# Load library
library(seminr)# Untuk SEM-PLS
## Warning: package 'seminr' was built under R version 4.3.3
library(readxl) # Untuk membaca file Excel
## Warning: package 'readxl' was built under R version 4.3.3
# Import data
data <- read_excel("sempls.xlsx")
# Cek struktur data
str(data)
## tibble [111 × 52] (S3: tbl_df/tbl/data.frame)
## $ C_1 : num [1:111] 3 3 3 2 3 3 3 3 3 3 ...
## $ C_2 : num [1:111] 3 3 3 2 3 3 2 2 3 4 ...
## $ C_3 : num [1:111] 3 2 2 4 4 4 3 3 3 3 ...
## $ C_4 : num [1:111] 3 3 2 2 4 3 2 4 3 4 ...
## $ C_5 : num [1:111] 1 3 3 3 3 4 3 4 2 3 ...
## $ C_6 : num [1:111] 2 2 3 3 4 3 2 3 2 3 ...
## $ C_7 : num [1:111] 1 4 2 3 4 3 3 3 2 3 ...
## $ C_8 : num [1:111] 1 2 2 2 4 3 3 3 2 3 ...
## $ C_9 : num [1:111] 3 4 2 2 4 3 4 3 3 3 ...
## $ C_10 : num [1:111] 3 1 2 3 1 3 2 3 2 4 ...
## $ I_1 : num [1:111] 2 2 3 3 2 3 2 3 2 3 ...
## $ I_2 : num [1:111] 2 2 3 3 4 4 4 3 2 2 ...
## $ I_3 : num [1:111] 2 2 2 2 4 3 3 3 2 3 ...
## $ I_4 : num [1:111] 3 4 2 3 4 3 4 3 2 4 ...
## $ I_5 : num [1:111] 3 4 2 2 4 3 3 3 2 4 ...
## $ I_6 : num [1:111] 3 4 3 3 4 3 4 3 2 3 ...
## $ I_7 : num [1:111] 3 3 2 3 3 4 3 2 2 2 ...
## $ I_8 : num [1:111] 3 3 2 2 4 3 3 3 2 3 ...
## $ I_9 : num [1:111] 3 3 3 2 4 3 4 3 2 4 ...
## $ I_10 : num [1:111] 3 3 3 2 4 3 3 3 2 4 ...
## $ I_11 : num [1:111] 3 3 2 3 4 3 3 3 2 3 ...
## $ I_12 : num [1:111] 3 3 3 3 4 3 3 3 1 3 ...
## $ PR_1 : num [1:111] 3 3 3 2 4 3 3 3 2 3 ...
## $ PR_2 : num [1:111] 2 2 2 2 4 2 2 2 2 2 ...
## $ PR_3 : num [1:111] 3 3 2 2 4 3 3 3 2 3 ...
## $ PR_4 : num [1:111] 3 2 3 3 4 3 3 3 2 3 ...
## $ PR_5 : num [1:111] 3 2 2 3 4 3 4 3 2 3 ...
## $ PR_6 : num [1:111] 2 3 2 3 4 3 3 2 2 3 ...
## $ PR_7 : num [1:111] 3 3 2 2 4 3 2 3 2 3 ...
## $ PR_8 : num [1:111] 2 2 2 3 4 3 2 4 2 3 ...
## $ PR_9 : num [1:111] 2 3 3 2 4 3 3 3 2 3 ...
## $ PR_10: num [1:111] 3 3 2 2 4 3 3 3 2 2 ...
## $ PR_11: num [1:111] 3 3 2 2 4 3 2 3 2 3 ...
## $ PR_12: num [1:111] 3 2 2 2 4 3 2 3 2 3 ...
## $ PR_13: num [1:111] 3 3 2 4 4 3 3 3 2 3 ...
## $ PR_14: num [1:111] 3 3 2 3 4 3 2 3 2 3 ...
## $ PR_15: num [1:111] 3 2 2 2 4 3 2 2 2 3 ...
## $ PR_16: num [1:111] 3 3 2 3 4 3 2 2 2 3 ...
## $ PR_17: num [1:111] 3 3 2 2 4 3 3 3 2 3 ...
## $ PR_18: num [1:111] 3 3 3 3 4 3 3 4 2 3 ...
## $ PD_1 : num [1:111] 4 3 3 3 3 3 3 4 2 3 ...
## $ PD_2 : num [1:111] 3 2 3 3 4 3 3 3 2 3 ...
## $ PD_3 : num [1:111] 3 2 2 3 4 3 3 3 2 3 ...
## $ PD_4 : num [1:111] 3 2 2 3 4 3 4 3 2 4 ...
## $ PD_5 : num [1:111] 3 3 2 3 4 2 3 4 2 3 ...
## $ PD_6 : num [1:111] 3 3 2 3 4 4 4 4 2 3 ...
## $ PD_7 : num [1:111] 2 3 3 3 4 3 3 4 2 4 ...
## $ PD_8 : num [1:111] 2 3 2 3 4 3 4 4 2 4 ...
## $ PD_9 : num [1:111] 3 2 2 4 4 3 3 4 3 4 ...
## $ PD_10: num [1:111] 2 3 2 3 4 3 3 3 2 3 ...
## $ PD_11: num [1:111] 3 3 2 3 4 3 3 3 2 3 ...
## $ PD_12: num [1:111] 4 3 3 3 4 3 4 3 2 3 ...
summary(data)
## C_1 C_2 C_3 C_4
## Min. :1.000 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.946 Mean :2.784 Mean :3.387 Mean :2.937
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## C_5 C_6 C_7 C_8 C_9
## Min. :1.000 Min. :2.000 Min. :1.00 Min. :1.00 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.00 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.00 Median :3.00 Median :3.000
## Mean :3.369 Mean :3.225 Mean :3.09 Mean :3.27 Mean :3.288
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.00 Max. :4.000
##
## C_10 I_1 I_2 I_3 I_4
## Min. :1.00 Min. :1.00 Min. :2.000 Min. :2.000 Min. :2.000
## 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.00 Median :3.00 Median :3.000 Median :3.000 Median :3.000
## Mean :2.91 Mean :2.82 Mean :3.036 Mean :3.153 Mean :3.378
## 3rd Qu.:3.00 3rd Qu.:3.00 3rd Qu.:3.500 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.00 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.000
##
## I_5 I_6 I_7 I_8 I_9
## Min. :1.000 Min. :2.00 Min. :2.000 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.00 Median :3.000 Median :3.000 Median :3.000
## Mean :3.081 Mean :3.27 Mean :3.054 Mean :3.279 Mean :3.342
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000 Max. :4.000
##
## I_10 I_11 I_12 PR_1 PR_2
## Min. :2.000 Min. :1.000 Min. :1.00 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.00 Median :3.000 Median :2.000
## Mean :3.108 Mean :3.108 Mean :3.18 Mean :3.279 Mean :2.486
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000
##
## PR_3 PR_4 PR_5 PR_6
## Min. :2.000 Min. :1.000 Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.261 Mean :3.234 Mean :3.198 Mean :3.216
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## PR_7 PR_8 PR_9 PR_10
## Min. :1.000 Min. :2.000 Min. :2.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.126 Mean :3.108 Mean :3.324 Mean :3.009
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## PR_11 PR_12 PR_13 PR_14
## Min. :2.000 Min. :2.000 Min. :2.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.072 Mean :3.081 Mean :3.279 Mean :3.153
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## PR_15 PR_16 PR_17 PR_18 PD_1
## Min. :2.000 Min. :1.000 Min. :2.000 Min. :2.000 Min. :2
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3
## Median :3.000 Median :3.000 Median :3.000 Median :3.000 Median :3
## Mean :2.982 Mean :3.054 Mean :3.234 Mean :3.414 Mean :3
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000 Max. :4
##
## PD_2 PD_3 PD_4 PD_5 PD_6
## Min. :2.00 Min. :2.000 Min. :2.00 Min. :2.000 Min. :2.000
## 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.00 Median :3.000 Median :3.00 Median :3.000 Median :3.000
## Mean :3.09 Mean :3.153 Mean :3.09 Mean :3.036 Mean :3.063
## 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.00 Max. :4.000 Max. :4.00 Max. :4.000 Max. :4.000
##
## PD_7 PD_8 PD_9 PD_10
## Min. :2.000 Min. :2.000 Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.225 Mean :3.198 Mean :3.117 Mean :3.189
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
##
## PD_11 PD_12
## Min. :1.000 Min. :2.000
## 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000
## Mean :3.209 Mean :3.252
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :4.000 Max. :4.000
## NA's :1
#######################################
#Visualisasi data statistik deskriptif
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
# Menghitung rata-rata untuk setiap elemen
means <- colMeans(data, na.rm = TRUE)
# Mengubah data menjadi format panjang untuk plotting
means_df <- data.frame(Elemen = names(means), RataRata = means)
# Menentukan kategori untuk elemen (Context, Input, Process, Product)
means_df$Kategori <- ifelse(grepl("^C_", means_df$Elemen), "Context",
ifelse(grepl("^I_", means_df$Elemen), "Input",
ifelse(grepl("^PR_", means_df$Elemen), "Process", "Product")))
ggplot(means_df, aes(x = Elemen, y = RataRata, fill = Kategori)) +
geom_bar(stat = "identity", width = 0.6) + # Menyesuaikan lebar bar agar lebih ramping
geom_text(aes(label = round(RataRata, 2)),
angle = 90,
vjust = 0.5,
hjust = -0.2,
color = "black",
size = 4) + # Ukuran teks label
scale_fill_manual(values = c("Context" = "lightblue",
"Input" = "lightgreen",
"Process" = "brown",
"Product" = "lightcoral")) + # Warna kategori
theme_minimal() +
labs(x = "Indikator Komponen CIPP",
y = "Rata-Rata",
fill = "Komponen") + # Label untuk sumbu dan legenda
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12), # Ukuran teks sumbu x
axis.text.y = element_text(size = 12), # Ukuran teks sumbu y
axis.title.x = element_text(size = 14), # Ukuran teks judul sumbu x
axis.title.y = element_text(size = 14)) # Ukuran teks judul sumbu y
#######################################
# Pastikan tidak ada missing value
data <- na.omit(data)
# Periksa apakah ada missing value
if (any(is.na(data))) {
cat("Terdapat missing value dalam dataset.\n")
# Menampilkan jumlah missing value per kolom
cat("Jumlah missing value per kolom:\n")
print(colSums(is.na(data)))
# Menghapus missing value
data <- na.omit(data)
cat("Missing value telah dihapus.\n")
} else {
cat("Tidak ada missing value dalam dataset.\n")
}
## Tidak ada missing value dalam dataset.
# Definisi konstruk dengan indikator reflektif (menyesuaikan indikator C_, I_, PR_, PD_)
cipp_mm <- constructs(
composite("Context", multi_items("C_", 1:10)),
composite("Input", multi_items("I_", 1:12)),
composite("Process", multi_items("PR_", 1:18)),
composite("Product", multi_items("PD_", 1:12))
)
# Definisi structural model (jalur antar variabel)
cipp_sm <- relationships(
paths(from = "Context", to = c("Input", "Process", "Product")),
paths(from = "Input", to = c("Process", "Product")),
paths(from = "Process", to = "Product")
)
# Estimasi model PLS-SEM
cipp_PLS <- estimate_pls(
data = data,
measurement_model = cipp_mm,
structural_model = cipp_sm
)
## Generating the seminr model
## All 110 observations are valid.
# Ringkasan hasil model
summary_cipp <- summary(cipp_PLS)
summary_cipp
##
## Results from package seminr (2.3.4)
##
## Path Coefficients:
## Input Process Product
## R^2 0.686 0.803 0.757
## AdjR^2 0.683 0.799 0.750
## Context 0.828 0.202 0.159
## Input . 0.721 0.045
## Process . . 0.697
##
## Reliability:
## alpha rhoC AVE rhoA
## Context 0.875 0.899 0.479 0.896
## Input 0.921 0.933 0.543 0.928
## Process 0.963 0.967 0.617 0.965
## Product 0.947 0.954 0.633 0.949
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5
####menampilkan nilai loading faktor tiap indikator####
# Ambil loading faktor dari ringkasan model
loadings <- summary_cipp$loadings
loadings
## Context Input Process Product
## C_1 0.383 0.000 0.000 0.000
## C_2 0.569 0.000 0.000 0.000
## C_3 0.686 0.000 0.000 0.000
## C_4 0.687 0.000 0.000 0.000
## C_5 0.732 0.000 0.000 0.000
## C_6 0.762 0.000 0.000 0.000
## C_7 0.821 0.000 0.000 0.000
## C_8 0.805 0.000 0.000 0.000
## C_9 0.735 0.000 0.000 0.000
## C_10 0.627 0.000 0.000 0.000
## I_1 0.000 0.513 0.000 0.000
## I_2 0.000 0.633 0.000 0.000
## I_3 0.000 0.781 0.000 0.000
## I_4 0.000 0.700 0.000 0.000
## I_5 0.000 0.744 0.000 0.000
## I_6 0.000 0.810 0.000 0.000
## I_7 0.000 0.587 0.000 0.000
## I_8 0.000 0.820 0.000 0.000
## I_9 0.000 0.790 0.000 0.000
## I_10 0.000 0.781 0.000 0.000
## I_11 0.000 0.852 0.000 0.000
## I_12 0.000 0.755 0.000 0.000
## PR_1 0.000 0.000 0.833 0.000
## PR_2 0.000 0.000 0.776 0.000
## PR_3 0.000 0.000 0.779 0.000
## PR_4 0.000 0.000 0.717 0.000
## PR_5 0.000 0.000 0.758 0.000
## PR_6 0.000 0.000 0.855 0.000
## PR_7 0.000 0.000 0.852 0.000
## PR_8 0.000 0.000 0.820 0.000
## PR_9 0.000 0.000 0.841 0.000
## PR_10 0.000 0.000 0.769 0.000
## PR_11 0.000 0.000 0.823 0.000
## PR_12 0.000 0.000 0.847 0.000
## PR_13 0.000 0.000 0.708 0.000
## PR_14 0.000 0.000 0.738 0.000
## PR_15 0.000 0.000 0.771 0.000
## PR_16 0.000 0.000 0.721 0.000
## PR_17 0.000 0.000 0.780 0.000
## PR_18 0.000 0.000 0.719 0.000
## PD_1 0.000 0.000 0.000 0.726
## PD_2 0.000 0.000 0.000 0.831
## PD_3 0.000 0.000 0.000 0.821
## PD_4 0.000 0.000 0.000 0.757
## PD_5 0.000 0.000 0.000 0.721
## PD_6 0.000 0.000 0.000 0.734
## PD_7 0.000 0.000 0.000 0.840
## PD_8 0.000 0.000 0.000 0.839
## PD_9 0.000 0.000 0.000 0.814
## PD_10 0.000 0.000 0.000 0.784
## PD_11 0.000 0.000 0.000 0.850
## PD_12 0.000 0.000 0.000 0.814
#######################################################
# Periksa jika hasil tidak NULL
if (!is.null(summary_cipp)) {
print("Ringkasan Model Berhasil")
} else {
stop("Error: Ringkasan model tidak dapat dihitung. Periksa data atau model.")
}
## [1] "Ringkasan Model Berhasil"
# Coba dengan layout lain
plot(cipp_PLS,
layout = "fr", # Force-directed layout
node.width = 2.5,
node.height = 2,
edge.arrow.size = 1.5,
edge.label.cex = 2.5,
node.label.cex = 2.5,
col.edge = "black",
col.node = "white",
col.text = "black",
font.node = 2,
cex.node = 2.5
)
# Bootstrap untuk uji signifikansi jalur
boot_cipp <- bootstrap_model(seminr_model = cipp_PLS,
nboot = 1000, cores = 2)
## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrapped
hasil_boot <- summary(boot_cipp)
hasil_boot
##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Context -> Input 0.828 0.833 0.031 27.120 0.771
## Context -> Process 0.202 0.207 0.079 2.550 0.055
## Context -> Product 0.159 0.159 0.126 1.258 -0.110
## Input -> Process 0.721 0.718 0.076 9.481 0.559
## Input -> Product 0.045 0.036 0.154 0.292 -0.242
## Process -> Product 0.697 0.704 0.132 5.289 0.442
## 97.5% CI
## Context -> Input 0.888
## Context -> Process 0.375
## Context -> Product 0.394
## Input -> Process 0.857
## Input -> Product 0.335
## Process -> Product 0.935
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## C_1 -> Context 0.066 0.065 0.025 2.620 0.010
## C_2 -> Context 0.092 0.092 0.019 4.811 0.054
## C_3 -> Context 0.146 0.146 0.014 10.692 0.121
## C_4 -> Context 0.135 0.135 0.019 7.129 0.093
## C_5 -> Context 0.146 0.145 0.015 9.632 0.118
## C_6 -> Context 0.178 0.178 0.016 10.905 0.148
## C_7 -> Context 0.176 0.175 0.015 12.086 0.150
## C_8 -> Context 0.179 0.179 0.017 10.470 0.148
## C_9 -> Context 0.166 0.164 0.015 11.429 0.139
## C_10 -> Context 0.120 0.119 0.020 6.132 0.076
## I_1 -> Input 0.086 0.085 0.012 7.177 0.060
## I_2 -> Input 0.103 0.103 0.009 10.973 0.087
## I_3 -> Input 0.128 0.128 0.009 14.888 0.114
## I_4 -> Input 0.108 0.107 0.009 11.447 0.089
## I_5 -> Input 0.105 0.106 0.008 12.789 0.088
## I_6 -> Input 0.119 0.119 0.008 14.315 0.104
## I_7 -> Input 0.092 0.092 0.011 8.093 0.068
## I_8 -> Input 0.123 0.123 0.009 13.849 0.107
## I_9 -> Input 0.123 0.123 0.008 14.699 0.107
## I_10 -> Input 0.121 0.121 0.009 14.154 0.107
## I_11 -> Input 0.129 0.129 0.009 14.878 0.114
## I_12 -> Input 0.111 0.112 0.008 13.353 0.096
## PR_1 -> Process 0.075 0.075 0.004 18.351 0.068
## PR_2 -> Process 0.075 0.075 0.006 13.116 0.066
## PR_3 -> Process 0.069 0.069 0.005 12.975 0.060
## PR_4 -> Process 0.070 0.070 0.005 14.277 0.062
## PR_5 -> Process 0.071 0.071 0.004 19.161 0.065
## PR_6 -> Process 0.078 0.079 0.005 17.260 0.071
## PR_7 -> Process 0.074 0.074 0.004 17.915 0.067
## PR_8 -> Process 0.080 0.080 0.005 16.238 0.072
## PR_9 -> Process 0.074 0.075 0.004 17.528 0.068
## PR_10 -> Process 0.074 0.074 0.005 14.277 0.065
## PR_11 -> Process 0.072 0.072 0.004 16.774 0.065
## PR_12 -> Process 0.076 0.076 0.004 18.296 0.068
## PR_13 -> Process 0.058 0.058 0.005 11.765 0.047
## PR_14 -> Process 0.062 0.061 0.005 12.116 0.050
## PR_15 -> Process 0.066 0.065 0.004 16.914 0.058
## PR_16 -> Process 0.065 0.065 0.005 13.921 0.057
## PR_17 -> Process 0.068 0.068 0.004 17.943 0.061
## PR_18 -> Process 0.065 0.065 0.005 14.154 0.056
## PD_1 -> Product 0.098 0.098 0.009 11.284 0.081
## PD_2 -> Product 0.115 0.115 0.008 14.085 0.102
## PD_3 -> Product 0.111 0.111 0.008 14.084 0.099
## PD_4 -> Product 0.102 0.101 0.007 14.247 0.086
## PD_5 -> Product 0.096 0.096 0.008 12.398 0.080
## PD_6 -> Product 0.088 0.088 0.008 11.502 0.072
## PD_7 -> Product 0.115 0.115 0.007 16.205 0.102
## PD_8 -> Product 0.104 0.105 0.006 18.036 0.094
## PD_9 -> Product 0.099 0.100 0.008 12.276 0.086
## PD_10 -> Product 0.107 0.107 0.009 12.507 0.090
## PD_11 -> Product 0.118 0.119 0.008 14.688 0.105
## PD_12 -> Product 0.102 0.102 0.007 15.148 0.090
## 97.5% CI
## C_1 -> Context 0.109
## C_2 -> Context 0.126
## C_3 -> Context 0.175
## C_4 -> Context 0.170
## C_5 -> Context 0.176
## C_6 -> Context 0.213
## C_7 -> Context 0.207
## C_8 -> Context 0.215
## C_9 -> Context 0.194
## C_10 -> Context 0.154
## I_1 -> Input 0.108
## I_2 -> Input 0.123
## I_3 -> Input 0.146
## I_4 -> Input 0.127
## I_5 -> Input 0.121
## I_6 -> Input 0.137
## I_7 -> Input 0.111
## I_8 -> Input 0.141
## I_9 -> Input 0.140
## I_10 -> Input 0.139
## I_11 -> Input 0.147
## I_12 -> Input 0.129
## PR_1 -> Process 0.084
## PR_2 -> Process 0.087
## PR_3 -> Process 0.081
## PR_4 -> Process 0.081
## PR_5 -> Process 0.079
## PR_6 -> Process 0.088
## PR_7 -> Process 0.083
## PR_8 -> Process 0.090
## PR_9 -> Process 0.084
## PR_10 -> Process 0.086
## PR_11 -> Process 0.081
## PR_12 -> Process 0.085
## PR_13 -> Process 0.066
## PR_14 -> Process 0.070
## PR_15 -> Process 0.073
## PR_16 -> Process 0.075
## PR_17 -> Process 0.077
## PR_18 -> Process 0.073
## PD_1 -> Product 0.116
## PD_2 -> Product 0.133
## PD_3 -> Product 0.129
## PD_4 -> Product 0.115
## PD_5 -> Product 0.111
## PD_6 -> Product 0.101
## PD_7 -> Product 0.131
## PD_8 -> Product 0.117
## PD_9 -> Product 0.118
## PD_10 -> Product 0.124
## PD_11 -> Product 0.137
## PD_12 -> Product 0.116
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## C_1 -> Context 0.383 0.379 0.120 3.193 0.132
## C_2 -> Context 0.569 0.568 0.084 6.783 0.386
## C_3 -> Context 0.686 0.686 0.049 14.000 0.581
## C_4 -> Context 0.687 0.685 0.075 9.199 0.518
## C_5 -> Context 0.732 0.731 0.041 17.945 0.647
## C_6 -> Context 0.762 0.763 0.036 21.163 0.683
## C_7 -> Context 0.821 0.819 0.038 21.496 0.743
## C_8 -> Context 0.805 0.805 0.033 24.296 0.739
## C_9 -> Context 0.735 0.735 0.049 14.988 0.631
## C_10 -> Context 0.627 0.622 0.083 7.519 0.437
## I_1 -> Input 0.513 0.510 0.081 6.352 0.351
## I_2 -> Input 0.633 0.630 0.064 9.954 0.496
## I_3 -> Input 0.781 0.780 0.033 23.862 0.715
## I_4 -> Input 0.700 0.693 0.063 11.030 0.554
## I_5 -> Input 0.744 0.745 0.053 14.024 0.639
## I_6 -> Input 0.810 0.807 0.036 22.475 0.729
## I_7 -> Input 0.587 0.583 0.077 7.580 0.418
## I_8 -> Input 0.820 0.819 0.033 24.856 0.747
## I_9 -> Input 0.790 0.787 0.045 17.737 0.688
## I_10 -> Input 0.781 0.781 0.040 19.607 0.695
## I_11 -> Input 0.852 0.852 0.027 31.573 0.793
## I_12 -> Input 0.755 0.754 0.044 17.077 0.661
## PR_1 -> Process 0.833 0.832 0.031 26.671 0.764
## PR_2 -> Process 0.776 0.779 0.037 21.166 0.698
## PR_3 -> Process 0.779 0.778 0.048 16.200 0.664
## PR_4 -> Process 0.717 0.718 0.049 14.760 0.617
## PR_5 -> Process 0.758 0.760 0.049 15.601 0.663
## PR_6 -> Process 0.855 0.854 0.028 30.680 0.795
## PR_7 -> Process 0.852 0.850 0.023 37.179 0.800
## PR_8 -> Process 0.820 0.817 0.037 22.402 0.738
## PR_9 -> Process 0.841 0.839 0.027 31.482 0.783
## PR_10 -> Process 0.769 0.768 0.035 22.197 0.697
## PR_11 -> Process 0.823 0.820 0.034 24.467 0.745
## PR_12 -> Process 0.847 0.845 0.033 25.628 0.769
## PR_13 -> Process 0.708 0.703 0.064 10.976 0.559
## PR_14 -> Process 0.738 0.732 0.065 11.409 0.591
## PR_15 -> Process 0.771 0.766 0.047 16.290 0.667
## PR_16 -> Process 0.721 0.720 0.054 13.449 0.609
## PR_17 -> Process 0.780 0.777 0.040 19.336 0.691
## PR_18 -> Process 0.719 0.712 0.055 12.999 0.590
## PD_1 -> Product 0.726 0.721 0.054 13.547 0.607
## PD_2 -> Product 0.831 0.829 0.034 24.335 0.757
## PD_3 -> Product 0.821 0.818 0.037 22.214 0.736
## PD_4 -> Product 0.757 0.751 0.058 13.036 0.618
## PD_5 -> Product 0.721 0.719 0.051 14.227 0.607
## PD_6 -> Product 0.734 0.729 0.052 14.019 0.614
## PD_7 -> Product 0.840 0.837 0.033 25.188 0.762
## PD_8 -> Product 0.839 0.838 0.034 24.698 0.761
## PD_9 -> Product 0.814 0.817 0.043 19.076 0.730
## PD_10 -> Product 0.784 0.781 0.044 17.665 0.683
## PD_11 -> Product 0.850 0.853 0.042 20.251 0.765
## PD_12 -> Product 0.814 0.810 0.040 20.571 0.724
## 97.5% CI
## C_1 -> Context 0.586
## C_2 -> Context 0.712
## C_3 -> Context 0.776
## C_4 -> Context 0.800
## C_5 -> Context 0.801
## C_6 -> Context 0.828
## C_7 -> Context 0.886
## C_8 -> Context 0.860
## C_9 -> Context 0.819
## C_10 -> Context 0.756
## I_1 -> Input 0.657
## I_2 -> Input 0.739
## I_3 -> Input 0.840
## I_4 -> Input 0.800
## I_5 -> Input 0.838
## I_6 -> Input 0.871
## I_7 -> Input 0.716
## I_8 -> Input 0.879
## I_9 -> Input 0.862
## I_10 -> Input 0.847
## I_11 -> Input 0.897
## I_12 -> Input 0.836
## PR_1 -> Process 0.886
## PR_2 -> Process 0.844
## PR_3 -> Process 0.856
## PR_4 -> Process 0.808
## PR_5 -> Process 0.846
## PR_6 -> Process 0.906
## PR_7 -> Process 0.891
## PR_8 -> Process 0.879
## PR_9 -> Process 0.886
## PR_10 -> Process 0.830
## PR_11 -> Process 0.877
## PR_12 -> Process 0.896
## PR_13 -> Process 0.819
## PR_14 -> Process 0.841
## PR_15 -> Process 0.844
## PR_16 -> Process 0.813
## PR_17 -> Process 0.849
## PR_18 -> Process 0.804
## PD_1 -> Product 0.821
## PD_2 -> Product 0.891
## PD_3 -> Product 0.883
## PD_4 -> Product 0.846
## PD_5 -> Product 0.808
## PD_6 -> Product 0.817
## PD_7 -> Product 0.895
## PD_8 -> Product 0.894
## PD_9 -> Product 0.890
## PD_10 -> Product 0.859
## PD_11 -> Product 0.920
## PD_12 -> Product 0.879
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## Context -> Input 0.906 0.908 0.030 0.846 0.964
## Context -> Process 0.847 0.848 0.038 0.766 0.913
## Context -> Product 0.793 0.794 0.048 0.695 0.879
## Input -> Process 0.938 0.938 0.022 0.892 0.973
## Input -> Product 0.849 0.847 0.045 0.746 0.919
## Process -> Product 0.901 0.900 0.035 0.821 0.957
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## Context -> Input 0.828 0.833 0.031 0.771 0.888
## Context -> Process 0.799 0.804 0.036 0.730 0.867
## Context -> Product 0.753 0.756 0.043 0.664 0.832
## Input -> Process 0.721 0.718 0.076 0.559 0.857
## Input -> Product 0.547 0.543 0.124 0.299 0.763
## Process -> Product 0.697 0.704 0.132 0.442 0.935
# Periksa hasil bootstrap
if (!is.null(hasil_boot)) {
print("Bootstrap berhasil dijalankan")
} else {
stop("Error: Hasil bootstrap tidak dapat dihitung. Periksa data atau model.")
}
## [1] "Bootstrap berhasil dijalankan"
# Uji efek spesifik (direct & indirect)
specific_effect_significance(boot_seminr_model = boot_cipp, from = "Context", to = "Input", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.82806881 0.83302344 0.03053332 27.12017052 0.77071710
## 97.5% CI
## 0.88807297
specific_effect_significance(boot_seminr_model = boot_cipp, from = "Input", to = "Product", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.04504955 0.03644425 0.15407828 0.29238091 -0.24214617
## 97.5% CI
## 0.33502225
specific_effect_significance(boot_seminr_model = boot_cipp, from = "Context", through = "Input", to = "Product", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.03730413 0.03091276 0.12845394 0.29040856 -0.19960126
## 97.5% CI
## 0.28280350
####### Modifikasi Model dengan mengerluartkan beberapa butir yang loading faktornya rendah#####################
cipp_mm2 <- constructs(
composite("Context", multi_items("C_", 3:9)),
composite("Input", multi_items("I_", c(3:6,8:12))),
composite("Process", multi_items("PR_", 1:18)),
composite("Product", multi_items("PD_", 1:12))
)
# Definisi structural model (jalur antar variabel)
cipp_sm2 <- relationships(
paths(from = "Context", to = c("Input", "Process", "Product")),
paths(from = "Input", to = c("Process", "Product")),
paths(from = "Process", to = "Product")
)
# Estimasi model PLS-SEM
cipp_PLS2 <- estimate_pls(
data = data,
measurement_model = cipp_mm2,
structural_model = cipp_sm2
)
## Generating the seminr model
## All 110 observations are valid.
# Ringkasan hasil model
summary_cipp_modif <- summary(cipp_PLS2)
summary_cipp_modif
##
## Results from package seminr (2.3.4)
##
## Path Coefficients:
## Input Process Product
## R^2 0.655 0.784 0.764
## AdjR^2 0.652 0.780 0.757
## Context 0.809 0.275 0.219
## Input . 0.648 0.019
## Process . . 0.672
##
## Reliability:
## alpha rhoC AVE rhoA
## Context 0.878 0.906 0.581 0.886
## Input 0.927 0.939 0.632 0.929
## Process 0.963 0.966 0.617 0.965
## Product 0.947 0.954 0.633 0.949
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5
# Periksa jika hasil tidak NULL
if (!is.null(summary_cipp_modif)) {
print("Ringkasan Model Berhasil")
} else {
stop("Error: Ringkasan model tidak dapat dihitung. Periksa data atau model.")
}
## [1] "Ringkasan Model Berhasil"
plot(cipp_PLS2, title = "PLS-SEM Evaluasi Implementasi Kurikulum Merdeka")
# Bootstrap untuk uji signifikansi jalur
boot_cipp2 <- bootstrap_model(seminr_model = cipp_PLS2,
nboot = 1000, cores = 2)
## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrapped
hasil_boot2 <- summary(boot_cipp2)
hasil_boot2
##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## Context -> Input 0.809 0.813 0.035 23.173 0.735
## Context -> Process 0.275 0.282 0.087 3.176 0.120
## Context -> Product 0.219 0.216 0.117 1.880 -0.001
## Input -> Process 0.648 0.643 0.084 7.710 0.469
## Input -> Product 0.019 0.019 0.129 0.145 -0.210
## Process -> Product 0.672 0.674 0.128 5.245 0.418
## 97.5% CI
## Context -> Input 0.876
## Context -> Process 0.460
## Context -> Product 0.429
## Input -> Process 0.793
## Input -> Product 0.278
## Process -> Product 0.920
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## C_3 -> Context 0.167 0.167 0.013 13.324 0.144
## C_4 -> Context 0.156 0.156 0.024 6.529 0.106
## C_5 -> Context 0.169 0.169 0.016 10.297 0.138
## C_6 -> Context 0.208 0.207 0.016 13.023 0.179
## C_7 -> Context 0.203 0.203 0.013 15.336 0.180
## C_8 -> Context 0.209 0.210 0.015 14.039 0.184
## C_9 -> Context 0.194 0.193 0.014 14.200 0.168
## I_3 -> Input 0.151 0.151 0.009 17.116 0.136
## I_4 -> Input 0.129 0.128 0.011 12.048 0.106
## I_5 -> Input 0.123 0.123 0.010 12.236 0.102
## I_6 -> Input 0.140 0.140 0.009 14.988 0.122
## I_8 -> Input 0.146 0.146 0.009 16.037 0.130
## I_9 -> Input 0.147 0.147 0.009 16.754 0.130
## I_10 -> Input 0.139 0.139 0.010 13.569 0.121
## I_11 -> Input 0.151 0.151 0.008 17.984 0.137
## I_12 -> Input 0.131 0.131 0.009 14.388 0.113
## PR_1 -> Process 0.075 0.075 0.004 18.755 0.068
## PR_2 -> Process 0.075 0.075 0.005 14.009 0.065
## PR_3 -> Process 0.070 0.070 0.005 12.890 0.059
## PR_4 -> Process 0.071 0.071 0.005 14.169 0.062
## PR_5 -> Process 0.070 0.071 0.004 19.088 0.064
## PR_6 -> Process 0.078 0.079 0.004 18.976 0.071
## PR_7 -> Process 0.074 0.074 0.004 19.668 0.067
## PR_8 -> Process 0.080 0.080 0.004 18.047 0.072
## PR_9 -> Process 0.075 0.075 0.004 17.219 0.067
## PR_10 -> Process 0.074 0.074 0.005 15.482 0.066
## PR_11 -> Process 0.072 0.072 0.004 16.784 0.064
## PR_12 -> Process 0.076 0.076 0.004 18.799 0.069
## PR_13 -> Process 0.057 0.057 0.005 11.245 0.046
## PR_14 -> Process 0.062 0.061 0.006 11.010 0.049
## PR_15 -> Process 0.065 0.064 0.004 16.943 0.056
## PR_16 -> Process 0.064 0.064 0.005 13.105 0.054
## PR_17 -> Process 0.068 0.067 0.004 18.147 0.060
## PR_18 -> Process 0.065 0.066 0.005 14.533 0.057
## PD_1 -> Product 0.096 0.096 0.009 10.988 0.080
## PD_2 -> Product 0.114 0.114 0.008 14.954 0.101
## PD_3 -> Product 0.111 0.111 0.007 14.981 0.099
## PD_4 -> Product 0.101 0.101 0.007 15.179 0.087
## PD_5 -> Product 0.097 0.097 0.007 12.898 0.083
## PD_6 -> Product 0.087 0.087 0.008 11.467 0.072
## PD_7 -> Product 0.116 0.115 0.007 15.906 0.103
## PD_8 -> Product 0.105 0.105 0.006 17.513 0.094
## PD_9 -> Product 0.099 0.100 0.008 12.371 0.085
## PD_10 -> Product 0.108 0.108 0.008 13.464 0.093
## PD_11 -> Product 0.118 0.118 0.008 14.922 0.105
## PD_12 -> Product 0.101 0.102 0.007 15.217 0.089
## 97.5% CI
## C_3 -> Context 0.192
## C_4 -> Context 0.200
## C_5 -> Context 0.202
## C_6 -> Context 0.242
## C_7 -> Context 0.232
## C_8 -> Context 0.241
## C_9 -> Context 0.220
## I_3 -> Input 0.171
## I_4 -> Input 0.147
## I_5 -> Input 0.141
## I_6 -> Input 0.160
## I_8 -> Input 0.164
## I_9 -> Input 0.165
## I_10 -> Input 0.161
## I_11 -> Input 0.169
## I_12 -> Input 0.148
## PR_1 -> Process 0.084
## PR_2 -> Process 0.087
## PR_3 -> Process 0.081
## PR_4 -> Process 0.081
## PR_5 -> Process 0.079
## PR_6 -> Process 0.087
## PR_7 -> Process 0.082
## PR_8 -> Process 0.089
## PR_9 -> Process 0.085
## PR_10 -> Process 0.084
## PR_11 -> Process 0.081
## PR_12 -> Process 0.085
## PR_13 -> Process 0.066
## PR_14 -> Process 0.071
## PR_15 -> Process 0.072
## PR_16 -> Process 0.074
## PR_17 -> Process 0.075
## PR_18 -> Process 0.075
## PD_1 -> Product 0.115
## PD_2 -> Product 0.131
## PD_3 -> Product 0.128
## PD_4 -> Product 0.114
## PD_5 -> Product 0.113
## PD_6 -> Product 0.102
## PD_7 -> Product 0.132
## PD_8 -> Product 0.119
## PD_9 -> Product 0.116
## PD_10 -> Product 0.124
## PD_11 -> Product 0.136
## PD_12 -> Product 0.116
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## C_3 -> Context 0.695 0.696 0.050 13.919 0.589
## C_4 -> Context 0.645 0.642 0.085 7.587 0.463
## C_5 -> Context 0.770 0.768 0.042 18.412 0.665
## C_6 -> Context 0.769 0.770 0.037 20.975 0.691
## C_7 -> Context 0.833 0.832 0.037 22.612 0.748
## C_8 -> Context 0.846 0.843 0.030 28.624 0.779
## C_9 -> Context 0.759 0.757 0.046 16.416 0.660
## I_3 -> Input 0.786 0.786 0.034 23.085 0.714
## I_4 -> Input 0.713 0.709 0.069 10.354 0.553
## I_5 -> Input 0.757 0.754 0.054 14.108 0.632
## I_6 -> Input 0.817 0.815 0.034 23.972 0.745
## I_8 -> Input 0.827 0.827 0.030 27.205 0.761
## I_9 -> Input 0.826 0.827 0.040 20.606 0.739
## I_10 -> Input 0.778 0.775 0.041 18.774 0.690
## I_11 -> Input 0.858 0.857 0.025 33.871 0.804
## I_12 -> Input 0.781 0.780 0.037 20.845 0.703
## PR_1 -> Process 0.833 0.834 0.030 27.705 0.768
## PR_2 -> Process 0.776 0.778 0.038 20.589 0.697
## PR_3 -> Process 0.779 0.780 0.049 15.907 0.675
## PR_4 -> Process 0.718 0.720 0.050 14.319 0.617
## PR_5 -> Process 0.758 0.760 0.047 16.194 0.664
## PR_6 -> Process 0.855 0.855 0.025 33.859 0.799
## PR_7 -> Process 0.853 0.852 0.022 38.541 0.805
## PR_8 -> Process 0.820 0.817 0.035 23.158 0.740
## PR_9 -> Process 0.842 0.842 0.025 34.265 0.791
## PR_10 -> Process 0.769 0.767 0.036 21.576 0.690
## PR_11 -> Process 0.823 0.821 0.032 25.485 0.752
## PR_12 -> Process 0.847 0.847 0.030 28.240 0.780
## PR_13 -> Process 0.707 0.704 0.064 11.069 0.571
## PR_14 -> Process 0.738 0.734 0.066 11.105 0.586
## PR_15 -> Process 0.771 0.766 0.046 16.782 0.664
## PR_16 -> Process 0.720 0.715 0.054 13.264 0.597
## PR_17 -> Process 0.780 0.777 0.039 19.863 0.695
## PR_18 -> Process 0.719 0.717 0.049 14.580 0.616
## PD_1 -> Product 0.724 0.723 0.053 13.716 0.606
## PD_2 -> Product 0.831 0.830 0.033 25.371 0.762
## PD_3 -> Product 0.820 0.816 0.040 20.345 0.729
## PD_4 -> Product 0.756 0.750 0.057 13.305 0.619
## PD_5 -> Product 0.721 0.720 0.048 14.888 0.620
## PD_6 -> Product 0.734 0.731 0.052 14.003 0.621
## PD_7 -> Product 0.841 0.840 0.034 24.961 0.771
## PD_8 -> Product 0.840 0.840 0.033 25.618 0.768
## PD_9 -> Product 0.814 0.815 0.042 19.281 0.724
## PD_10 -> Product 0.785 0.785 0.044 18.022 0.688
## PD_11 -> Product 0.851 0.853 0.041 20.685 0.767
## PD_12 -> Product 0.814 0.813 0.038 21.340 0.730
## 97.5% CI
## C_3 -> Context 0.782
## C_4 -> Context 0.784
## C_5 -> Context 0.837
## C_6 -> Context 0.833
## C_7 -> Context 0.896
## C_8 -> Context 0.892
## C_9 -> Context 0.835
## I_3 -> Input 0.849
## I_4 -> Input 0.825
## I_5 -> Input 0.847
## I_6 -> Input 0.878
## I_8 -> Input 0.880
## I_9 -> Input 0.893
## I_10 -> Input 0.849
## I_11 -> Input 0.903
## I_12 -> Input 0.847
## PR_1 -> Process 0.885
## PR_2 -> Process 0.846
## PR_3 -> Process 0.863
## PR_4 -> Process 0.811
## PR_5 -> Process 0.846
## PR_6 -> Process 0.901
## PR_7 -> Process 0.893
## PR_8 -> Process 0.879
## PR_9 -> Process 0.889
## PR_10 -> Process 0.831
## PR_11 -> Process 0.878
## PR_12 -> Process 0.899
## PR_13 -> Process 0.812
## PR_14 -> Process 0.843
## PR_15 -> Process 0.843
## PR_16 -> Process 0.812
## PR_17 -> Process 0.847
## PR_18 -> Process 0.803
## PD_1 -> Product 0.817
## PD_2 -> Product 0.888
## PD_3 -> Product 0.885
## PD_4 -> Product 0.846
## PD_5 -> Product 0.804
## PD_6 -> Product 0.824
## PD_7 -> Product 0.900
## PD_8 -> Product 0.894
## PD_9 -> Product 0.887
## PD_10 -> Product 0.859
## PD_11 -> Product 0.918
## PD_12 -> Product 0.881
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## Context -> Input 0.892 0.894 0.033 0.822 0.954
## Context -> Process 0.861 0.862 0.042 0.777 0.938
## Context -> Product 0.838 0.837 0.041 0.750 0.909
## Input -> Process 0.914 0.914 0.024 0.862 0.957
## Input -> Product 0.831 0.830 0.044 0.736 0.906
## Process -> Product 0.901 0.899 0.033 0.828 0.953
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## Context -> Input 0.809 0.813 0.035 0.735 0.876
## Context -> Process 0.799 0.804 0.039 0.723 0.875
## Context -> Product 0.772 0.773 0.041 0.689 0.845
## Input -> Process 0.648 0.643 0.084 0.469 0.793
## Input -> Product 0.454 0.452 0.108 0.231 0.658
## Process -> Product 0.672 0.674 0.128 0.418 0.920
plot(boot_cipp2)
# Periksa hasil bootstrap
if (!is.null(hasil_boot2)) {
print("Bootstrap berhasil dijalankan")
} else {
stop("Error: Hasil bootstrap tidak dapat dihitung. Periksa data atau model.")
}
## [1] "Bootstrap berhasil dijalankan"
# Uji efek spesifik (direct & indirect)
specific_effect_significance(boot_seminr_model = boot_cipp2, from = "Context", to = "Input", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.80914785 0.81328188 0.03491785 23.17289968 0.73522789
## 97.5% CI
## 0.87550100
specific_effect_significance(boot_seminr_model = boot_cipp2, from = "Input", to = "Product", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.01872563 0.01942093 0.12895861 0.14520648 -0.20967080
## 97.5% CI
## 0.27759559
specific_effect_significance(boot_seminr_model = boot_cipp2, from = "Context", through = "Input", to = "Product", alpha = 0.05)
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.0151518 0.0159428 0.1052686 0.1439346 -0.1697772
## 97.5% CI
## 0.2249935