Proyecto de Investigación
Diego Calderón
Abril 2024
library(readxl)
library(parameters)
library (apa)
library (haven)
library (ggplot2)
library (ggpubr)
library (gridExtra)
library (apaTables)
library (reshape)
library (GPArotation)
library (mvtnorm)
library (psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:GPArotation':
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## equamax, varimin## The following objects are masked from 'package:ggplot2':
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## %+%, alpha## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following object is masked from 'package:reshape':
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## rename## The following object is masked from 'package:gridExtra':
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## combine## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## Loading required package: multilevel## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
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## collapse## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
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## select## Loading required package: purrr##
## Attaching package: 'psychometric'## The following object is masked from 'package:psych':
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## alpha## The following object is masked from 'package:ggplot2':
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## alpha## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
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## cor2cov## Loading required package: lattice##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
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## parallel## ## ################################################################################# This is semTools 0.5-6## All users of R (or SEM) are invited to submit functions or ideas for functions.## #################################################################################
## Attaching package: 'semTools'## The following objects are masked from 'package:psych':
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## reliability, skew## The following object is masked from 'package:parameters':
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## kurtosis##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
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## lift## The following object is masked from 'package:parameters':
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## compare_models##
## Attaching package: 'gtsummary'## The following object is masked from 'package:MASS':
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## selectCaraceríticas sociodemográficas
## New names:
## • `` -> `...58`
## • `` -> `...59`| Characteristic | N = 1341 |
|---|---|
| ¿Con qué género te identificas? | |
| Femenino | 79 (59%) |
| Masculino | 47 (35%) |
| No binario | 6 (4.5%) |
| Otros | 2 (1.5%) |
| ¿A qué máster estás vinculado? | |
| Dirección y Gestión de Centros Educativos | 1 (0.7%) |
| Educación Interdisciplinaria de las Artes | 14 (10%) |
| Entornos de Enseñanza y Aprendizaje con Tecnologías Digitales | 31 (23%) |
| Formación del Profesorado de Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanza de Idiomas | 87 (65%) |
| Intervenciones Sociales y Educativas | 1 (0.7%) |
| 1 n (%) | |
| Characteristic | N = 1341 |
|---|---|
| ¿A qué máster estás vinculado? | |
| Dirección y Gestión de Centros Educativos | 1 (0.7%) |
| Educación Interdisciplinaria de las Artes | 14 (10%) |
| Entornos de Enseñanza y Aprendizaje con Tecnologías Digitales | 31 (23%) |
| Formación del Profesorado de Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanza de Idiomas | 87 (65%) |
| Intervenciones Sociales y Educativas | 1 (0.7%) |
| 1 n (%) | |
| Name | Demográfico[, 2] |
| Number of rows | 134 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ¿Cuál es tu edad? | 0 | 1 | 32.1 | 9.5 | 22 | 25 | 30 | 36.75 | 63 | ▇▅▂▁▁ |
Alfabetización respecto a la Inteligencia Artificial
Base_de_datos_IA <- read_excel("Base de datos_IA.xlsx",
sheet = "AF")
ASI <- Base_de_datos_IA
dim(ASI)## [1] 134 32## IM01 IM02 IM03 IM04 SE01
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :2.000 Median :4.0 Median :4.000
## Mean :2.843 Mean :3.903 Mean :2.381 Mean :3.5 Mean :3.701
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:5.0 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.0 Max. :5.000
## SE02 SE03 SE04 CL01
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.597 Mean :3.761 Mean :3.403 Mean :3.754
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## CL02 BI01 BI02 BI03 EN01
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.00
## Median :4.000 Median :4.000 Median :4.000 Median :3.000 Median :2.00
## Mean :3.522 Mean :3.858 Mean :3.664 Mean :3.194 Mean :2.53
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:3.75
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## EN02 SI01 SI02 SI03
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.164 Mean :2.351 Mean :2.164 Mean :2.269
## 3rd Qu.:3.000 3rd Qu.:3.750 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## AIE01 AIE02 AIE03 AIE04 AIE05
## Min. :1.000 Min. :2.000 Min. :1 Min. :1.00 Min. :2.000
## 1st Qu.:4.000 1st Qu.:4.250 1st Qu.:3 1st Qu.:4.00 1st Qu.:5.000
## Median :5.000 Median :5.000 Median :4 Median :5.00 Median :5.000
## Mean :4.515 Mean :4.634 Mean :4 Mean :4.53 Mean :4.694
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5 Max. :5.00 Max. :5.000
## AIE06 AIE07 AIE08 KU01
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :4.000
## Mean :4.537 Mean :4.769 Mean :4.299 Mean :3.746
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## KU02 KU03 EC01 EC02 EC03
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:1.00 1st Qu.:1.000
## Median :4.000 Median :2.000 Median :4.00 Median :2.00 Median :3.000
## Mean :3.873 Mean :2.627 Mean :3.47 Mean :2.53 Mean :2.716
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.00 Max. :5.000## IM01 IM02 IM03 IM04 SE01 SE02 SE03 SE04
## 2.843284 3.902985 2.380597 3.500000 3.701493 3.597015 3.761194 3.402985
## CL01 CL02 BI01 BI02 BI03 EN01 EN02 SI01
## 3.753731 3.522388 3.858209 3.664179 3.194030 2.529851 2.164179 2.350746
## SI02 SI03 AIE01 AIE02 AIE03 AIE04 AIE05 AIE06
## 2.164179 2.268657 4.514925 4.634328 4.000000 4.529851 4.694030 4.537313
## AIE07 AIE08 KU01 KU02 KU03 EC01 EC02 EC03
## 4.768657 4.298507 3.746269 3.873134 2.626866 3.470149 2.529851 2.716418## IM01 IM02 IM03 IM04 SE01 SE02 SE03 SE04
## 1.8173606 1.5017955 1.3803726 2.0263158 1.3688699 1.4454046 1.1004377 1.4303670
## CL01 CL02 BI01 BI02 BI03 EN01 EN02 SI01
## 1.2847604 1.3791943 1.7917742 1.3525418 1.9019190 1.8449669 1.8976546 1.9587588
## SI02 SI03 AIE01 AIE02 AIE03 AIE04 AIE05 AIE06
## 1.7172035 1.8520929 0.6125575 0.5043766 1.1729323 0.7171473 0.3643250 0.6564920
## AIE07 AIE08 KU01 KU02 KU03 EC01 EC02 EC03
## 0.4047245 0.9628549 1.1080687 1.2845360 1.7694984 1.4840646 2.0554932 1.8437886## [1] 0.9413436## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = ASI)
## Overall MSA = 0.89
## MSA for each item =
## IM01 IM02 IM03 IM04 SE01 SE02 SE03 SE04 CL01 CL02 BI01 BI02 BI03
## 0.91 0.90 0.93 0.91 0.90 0.90 0.93 0.89 0.92 0.92 0.90 0.94 0.93
## EN01 EN02 SI01 SI02 SI03 AIE01 AIE02 AIE03 AIE04 AIE05 AIE06 AIE07 AIE08
## 0.90 0.84 0.89 0.90 0.91 0.80 0.69 0.74 0.82 0.76 0.55 0.61 0.85
## KU01 KU02 KU03 EC01 EC02 EC03
## 0.75 0.90 0.91 0.93 0.78 0.87## R was not square, finding R from data## $chisq
## [1] 2997.789
##
## $p.value
## [1] 0
##
## $df
## [1] 496set.seed(123)
training.samples <- ASI$IM01 %>% createDataPartition(p = 0.70, list = FALSE)
train.data <- ASI [training.samples, ]
test.data <- ASI [-training.samples, ]
skim(train.data)| Name | train.data |
| Number of rows | 96 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| numeric | 32 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| IM01 | 0 | 1 | 2.85 | 1.33 | 1 | 2.00 | 3.0 | 4 | 5 | ▆▅▇▅▃ |
| IM02 | 0 | 1 | 3.92 | 1.19 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▂▂▆▇ |
| IM03 | 0 | 1 | 2.46 | 1.17 | 1 | 1.75 | 2.0 | 3 | 5 | ▇▇▇▃▂ |
| IM04 | 0 | 1 | 3.54 | 1.39 | 1 | 2.00 | 4.0 | 5 | 5 | ▂▃▃▅▇ |
| SE01 | 0 | 1 | 3.71 | 1.13 | 1 | 3.00 | 4.0 | 5 | 5 | ▂▂▅▇▆ |
| SE02 | 0 | 1 | 3.62 | 1.17 | 1 | 3.00 | 4.0 | 5 | 5 | ▂▂▇▇▇ |
| SE03 | 0 | 1 | 3.75 | 1.03 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▂▆▇▆ |
| SE04 | 0 | 1 | 3.38 | 1.15 | 1 | 3.00 | 4.0 | 4 | 5 | ▂▂▆▇▃ |
| CL01 | 0 | 1 | 3.71 | 1.18 | 1 | 3.00 | 4.0 | 5 | 5 | ▂▂▃▇▅ |
| CL02 | 0 | 1 | 3.49 | 1.17 | 1 | 3.00 | 4.0 | 4 | 5 | ▂▂▆▇▅ |
| BI01 | 0 | 1 | 3.90 | 1.33 | 1 | 3.00 | 4.0 | 5 | 5 | ▂▂▂▅▇ |
| BI02 | 0 | 1 | 3.76 | 1.09 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▂▃▇▅ |
| BI03 | 0 | 1 | 3.28 | 1.34 | 1 | 2.00 | 3.0 | 4 | 5 | ▃▇▆▇▇ |
| EN01 | 0 | 1 | 2.67 | 1.35 | 1 | 2.00 | 2.0 | 4 | 5 | ▇▇▆▅▃ |
| EN02 | 0 | 1 | 2.23 | 1.38 | 1 | 1.00 | 2.0 | 3 | 5 | ▇▅▂▂▂ |
| SI01 | 0 | 1 | 2.44 | 1.43 | 1 | 1.00 | 2.0 | 4 | 5 | ▇▅▂▃▃ |
| SI02 | 0 | 1 | 2.22 | 1.32 | 1 | 1.00 | 2.0 | 3 | 5 | ▇▅▂▂▂ |
| SI03 | 0 | 1 | 2.28 | 1.41 | 1 | 1.00 | 2.0 | 3 | 5 | ▇▃▂▂▂ |
| AIE01 | 0 | 1 | 4.49 | 0.81 | 1 | 4.00 | 5.0 | 5 | 5 | ▁▁▁▃▇ |
| AIE02 | 0 | 1 | 4.67 | 0.64 | 2 | 4.75 | 5.0 | 5 | 5 | ▁▁▁▂▇ |
| AIE03 | 0 | 1 | 4.11 | 1.04 | 1 | 3.75 | 4.0 | 5 | 5 | ▁▁▃▅▇ |
| AIE04 | 0 | 1 | 4.51 | 0.91 | 1 | 4.00 | 5.0 | 5 | 5 | ▁▁▁▂▇ |
| AIE05 | 0 | 1 | 4.70 | 0.60 | 2 | 5.00 | 5.0 | 5 | 5 | ▁▁▁▂▇ |
| AIE06 | 0 | 1 | 4.53 | 0.85 | 1 | 4.00 | 5.0 | 5 | 5 | ▁▁▁▂▇ |
| AIE07 | 0 | 1 | 4.75 | 0.68 | 1 | 5.00 | 5.0 | 5 | 5 | ▁▁▁▁▇ |
| AIE08 | 0 | 1 | 4.39 | 0.88 | 2 | 4.00 | 5.0 | 5 | 5 | ▁▂▁▃▇ |
| KU01 | 0 | 1 | 3.75 | 1.07 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▂▇▇▇ |
| KU02 | 0 | 1 | 3.88 | 1.11 | 1 | 3.00 | 4.0 | 5 | 5 | ▁▂▅▅▇ |
| KU03 | 0 | 1 | 2.77 | 1.36 | 1 | 2.00 | 3.0 | 4 | 5 | ▇▇▇▇▅ |
| EC01 | 0 | 1 | 3.51 | 1.19 | 1 | 3.00 | 4.0 | 4 | 5 | ▁▅▅▇▅ |
| EC02 | 0 | 1 | 2.62 | 1.44 | 1 | 1.00 | 2.5 | 4 | 5 | ▇▅▅▅▃ |
| EC03 | 0 | 1 | 2.79 | 1.34 | 1 | 2.00 | 3.0 | 4 | 5 | ▇▇▆▇▃ |
Análisis Factorial Exploratorio (AFE)
## Parallel analysis suggests that the number of factors = 3 and the number of components = NA##
## Call:
## factanal(x = train.data, factors = 4, rotation = "varimax")
##
## Uniquenesses:
## IM01 IM02 IM03 IM04 SE01 SE02 SE03 SE04 CL01 CL02 BI01 BI02 BI03
## 0.430 0.308 0.462 0.284 0.238 0.225 0.108 0.391 0.298 0.246 0.266 0.300 0.421
## EN01 EN02 SI01 SI02 SI03 AIE01 AIE02 AIE03 AIE04 AIE05 AIE06 AIE07 AIE08
## 0.360 0.407 0.265 0.429 0.694 0.786 0.852 0.805 0.584 0.762 0.842 0.772 0.766
## KU01 KU02 KU03 EC01 EC02 EC03
## 0.631 0.337 0.440 0.333 0.460 0.431
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## IM01 0.670 0.244 0.239
## IM02 0.804 0.171
## IM03 0.707 0.150 -0.101
## IM04 0.836
## SE01 0.565 0.277 0.573 0.193
## SE02 0.628 0.265 0.545 0.115
## SE03 0.488 0.409 0.696
## SE04 0.533 0.498 0.278
## CL01 0.693 0.213 0.357 0.223
## CL02 0.601 0.236 0.554 0.175
## BI01 0.733 0.347 0.161 0.224
## BI02 0.649 0.418 0.312
## BI03 0.716 0.194 0.168
## EN01 0.566 0.482 -0.288
## EN02 0.429 0.471 0.107 -0.418
## SI01 0.543 0.543 0.123 -0.362
## SI02 0.588 0.379 0.142 -0.249
## SI03 0.465 0.225 0.193
## AIE01 0.346 0.271 0.140
## AIE02 0.134 0.355
## AIE03 0.114 0.149 0.392
## AIE04 0.205 0.122 0.595
## AIE05 0.256 0.242 0.326
## AIE06 0.376 0.123
## AIE07 0.459
## AIE08 0.214 0.219 0.126 0.352
## KU01 0.175 0.550 0.188
## KU02 0.231 0.629 0.185 0.423
## KU03 0.199 0.660 0.276
## EC01 0.317 0.586 0.196 0.430
## EC02 0.246 0.669 0.178
## EC03 0.289 0.657 0.173 0.154
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 7.645 4.461 2.575 2.387
## Proportion Var 0.239 0.139 0.080 0.075
## Cumulative Var 0.239 0.378 0.459 0.533
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 641.06 on 374 degrees of freedom.
## The p-value is 2.37e-16##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## IM01 0.670 0.244 0.239
## IM02 0.804 0.171
## IM03 0.707 0.150 -0.101
## IM04 0.836
## SE01 0.565 0.277 0.573 0.193
## SE02 0.628 0.265 0.545 0.115
## SE03 0.488 0.409 0.696
## SE04 0.533 0.498 0.278
## CL01 0.693 0.213 0.357 0.223
## CL02 0.601 0.236 0.554 0.175
## BI01 0.733 0.347 0.161 0.224
## BI02 0.649 0.418 0.312
## BI03 0.716 0.194 0.168
## EN01 0.566 0.482 -0.288
## EN02 0.429 0.471 0.107 -0.418
## SI01 0.543 0.543 0.123 -0.362
## SI02 0.588 0.379 0.142 -0.249
## SI03 0.465 0.225 0.193
## AIE01 0.346 0.271 0.140
## AIE02 0.134 0.355
## AIE03 0.114 0.149 0.392
## AIE04 0.205 0.122 0.595
## AIE05 0.256 0.242 0.326
## AIE06 0.376 0.123
## AIE07 0.459
## AIE08 0.214 0.219 0.126 0.352
## KU01 0.175 0.550 0.188
## KU02 0.231 0.629 0.185 0.423
## KU03 0.199 0.660 0.276
## EC01 0.317 0.586 0.196 0.430
## EC02 0.246 0.669 0.178
## EC03 0.289 0.657 0.173 0.154
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 7.647 4.461 2.574 2.387
## Proportion Var 0.239 0.139 0.080 0.075
## Cumulative Var 0.239 0.378 0.459 0.533
Análisis Factorial Confirmatorio (AFC)
ASIconf <- ASI
attach(ASIconf)
Onefactor<- 'IA =~ IM01 + IM02 + IM03+ IM04 + SE01 + SE02 + SE03 + SE04 + CL01 + CL02 +
BI01 + BI02 + BI03 + EN01 + EN02+ SI01 + SI02 + SI03 +
AIE01 + AIE02 + AIE03 + AIE04 + AIE05 + AIE06 + AIE07 + AIE08 +
KU01 + KU02 + KU03 + EC01 + EC02 + EC03'
Fourfactor<-'MR1 =~IM01 + IM02 + IM03+ IM04 + SE01 + SE02 + SE03 + SE04 + CL01 + CL02
MR2 =~ BI01 + BI02 + BI03 + EN01 + EN02+ SI01 + SI02 + SI03
MR3 =~ AIE01 + AIE02 + AIE03 + AIE04 + AIE05 + AIE06 + AIE07 + AIE08
MR4 =~ KU01 + KU02 + KU03 + EC01 + EC02 + EC03'
CFAone <- cfa(Onefactor,orthogonal=TRUE, data=test.data , estimator="WLSMV",ordered =names(test.data))## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -4.899091e-15) is smaller than zero. This may be a symptom that
## the model is not identified.## lavaan 0.6.16 ended normally after 65 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 148
##
## Number of observations 38
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1121.975 768.065
## Degrees of freedom 464 464
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 2.730
## Shift parameter 357.045
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 12189.016 2735.539
## Degrees of freedom 496 496
## P-value 0.000 0.000
## Scaling correction factor 5.221
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.944 0.864
## Tucker-Lewis Index (TLI) 0.940 0.855
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.196 0.133
## 90 Percent confidence interval - lower 0.181 0.116
## 90 Percent confidence interval - upper 0.210 0.150
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.200 0.200
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## IA =~
## IM01 1.000
## IM02 0.859 0.105 8.181 0.000
## IM03 0.943 0.061 15.545 0.000
## IM04 1.065 0.079 13.408 0.000
## SE01 1.132 0.066 17.106 0.000
## SE02 1.141 0.067 16.920 0.000
## SE03 1.029 0.076 13.549 0.000
## SE04 0.845 0.096 8.812 0.000
## CL01 1.000 0.082 12.185 0.000
## CL02 1.144 0.074 15.371 0.000
## BI01 1.158 0.080 14.505 0.000
## BI02 1.183 0.082 14.463 0.000
## BI03 1.098 0.072 15.257 0.000
## EN01 1.044 0.070 14.894 0.000
## EN02 1.080 0.084 12.798 0.000
## SI01 1.003 0.083 12.154 0.000
## SI02 0.982 0.105 9.365 0.000
## SI03 0.693 0.136 5.113 0.000
## AIE01 -0.187 0.193 -0.971 0.332
## AIE02 -0.121 0.232 -0.524 0.600
## AIE03 0.740 0.106 6.993 0.000
## AIE04 0.600 0.168 3.572 0.000
## AIE05 -0.418 0.200 -2.087 0.037
## AIE06 0.015 0.223 0.065 0.948
## AIE07 -0.641 0.165 -3.893 0.000
## AIE08 0.515 0.131 3.945 0.000
## KU01 0.505 0.143 3.528 0.000
## KU02 0.739 0.121 6.112 0.000
## KU03 0.738 0.109 6.752 0.000
## EC01 0.976 0.077 12.732 0.000
## EC02 0.899 0.113 7.956 0.000
## EC03 0.786 0.106 7.393 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IM01 0.000
## .IM02 0.000
## .IM03 0.000
## .IM04 0.000
## .SE01 0.000
## .SE02 0.000
## .SE03 0.000
## .SE04 0.000
## .CL01 0.000
## .CL02 0.000
## .BI01 0.000
## .BI02 0.000
## .BI03 0.000
## .EN01 0.000
## .EN02 0.000
## .SI01 0.000
## .SI02 0.000
## .SI03 0.000
## .AIE01 0.000
## .AIE02 0.000
## .AIE03 0.000
## .AIE04 0.000
## .AIE05 0.000
## .AIE06 0.000
## .AIE07 0.000
## .AIE08 0.000
## .KU01 0.000
## .KU02 0.000
## .KU03 0.000
## .EC01 0.000
## .EC02 0.000
## .EC03 0.000
## IA 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IM01|t1 -0.634 0.222 -2.857 0.004
## IM01|t2 -0.267 0.209 -1.279 0.201
## IM01|t3 0.480 0.215 2.231 0.026
## IM01|t4 1.003 0.249 4.036 0.000
## IM02|t1 -1.252 0.277 -4.521 0.000
## IM02|t2 -1.003 0.249 -4.036 0.000
## IM02|t3 -0.555 0.218 -2.546 0.011
## IM02|t4 0.199 0.208 0.960 0.337
## IM03|t1 -0.267 0.209 -1.279 0.201
## IM03|t2 0.267 0.209 1.279 0.201
## IM03|t3 1.003 0.249 4.036 0.000
## IM03|t4 1.938 0.431 4.493 0.000
## IM04|t1 -1.003 0.249 -4.036 0.000
## IM04|t2 -0.336 0.210 -1.598 0.110
## IM04|t3 -0.267 0.209 -1.279 0.201
## IM04|t4 0.480 0.215 2.231 0.026
## SE01|t1 -1.412 0.301 -4.689 0.000
## SE01|t2 -0.805 0.232 -3.465 0.001
## SE01|t3 -0.407 0.212 -1.915 0.055
## SE01|t4 0.480 0.215 2.231 0.026
## SE02|t1 -1.252 0.277 -4.521 0.000
## SE02|t2 -0.805 0.232 -3.465 0.001
## SE02|t3 -0.199 0.208 -0.960 0.337
## SE02|t4 0.634 0.222 2.857 0.004
## SE03|t1 -1.620 0.342 -4.741 0.000
## SE03|t2 -1.119 0.261 -4.295 0.000
## SE03|t3 -0.480 0.215 -2.231 0.026
## SE03|t4 0.555 0.218 2.546 0.011
## SE04|t1 -1.252 0.277 -4.521 0.000
## SE04|t2 -0.716 0.226 -3.164 0.002
## SE04|t3 -0.132 0.207 -0.640 0.522
## SE04|t4 0.634 0.222 2.857 0.004
## CL01|t1 -1.620 0.342 -4.741 0.000
## CL01|t2 -0.480 0.215 -2.231 0.026
## CL01|t3 0.555 0.218 2.546 0.011
## CL02|t1 -1.620 0.342 -4.741 0.000
## CL02|t2 -0.805 0.232 -3.465 0.001
## CL02|t3 -0.267 0.209 -1.279 0.201
## CL02|t4 0.634 0.222 2.857 0.004
## BI01|t1 -1.412 0.301 -4.689 0.000
## BI01|t2 -0.716 0.226 -3.164 0.002
## BI01|t3 -0.407 0.212 -1.915 0.055
## BI01|t4 0.199 0.208 0.960 0.337
## BI02|t1 -1.252 0.277 -4.521 0.000
## BI02|t2 -0.634 0.222 -2.857 0.004
## BI02|t3 -0.132 0.207 -0.640 0.522
## BI02|t4 0.716 0.226 3.164 0.002
## BI03|t1 -0.634 0.222 -2.857 0.004
## BI03|t2 -0.336 0.210 -1.598 0.110
## BI03|t3 0.132 0.207 0.640 0.522
## BI03|t4 1.003 0.249 4.036 0.000
## EN01|t1 -0.132 0.207 -0.640 0.522
## EN01|t2 0.336 0.210 1.598 0.110
## EN01|t3 0.899 0.239 3.757 0.000
## EN01|t4 1.412 0.301 4.689 0.000
## EN02|t1 0.199 0.208 0.960 0.337
## EN02|t2 0.480 0.215 2.231 0.026
## EN02|t3 0.899 0.239 3.757 0.000
## EN02|t4 1.412 0.301 4.689 0.000
## SI01|t1 -0.066 0.206 -0.320 0.749
## SI01|t2 0.336 0.210 1.598 0.110
## SI01|t3 0.899 0.239 3.757 0.000
## SI01|t4 1.620 0.342 4.741 0.000
## SI02|t1 0.066 0.206 0.320 0.749
## SI02|t2 0.480 0.215 2.231 0.026
## SI02|t3 0.805 0.232 3.465 0.001
## SI02|t4 1.938 0.431 4.493 0.000
## SI03|t1 -0.336 0.210 -1.598 0.110
## SI03|t2 0.336 0.210 1.598 0.110
## SI03|t3 0.899 0.239 3.757 0.000
## SI03|t4 1.620 0.342 4.741 0.000
## AIE01|t1 -1.938 0.431 -4.493 0.000
## AIE01|t2 -1.412 0.301 -4.689 0.000
## AIE01|t3 -0.480 0.215 -2.231 0.026
## AIE02|t1 -1.620 0.342 -4.741 0.000
## AIE02|t2 -1.119 0.261 -4.295 0.000
## AIE02|t3 -0.634 0.222 -2.857 0.004
## AIE03|t1 -1.938 0.431 -4.493 0.000
## AIE03|t2 -1.003 0.249 -4.036 0.000
## AIE03|t3 -0.199 0.208 -0.960 0.337
## AIE03|t4 0.480 0.215 2.231 0.026
## AIE04|t1 -1.252 0.277 -4.521 0.000
## AIE04|t2 -0.480 0.215 -2.231 0.026
## AIE05|t1 -1.412 0.301 -4.689 0.000
## AIE05|t2 -0.716 0.226 -3.164 0.002
## AIE06|t1 -1.119 0.261 -4.295 0.000
## AIE06|t2 -0.480 0.215 -2.231 0.026
## AIE07|t1 -1.620 0.342 -4.741 0.000
## AIE07|t2 -1.119 0.261 -4.295 0.000
## AIE08|t1 -1.620 0.342 -4.741 0.000
## AIE08|t2 -1.119 0.261 -4.295 0.000
## AIE08|t3 -0.716 0.226 -3.164 0.002
## AIE08|t4 0.000 0.206 0.000 1.000
## KU01|t1 -1.119 0.261 -4.295 0.000
## KU01|t2 -0.199 0.208 -0.960 0.337
## KU01|t3 0.555 0.218 2.546 0.011
## KU02|t1 -1.412 0.301 -4.689 0.000
## KU02|t2 -1.119 0.261 -4.295 0.000
## KU02|t3 -0.555 0.218 -2.546 0.011
## KU02|t4 0.336 0.210 1.598 0.110
## KU03|t1 -0.480 0.215 -2.231 0.026
## KU03|t2 0.407 0.212 1.915 0.055
## KU03|t3 0.899 0.239 3.757 0.000
## KU03|t4 1.620 0.342 4.741 0.000
## EC01|t1 -1.119 0.261 -4.295 0.000
## EC01|t2 -0.634 0.222 -2.857 0.004
## EC01|t3 -0.199 0.208 -0.960 0.337
## EC01|t4 0.899 0.239 3.757 0.000
## EC02|t1 -0.132 0.207 -0.640 0.522
## EC02|t2 0.336 0.210 1.598 0.110
## EC02|t3 0.480 0.215 2.231 0.026
## EC02|t4 1.620 0.342 4.741 0.000
## EC03|t1 -0.407 0.212 -1.915 0.055
## EC03|t2 0.000 0.206 0.000 1.000
## EC03|t3 0.634 0.222 2.857 0.004
## EC03|t4 1.252 0.277 4.521 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IM01 0.379
## .IM02 0.542
## .IM03 0.448
## .IM04 0.295
## .SE01 0.204
## .SE02 0.191
## .SE03 0.343
## .SE04 0.556
## .CL01 0.379
## .CL02 0.188
## .BI01 0.167
## .BI02 0.131
## .BI03 0.251
## .EN01 0.324
## .EN02 0.276
## .SI01 0.375
## .SI02 0.401
## .SI03 0.702
## .AIE01 0.978
## .AIE02 0.991
## .AIE03 0.660
## .AIE04 0.776
## .AIE05 0.891
## .AIE06 1.000
## .AIE07 0.745
## .AIE08 0.835
## .KU01 0.841
## .KU02 0.661
## .KU03 0.662
## .EC01 0.408
## .EC02 0.498
## .EC03 0.616
## IA 0.621 0.078 7.917 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IM01 1.000
## IM02 1.000
## IM03 1.000
## IM04 1.000
## SE01 1.000
## SE02 1.000
## SE03 1.000
## SE04 1.000
## CL01 1.000
## CL02 1.000
## BI01 1.000
## BI02 1.000
## BI03 1.000
## EN01 1.000
## EN02 1.000
## SI01 1.000
## SI02 1.000
## SI03 1.000
## AIE01 1.000
## AIE02 1.000
## AIE03 1.000
## AIE04 1.000
## AIE05 1.000
## AIE06 1.000
## AIE07 1.000
## AIE08 1.000
## KU01 1.000
## KU02 1.000
## KU03 1.000
## EC01 1.000
## EC02 1.000
## EC03 1.000CFAtworele <- cfa(Fourfactor,orthogonal=FALSE, data=test.data , estimator="WLSMV",ordered =names(test.data ))## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.034490e-13) is smaller than zero. This may be a symptom that
## the model is not identified.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## lavaan 0.6.16 ended normally after 97 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 154
##
## Number of observations 38
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 874.960 681.557
## Degrees of freedom 458 458
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 2.634
## Shift parameter 349.339
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 12189.016 2735.539
## Degrees of freedom 496 496
## P-value 0.000 0.000
## Scaling correction factor 5.221
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964 0.900
## Tucker-Lewis Index (TLI) 0.961 0.892
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.157 0.115
## 90 Percent confidence interval - lower 0.141 0.096
## 90 Percent confidence interval - upper 0.173 0.133
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.999
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.185 0.185
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## MR1 =~
## IM01 1.000
## IM02 0.827 0.100 8.264 0.000
## IM03 0.921 0.063 14.676 0.000
## IM04 1.056 0.079 13.302 0.000
## SE01 1.059 0.062 17.036 0.000
## SE02 1.070 0.064 16.839 0.000
## SE03 0.965 0.070 13.759 0.000
## SE04 0.812 0.092 8.856 0.000
## CL01 0.944 0.078 12.087 0.000
## CL02 1.073 0.069 15.576 0.000
## MR2 =~
## BI01 1.000
## BI02 1.007 0.054 18.530 0.000
## BI03 0.923 0.041 22.601 0.000
## EN01 0.891 0.056 15.989 0.000
## EN02 0.908 0.064 14.227 0.000
## SI01 0.858 0.071 12.041 0.000
## SI02 0.849 0.069 12.322 0.000
## SI03 0.605 0.105 5.743 0.000
## MR3 =~
## AIE01 1.000
## AIE02 0.627 0.806 0.778 0.437
## AIE03 -3.603 2.946 -1.223 0.221
## AIE04 -2.621 2.211 -1.185 0.236
## AIE05 1.805 1.529 1.181 0.238
## AIE06 -0.284 1.026 -0.277 0.782
## AIE07 2.893 2.746 1.053 0.292
## AIE08 -2.462 1.894 -1.300 0.194
## MR4 =~
## KU01 1.000
## KU02 1.367 0.266 5.141 0.000
## KU03 1.266 0.273 4.637 0.000
## EC01 1.771 0.334 5.307 0.000
## EC02 1.484 0.294 5.048 0.000
## EC03 1.358 0.296 4.582 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## MR1 ~~
## MR2 0.678 0.064 10.657 0.000
## MR3 -0.125 0.104 -1.202 0.229
## MR4 0.303 0.074 4.074 0.000
## MR2 ~~
## MR3 -0.154 0.135 -1.145 0.252
## MR4 0.322 0.087 3.685 0.000
## MR3 ~~
## MR4 -0.075 0.069 -1.088 0.277
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .IM01 0.000
## .IM02 0.000
## .IM03 0.000
## .IM04 0.000
## .SE01 0.000
## .SE02 0.000
## .SE03 0.000
## .SE04 0.000
## .CL01 0.000
## .CL02 0.000
## .BI01 0.000
## .BI02 0.000
## .BI03 0.000
## .EN01 0.000
## .EN02 0.000
## .SI01 0.000
## .SI02 0.000
## .SI03 0.000
## .AIE01 0.000
## .AIE02 0.000
## .AIE03 0.000
## .AIE04 0.000
## .AIE05 0.000
## .AIE06 0.000
## .AIE07 0.000
## .AIE08 0.000
## .KU01 0.000
## .KU02 0.000
## .KU03 0.000
## .EC01 0.000
## .EC02 0.000
## .EC03 0.000
## MR1 0.000
## MR2 0.000
## MR3 0.000
## MR4 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## IM01|t1 -0.634 0.222 -2.857 0.004
## IM01|t2 -0.267 0.209 -1.279 0.201
## IM01|t3 0.480 0.215 2.231 0.026
## IM01|t4 1.003 0.249 4.036 0.000
## IM02|t1 -1.252 0.277 -4.521 0.000
## IM02|t2 -1.003 0.249 -4.036 0.000
## IM02|t3 -0.555 0.218 -2.546 0.011
## IM02|t4 0.199 0.208 0.960 0.337
## IM03|t1 -0.267 0.209 -1.279 0.201
## IM03|t2 0.267 0.209 1.279 0.201
## IM03|t3 1.003 0.249 4.036 0.000
## IM03|t4 1.938 0.431 4.493 0.000
## IM04|t1 -1.003 0.249 -4.036 0.000
## IM04|t2 -0.336 0.210 -1.598 0.110
## IM04|t3 -0.267 0.209 -1.279 0.201
## IM04|t4 0.480 0.215 2.231 0.026
## SE01|t1 -1.412 0.301 -4.689 0.000
## SE01|t2 -0.805 0.232 -3.465 0.001
## SE01|t3 -0.407 0.212 -1.915 0.055
## SE01|t4 0.480 0.215 2.231 0.026
## SE02|t1 -1.252 0.277 -4.521 0.000
## SE02|t2 -0.805 0.232 -3.465 0.001
## SE02|t3 -0.199 0.208 -0.960 0.337
## SE02|t4 0.634 0.222 2.857 0.004
## SE03|t1 -1.620 0.342 -4.741 0.000
## SE03|t2 -1.119 0.261 -4.295 0.000
## SE03|t3 -0.480 0.215 -2.231 0.026
## SE03|t4 0.555 0.218 2.546 0.011
## SE04|t1 -1.252 0.277 -4.521 0.000
## SE04|t2 -0.716 0.226 -3.164 0.002
## SE04|t3 -0.132 0.207 -0.640 0.522
## SE04|t4 0.634 0.222 2.857 0.004
## CL01|t1 -1.620 0.342 -4.741 0.000
## CL01|t2 -0.480 0.215 -2.231 0.026
## CL01|t3 0.555 0.218 2.546 0.011
## CL02|t1 -1.620 0.342 -4.741 0.000
## CL02|t2 -0.805 0.232 -3.465 0.001
## CL02|t3 -0.267 0.209 -1.279 0.201
## CL02|t4 0.634 0.222 2.857 0.004
## BI01|t1 -1.412 0.301 -4.689 0.000
## BI01|t2 -0.716 0.226 -3.164 0.002
## BI01|t3 -0.407 0.212 -1.915 0.055
## BI01|t4 0.199 0.208 0.960 0.337
## BI02|t1 -1.252 0.277 -4.521 0.000
## BI02|t2 -0.634 0.222 -2.857 0.004
## BI02|t3 -0.132 0.207 -0.640 0.522
## BI02|t4 0.716 0.226 3.164 0.002
## BI03|t1 -0.634 0.222 -2.857 0.004
## BI03|t2 -0.336 0.210 -1.598 0.110
## BI03|t3 0.132 0.207 0.640 0.522
## BI03|t4 1.003 0.249 4.036 0.000
## EN01|t1 -0.132 0.207 -0.640 0.522
## EN01|t2 0.336 0.210 1.598 0.110
## EN01|t3 0.899 0.239 3.757 0.000
## EN01|t4 1.412 0.301 4.689 0.000
## EN02|t1 0.199 0.208 0.960 0.337
## EN02|t2 0.480 0.215 2.231 0.026
## EN02|t3 0.899 0.239 3.757 0.000
## EN02|t4 1.412 0.301 4.689 0.000
## SI01|t1 -0.066 0.206 -0.320 0.749
## SI01|t2 0.336 0.210 1.598 0.110
## SI01|t3 0.899 0.239 3.757 0.000
## SI01|t4 1.620 0.342 4.741 0.000
## SI02|t1 0.066 0.206 0.320 0.749
## SI02|t2 0.480 0.215 2.231 0.026
## SI02|t3 0.805 0.232 3.465 0.001
## SI02|t4 1.938 0.431 4.493 0.000
## SI03|t1 -0.336 0.210 -1.598 0.110
## SI03|t2 0.336 0.210 1.598 0.110
## SI03|t3 0.899 0.239 3.757 0.000
## SI03|t4 1.620 0.342 4.741 0.000
## AIE01|t1 -1.938 0.431 -4.493 0.000
## AIE01|t2 -1.412 0.301 -4.689 0.000
## AIE01|t3 -0.480 0.215 -2.231 0.026
## AIE02|t1 -1.620 0.342 -4.741 0.000
## AIE02|t2 -1.119 0.261 -4.295 0.000
## AIE02|t3 -0.634 0.222 -2.857 0.004
## AIE03|t1 -1.938 0.431 -4.493 0.000
## AIE03|t2 -1.003 0.249 -4.036 0.000
## AIE03|t3 -0.199 0.208 -0.960 0.337
## AIE03|t4 0.480 0.215 2.231 0.026
## AIE04|t1 -1.252 0.277 -4.521 0.000
## AIE04|t2 -0.480 0.215 -2.231 0.026
## AIE05|t1 -1.412 0.301 -4.689 0.000
## AIE05|t2 -0.716 0.226 -3.164 0.002
## AIE06|t1 -1.119 0.261 -4.295 0.000
## AIE06|t2 -0.480 0.215 -2.231 0.026
## AIE07|t1 -1.620 0.342 -4.741 0.000
## AIE07|t2 -1.119 0.261 -4.295 0.000
## AIE08|t1 -1.620 0.342 -4.741 0.000
## AIE08|t2 -1.119 0.261 -4.295 0.000
## AIE08|t3 -0.716 0.226 -3.164 0.002
## AIE08|t4 0.000 0.206 0.000 1.000
## KU01|t1 -1.119 0.261 -4.295 0.000
## KU01|t2 -0.199 0.208 -0.960 0.337
## KU01|t3 0.555 0.218 2.546 0.011
## KU02|t1 -1.412 0.301 -4.689 0.000
## KU02|t2 -1.119 0.261 -4.295 0.000
## KU02|t3 -0.555 0.218 -2.546 0.011
## KU02|t4 0.336 0.210 1.598 0.110
## KU03|t1 -0.480 0.215 -2.231 0.026
## KU03|t2 0.407 0.212 1.915 0.055
## KU03|t3 0.899 0.239 3.757 0.000
## KU03|t4 1.620 0.342 4.741 0.000
## EC01|t1 -1.119 0.261 -4.295 0.000
## EC01|t2 -0.634 0.222 -2.857 0.004
## EC01|t3 -0.199 0.208 -0.960 0.337
## EC01|t4 0.899 0.239 3.757 0.000
## EC02|t1 -0.132 0.207 -0.640 0.522
## EC02|t2 0.336 0.210 1.598 0.110
## EC02|t3 0.480 0.215 2.231 0.026
## EC02|t4 1.620 0.342 4.741 0.000
## EC03|t1 -0.407 0.212 -1.915 0.055
## EC03|t2 0.000 0.206 0.000 1.000
## EC03|t3 0.634 0.222 2.857 0.004
## EC03|t4 1.252 0.277 4.521 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .IM01 0.255
## .IM02 0.491
## .IM03 0.369
## .IM04 0.169
## .SE01 0.165
## .SE02 0.148
## .SE03 0.305
## .SE04 0.509
## .CL01 0.336
## .CL02 0.142
## .BI01 0.055
## .BI02 0.041
## .BI03 0.196
## .EN01 0.249
## .EN02 0.220
## .SI01 0.304
## .SI02 0.318
## .SI03 0.654
## .AIE01 0.953
## .AIE02 0.982
## .AIE03 0.394
## .AIE04 0.680
## .AIE05 0.848
## .AIE06 0.996
## .AIE07 0.610
## .AIE08 0.717
## .KU01 0.675
## .KU02 0.393
## .KU03 0.479
## .EC01 -0.020
## .EC02 0.284
## .EC03 0.401
## MR1 0.745 0.085 8.738 0.000
## MR2 0.945 0.079 12.015 0.000
## MR3 0.047 0.073 0.635 0.525
## MR4 0.325 0.125 2.605 0.009
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## IM01 1.000
## IM02 1.000
## IM03 1.000
## IM04 1.000
## SE01 1.000
## SE02 1.000
## SE03 1.000
## SE04 1.000
## CL01 1.000
## CL02 1.000
## BI01 1.000
## BI02 1.000
## BI03 1.000
## EN01 1.000
## EN02 1.000
## SI01 1.000
## SI02 1.000
## SI03 1.000
## AIE01 1.000
## AIE02 1.000
## AIE03 1.000
## AIE04 1.000
## AIE05 1.000
## AIE06 1.000
## AIE07 1.000
## AIE08 1.000
## KU01 1.000
## KU02 1.000
## KU03 1.000
## EC01 1.000
## EC02 1.000
## EC03 1.000## npar fmin
## 154.000 11.513
## chisq df
## 874.960 458.000
## pvalue chisq.scaled
## 0.000 681.557
## df.scaled pvalue.scaled
## 458.000 0.000
## chisq.scaling.factor baseline.chisq
## 2.634 12189.016
## baseline.df baseline.pvalue
## 496.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 2735.539 496.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 5.221
## cfi tli
## 0.964 0.961
## cfi.scaled tli.scaled
## 0.900 0.892
## cfi.robust tli.robust
## NA NA
## nnfi rfi
## 0.961 0.922
## nfi pnfi
## 0.928 0.857
## ifi rni
## 0.964 0.964
## nnfi.scaled rfi.scaled
## 0.892 0.730
## nfi.scaled pnfi.scaled
## 0.751 0.693
## ifi.scaled rni.scaled
## 0.902 0.900
## nnfi.robust rni.robust
## NA NA
## rmsea rmsea.ci.lower
## 0.157 0.141
## rmsea.ci.upper rmsea.ci.level
## 0.173 0.900
## rmsea.pvalue rmsea.close.h0
## 0.000 0.050
## rmsea.notclose.pvalue rmsea.notclose.h0
## 1.000 0.080
## rmsea.scaled rmsea.ci.lower.scaled
## 0.115 0.096
## rmsea.ci.upper.scaled rmsea.pvalue.scaled
## 0.133 0.000
## rmsea.notclose.pvalue.scaled rmsea.robust
## 0.999 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmsea.notclose.pvalue.robust
## NA NA
## rmr rmr_nomean
## 0.167 0.185
## srmr srmr_bentler
## 0.185 0.167
## srmr_bentler_nomean crmr
## 0.185 0.172
## crmr_nomean srmr_mplus
## 0.191 NA
## srmr_mplus_nomean cn_05
## NA 22.520
## cn_01 gfi
## 23.469 0.935
## agfi pgfi
## 0.913 0.700
## mfi wrmr
## 0.004 1.196semPaths(CFAtworele, intercepts = FALSE,edge.label.cex=0.5, optimizeLatRes = TRUE, groups = "lat",pastel = TRUE, exoVar = FALSE, sizeInt=5,edge.color ="black",esize = 6, label.prop=1,sizeLat = 6,"std", layout="circle2")
Modelo de Aceptación tecnológica
Base_de_datos_IA_TAM <- read_excel("Base de datos_IA.xlsx",
sheet = "AF_TAM")
ASI_TAM <- Base_de_datos_IA_TAM
dim(ASI_TAM)## [1] 134 8## PU1 PU2 PU3 PEU1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000
## Median :5.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.299 Mean :4.067 Mean :3.858 Mean :4.075
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## PEU2 PEU3 BI1 BI2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :3.000 Median :4.000
## Mean :3.836 Mean :3.828 Mean :3.239 Mean :3.679
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000## PU1 PU2 PU3 PEU1 PEU2 PEU3 BI1 BI2
## 4.298507 4.067164 3.858209 4.074627 3.835821 3.828358 3.238806 3.679104## PU1 PU2 PU3 PEU1 PEU2 PEU3 BI1 BI2
## 0.8575917 1.2360566 1.2955336 1.0921333 0.9502862 1.0455056 1.8222422 1.6330939## [1] 0.8112004set.seed(123)
training.samples <- ASI_TAM$PU1 %>% createDataPartition(p = 0.60, list = FALSE)
train.data <- ASI_TAM [training.samples, ]
test.data <- ASI_TAM [-training.samples, ]
KMO(train.data)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = train.data)
## Overall MSA = 0.79
## MSA for each item =
## PU1 PU2 PU3 PEU1 PEU2 PEU3 BI1 BI2
## 0.85 0.73 0.79 0.77 0.82 0.80 0.63 0.82## R was not square, finding R from data## $chisq
## [1] 369.6452
##
## $p.value
## [1] 2.73898e-61
##
## $df
## [1] 28Análisis Factorial Exploratorio (AFE)
## Parallel analysis suggests that the number of factors = 2 and the number of components = NA##
## Call:
## factanal(x = train.data, factors = 3, rotation = "varimax")
##
## Uniquenesses:
## PU1 PU2 PU3 PEU1 PEU2 PEU3 BI1 BI2
## 0.311 0.021 0.258 0.125 0.294 0.268 0.744 0.498
##
## Loadings:
## Factor1 Factor2 Factor3
## PU1 0.276 0.720 0.307
## PU2 0.264 0.953
## PU3 0.195 0.759 0.358
## PEU1 0.893 0.279
## PEU2 0.803 0.200 0.149
## PEU3 0.808 0.174 0.222
## BI1 0.498
## BI2 0.131 0.332 0.612
##
## Factor1 Factor2 Factor3
## SS loadings 2.300 2.265 0.918
## Proportion Var 0.287 0.283 0.115
## Cumulative Var 0.287 0.571 0.685
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 5.59 on 7 degrees of freedom.
## The p-value is 0.588##
## Loadings:
## Factor1 Factor2 Factor3
## PU1 0.276 0.720 0.307
## PU2 0.264 0.953
## PU3 0.195 0.759 0.358
## PEU1 0.893 0.279
## PEU2 0.803 0.200 0.149
## PEU3 0.808 0.174 0.222
## BI1 0.498
## BI2 0.131 0.332 0.612
##
## Factor1 Factor2 Factor3
## SS loadings 2.301 2.265 0.918
## Proportion Var 0.288 0.283 0.115
## Cumulative Var 0.288 0.571 0.685modelo_varimax = fa(train.data,nfactors = 3,rotate = "varimax",
fa="minres")
fa.diagram(modelo_varimax)
Análisis Factorial Confirmatorio (AFC)
ASIconf <- test.data
attach(ASIconf)
Onefactor<- 'TAM =~ PU1 +PU2 + PU3 +PEU1 + PEU2+ PEU3 + BI1 + BI2'
Fourfactor<-'MR1_TAM =~ PU1 + PU2 + PU3
MR2_TAM =~ PEU1 + PEU2 + PEU3
MR3_TAM =~ BI1 + BI2'
CFAone <- cfa(Onefactor,orthogonal=TRUE, data=ASIconf, estimator="WLSMV",ordered =names(ASIconf))## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.446814e-16) is smaller than zero. This may be a symptom that
## the model is not identified.## lavaan 0.6.16 ended normally after 24 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 53
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 78.319 84.429
## Degrees of freedom 20 20
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.023
## Shift parameter 7.861
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 2533.056 1423.625
## Degrees of freedom 28 28
## P-value 0.000 0.000
## Scaling correction factor 1.795
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.977 0.954
## Tucker-Lewis Index (TLI) 0.967 0.935
##
## Robust Comparative Fit Index (CFI) 0.715
## Robust Tucker-Lewis Index (TLI) 0.601
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.237 0.249
## 90 Percent confidence interval - lower 0.183 0.195
## 90 Percent confidence interval - upper 0.293 0.305
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.311
## 90 Percent confidence interval - lower 0.217
## 90 Percent confidence interval - upper 0.408
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.154 0.154
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## TAM =~
## PU1 1.000
## PU2 1.097 0.110 9.975 0.000
## PU3 1.027 0.120 8.548 0.000
## PEU1 1.298 0.117 11.108 0.000
## PEU2 1.261 0.111 11.407 0.000
## PEU3 1.203 0.103 11.699 0.000
## BI1 -0.218 0.140 -1.558 0.119
## BI2 0.364 0.112 3.239 0.001
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .PU1 0.000
## .PU2 0.000
## .PU3 0.000
## .PEU1 0.000
## .PEU2 0.000
## .PEU3 0.000
## .BI1 0.000
## .BI2 0.000
## TAM 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## PU1|t1 -2.078 0.409 -5.074 0.000
## PU1|t2 -1.778 0.322 -5.528 0.000
## PU1|t3 -1.032 0.212 -4.869 0.000
## PU1|t4 -0.119 0.174 -0.680 0.496
## PU2|t1 -2.078 0.409 -5.074 0.000
## PU2|t2 -1.314 0.241 -5.453 0.000
## PU2|t3 -0.631 0.187 -3.373 0.001
## PU2|t4 0.119 0.174 0.680 0.496
## PU3|t1 -2.078 0.409 -5.074 0.000
## PU3|t2 -1.032 0.212 -4.869 0.000
## PU3|t3 -0.466 0.181 -2.575 0.010
## PU3|t4 0.362 0.178 2.036 0.042
## PEU1|t1 -1.584 0.282 -5.625 0.000
## PEU1|t2 -1.210 0.229 -5.284 0.000
## PEU1|t3 -0.751 0.193 -3.893 0.000
## PEU1|t4 0.166 0.175 0.952 0.341
## PEU2|t1 -1.778 0.322 -5.528 0.000
## PEU2|t2 -1.210 0.229 -5.284 0.000
## PEU2|t3 -0.362 0.178 -2.036 0.042
## PEU2|t4 0.362 0.178 2.036 0.042
## PEU3|t1 -1.778 0.322 -5.528 0.000
## PEU3|t2 -1.117 0.220 -5.086 0.000
## PEU3|t3 -0.519 0.183 -2.842 0.004
## PEU3|t4 0.574 0.185 3.109 0.002
## BI1|t1 -0.955 0.206 -4.638 0.000
## BI1|t2 -0.466 0.181 -2.575 0.010
## BI1|t3 0.071 0.174 0.408 0.683
## BI1|t4 0.751 0.193 3.893 0.000
## BI2|t1 -1.778 0.322 -5.528 0.000
## BI2|t2 -1.032 0.212 -4.869 0.000
## BI2|t3 -0.263 0.176 -1.495 0.135
## BI2|t4 0.413 0.179 2.306 0.021
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .PU1 0.452
## .PU2 0.341
## .PU3 0.422
## .PEU1 0.076
## .PEU2 0.129
## .PEU3 0.206
## .BI1 0.974
## .BI2 0.927
## TAM 0.548 0.096 5.712 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## PU1 1.000
## PU2 1.000
## PU3 1.000
## PEU1 1.000
## PEU2 1.000
## PEU3 1.000
## BI1 1.000
## BI2 1.000CFAtworele <- cfa(Fourfactor,orthogonal=FALSE, data=ASIconf, estimator="WLSMV",ordered =names(ASIconf))## Warning in lavaan::lavaan(model = Fourfactor, data = ASIconf, ordered = names(ASIconf), : lavaan WARNING:
## the optimizer warns that a solution has NOT been found!## Warning in lav_object_summary(object = object, header = header, fit.measures = fit.measures, : lavaan WARNING: fit measures not available if model did not converge## lavaan 0.6.16 did NOT end normally after 818 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 43
##
## Number of observations 53
##
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## MR1_TAM =~
## PU1 1.000
## PU2 1.027 NA
## PU3 0.986 NA
## MR2_TAM =~
## PEU1 1.000
## PEU2 0.976 NA
## PEU3 0.938 NA
## MR3_TAM =~
## BI1 1.000
## BI2 172408.514 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## MR1_TAM ~~
## MR2_TAM 0.545 NA
## MR3_TAM 0.000 NA
## MR2_TAM ~~
## MR3_TAM 0.000 NA
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .PU1 0.000
## .PU2 0.000
## .PU3 0.000
## .PEU1 0.000
## .PEU2 0.000
## .PEU3 0.000
## .BI1 0.000
## .BI2 0.000
## MR1_TAM 0.000
## MR2_TAM 0.000
## MR3_TAM 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## PU1|t1 -2.078 NA
## PU1|t2 -1.778 NA
## PU1|t3 -1.032 NA
## PU1|t4 -0.119 NA
## PU2|t1 -2.078 NA
## PU2|t2 -1.314 NA
## PU2|t3 -0.631 NA
## PU2|t4 0.119 NA
## PU3|t1 -2.078 NA
## PU3|t2 -1.032 NA
## PU3|t3 -0.466 NA
## PU3|t4 0.362 NA
## PEU1|t1 -1.584 NA
## PEU1|t2 -1.210 NA
## PEU1|t3 -0.751 NA
## PEU1|t4 0.166 NA
## PEU2|t1 -1.778 NA
## PEU2|t2 -1.210 NA
## PEU2|t3 -0.362 NA
## PEU2|t4 0.362 NA
## PEU3|t1 -1.778 NA
## PEU3|t2 -1.117 NA
## PEU3|t3 -0.519 NA
## PEU3|t4 0.574 NA
## BI1|t1 -0.955 NA
## BI1|t2 -0.466 NA
## BI1|t3 0.071 NA
## BI1|t4 0.751 NA
## BI2|t1 -1.778 NA
## BI2|t2 -1.032 NA
## BI2|t3 -0.263 NA
## BI2|t4 0.413 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .PU1 0.255
## .PU2 0.215
## .PU3 0.276
## .PEU1 0.060
## .PEU2 0.105
## .PEU3 0.174
## .BI1 1.000
## .BI2 -78929.546
## MR1_TAM 0.745 NA
## MR2_TAM 0.940 NA
## MR3_TAM 0.000 NA
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## PU1 1.000
## PU2 1.000
## PU3 1.000
## PEU1 1.000
## PEU2 1.000
## PEU3 1.000
## BI1 1.000
## BI2 1.000semPaths(CFAtworele, intercepts = FALSE,edge.label.cex=0.5, optimizeLatRes = TRUE, groups = "lat",pastel = TRUE, exoVar = FALSE, sizeInt=5,edge.color ="black",esize = 6, label.prop=1,sizeLat = 6,"std", layout="circle2")