tidak signifikan: PQA → FQA, FQA → QAPI, SSS → ESP
Nilai R² menunjukkan kemampuan penjelasan model dari rendah hingga sedang, dengan nilai tertinggi pada GE (0,448), SSS (0,442), dan PC (0,431).
16. Kesimpulan
- Konstruk dalam model secara umum telah memenuhi validitas dan reliabilitas yang baik.
- QAPI merupakan konstruk paling dominan — pengaruh positif dan signifikan terhadap SSS dan GE.
- ESP terbukti menjadi prediktor penting terhadap PC.
- QAH memiliki pengaruh negatif signifikan terhadap PQA, sedangkan QP dan QAU positif signifikan.
- Jalur PQA → FQA dan FQA → QAPI tidak signifikan → ada kesenjangan persepsi mahasiswa.
- Model lebih kuat menjelaskan variabel akhir (PC, GE, SSS) dibanding variabel perantara awal (FQA, QAPI).
Sesi R Markdown ini menggunakan seminr untuk
estimasi PLS-SEM dan ggplot2 / patchwork untuk
visualisasi.