Script Metode Pemodelan Niat
Kewirausahaan Mahasiswa
Berdasarkan Kepribadian, Modal Sosial, dan Faktor Kognitif
Menggunakan SEM dan CFA
Kelompok 6
1. Nanik Erawati (23031554066)
2. Hani’a Tsabita F. K (23031554073)
3. Rizqika Naura Fuady (23031554120)
2025-05-22
1 Preprocessing Data (Missing Value & Outlier)
1.1 Read Dataset
data <- read.csv("C:/Users/ASUS/Downloads/Dataset.csv")
komposit_vars <- c("PBC", "Attitude", "Innova", "Intention", "Opportunity", "SocialCap", "Locus", "Risk")
knitr::kable(head(data[komposit_vars]), caption = "Tabel: 6 Baris Pertama Variabel Komposit")| PBC | Attitude | Innova | Intention | Opportunity | SocialCap | Locus | Risk |
|---|---|---|---|---|---|---|---|
| 4.333333 | 4.8 | 4.50 | 4.833333 | 4.2 | 4.333333 | 4.2 | 4.000000 |
| 4.333333 | 5.8 | 5.50 | 5.500000 | 5.2 | 4.333333 | 5.2 | 4.000000 |
| 4.666667 | 4.6 | 4.75 | 4.500000 | 3.8 | 3.000000 | 4.6 | 6.000000 |
| 7.000000 | 5.0 | 6.25 | 7.000000 | 5.8 | 5.333333 | 7.0 | 6.666667 |
| 3.333333 | 3.0 | 2.75 | 3.000000 | 3.2 | 1.333333 | 3.2 | 2.666667 |
| 5.000000 | 6.2 | 5.00 | 7.000000 | 5.0 | 4.333333 | 6.6 | 6.333333 |
1.2 Konversi dan cek missing value
library(knitr)
data[komposit_vars] <- lapply(data[komposit_vars], function(x) as.numeric(as.character(x)))
na_counts <- colSums(is.na(data))
kable(as.data.frame(na_counts), col.names = c("Missing Values"))| Missing Values | |
|---|---|
| ID | 0 |
| PBC1 | 0 |
| PBC2 | 0 |
| PBC3 | 0 |
| EI1 | 0 |
| EI2 | 0 |
| EI3 | 0 |
| EI4 | 0 |
| EI5 | 0 |
| EI6 | 0 |
| SSN1 | 0 |
| SSN2 | 0 |
| SSN3 | 0 |
| ATT1 | 0 |
| ATT2 | 0 |
| ATT3 | 0 |
| ATT4 | 0 |
| ATT5 | 0 |
| OR1 | 0 |
| OR2 | 0 |
| OR3 | 0 |
| OR4 | 0 |
| OR5 | 0 |
| SC1 | 0 |
| SC2 | 0 |
| SC3 | 0 |
| EE1 | 0 |
| EE2 | 0 |
| EE3 | 0 |
| EE4 | 0 |
| EE5 | 0 |
| INNOV1 | 0 |
| INNOV2 | 0 |
| INNOV3 | 0 |
| INNOV4 | 0 |
| LOC1 | 0 |
| LOC2 | 0 |
| LOC3 | 0 |
| LOC4 | 0 |
| LOC5 | 0 |
| RTP1 | 0 |
| RTP2 | 0 |
| RTP3 | 0 |
| Inclination | 0 |
| Age | 0 |
| Gender | 0 |
| Class | 0 |
| PBC | 0 |
| Attitude | 0 |
| Innova | 0 |
| Intention | 0 |
| SSN | 0 |
| Opportunity | 0 |
| SocialCap | 0 |
| Education | 0 |
| Locus | 0 |
| Risk | 0 |
1.3 Winsorize (outlier handling)
# Boxplot sebelum winsorize
par(mfrow = c(3, 3))
for (var in komposit_vars) {
boxplot(data[[var]], main = paste("Sebelum Winsorize:", var), col = "lightblue", horizontal = TRUE)
}
# Terapkan winsorize
winsorize <- function(x) {
q1 <- quantile(x, 0.25, na.rm = TRUE)
q3 <- quantile(x, 0.75, na.rm = TRUE)
iqr <- q3 - q1
lower <- q1 - 1.5 * iqr
upper <- q3 + 1.5 * iqr
x[x < lower] <- lower
x[x > upper] <- upper
return(x)
}
data_winsor <- data # salin data asli
data_winsor[komposit_vars] <- lapply(data_winsor[komposit_vars], winsorize)
# Boxplot setelah winsorize
par(mfrow = c(3, 3))
for (var in komposit_vars) {
boxplot(data_winsor[[var]], main = paste("Setelah Winsorize:", var), col = "salmon", horizontal = TRUE)
}
par(mfrow = c(1, 1)) # reset layout plot
# EDA (Exploratory Data Analysis)
summary(data)## ID PBC1 PBC2 PBC3 EI1
## Length:276 Min. :1.000 Min. :2 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.000 1st Qu.:4 1st Qu.:4.000 1st Qu.:5.000
## Mode :character Median :4.000 Median :5 Median :5.000 Median :6.000
## Mean :4.529 Mean :5 Mean :4.725 Mean :5.761
## 3rd Qu.:5.000 3rd Qu.:6 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7 Max. :7.000 Max. :7.000
## EI2 EI3 EI4 EI5 EI6
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :2.00 Min. :2.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.00 Median :6.000
## Mean :5.859 Mean :5.529 Mean :6.033 Mean :5.96 Mean :5.688
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## SSN1 SSN2 SSN3 ATT1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:5.000
## Median :5.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.011 Mean :5.344 Mean :5.435 Mean :5.732
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## ATT2 ATT3 ATT4 ATT5 OR1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :2.00 Min. :2.000
## 1st Qu.:6.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:5.000
## Median :7.000 Median :6.000 Median :6.000 Median :6.00 Median :6.000
## Mean :6.185 Mean :5.851 Mean :5.645 Mean :5.63 Mean :5.525
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## OR2 OR3 OR4 OR5
## Min. :1.000 Min. :2.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :6.000 Median :5.000 Median :5.000
## Mean :4.855 Mean :5.428 Mean :5.087 Mean :5.141
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## SC1 SC2 SC3 EE1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :4.000 Median :5.000 Median :6.000
## Mean :4.181 Mean :4.243 Mean :4.464 Mean :5.554
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## EE2 EE3 EE4 EE5
## Min. :2.000 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:4.750 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000
## Median :5.000 Median :5.000 Median :5.000 Median :6.000
## Mean :5.279 Mean :5.348 Mean :5.373 Mean :5.406
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## INNOV1 INNOV2 INNOV3 INNOV4 LOC1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:5.000
## Median :5.000 Median :5.000 Median :6.000 Median :5.00 Median :6.000
## Mean :4.946 Mean :5.134 Mean :5.257 Mean :5.29 Mean :5.576
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## LOC2 LOC3 LOC4 LOC5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.388 Mean :5.884 Mean :5.659 Mean :5.754
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## RTP1 RTP2 RTP3 Inclination
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:1.000
## Median :6.000 Median :5.000 Median :5.000 Median :2.000
## Mean :5.525 Mean :5.138 Mean :5.083 Mean :1.598
## 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:2.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :2.000
## Age Gender Class PBC
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.667
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:4.000
## Median :1.000 Median :1.000 Median :1.000 Median :5.000
## Mean :1.833 Mean :1.482 Mean :1.355 Mean :4.829
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:5.750
## Max. :3.000 Max. :2.000 Max. :2.000 Max. :7.000
## Attitude Innova Intention SSN
## Min. :2.000 Min. :1.500 Min. :1.833 Min. :1.000
## 1st Qu.:5.200 1st Qu.:4.250 1st Qu.:5.000 1st Qu.:4.333
## Median :6.000 Median :5.250 Median :6.083 Median :5.333
## Mean :5.809 Mean :5.157 Mean :5.805 Mean :5.263
## 3rd Qu.:6.600 3rd Qu.:6.000 3rd Qu.:6.667 3rd Qu.:6.333
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Opportunity SocialCap Education Locus
## Min. :2.600 Min. :1.000 Min. :2.000 Min. :2.000
## 1st Qu.:4.400 1st Qu.:3.333 1st Qu.:4.600 1st Qu.:5.000
## Median :5.200 Median :4.333 Median :5.600 Median :5.800
## Mean :5.207 Mean :4.296 Mean :5.392 Mean :5.652
## 3rd Qu.:6.000 3rd Qu.:5.333 3rd Qu.:6.200 3rd Qu.:6.600
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## Risk
## Min. :1.333
## 1st Qu.:4.333
## Median :5.333
## Mean :5.249
## 3rd Qu.:6.333
## Max. :7.000str(data)## 'data.frame': 276 obs. of 57 variables:
## $ ID : chr "#1" "#2" "#3" "#4" ...
## $ PBC1 : int 5 4 4 2 4 5 4 3 3 5 ...
## $ PBC2 : int 4 4 5 7 5 5 4 3 4 4 ...
## $ PBC3 : int 6 4 4 7 2 5 4 3 3 5 ...
## $ EI1 : int 5 6 5 7 4 7 3 3 3 7 ...
## $ EI2 : int 3 6 5 7 3 7 5 4 3 7 ...
## $ EI3 : int 5 4 4 7 2 7 2 3 3 7 ...
## $ EI4 : int 6 6 5 7 3 7 4 3 4 7 ...
## $ EI5 : int 5 6 4 7 3 7 3 3 4 7 ...
## $ EI6 : int 5 5 4 7 3 7 2 3 3 7 ...
## $ SSN1 : int 4 5 5 6 3 4 4 3 3 6 ...
## $ SSN2 : int 5 6 5 6 3 6 2 3 3 6 ...
## $ SSN3 : int 5 5 5 6 3 7 4 2 3 6 ...
## $ ATT1 : int 5 5 5 7 3 7 4 3 3 7 ...
## $ ATT2 : int 4 6 5 4 3 5 4 3 4 7 ...
## $ ATT3 : int 5 6 4 5 3 7 3 3 4 7 ...
## $ ATT4 : int 5 6 4 5 3 5 3 3 3 7 ...
## $ ATT5 : int 5 6 5 4 3 7 3 3 4 7 ...
## $ OR1 : int 4 5 4 5 4 5 3 4 4 6 ...
## $ OR2 : int 4 5 4 6 3 4 3 3 4 6 ...
## $ OR3 : int 5 6 4 6 5 5 4 4 4 6 ...
## $ OR4 : int 4 5 4 7 2 5 3 3 4 6 ...
## $ OR5 : int 4 5 3 5 2 6 3 4 4 6 ...
## $ SC1 : int 3 5 3 5 1 5 4 3 3 5 ...
## $ SC2 : int 5 5 3 5 1 3 3 3 4 5 ...
## $ SC3 : int 5 3 3 6 2 5 1 3 3 6 ...
## $ EE1 : int 5 5 5 7 2 7 3 3 3 7 ...
## $ EE2 : int 4 4 5 7 4 7 4 4 4 6 ...
## $ EE3 : int 5 5 6 7 3 7 4 4 4 6 ...
## $ EE4 : int 5 5 5 7 3 7 4 4 4 6 ...
## $ EE5 : int 5 6 4 7 3 7 3 3 4 6 ...
## $ INNOV1 : int 5 5 4 4 2 4 4 4 4 6 ...
## $ INNOV2 : int 5 6 5 7 3 5 2 3 3 6 ...
## $ INNOV3 : int 4 6 5 7 3 6 3 3 4 7 ...
## $ INNOV4 : int 4 5 5 7 3 5 4 4 4 6 ...
## $ LOC1 : int 4 5 4 7 2 7 4 4 4 5 ...
## $ LOC2 : int 4 5 4 7 5 7 4 4 4 6 ...
## $ LOC3 : int 5 5 4 7 3 6 3 4 4 6 ...
## $ LOC4 : int 3 6 6 7 3 7 5 4 4 5 ...
## $ LOC5 : int 5 5 5 7 3 6 5 4 4 6 ...
## $ RTP1 : int 5 4 6 7 2 6 5 4 4 5 ...
## $ RTP2 : int 2 4 6 7 3 6 5 3 4 6 ...
## $ RTP3 : int 5 4 6 6 3 7 3 4 3 5 ...
## $ Inclination: int 2 1 2 2 1 2 1 1 1 1 ...
## $ Age : int 2 3 3 3 3 2 3 3 3 3 ...
## $ Gender : int 1 1 1 1 1 2 2 1 1 1 ...
## $ Class : int 1 2 2 2 2 1 2 2 2 1 ...
## $ PBC : num 4.33 4.33 4.67 7 3.33 ...
## $ Attitude : num 4.8 5.8 4.6 5 3 6.2 3.4 3 3.6 7 ...
## $ Innova : num 4.5 5.5 4.75 6.25 2.75 5 3.25 3.5 3.75 6.25 ...
## $ Intention : num 4.83 5.5 4.5 7 3 ...
## $ SSN : num 4.67 5.33 5 6 3 ...
## $ Opportunity: num 4.2 5.2 3.8 5.8 3.2 5 3.2 3.6 4 6 ...
## $ SocialCap : num 4.33 4.33 3 5.33 1.33 ...
## $ Education : num 4.8 5 5 7 3 7 3.6 3.6 3.8 6.2 ...
## $ Locus : num 4.2 5.2 4.6 7 3.2 6.6 4.2 4 4 5.6 ...
## $ Risk : num 4 4 6 6.67 2.67 ...par(mfrow=c(3,3))
for (var in komposit_vars) {
hist(data[[var]], main=paste("Histogram:", var), xlab=var, col="lightblue", breaks=10)
}
par(mfrow=c(1,1))
# Confirmatory Factor Analysis (CFA)
library(lavaan)
library(semPlot)
library(semTools)
model_cfa <- '
Kepribadian =~ Attitude + Risk + Locus
ModalSosial =~ SocialCap + Opportunity
FaktorKognitif =~ PBC + Innova
'
cfa_fit <- cfa(model_cfa, data = data)
summary(cfa_fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-19 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 17
##
## Number of observations 276
##
## Model Test User Model:
##
## Test statistic 50.645
## Degrees of freedom 11
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 976.526
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.959
## Tucker-Lewis Index (TLI) 0.921
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2602.307
## Loglikelihood unrestricted model (H1) -2576.985
##
## Akaike (AIC) 5238.614
## Bayesian (BIC) 5300.161
## Sample-size adjusted Bayesian (SABIC) 5246.257
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.114
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.147
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.966
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.037
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Kepribadian =~
## Attitude 1.000 0.824 0.763
## Risk 1.040 0.094 11.093 0.000 0.857 0.676
## Locus 1.030 0.081 12.658 0.000 0.848 0.762
## ModalSosial =~
## SocialCap 1.000 0.775 0.545
## Opportunity 1.329 0.151 8.816 0.000 1.029 0.954
## FaktorKognitif =~
## PBC 1.000 0.818 0.673
## Innova 1.185 0.099 11.980 0.000 0.969 0.851
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Kepribadian ~~
## ModalSosial 0.559 0.087 6.457 0.000 0.876 0.876
## FaktorKognitif 0.646 0.082 7.895 0.000 0.958 0.958
## ModalSosial ~~
## FaktorKognitif 0.562 0.091 6.201 0.000 0.887 0.887
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Attitude 0.487 0.052 9.346 0.000 0.487 0.418
## .Risk 0.875 0.084 10.387 0.000 0.875 0.544
## .Locus 0.519 0.055 9.364 0.000 0.519 0.419
## .SocialCap 1.419 0.128 11.109 0.000 1.419 0.703
## .Opportunity 0.104 0.074 1.405 0.160 0.104 0.089
## .PBC 0.807 0.078 10.413 0.000 0.807 0.547
## .Innova 0.358 0.059 6.084 0.000 0.358 0.276
## Kepribadian 0.679 0.096 7.107 0.000 1.000 1.000
## ModalSosial 0.600 0.129 4.648 0.000 1.000 1.000
## FaktorKognitif 0.669 0.111 6.017 0.000 1.000 1.000semPaths(cfa_fit, "std", layout = "tree", whatLabels = "std", edge.label.cex = 0.8)
cfa_reliability <- reliability(cfa_fit)
cfa_reliability## Kepribadian ModalSosial FaktorKognitif
## alpha 0.7760993 0.6677692 0.7272651
## omega 0.7727700 0.6813312 0.7324951
## omega2 0.7727700 0.6813312 0.7324951
## omega3 0.7691186 0.6813315 0.7324951
## avevar 0.5313799 0.5216010 0.57963782 Structural Equation Modeling (SEM)
model_sem <- '
Kepribadian =~ Attitude + Risk + Locus
ModalSosial =~ SSN + SocialCap + Opportunity
FaktorKognitif =~ PBC + Innova
Intention ~ Kepribadian + ModalSosial + FaktorKognitif
'
sem_fit <- sem(model_sem, data = data, estimator = "ML")
summary(sem_fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-19 ended normally after 94 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 276
##
## Model Test User Model:
##
## Test statistic 104.518
## Degrees of freedom 22
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1351.444
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.937
## Tucker-Lewis Index (TLI) 0.897
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3316.178
## Loglikelihood unrestricted model (H1) -3263.919
##
## Akaike (AIC) 6678.356
## Bayesian (BIC) 6761.625
## Sample-size adjusted Bayesian (SABIC) 6688.696
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.117
## 90 Percent confidence interval - lower 0.095
## 90 Percent confidence interval - upper 0.140
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.996
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Kepribadian =~
## Attitude 1.000 0.878 0.813
## Risk 0.913 0.080 11.417 0.000 0.802 0.632
## Locus 0.903 0.068 13.303 0.000 0.793 0.712
## ModalSosial =~
## SSN 1.000 0.653 0.509
## SocialCap 1.188 0.173 6.856 0.000 0.775 0.546
## Opportunity 1.473 0.170 8.675 0.000 0.962 0.891
## FaktorKognitif =~
## PBC 1.000 0.814 0.670
## Innova 1.195 0.100 11.964 0.000 0.973 0.854
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Intention ~
## Kepribadian 3.627 2.561 1.416 0.157 3.183 2.935
## ModalSosial -0.898 1.628 -0.552 0.581 -0.586 -0.541
## FaktorKognitif -2.141 2.437 -0.879 0.380 -1.743 -1.608
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Kepribadian ~~
## ModalSosial 0.543 0.081 6.749 0.000 0.949 0.949
## FaktorKognitif 0.688 0.084 8.149 0.000 0.962 0.962
## ModalSosial ~~
## FaktorKognitif 0.501 0.080 6.288 0.000 0.943 0.943
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Attitude 0.395 0.043 9.224 0.000 0.395 0.339
## .Risk 0.967 0.086 11.203 0.000 0.967 0.601
## .Locus 0.611 0.057 10.742 0.000 0.611 0.493
## .SSN 1.220 0.108 11.273 0.000 1.220 0.741
## .SocialCap 1.418 0.127 11.170 0.000 1.418 0.702
## .Opportunity 0.239 0.051 4.681 0.000 0.239 0.205
## .PBC 0.813 0.078 10.460 0.000 0.813 0.551
## .Innova 0.350 0.059 5.971 0.000 0.350 0.270
## .Intention -0.044 0.387 -0.113 0.910 -0.044 -0.037
## Kepribadian 0.770 0.097 7.936 0.000 1.000 1.000
## ModalSosial 0.426 0.099 4.310 0.000 1.000 1.000
## FaktorKognitif 0.663 0.111 5.991 0.000 1.000 1.0003 Evaluasi Model SEM
# Visualisasi model SEM
semPaths(sem_fit, "std", layout="tree", whatLabels="std", edge.label.cex=0.8)
# Interpretasi Hasil dan Hubungan Antar Konstruk
sem_est <- parameterEstimates(sem_fit, standardized = TRUE)
sem_est## lhs op rhs est se z pvalue ci.lower
## 1 Kepribadian =~ Attitude 1.000 0.000 NA NA 1.000
## 2 Kepribadian =~ Risk 0.913 0.080 11.417 0.000 0.757
## 3 Kepribadian =~ Locus 0.903 0.068 13.303 0.000 0.770
## 4 ModalSosial =~ SSN 1.000 0.000 NA NA 1.000
## 5 ModalSosial =~ SocialCap 1.188 0.173 6.856 0.000 0.848
## 6 ModalSosial =~ Opportunity 1.473 0.170 8.675 0.000 1.140
## 7 FaktorKognitif =~ PBC 1.000 0.000 NA NA 1.000
## 8 FaktorKognitif =~ Innova 1.195 0.100 11.964 0.000 0.999
## 9 Intention ~ Kepribadian 3.627 2.561 1.416 0.157 -1.393
## 10 Intention ~ ModalSosial -0.898 1.628 -0.552 0.581 -4.089
## 11 Intention ~ FaktorKognitif -2.141 2.437 -0.879 0.380 -6.916
## 12 Attitude ~~ Attitude 0.395 0.043 9.224 0.000 0.311
## 13 Risk ~~ Risk 0.967 0.086 11.203 0.000 0.798
## 14 Locus ~~ Locus 0.611 0.057 10.742 0.000 0.499
## 15 SSN ~~ SSN 1.220 0.108 11.273 0.000 1.008
## 16 SocialCap ~~ SocialCap 1.418 0.127 11.170 0.000 1.169
## 17 Opportunity ~~ Opportunity 0.239 0.051 4.681 0.000 0.139
## 18 PBC ~~ PBC 0.813 0.078 10.460 0.000 0.661
## 19 Innova ~~ Innova 0.350 0.059 5.971 0.000 0.235
## 20 Intention ~~ Intention -0.044 0.387 -0.113 0.910 -0.802
## 21 Kepribadian ~~ Kepribadian 0.770 0.097 7.936 0.000 0.580
## 22 ModalSosial ~~ ModalSosial 0.426 0.099 4.310 0.000 0.232
## 23 FaktorKognitif ~~ FaktorKognitif 0.663 0.111 5.991 0.000 0.446
## 24 Kepribadian ~~ ModalSosial 0.543 0.081 6.749 0.000 0.386
## 25 Kepribadian ~~ FaktorKognitif 0.688 0.084 8.149 0.000 0.522
## 26 ModalSosial ~~ FaktorKognitif 0.501 0.080 6.288 0.000 0.345
## ci.upper std.lv std.all
## 1 1.000 0.878 0.813
## 2 1.070 0.802 0.632
## 3 1.037 0.793 0.712
## 4 1.000 0.653 0.509
## 5 1.527 0.775 0.546
## 6 1.806 0.962 0.891
## 7 1.000 0.814 0.670
## 8 1.391 0.973 0.854
## 9 8.647 3.183 2.935
## 10 2.293 -0.586 -0.541
## 11 2.635 -1.743 -1.608
## 12 0.479 0.395 0.339
## 13 1.136 0.967 0.601
## 14 0.722 0.611 0.493
## 15 1.432 1.220 0.741
## 16 1.667 1.418 0.702
## 17 0.339 0.239 0.205
## 18 0.966 0.813 0.551
## 19 0.465 0.350 0.270
## 20 0.714 -0.044 -0.037
## 21 0.960 1.000 1.000
## 22 0.620 1.000 1.000
## 23 0.880 1.000 1.000
## 24 0.701 0.949 0.949
## 25 0.853 0.962 0.962
## 26 0.657 0.943 0.943