Analisis SEM terhadap Faktor-Faktor yang Mempengaruhi Niat Mahasiswa dalam Penggunaan Teknologi Artificial Intelligence untuk Pembelajaran dengan Analisis Lanjutan melalui Pendekatan CFA
Muhammad Dafa Alvian Ramadhani (23031554017) Muhammad Alif Mahbubi (23031554031) Yogi Ramadhani (23031554198)
2025-05-23
library(readxl)## Warning: package 'readxl' was built under R version 4.4.3data <- read_excel("C:/Users/Yogi Ramadhani/Documents/KULIAH/SEM4/Analisis Multivariat/Project/dataset/Dataset Anmul.xlsx")
data <- data[, !(names(data) %in% c("Respondent", "Gender","University","Level.of.Education","Province","Fields of Study", "Type of AI"))]
head(data)## # A tibble: 6 × 39
## PE1 PE2 PE3 PE4 PE5 PE6 PE7 PE8 PE9 PE10 PE11 PE12 PE13
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 3 3 3 3 3 3 3 3 3 3 3 3
## 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3 3 2 3 2 3 2 3 3 3 3 3 2 3
## 4 3 3 3 3 4 3 4 3 3 2 3 3 3
## 5 3 3 3 3 3 3 3 3 3 3 3 3 3
## 6 3 3 2 1 2 3 2 2 2 2 2 3 2
## # ℹ 26 more variables: PE14 <dbl>, PE15 <dbl>, PE16 <dbl>, PE17 <dbl>,
## # PE18 <dbl>, PE19 <dbl>, PE20 <dbl>, CU1 <dbl>, CU2 <dbl>, CU3 <dbl>,
## # CU4 <dbl>, ATU1 <dbl>, ATU2 <dbl>, ATU3 <dbl>, ATU4 <dbl>, ATU5 <dbl>,
## # AUP1 <dbl>, AUP2 <dbl>, AUP3 <dbl>, AUP4 <dbl>, AUP5 <dbl>, MIUA1 <dbl>,
## # MIUA2 <dbl>, MIUA3 <dbl>, MIUA4 <dbl>, MIUA5 <dbl>Handling Missing Value with Mean
numeric_vars <- sapply(data, is.numeric)
for (col in names(data)[numeric_vars]) {
if (any(is.na(data[[col]]))) {
data[[col]][is.na(data[[col]])] <- mean(data[[col]], na.rm = TRUE)
}
}
colSums(is.na(data))## PE1 PE2 PE3 PE4 PE5 PE6 PE7 PE8 PE9 PE10 PE11 PE12 PE13
## 0 0 0 0 0 0 0 0 0 0 0 0 0
## PE14 PE15 PE16 PE17 PE18 PE19 PE20 CU1 CU2 CU3 CU4 ATU1 ATU2
## 0 0 0 0 0 0 0 0 0 0 0 0 0
## ATU3 ATU4 ATU5 AUP1 AUP2 AUP3 AUP4 AUP5 MIUA1 MIUA2 MIUA3 MIUA4 MIUA5
## 0 0 0 0 0 0 0 0 0 0 0 0 0Uji Validitas
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionTotal_PE <- rowSums(select(data, PE1:PE20), na.rm = TRUE)
Total_CU <- rowSums(select(data, CU1:CU4), na.rm = TRUE)
Total_ATU <- rowSums(select(data, ATU1:ATU5), na.rm = TRUE)
Total_AUP <- rowSums(select(data, AUP1:AUP5), na.rm = TRUE)
Total_MIUA <- rowSums(select(data, MIUA1:MIUA5), na.rm = TRUE)
valid_PE <- cor(cbind(select(data, PE1:PE20), Total_PE), method = "pearson")
valid_CU <- cor(cbind(select(data, CU1:CU4), Total_CU), method = "pearson")
valid_ATU <- cor(cbind(select(data, ATU1:ATU5), Total_ATU), method = "pearson")
valid_AUP <- cor(cbind(select(data, AUP1:AUP5), Total_AUP), method = "pearson")
valid_MIUA <- cor(cbind(select(data, MIUA1:MIUA5), Total_MIUA), method = "pearson")valid_PE## PE1 PE2 PE3 PE4 PE5 PE6 PE7
## PE1 1.0000000 0.6277398 0.6242032 0.5238684 0.5617807 0.5507004 0.5364868
## PE2 0.6277398 1.0000000 0.6353586 0.5556129 0.5939141 0.5403536 0.5338162
## PE3 0.6242032 0.6353586 1.0000000 0.5935077 0.5552296 0.5406125 0.4906201
## PE4 0.5238684 0.5556129 0.5935077 1.0000000 0.5950555 0.5977996 0.5186781
## PE5 0.5617807 0.5939141 0.5552296 0.5950555 1.0000000 0.6071837 0.6006074
## PE6 0.5507004 0.5403536 0.5406125 0.5977996 0.6071837 1.0000000 0.6175563
## PE7 0.5364868 0.5338162 0.4906201 0.5186781 0.6006074 0.6175563 1.0000000
## PE8 0.5731472 0.5769760 0.5431667 0.5821213 0.5505614 0.5847671 0.5727334
## PE9 0.4693823 0.4730326 0.4648374 0.4297308 0.4986458 0.4922610 0.5869235
## PE10 0.4099547 0.4726995 0.4489949 0.4659827 0.4672390 0.4956342 0.5639892
## PE11 0.4870641 0.5034137 0.5285818 0.5398678 0.4985511 0.4933771 0.4971232
## PE12 0.4342367 0.4386617 0.4152179 0.4169545 0.4245570 0.4667804 0.5241551
## PE13 0.4479060 0.4179520 0.4148928 0.4291469 0.4208987 0.4534617 0.5265676
## PE14 0.5040053 0.5093138 0.5030818 0.4799485 0.5020536 0.5117141 0.4543857
## PE15 0.5444749 0.5493541 0.5888175 0.6239732 0.5611436 0.5625572 0.5486691
## PE16 0.4875227 0.5299355 0.5161034 0.5588977 0.5522408 0.4871027 0.5280734
## PE17 0.4462551 0.5226418 0.4657337 0.5966963 0.5194670 0.4737026 0.4705746
## PE18 0.4592819 0.5589592 0.5104100 0.5647432 0.5887561 0.5605568 0.5296781
## PE19 0.4667481 0.5100662 0.4798259 0.4918437 0.4716203 0.5100927 0.4741386
## PE20 0.5502205 0.5655489 0.5583390 0.5325574 0.5494552 0.5459044 0.5075899
## Total_PE 0.7208547 0.7470896 0.7313494 0.7487313 0.7481766 0.7487591 0.7509924
## PE8 PE9 PE10 PE11 PE12 PE13 PE14
## PE1 0.5731472 0.4693823 0.4099547 0.4870641 0.4342367 0.4479060 0.5040053
## PE2 0.5769760 0.4730326 0.4726995 0.5034137 0.4386617 0.4179520 0.5093138
## PE3 0.5431667 0.4648374 0.4489949 0.5285818 0.4152179 0.4148928 0.5030818
## PE4 0.5821213 0.4297308 0.4659827 0.5398678 0.4169545 0.4291469 0.4799485
## PE5 0.5505614 0.4986458 0.4672390 0.4985511 0.4245570 0.4208987 0.5020536
## PE6 0.5847671 0.4922610 0.4956342 0.4933771 0.4667804 0.4534617 0.5117141
## PE7 0.5727334 0.5869235 0.5639892 0.4971232 0.5241551 0.5265676 0.4543857
## PE8 1.0000000 0.6042841 0.5310728 0.6497344 0.5994982 0.5739240 0.4853984
## PE9 0.6042841 1.0000000 0.6376662 0.5490478 0.5897742 0.5563170 0.4170767
## PE10 0.5310728 0.6376662 1.0000000 0.5776781 0.5718311 0.5660748 0.3903734
## PE11 0.6497344 0.5490478 0.5776781 1.0000000 0.6047186 0.5756760 0.4866322
## PE12 0.5994982 0.5897742 0.5718311 0.6047186 1.0000000 0.7256365 0.4174561
## PE13 0.5739240 0.5563170 0.5660748 0.5756760 0.7256365 1.0000000 0.4169262
## PE14 0.4853984 0.4170767 0.3903734 0.4866322 0.4174561 0.4169262 1.0000000
## PE15 0.5782713 0.4687738 0.4953869 0.5878109 0.4588219 0.4744591 0.6200235
## PE16 0.5870587 0.5342415 0.5607313 0.5761340 0.5226842 0.4765910 0.5666065
## PE17 0.5228360 0.4520669 0.5100357 0.5358757 0.4352210 0.4328262 0.5036208
## PE18 0.5358498 0.4796957 0.5259698 0.5514267 0.4760056 0.4655334 0.5222346
## PE19 0.4949235 0.4318593 0.4313362 0.4942663 0.3558331 0.3783897 0.5458468
## PE20 0.5853391 0.4906331 0.4928994 0.5726330 0.5216034 0.5060211 0.5490249
## Total_PE 0.7912134 0.7211269 0.7223910 0.7631862 0.7072656 0.6989027 0.6982237
## PE15 PE16 PE17 PE18 PE19 PE20 Total_PE
## PE1 0.5444749 0.4875227 0.4462551 0.4592819 0.4667481 0.5502205 0.7208547
## PE2 0.5493541 0.5299355 0.5226418 0.5589592 0.5100662 0.5655489 0.7470896
## PE3 0.5888175 0.5161034 0.4657337 0.5104100 0.4798259 0.5583390 0.7313494
## PE4 0.6239732 0.5588977 0.5966963 0.5647432 0.4918437 0.5325574 0.7487313
## PE5 0.5611436 0.5522408 0.5194670 0.5887561 0.4716203 0.5494552 0.7481766
## PE6 0.5625572 0.4871027 0.4737026 0.5605568 0.5100927 0.5459044 0.7487591
## PE7 0.5486691 0.5280734 0.4705746 0.5296781 0.4741386 0.5075899 0.7509924
## PE8 0.5782713 0.5870587 0.5228360 0.5358498 0.4949235 0.5853391 0.7912134
## PE9 0.4687738 0.5342415 0.4520669 0.4796957 0.4318593 0.4906331 0.7211269
## PE10 0.4953869 0.5607313 0.5100357 0.5259698 0.4313362 0.4928994 0.7223910
## PE11 0.5878109 0.5761340 0.5358757 0.5514267 0.4942663 0.5726330 0.7631862
## PE12 0.4588219 0.5226842 0.4352210 0.4760056 0.3558331 0.5216034 0.7072656
## PE13 0.4744591 0.4765910 0.4328262 0.4655334 0.3783897 0.5060211 0.6989027
## PE14 0.6200235 0.5666065 0.5036208 0.5222346 0.5458468 0.5490249 0.6982237
## PE15 1.0000000 0.6582014 0.6145637 0.5981663 0.6052196 0.6011045 0.7900978
## PE16 0.6582014 1.0000000 0.5723160 0.6269710 0.5521735 0.5778620 0.7725529
## PE17 0.6145637 0.5723160 1.0000000 0.6408302 0.4986851 0.5225760 0.7244990
## PE18 0.5981663 0.6269710 0.6408302 1.0000000 0.5699056 0.6121033 0.7646007
## PE19 0.6052196 0.5521735 0.4986851 0.5699056 1.0000000 0.6488564 0.6973285
## PE20 0.6011045 0.5778620 0.5225760 0.6121033 0.6488564 1.0000000 0.7712929
## Total_PE 0.7900978 0.7725529 0.7244990 0.7646007 0.6973285 0.7712929 1.0000000valid_CU## CU1 CU2 CU3 CU4 Total_CU
## CU1 1.0000000 0.6236553 0.3380980 0.3592228 0.7743097
## CU2 0.6236553 1.0000000 0.3997743 0.3815153 0.8095394
## CU3 0.3380980 0.3997743 1.0000000 0.2363142 0.6739965
## CU4 0.3592228 0.3815153 0.2363142 1.0000000 0.6867894
## Total_CU 0.7743097 0.8095394 0.6739965 0.6867894 1.0000000valid_ATU## ATU1 ATU2 ATU3 ATU4 ATU5 Total_ATU
## ATU1 1.0000000 0.4533796 0.4661675 0.4140088 0.4207411 0.7028879
## ATU2 0.4533796 1.0000000 0.6015316 0.5203806 0.5131822 0.8119109
## ATU3 0.4661675 0.6015316 1.0000000 0.6106180 0.5147857 0.8256832
## ATU4 0.4140088 0.5203806 0.6106180 1.0000000 0.4657914 0.7675091
## ATU5 0.4207411 0.5131822 0.5147857 0.4657914 1.0000000 0.7584013
## Total_ATU 0.7028879 0.8119109 0.8256832 0.7675091 0.7584013 1.0000000valid_AUP## AUP1 AUP2 AUP3 AUP4 AUP5 Total_AUP
## AUP1 1.00000000 0.2773411 0.07327707 0.3544087 0.3337524 0.6506730
## AUP2 0.27734114 1.0000000 0.38698181 0.3087289 0.3382597 0.6908068
## AUP3 0.07327707 0.3869818 1.00000000 0.1814437 0.2086779 0.5539612
## AUP4 0.35440874 0.3087289 0.18144373 1.0000000 0.5337347 0.6960207
## AUP5 0.33375238 0.3382597 0.20867791 0.5337347 1.0000000 0.7196733
## Total_AUP 0.65067296 0.6908068 0.55396115 0.6960207 0.7196733 1.0000000valid_MIUA## MIUA1 MIUA2 MIUA3 MIUA4 MIUA5 Total_MIUA
## MIUA1 1.0000000 0.6388931 0.5935107 0.5282506 0.5085203 0.8171874
## MIUA2 0.6388931 1.0000000 0.5847300 0.5139989 0.5519228 0.8167285
## MIUA3 0.5935107 0.5847300 1.0000000 0.5885623 0.5759731 0.8354789
## MIUA4 0.5282506 0.5139989 0.5885623 1.0000000 0.4278602 0.7759114
## MIUA5 0.5085203 0.5519228 0.5759731 0.4278602 1.0000000 0.7569022
## Total_MIUA 0.8171874 0.8167285 0.8354789 0.7759114 0.7569022 1.0000000r tabel , α = 0.05
n <- 535
df <- n - 2
t_tabel <- qt(0.975, df)
r_tabel <- t_tabel / sqrt(df + t_tabel^2)
r_tabel## [1] 0.08478232Uji reliabilitas
alpha_PE <- psych::alpha(select(data, PE1:PE20))## Number of categories should be increased in order to count frequencies.alpha_CU <- psych::alpha(select(data, CU1:CU4))
alpha_ATU <- psych::alpha(select(data, ATU1:ATU5))
alpha_AUP <- psych::alpha(select(data, AUP1:AUP5))
alpha_MIUA <- psych::alpha(select(data, MIUA1:MIUA5))
alpha_PE$total$raw_alpha## [1] 0.9562628alpha_CU$total$raw_alpha## [1] 0.7132583alpha_ATU$total$raw_alpha## [1] 0.8317695alpha_AUP$total$raw_alpha## [1] 0.6718383alpha_MIUA$total$raw_alpha## [1] 0.8588062Normalisasi (MinMax Scaler)
data <- as.data.frame(lapply(data, function(x) {
if (is.numeric(x)) {
min_x <- min(x, na.rm = TRUE)
max_x <- max(x, na.rm = TRUE)
if (min_x == max_x) {
return(rep(0, length(x)))
} else {
return((x - min_x) / (max_x - min_x))
}
} else {
return(x)
}
}))
head(data)## PE1 PE2 PE3 PE4 PE5 PE6 PE7
## 1 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 2 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 3 0.6666667 0.3333333 0.6666667 0.3333333 0.6666667 0.3333333 0.6666667
## 4 0.6666667 0.6666667 0.6666667 0.6666667 1.0000000 0.6666667 1.0000000
## 5 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 6 0.6666667 0.6666667 0.3333333 0.0000000 0.3333333 0.6666667 0.3333333
## PE8 PE9 PE10 PE11 PE12 PE13 PE14
## 1 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 2 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 3 0.6666667 0.6666667 0.6666667 0.6666667 0.3333333 0.6666667 0.6666667
## 4 0.6666667 0.6666667 0.3333333 0.6666667 0.6666667 0.6666667 0.6666667
## 5 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 6 0.3333333 0.3333333 0.3333333 0.3333333 0.6666667 0.3333333 0.3333333
## PE15 PE16 PE17 PE18 PE19 PE20 CU1
## 1 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.0000000
## 2 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.3333333
## 3 0.6666667 0.3333333 0.3333333 0.6666667 0.3333333 0.6666667 0.3333333
## 4 0.3333333 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 5 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 6 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333
## CU2 CU3 CU4 ATU1 ATU2 ATU3 ATU4 ATU5
## 1 0.0000000 0.6666667 0.0000000 1.0000000 0.6666667 0.50 0.6535335 0.50
## 2 0.3333333 0.0000000 0.0000000 0.3333333 0.0000000 0.00 1.0000000 0.00
## 3 0.6666667 0.3333333 0.3333333 0.3333333 0.3333333 0.50 0.3333333 0.25
## 4 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.75 0.6666667 0.75
## 5 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.75 0.6666667 0.75
## 6 0.3333333 0.3333333 0.0000000 0.3333333 0.3333333 0.00 0.3333333 0.25
## AUP1 AUP2 AUP3 AUP4 AUP5 MIUA1 MIUA2
## 1 0.0000000 0.3333333 0.6666667 0.3333333 0.0000000 0.3333333 0.3333333
## 2 0.0000000 0.0000000 0.3333333 0.0000000 0.0000000 0.0000000 0.0000000
## 3 0.0000000 0.3333333 0.6666667 0.3333333 0.0000000 0.3333333 0.3333333
## 4 0.3333333 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 5 0.3333333 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 6 0.6666667 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333
## MIUA3 MIUA4 MIUA5
## 1 0.3333333 0.3333333 0.3333333
## 2 0.0000000 0.0000000 0.0000000
## 3 0.3333333 0.3333333 0.3333333
## 4 0.6666667 0.6666667 0.6666667
## 5 0.6666667 0.6666667 0.6666667
## 6 0.3333333 0.3333333 0.3333333CFA
library(lavaan)## Warning: package 'lavaan' was built under R version 4.4.3## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.PE.model <- 'PE =~ PE1 + PE2 + PE3 + PE4 + PE5 + PE6 + PE7 + PE8 + PE9 + PE10 +
PE11 + PE12 + PE13 + PE14 + PE15 + PE16 + PE17 + PE18 + PE19 + PE20'
fit.PE <- cfa(PE.model, data = data)
summary(fit.PE, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 152 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 946.425
## Degrees of freedom 170
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7313.284
## Degrees of freedom 190
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.891
## Tucker-Lewis Index (TLI) 0.878
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 2478.071
## Loglikelihood unrestricted model (H1) 2951.284
##
## Akaike (AIC) -4876.142
## Bayesian (BIC) -4704.851
## Sample-size adjusted Bayesian (SABIC) -4831.824
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092
## 90 Percent confidence interval - lower 0.087
## 90 Percent confidence interval - upper 0.098
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050
##
## 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
## PE =~
## PE1 1.000 0.182 0.705
## PE2 1.003 0.061 16.556 0.000 0.183 0.736
## PE3 0.990 0.061 16.194 0.000 0.180 0.719
## PE4 1.102 0.066 16.614 0.000 0.201 0.738
## PE5 1.006 0.061 16.597 0.000 0.183 0.737
## PE6 1.065 0.065 16.503 0.000 0.194 0.733
## PE7 1.096 0.067 16.410 0.000 0.200 0.729
## PE8 1.051 0.060 17.537 0.000 0.191 0.780
## PE9 1.024 0.066 15.598 0.000 0.186 0.693
## PE10 1.097 0.070 15.597 0.000 0.200 0.693
## PE11 1.025 0.061 16.828 0.000 0.187 0.748
## PE12 1.017 0.067 15.214 0.000 0.185 0.675
## PE13 1.047 0.070 14.970 0.000 0.191 0.664
## PE14 0.962 0.063 15.377 0.000 0.175 0.683
## PE15 1.100 0.062 17.637 0.000 0.200 0.784
## PE16 1.052 0.061 17.171 0.000 0.192 0.763
## PE17 1.044 0.065 15.985 0.000 0.190 0.710
## PE18 0.997 0.059 17.015 0.000 0.181 0.756
## PE19 0.907 0.059 15.480 0.000 0.165 0.687
## PE20 0.998 0.058 17.157 0.000 0.182 0.763
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PE1 0.034 0.002 15.644 0.000 0.034 0.503
## .PE2 0.028 0.002 15.505 0.000 0.028 0.459
## .PE3 0.030 0.002 15.583 0.000 0.030 0.483
## .PE4 0.034 0.002 15.492 0.000 0.034 0.455
## .PE5 0.028 0.002 15.496 0.000 0.028 0.456
## .PE6 0.032 0.002 15.517 0.000 0.032 0.462
## .PE7 0.035 0.002 15.538 0.000 0.035 0.469
## .PE8 0.024 0.002 15.237 0.000 0.024 0.392
## .PE9 0.038 0.002 15.691 0.000 0.038 0.520
## .PE10 0.043 0.003 15.691 0.000 0.043 0.520
## .PE11 0.027 0.002 15.441 0.000 0.027 0.441
## .PE12 0.041 0.003 15.751 0.000 0.041 0.544
## .PE13 0.046 0.003 15.786 0.000 0.046 0.559
## .PE14 0.035 0.002 15.727 0.000 0.035 0.534
## .PE15 0.025 0.002 15.203 0.000 0.025 0.385
## .PE16 0.026 0.002 15.349 0.000 0.026 0.417
## .PE17 0.036 0.002 15.623 0.000 0.036 0.496
## .PE18 0.025 0.002 15.392 0.000 0.025 0.428
## .PE19 0.030 0.002 15.710 0.000 0.030 0.528
## .PE20 0.024 0.002 15.353 0.000 0.024 0.418
## PE 0.033 0.004 9.256 0.000 1.000 1.000fitMeasures(fit.PE)## npar fmin chisq
## 40.000 0.885 946.425
## df pvalue baseline.chisq
## 170.000 0.000 7313.284
## baseline.df baseline.pvalue cfi
## 190.000 0.000 0.891
## tli nnfi rfi
## 0.878 0.878 0.855
## nfi pnfi ifi
## 0.871 0.779 0.891
## rni logl unrestricted.logl
## 0.891 2478.071 2951.284
## aic bic ntotal
## -4876.142 -4704.851 535.000
## bic2 rmsea rmsea.ci.lower
## -4831.824 0.092 0.087
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.098 0.900 0.000
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 1.000 0.080
## rmr rmr_nomean srmr
## 0.004 0.004 0.050
## srmr_bentler srmr_bentler_nomean crmr
## 0.050 0.050 0.053
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.053 0.050 0.050
## cn_05 cn_01 gfi
## 114.862 122.995 0.819
## agfi pgfi mfi
## 0.777 0.663 0.484
## ecvi
## 1.919CU.model <- 'CU =~ CU1 + CU2 + CU3 + CU4'
fit.CU <- cfa(CU.model, data = data)
summary(fit.CU, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 0.954
## Degrees of freedom 2
## P-value (Chi-square) 0.621
##
## Model Test Baseline Model:
##
## Test statistic 467.834
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.007
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -221.191
## Loglikelihood unrestricted model (H1) -220.714
##
## Akaike (AIC) 458.381
## Bayesian (BIC) 492.640
## Sample-size adjusted Bayesian (SABIC) 467.245
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.069
## P-value H_0: RMSEA <= 0.050 0.868
## P-value H_0: RMSEA >= 0.080 0.024
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008
##
## 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
## CU =~
## CU1 1.000 0.209 0.747
## CU2 1.170 0.093 12.593 0.000 0.245 0.834
## CU3 0.690 0.072 9.608 0.000 0.144 0.473
## CU4 0.716 0.075 9.530 0.000 0.150 0.469
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .CU1 0.035 0.004 9.460 0.000 0.035 0.443
## .CU2 0.026 0.004 6.019 0.000 0.026 0.305
## .CU3 0.072 0.005 15.140 0.000 0.072 0.777
## .CU4 0.080 0.005 15.168 0.000 0.080 0.780
## CU 0.044 0.005 8.342 0.000 1.000 1.000fitMeasures(fit.CU)## npar fmin chisq
## 8.000 0.001 0.954
## df pvalue baseline.chisq
## 2.000 0.621 467.834
## baseline.df baseline.pvalue cfi
## 6.000 0.000 1.000
## tli nnfi rfi
## 1.007 1.007 0.994
## nfi pnfi ifi
## 0.998 0.333 1.002
## rni logl unrestricted.logl
## 1.002 -221.191 -220.714
## aic bic ntotal
## 458.381 492.640 535.000
## bic2 rmsea rmsea.ci.lower
## 467.245 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.069 0.900 0.868
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.024 0.080
## rmr rmr_nomean srmr
## 0.001 0.001 0.008
## srmr_bentler srmr_bentler_nomean crmr
## 0.008 0.008 0.010
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.010 0.008 0.008
## cn_05 cn_01 gfi
## 3360.451 5165.294 0.999
## agfi pgfi mfi
## 0.996 0.200 1.001
## ecvi
## 0.032ATU.model <- 'ATU =~ ATU1 + ATU2 + ATU3 + ATU4 + ATU5'
fit.ATU <- cfa(ATU.model, data = data)
summary(fit.ATU, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 7.264
## Degrees of freedom 5
## P-value (Chi-square) 0.202
##
## Model Test Baseline Model:
##
## Test statistic 938.543
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998
## Tucker-Lewis Index (TLI) 0.995
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 535.570
## Loglikelihood unrestricted model (H1) 539.202
##
## Akaike (AIC) -1051.141
## Bayesian (BIC) -1008.318
## Sample-size adjusted Bayesian (SABIC) -1040.061
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.029
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.071
## P-value H_0: RMSEA <= 0.050 0.747
## P-value H_0: RMSEA >= 0.080 0.020
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015
##
## 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
## ATU =~
## ATU1 1.000 0.148 0.593
## ATU2 1.490 0.117 12.707 0.000 0.220 0.746
## ATU3 1.095 0.083 13.250 0.000 0.162 0.809
## ATU4 1.186 0.095 12.471 0.000 0.175 0.723
## ATU5 0.916 0.078 11.744 0.000 0.135 0.660
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ATU1 0.040 0.003 14.733 0.000 0.040 0.649
## .ATU2 0.039 0.003 12.489 0.000 0.039 0.443
## .ATU3 0.014 0.001 10.539 0.000 0.014 0.345
## .ATU4 0.028 0.002 12.997 0.000 0.028 0.477
## .ATU5 0.024 0.002 14.025 0.000 0.024 0.565
## ATU 0.022 0.003 6.982 0.000 1.000 1.000fitMeasures(fit.ATU)## npar fmin chisq
## 10.000 0.007 7.264
## df pvalue baseline.chisq
## 5.000 0.202 938.543
## baseline.df baseline.pvalue cfi
## 10.000 0.000 0.998
## tli nnfi rfi
## 0.995 0.995 0.985
## nfi pnfi ifi
## 0.992 0.496 0.998
## rni logl unrestricted.logl
## 0.998 535.570 539.202
## aic bic ntotal
## -1051.141 -1008.318 535.000
## bic2 rmsea rmsea.ci.lower
## -1040.061 0.029 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.071 0.900 0.747
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.020 0.080
## rmr rmr_nomean srmr
## 0.001 0.001 0.015
## srmr_bentler srmr_bentler_nomean crmr
## 0.015 0.015 0.018
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.018 0.015 0.015
## cn_05 cn_01 gfi
## 816.368 1112.139 0.994
## agfi pgfi mfi
## 0.983 0.331 0.998
## ecvi
## 0.051AUP.model <- 'AUP =~ AUP1 + AUP2 + AUP3 + AUP4 + AUP5'
fit.AUP <- cfa(AUP.model, data = data)
summary(fit.AUP, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 57.789
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 456.035
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.882
## Tucker-Lewis Index (TLI) 0.763
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -256.931
## Loglikelihood unrestricted model (H1) -228.037
##
## Akaike (AIC) 533.862
## Bayesian (BIC) 576.685
## Sample-size adjusted Bayesian (SABIC) 544.942
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.140
## 90 Percent confidence interval - lower 0.109
## 90 Percent confidence interval - upper 0.174
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.999
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.066
##
## 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
## AUP =~
## AUP1 1.000 0.161 0.479
## AUP2 0.868 0.114 7.637 0.000 0.140 0.503
## AUP3 0.580 0.103 5.645 0.000 0.093 0.322
## AUP4 1.109 0.126 8.787 0.000 0.179 0.704
## AUP5 1.267 0.144 8.806 0.000 0.204 0.723
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .AUP1 0.087 0.006 14.586 0.000 0.087 0.771
## .AUP2 0.058 0.004 14.327 0.000 0.058 0.747
## .AUP3 0.075 0.005 15.678 0.000 0.075 0.896
## .AUP4 0.032 0.003 10.030 0.000 0.032 0.504
## .AUP5 0.038 0.004 9.412 0.000 0.038 0.477
## AUP 0.026 0.005 5.018 0.000 1.000 1.000fitMeasures(fit.AUP)## npar fmin chisq
## 10.000 0.054 57.789
## df pvalue baseline.chisq
## 5.000 0.000 456.035
## baseline.df baseline.pvalue cfi
## 10.000 0.000 0.882
## tli nnfi rfi
## 0.763 0.763 0.747
## nfi pnfi ifi
## 0.873 0.437 0.883
## rni logl unrestricted.logl
## 0.882 -256.931 -228.037
## aic bic ntotal
## 533.862 576.685 535.000
## bic2 rmsea rmsea.ci.lower
## 544.942 0.140 0.109
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.174 0.900 0.000
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.999 0.080
## rmr rmr_nomean srmr
## 0.005 0.005 0.066
## srmr_bentler srmr_bentler_nomean crmr
## 0.066 0.066 0.081
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.081 0.066 0.066
## cn_05 cn_01 gfi
## 103.489 140.666 0.960
## agfi pgfi mfi
## 0.880 0.320 0.952
## ecvi
## 0.145MIUA.model <- 'MIUA =~ MIUA1 + MIUA2 + MIUA3 + MIUA4 + MIUA5'
fit.MIUA <- cfa(MIUA.model, data = data)
summary(fit.MIUA, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 24.873
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1143.133
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.982
## Tucker-Lewis Index (TLI) 0.965
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 270.635
## Loglikelihood unrestricted model (H1) 283.072
##
## Akaike (AIC) -521.271
## Bayesian (BIC) -478.448
## Sample-size adjusted Bayesian (SABIC) -510.191
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.086
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.121
## P-value H_0: RMSEA <= 0.050 0.032
## P-value H_0: RMSEA >= 0.080 0.658
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.024
##
## 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
## MIUA =~
## MIUA1 1.000 0.211 0.773
## MIUA2 0.955 0.054 17.720 0.000 0.201 0.778
## MIUA3 1.014 0.056 18.028 0.000 0.214 0.792
## MIUA4 0.952 0.062 15.485 0.000 0.201 0.685
## MIUA5 0.833 0.054 15.541 0.000 0.176 0.687
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .MIUA1 0.030 0.002 12.478 0.000 0.030 0.402
## .MIUA2 0.026 0.002 12.346 0.000 0.026 0.394
## .MIUA3 0.027 0.002 11.956 0.000 0.027 0.372
## .MIUA4 0.046 0.003 14.079 0.000 0.046 0.531
## .MIUA5 0.034 0.002 14.050 0.000 0.034 0.527
## MIUA 0.044 0.004 10.012 0.000 1.000 1.000fitMeasures(fit.MIUA)## npar fmin chisq
## 10.000 0.023 24.873
## df pvalue baseline.chisq
## 5.000 0.000 1143.133
## baseline.df baseline.pvalue cfi
## 10.000 0.000 0.982
## tli nnfi rfi
## 0.965 0.965 0.956
## nfi pnfi ifi
## 0.978 0.489 0.983
## rni logl unrestricted.logl
## 0.982 270.635 283.072
## aic bic ntotal
## -521.271 -478.448 535.000
## bic2 rmsea rmsea.ci.lower
## -510.191 0.086 0.054
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.121 0.900 0.032
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.658 0.080
## rmr rmr_nomean srmr
## 0.002 0.002 0.024
## srmr_bentler srmr_bentler_nomean crmr
## 0.024 0.024 0.030
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.030 0.024 0.024
## cn_05 cn_01 gfi
## 239.115 325.490 0.982
## agfi pgfi mfi
## 0.946 0.327 0.982
## ecvi
## 0.084SEM
model <- '
# measurement model
PE =~ PE1 + PE2 + PE3 + PE4 + PE5 + PE6 + PE7 + PE8 + PE9 + PE10 +
PE11 + PE12 + PE13 + PE14 + PE15 + PE16 + PE17 + PE18 + PE19 + PE20
CU =~ CU1 + CU2 + CU3 + CU4
ATU =~ ATU1 + ATU2 + ATU3 + ATU4 + ATU5
AUP =~ AUP1 + AUP2 + AUP3 + AUP4 + AUP5
MIUA =~ MIUA1 + MIUA2 + MIUA3 + MIUA4 + MIUA5
# structural model
ATU ~ CU + PE + AUP
AUP ~ PE
MIUA ~ ATU + AUP
'
library(lavaan)
fitsem <- sem(model, data = data)
summary(fitsem,
fit.measures = TRUE,
standardized = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 269 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 85
##
## Number of observations 535
##
## Model Test User Model:
##
## Test statistic 2245.335
## Degrees of freedom 695
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 12490.373
## Degrees of freedom 741
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.868
## Tucker-Lewis Index (TLI) 0.859
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) 3287.913
## Loglikelihood unrestricted model (H1) 4410.580
##
## Akaike (AIC) -6405.826
## Bayesian (BIC) -6041.833
## Sample-size adjusted Bayesian (SABIC) -6311.651
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.065
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.068
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.071
##
## 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
## PE =~
## PE1 1.000 0.181 0.700
## PE2 1.010 0.061 16.468 0.000 0.183 0.736
## PE3 0.996 0.062 16.094 0.000 0.180 0.719
## PE4 1.111 0.067 16.537 0.000 0.201 0.739
## PE5 1.009 0.061 16.455 0.000 0.183 0.735
## PE6 1.071 0.065 16.406 0.000 0.194 0.733
## PE7 1.099 0.068 16.260 0.000 0.199 0.726
## PE8 1.053 0.061 17.349 0.000 0.190 0.777
## PE9 1.026 0.066 15.451 0.000 0.186 0.690
## PE10 1.107 0.071 15.571 0.000 0.200 0.695
## PE11 1.030 0.062 16.715 0.000 0.186 0.747
## PE12 1.024 0.068 15.152 0.000 0.185 0.676
## PE13 1.056 0.071 14.939 0.000 0.191 0.666
## PE14 0.968 0.063 15.306 0.000 0.175 0.683
## PE15 1.106 0.063 17.511 0.000 0.200 0.784
## PE16 1.059 0.062 17.064 0.000 0.192 0.763
## PE17 1.056 0.066 15.985 0.000 0.191 0.714
## PE18 1.010 0.059 17.017 0.000 0.183 0.761
## PE19 0.913 0.059 15.412 0.000 0.165 0.688
## PE20 1.004 0.059 17.046 0.000 0.182 0.763
## CU =~
## CU1 1.000 0.212 0.758
## CU2 1.120 0.087 12.942 0.000 0.238 0.811
## CU3 0.684 0.071 9.624 0.000 0.145 0.476
## CU4 0.734 0.074 9.864 0.000 0.156 0.488
## ATU =~
## ATU1 1.000 0.154 0.619
## ATU2 1.392 0.104 13.437 0.000 0.214 0.728
## ATU3 1.036 0.072 14.315 0.000 0.160 0.800
## ATU4 1.113 0.084 13.184 0.000 0.171 0.709
## ATU5 0.905 0.071 12.803 0.000 0.139 0.681
## AUP =~
## AUP1 1.000 0.150 0.444
## AUP2 0.934 0.117 7.968 0.000 0.140 0.503
## AUP3 0.548 0.102 5.375 0.000 0.082 0.283
## AUP4 1.130 0.124 9.113 0.000 0.169 0.666
## AUP5 1.461 0.152 9.609 0.000 0.219 0.774
## MIUA =~
## MIUA1 1.000 0.209 0.767
## MIUA2 0.949 0.053 17.988 0.000 0.198 0.767
## MIUA3 1.025 0.055 18.701 0.000 0.214 0.794
## MIUA4 0.976 0.060 16.138 0.000 0.204 0.696
## MIUA5 0.850 0.053 16.140 0.000 0.178 0.696
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ATU ~
## CU 0.017 0.029 0.595 0.552 0.024 0.024
## PE 0.182 0.049 3.697 0.000 0.214 0.214
## AUP 0.651 0.095 6.857 0.000 0.632 0.632
## AUP ~
## PE 0.529 0.064 8.261 0.000 0.639 0.639
## MIUA ~
## ATU 0.397 0.103 3.867 0.000 0.293 0.293
## AUP 0.879 0.137 6.410 0.000 0.629 0.629
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PE ~~
## CU 0.008 0.002 3.930 0.000 0.204 0.204
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PE1 0.034 0.002 15.682 0.000 0.034 0.509
## .PE2 0.028 0.002 15.529 0.000 0.028 0.458
## .PE3 0.030 0.002 15.608 0.000 0.030 0.483
## .PE4 0.034 0.002 15.513 0.000 0.034 0.454
## .PE5 0.028 0.002 15.532 0.000 0.028 0.459
## .PE6 0.032 0.002 15.543 0.000 0.032 0.462
## .PE7 0.035 0.002 15.574 0.000 0.035 0.472
## .PE8 0.024 0.002 15.293 0.000 0.024 0.397
## .PE9 0.038 0.002 15.722 0.000 0.038 0.525
## .PE10 0.043 0.003 15.702 0.000 0.043 0.517
## .PE11 0.027 0.002 15.471 0.000 0.027 0.441
## .PE12 0.041 0.003 15.768 0.000 0.041 0.543
## .PE13 0.046 0.003 15.798 0.000 0.046 0.556
## .PE14 0.035 0.002 15.745 0.000 0.035 0.534
## .PE15 0.025 0.002 15.240 0.000 0.025 0.385
## .PE16 0.026 0.002 15.379 0.000 0.026 0.417
## .PE17 0.035 0.002 15.629 0.000 0.035 0.490
## .PE18 0.024 0.002 15.392 0.000 0.024 0.420
## .PE19 0.030 0.002 15.728 0.000 0.030 0.527
## .PE20 0.024 0.002 15.384 0.000 0.024 0.419
## .CU1 0.033 0.004 9.246 0.000 0.033 0.425
## .CU2 0.030 0.004 7.177 0.000 0.030 0.343
## .CU3 0.072 0.005 15.088 0.000 0.072 0.773
## .CU4 0.078 0.005 14.995 0.000 0.078 0.761
## .ATU1 0.038 0.003 14.901 0.000 0.038 0.617
## .ATU2 0.041 0.003 13.686 0.000 0.041 0.470
## .ATU3 0.014 0.001 12.125 0.000 0.014 0.360
## .ATU4 0.029 0.002 13.968 0.000 0.029 0.498
## .ATU5 0.022 0.002 14.315 0.000 0.022 0.536
## .AUP1 0.091 0.006 15.617 0.000 0.091 0.802
## .AUP2 0.058 0.004 15.333 0.000 0.058 0.747
## .AUP3 0.077 0.005 16.097 0.000 0.077 0.920
## .AUP4 0.036 0.003 13.875 0.000 0.036 0.557
## .AUP5 0.032 0.003 11.421 0.000 0.032 0.401
## .MIUA1 0.031 0.002 13.531 0.000 0.031 0.412
## .MIUA2 0.028 0.002 13.534 0.000 0.028 0.412
## .MIUA3 0.027 0.002 12.973 0.000 0.027 0.370
## .MIUA4 0.044 0.003 14.511 0.000 0.044 0.516
## .MIUA5 0.034 0.002 14.510 0.000 0.034 0.516
## PE 0.033 0.004 9.184 0.000 1.000 1.000
## CU 0.045 0.005 8.569 0.000 1.000 1.000
## .ATU 0.009 0.001 6.210 0.000 0.374 0.374
## .AUP 0.013 0.003 4.857 0.000 0.591 0.591
## .MIUA 0.010 0.002 6.374 0.000 0.233 0.233
##
## R-Square:
## Estimate
## PE1 0.491
## PE2 0.542
## PE3 0.517
## PE4 0.546
## PE5 0.541
## PE6 0.538
## PE7 0.528
## PE8 0.603
## PE9 0.475
## PE10 0.483
## PE11 0.559
## PE12 0.457
## PE13 0.444
## PE14 0.466
## PE15 0.615
## PE16 0.583
## PE17 0.510
## PE18 0.580
## PE19 0.473
## PE20 0.581
## CU1 0.575
## CU2 0.657
## CU3 0.227
## CU4 0.239
## ATU1 0.383
## ATU2 0.530
## ATU3 0.640
## ATU4 0.502
## ATU5 0.464
## AUP1 0.198
## AUP2 0.253
## AUP3 0.080
## AUP4 0.443
## AUP5 0.599
## MIUA1 0.588
## MIUA2 0.588
## MIUA3 0.630
## MIUA4 0.484
## MIUA5 0.484
## ATU 0.626
## AUP 0.409
## MIUA 0.767fitMeasures(fitsem)## npar fmin chisq
## 85.000 2.098 2245.335
## df pvalue baseline.chisq
## 695.000 0.000 12490.373
## baseline.df baseline.pvalue cfi
## 741.000 0.000 0.868
## tli nnfi rfi
## 0.859 0.859 0.808
## nfi pnfi ifi
## 0.820 0.769 0.869
## rni logl unrestricted.logl
## 0.868 3287.913 4410.580
## aic bic ntotal
## -6405.826 -6041.833 535.000
## bic2 rmsea rmsea.ci.lower
## -6311.651 0.065 0.062
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.068 0.900 0.000
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.006 0.006 0.071
## srmr_bentler srmr_bentler_nomean crmr
## 0.071 0.071 0.073
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.073 0.071 0.071
## cn_05 cn_01 gfi
## 181.477 187.963 0.800
## agfi pgfi mfi
## 0.775 0.713 0.235
## ecvi
## 4.515library('semPlot')## Warning: package 'semPlot' was built under R version 4.4.3semPaths(
object = fitsem,
what = "path",
whatLabels = "par"
)
P <- semPaths(
object = fitsem,
what = "path",
whatLabels = "par",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 7,
sizeLat = 7,
color = "lightgray",
edge.label.cex = 1.2,
label.cex=1.3
)