Entregable 3 Gerardo Cedillo Corona
Gerardo Cedillo Corona
2025-02-19
#Teoría Los Modelos de Ecuaciones Estructurales (SEM) es una técnica de análisis de estadística multicariada, que permite analizar patrones complejos de relaciones entre variables, realizar copraciones entre intragrupos, y validar modelos teoricos y empiricos.
Ejemplo 1. Estudio de Holzinger y Swinefor (1939)
##Contexto Holzimger y Swineford realizaron examenes de habilidad mental a adolescentes de 7 y 8vo de dos escuela (Pateur y Grand-White)
La base de datos esta incluida comp paquete en R, e incluye las siguientes columnas:
- sex: Género (1=male, 2=female) x1: Percepción visual x2: Juegos con cubos x3: Juego con pastillas/espacial x4: Comprensión de parrafos x5: Completar oraciones x6: Significado de palabras x7: Percepción Sumas aceleradas x8: Percepción Conteo acelerado de puntos *x9: Discriminación acelerada de mayusculos rectas y curvas
Se busca identificar las relaciones entre habilidades visual (x1,x2,x3), textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolescentes.
Instalar paquetes y llamar librerias
#install.packages ("lavaan") #Latent Variable Analysis
library(lavaan)
#install.packages("lavaanPlot")
library(lavaanPlot)Importar la base de datos
df1 <- HolzingerSwineford1939Entender la base de datos
summary(df1)## id sex ageyr agemo
## Min. : 1.0 Min. :1.000 Min. :11 Min. : 0.000
## 1st Qu.: 82.0 1st Qu.:1.000 1st Qu.:12 1st Qu.: 2.000
## Median :163.0 Median :2.000 Median :13 Median : 5.000
## Mean :176.6 Mean :1.515 Mean :13 Mean : 5.375
## 3rd Qu.:272.0 3rd Qu.:2.000 3rd Qu.:14 3rd Qu.: 8.000
## Max. :351.0 Max. :2.000 Max. :16 Max. :11.000
##
## school grade x1 x2
## Grant-White:145 Min. :7.000 Min. :0.6667 Min. :2.250
## Pasteur :156 1st Qu.:7.000 1st Qu.:4.1667 1st Qu.:5.250
## Median :7.000 Median :5.0000 Median :6.000
## Mean :7.477 Mean :4.9358 Mean :6.088
## 3rd Qu.:8.000 3rd Qu.:5.6667 3rd Qu.:6.750
## Max. :8.000 Max. :8.5000 Max. :9.250
## NA's :1
## x3 x4 x5 x6
## Min. :0.250 Min. :0.000 Min. :1.000 Min. :0.1429
## 1st Qu.:1.375 1st Qu.:2.333 1st Qu.:3.500 1st Qu.:1.4286
## Median :2.125 Median :3.000 Median :4.500 Median :2.0000
## Mean :2.250 Mean :3.061 Mean :4.341 Mean :2.1856
## 3rd Qu.:3.125 3rd Qu.:3.667 3rd Qu.:5.250 3rd Qu.:2.7143
## Max. :4.500 Max. :6.333 Max. :7.000 Max. :6.1429
##
## x7 x8 x9
## Min. :1.304 Min. : 3.050 Min. :2.778
## 1st Qu.:3.478 1st Qu.: 4.850 1st Qu.:4.750
## Median :4.087 Median : 5.500 Median :5.417
## Mean :4.186 Mean : 5.527 Mean :5.374
## 3rd Qu.:4.913 3rd Qu.: 6.100 3rd Qu.:6.083
## Max. :7.435 Max. :10.000 Max. :9.250
## str(df1)## 'data.frame': 301 obs. of 15 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 11 ...
## $ sex : int 1 2 2 1 2 2 1 2 2 2 ...
## $ ageyr : int 13 13 13 13 12 14 12 12 13 12 ...
## $ agemo : int 1 7 1 2 2 1 1 2 0 5 ...
## $ school: Factor w/ 2 levels "Grant-White",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ grade : int 7 7 7 7 7 7 7 7 7 7 ...
## $ x1 : num 3.33 5.33 4.5 5.33 4.83 ...
## $ x2 : num 7.75 5.25 5.25 7.75 4.75 5 6 6.25 5.75 5.25 ...
## $ x3 : num 0.375 2.125 1.875 3 0.875 ...
## $ x4 : num 2.33 1.67 1 2.67 2.67 ...
## $ x5 : num 5.75 3 1.75 4.5 4 3 6 4.25 5.75 5 ...
## $ x6 : num 1.286 1.286 0.429 2.429 2.571 ...
## $ x7 : num 3.39 3.78 3.26 3 3.7 ...
## $ x8 : num 5.75 6.25 3.9 5.3 6.3 6.65 6.2 5.15 4.65 4.55 ...
## $ x9 : num 6.36 7.92 4.42 4.86 5.92 ...head(df1)## id sex ageyr agemo school grade x1 x2 x3 x4 x5 x6
## 1 1 1 13 1 Pasteur 7 3.333333 7.75 0.375 2.333333 5.75 1.2857143
## 2 2 2 13 7 Pasteur 7 5.333333 5.25 2.125 1.666667 3.00 1.2857143
## 3 3 2 13 1 Pasteur 7 4.500000 5.25 1.875 1.000000 1.75 0.4285714
## 4 4 1 13 2 Pasteur 7 5.333333 7.75 3.000 2.666667 4.50 2.4285714
## 5 5 2 12 2 Pasteur 7 4.833333 4.75 0.875 2.666667 4.00 2.5714286
## 6 6 2 14 1 Pasteur 7 5.333333 5.00 2.250 1.000000 3.00 0.8571429
## x7 x8 x9
## 1 3.391304 5.75 6.361111
## 2 3.782609 6.25 7.916667
## 3 3.260870 3.90 4.416667
## 4 3.000000 5.30 4.861111
## 5 3.695652 6.30 5.916667
## 6 4.347826 6.65 7.500000##Tipos de formulas 1.Regresión (~) Variable que depende de otras. 2.Variables latentes (=~) No se observa, se infiere. 3.Varianza y covarianza(~~) Relaciones entre variables latentes y observada (Varianza: Entre si misma, Covarianza: Entre otras) 4. Intercepto (~1) Valor esperado cuando las demas variables son cero.
Estructurar el modelo
modelo1 <- ' #Regresiones
#Variables Latantes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7+ x8 + x9
# Varianza y Covarianza
visual ~~ visual
textual ~~ textual
velocidad ~~ velocidad
visual ~~ textual + velocidad
textual ~~ velocidad
# Intercepto
' Generar analisis factorial confirmatorio
cfa1 <- sem(modelo1, data=df1)
summary(cfa1)## lavaan 0.6-19 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 301
##
## Model Test User Model:
##
## Test statistic 85.306
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## visual =~
## x1 1.000
## x2 0.554 0.100 5.554 0.000
## x3 0.729 0.109 6.685 0.000
## textual =~
## x4 1.000
## x5 1.113 0.065 17.014 0.000
## x6 0.926 0.055 16.703 0.000
## velocidad =~
## x7 1.000
## x8 1.180 0.165 7.152 0.000
## x9 1.082 0.151 7.155 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## visual ~~
## textual 0.408 0.074 5.552 0.000
## velocidad 0.262 0.056 4.660 0.000
## textual ~~
## velocidad 0.173 0.049 3.518 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## visual 0.809 0.145 5.564 0.000
## textual 0.979 0.112 8.737 0.000
## velocidad 0.384 0.086 4.451 0.000
## .x1 0.549 0.114 4.833 0.000
## .x2 1.134 0.102 11.146 0.000
## .x3 0.844 0.091 9.317 0.000
## .x4 0.371 0.048 7.779 0.000
## .x5 0.446 0.058 7.642 0.000
## .x6 0.356 0.043 8.277 0.000
## .x7 0.799 0.081 9.823 0.000
## .x8 0.488 0.074 6.573 0.000
## .x9 0.566 0.071 8.003 0.000lavaanPlot(cfa1, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa1, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 301
##
## Model Test User Model:
##
## Test statistic 85.306
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 918.852
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.931
## Tucker-Lewis Index (TLI) 0.896
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3737.745
## Loglikelihood unrestricted model (H1) -3695.092
##
## Akaike (AIC) 7517.490
## Bayesian (BIC) 7595.339
## Sample-size adjusted Bayesian (SABIC) 7528.739
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092
## 90 Percent confidence interval - lower 0.071
## 90 Percent confidence interval - upper 0.114
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.840
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## visual =~
## x1 1.000
## x2 0.554 0.100 5.554 0.000
## x3 0.729 0.109 6.685 0.000
## textual =~
## x4 1.000
## x5 1.113 0.065 17.014 0.000
## x6 0.926 0.055 16.703 0.000
## velocidad =~
## x7 1.000
## x8 1.180 0.165 7.152 0.000
## x9 1.082 0.151 7.155 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## visual ~~
## textual 0.408 0.074 5.552 0.000
## velocidad 0.262 0.056 4.660 0.000
## textual ~~
## velocidad 0.173 0.049 3.518 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## visual 0.809 0.145 5.564 0.000
## textual 0.979 0.112 8.737 0.000
## velocidad 0.384 0.086 4.451 0.000
## .x1 0.549 0.114 4.833 0.000
## .x2 1.134 0.102 11.146 0.000
## .x3 0.844 0.091 9.317 0.000
## .x4 0.371 0.048 7.779 0.000
## .x5 0.446 0.058 7.642 0.000
## .x6 0.356 0.043 8.277 0.000
## .x7 0.799 0.081 9.823 0.000
## .x8 0.488 0.074 6.573 0.000
## .x9 0.566 0.071 8.003 0.000# Revisar los valores de Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI)
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente <0.90Conclusión: Modelo aceptado
Ejercicio 1. Democracia Politica e Industralización
##Contexto La base de datos contiene distintos mediciones sobre democracia politica e industralización en paises en desarrollo durante 1960 y 1965
La tabla incluye los siguientes datos * y1: Calificaciones sobre libertad de prensa en 1960 * y2: Libertad de la oposición política en 1960 * y3: Imparcialidad de elecciones en 1960 * y4: Eficacia de la legislatura electa en 1960 * y5: Calificaciones sobre libertad de prensa en 1965 * y6: Libertad de la oposición política en 1965 * y7: Imparcialidad de elecciones en 1965 * y8: Eficacia de la legislatura electa en 1965 * x1: PIB per cápita en 1960 * x2: Consumo de energía inanimada per cápita en 1960 * x3: Porcentaje de la fuerza laboral en la industria en 1960)
##Importar la base de datos
df2 <- PoliticalDemocracy ##Entender la base de datos
summary(df2)## y1 y2 y3 y4
## Min. : 1.250 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.900 1st Qu.: 0.000 1st Qu.: 3.767 1st Qu.: 1.581
## Median : 5.400 Median : 3.333 Median : 6.667 Median : 3.333
## Mean : 5.465 Mean : 4.256 Mean : 6.563 Mean : 4.453
## 3rd Qu.: 7.500 3rd Qu.: 8.283 3rd Qu.:10.000 3rd Qu.: 6.667
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## y5 y6 y7 y8
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.692 1st Qu.: 0.000 1st Qu.: 3.478 1st Qu.: 1.301
## Median : 5.000 Median : 2.233 Median : 6.667 Median : 3.333
## Mean : 5.136 Mean : 2.978 Mean : 6.196 Mean : 4.043
## 3rd Qu.: 7.500 3rd Qu.: 4.207 3rd Qu.:10.000 3rd Qu.: 6.667
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## x1 x2 x3
## Min. :3.784 Min. :1.386 Min. :1.002
## 1st Qu.:4.477 1st Qu.:3.663 1st Qu.:2.300
## Median :5.075 Median :4.963 Median :3.568
## Mean :5.054 Mean :4.792 Mean :3.558
## 3rd Qu.:5.515 3rd Qu.:5.830 3rd Qu.:4.523
## Max. :6.737 Max. :7.872 Max. :6.425str(df2)## 'data.frame': 75 obs. of 11 variables:
## $ y1: num 2.5 1.25 7.5 8.9 10 7.5 7.5 7.5 2.5 10 ...
## $ y2: num 0 0 8.8 8.8 3.33 ...
## $ y3: num 3.33 3.33 10 10 10 ...
## $ y4: num 0 0 9.2 9.2 6.67 ...
## $ y5: num 1.25 6.25 8.75 8.91 7.5 ...
## $ y6: num 0 1.1 8.09 8.13 3.33 ...
## $ y7: num 3.73 6.67 10 10 10 ...
## $ y8: num 3.333 0.737 8.212 4.615 6.667 ...
## $ x1: num 4.44 5.38 5.96 6.29 5.86 ...
## $ x2: num 3.64 5.06 6.26 7.57 6.82 ...
## $ x3: num 2.56 3.57 5.22 6.27 4.57 ...head(df2)## y1 y2 y3 y4 y5 y6 y7 y8 x1
## 1 2.50 0.000000 3.333333 0.000000 1.250000 0.000000 3.726360 3.333333 4.442651
## 2 1.25 0.000000 3.333333 0.000000 6.250000 1.100000 6.666666 0.736999 5.384495
## 3 7.50 8.800000 9.999998 9.199991 8.750000 8.094061 9.999998 8.211809 5.961005
## 4 8.90 8.800000 9.999998 9.199991 8.907948 8.127979 9.999998 4.615086 6.285998
## 5 10.00 3.333333 9.999998 6.666666 7.500000 3.333333 9.999998 6.666666 5.863631
## 6 7.50 3.333333 6.666666 6.666666 6.250000 1.100000 6.666666 0.368500 5.533389
## x2 x3
## 1 3.637586 2.557615
## 2 5.062595 3.568079
## 3 6.255750 5.224433
## 4 7.567863 6.267495
## 5 6.818924 4.573679
## 6 5.135798 3.892270##Estructura del modelo
modelo2 <- ' # Regresiones
# Variables Latentes
Politica_60 =~ y1 + y2 + y3 + y4
Politica_65 =~ y5 + y6 + y7 + y8
Industrializacion =~ x1 +x2 + x3
# Varianzas y Covarianzas
Politica_60 ~~ Politica_60
Politica_65 ~~ Politica_65
Industrializacion ~~ Industrializacion
Politica_60 ~~ Politica_65 + Industrializacion
Politica_65 ~~ Industrializacion
# Intercepto
'##Generar analisis factorial confirmatorio
cfa2 <- sem(modelo2, data=df2)
summary(cfa2)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 72.462
## Degrees of freedom 41
## P-value (Chi-square) 0.002
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 =~
## y1 1.000
## y2 1.354 0.175 7.755 0.000
## y3 1.044 0.150 6.961 0.000
## y4 1.300 0.138 9.412 0.000
## Politica_65 =~
## y5 1.000
## y6 1.258 0.164 7.651 0.000
## y7 1.282 0.158 8.137 0.000
## y8 1.310 0.154 8.529 0.000
## Industrializacion =~
## x1 1.000
## x2 2.182 0.139 15.714 0.000
## x3 1.819 0.152 11.956 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 ~~
## Politica_65 4.487 0.911 4.924 0.000
## Industrializcn 0.660 0.206 3.202 0.001
## Politica_65 ~~
## Industrializcn 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 4.845 1.088 4.453 0.000
## Politica_65 4.345 1.051 4.134 0.000
## Industrializcn 0.448 0.087 5.169 0.000
## .y1 1.942 0.395 4.910 0.000
## .y2 6.490 1.185 5.479 0.000
## .y3 5.340 0.943 5.662 0.000
## .y4 2.887 0.610 4.731 0.000
## .y5 2.390 0.447 5.351 0.000
## .y6 4.343 0.796 5.456 0.000
## .y7 3.510 0.668 5.252 0.000
## .y8 2.940 0.586 5.019 0.000
## .x1 0.082 0.020 4.180 0.000
## .x2 0.118 0.070 1.689 0.091
## .x3 0.467 0.090 5.174 0.000lavaanPlot(cfa2, coef=TRUE, cov=TRUE)##Evaluar el modelo
summary(cfa2, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 72.462
## Degrees of freedom 41
## P-value (Chi-square) 0.002
##
## Model Test Baseline Model:
##
## Test statistic 730.654
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.953
## Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1564.959
## Loglikelihood unrestricted model (H1) -1528.728
##
## Akaike (AIC) 3179.918
## Bayesian (BIC) 3237.855
## Sample-size adjusted Bayesian (SABIC) 3159.062
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.101
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.139
## P-value H_0: RMSEA <= 0.050 0.021
## P-value H_0: RMSEA >= 0.080 0.827
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 =~
## y1 1.000
## y2 1.354 0.175 7.755 0.000
## y3 1.044 0.150 6.961 0.000
## y4 1.300 0.138 9.412 0.000
## Politica_65 =~
## y5 1.000
## y6 1.258 0.164 7.651 0.000
## y7 1.282 0.158 8.137 0.000
## y8 1.310 0.154 8.529 0.000
## Industrializacion =~
## x1 1.000
## x2 2.182 0.139 15.714 0.000
## x3 1.819 0.152 11.956 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 ~~
## Politica_65 4.487 0.911 4.924 0.000
## Industrializcn 0.660 0.206 3.202 0.001
## Politica_65 ~~
## Industrializcn 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## Politica_60 4.845 1.088 4.453 0.000
## Politica_65 4.345 1.051 4.134 0.000
## Industrializcn 0.448 0.087 5.169 0.000
## .y1 1.942 0.395 4.910 0.000
## .y2 6.490 1.185 5.479 0.000
## .y3 5.340 0.943 5.662 0.000
## .y4 2.887 0.610 4.731 0.000
## .y5 2.390 0.447 5.351 0.000
## .y6 4.343 0.796 5.456 0.000
## .y7 3.510 0.668 5.252 0.000
## .y8 2.940 0.586 5.019 0.000
## .x1 0.082 0.020 4.180 0.000
## .x2 0.118 0.070 1.689 0.091
## .x3 0.467 0.090 5.174 0.000# Revisar los valores de Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI)
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente <0.90#Actividad 3
##Importar y llamar librerias
#install.packages("readxl")
library(readxl)##Importar la base de datos
df3 <- read_excel( "C:\\Users\\rodri\\Desktop\\Octavo\\Analitica\\Modulo 1\\Datos_SEM_Eng.xlsx")##Entender la base de datos
summary(df3)## ID GEN EXPER EDAD
## Min. : 1.0 Min. :0.0000 Min. : 0.00 Min. :22.00
## 1st Qu.: 56.5 1st Qu.:0.0000 1st Qu.:15.00 1st Qu.:37.50
## Median :112.0 Median :1.0000 Median :20.00 Median :44.00
## Mean :112.0 Mean :0.5919 Mean :21.05 Mean :43.95
## 3rd Qu.:167.5 3rd Qu.:1.0000 3rd Qu.:27.50 3rd Qu.:51.00
## Max. :223.0 Max. :1.0000 Max. :50.00 Max. :72.00
## RPD01 RPD02 RPD03 RPD05 RPD06
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :5.000 Median :4.00 Median :5.000 Median :5.000 Median :5.000
## Mean :4.596 Mean :4.09 Mean :4.789 Mean :4.327 Mean :4.798
## 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.000
## RPD07 RPD08 RPD09 RPD10
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.500
## Median :4.000 Median :5.000 Median :5.000 Median :5.000
## Mean :3.794 Mean :4.735 Mean :4.466 Mean :4.435
## 3rd Qu.:5.500 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## RRE02 RRE03 RRE04 RRE05 RRE06
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:4.0
## Median :6.000 Median :6.000 Median :6.000 Median :6.000 Median :6.0
## Mean :5.691 Mean :5.534 Mean :5.668 Mean :5.623 Mean :5.3
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.0
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.0
## RRE07 RRE10 RMA02 RMA03
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:3.000 1st Qu.:3.000
## Median :6.000 Median :6.000 Median :4.000 Median :5.000
## Mean :5.305 Mean :5.664 Mean :4.215 Mean :4.377
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## RMA04 RMA05 RMA06 RMA07
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:5.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :6.000 Median :5.000
## Mean :4.686 Mean :4.637 Mean :5.511 Mean :4.767
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## RMA08 RMA09 RMA10 RCO02 RCO03
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:5.000 1st Qu.:5.000
## Median :5.000 Median :5.000 Median :5.00 Median :6.000 Median :6.000
## Mean :4.942 Mean :4.614 Mean :4.43 Mean :5.336 Mean :5.574
## 3rd Qu.:6.500 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000
## RCO04 RCO05 RCO06 RCO07
## 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 :5.704 Mean :5.668 Mean :5.619 Mean :5.632
## 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
## EN01 EN02 EN04 EN05
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 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 :4.717 Mean :5.004 Mean :4.883 Mean :4.928
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## EN06 EN07 EN08 EVI01
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.767 Mean :4.578 Mean :4.776 Mean :5.013
## 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
## EVI02 EVI03 EDE01 EDE02
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:5.000 1st Qu.:5.000
## Median :6.000 Median :6.000 Median :6.000 Median :6.000
## Mean :5.076 Mean :4.973 Mean :5.305 Mean :5.543
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## EDE03 EAB01 EAB02 EAB03
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:6.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:5.000
## Median :7.000 Median :6.000 Median :6.000 Median :6.000
## Mean :6.135 Mean :5.605 Mean :5.821 Mean :5.363
## 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. :7.000str(df3)## tibble [223 × 51] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:223] 1 2 3 4 5 6 7 8 9 10 ...
## $ GEN : num [1:223] 1 1 1 1 1 0 0 1 1 1 ...
## $ EXPER: num [1:223] 22 22 30 17 23 31 26 30 15 15 ...
## $ EDAD : num [1:223] 45 44 52 41 51 52 53 48 40 38 ...
## $ RPD01: num [1:223] 5 4 7 5 7 3 5 6 4 2 ...
## $ RPD02: num [1:223] 1 4 7 5 6 4 5 7 4 3 ...
## $ RPD03: num [1:223] 3 6 7 1 7 5 4 6 4 2 ...
## $ RPD05: num [1:223] 2 5 7 1 6 4 4 7 4 3 ...
## $ RPD06: num [1:223] 3 3 7 3 7 3 5 2 6 7 ...
## $ RPD07: num [1:223] 1 2 6 5 6 5 6 5 4 1 ...
## $ RPD08: num [1:223] 3 3 7 3 7 4 6 2 5 3 ...
## $ RPD09: num [1:223] 2 4 7 2 6 4 7 4 4 2 ...
## $ RPD10: num [1:223] 4 4 7 2 6 4 7 1 6 2 ...
## $ RRE02: num [1:223] 6 6 7 6 7 5 7 5 6 7 ...
## $ RRE03: num [1:223] 6 6 7 6 7 4 7 4 4 7 ...
## $ RRE04: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE05: num [1:223] 6 6 7 6 7 5 7 4 6 7 ...
## $ RRE06: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE07: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE10: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
## $ RMA02: num [1:223] 4 6 4 3 4 7 5 2 6 7 ...
## $ RMA03: num [1:223] 5 6 5 4 4 7 5 1 2 7 ...
## $ RMA04: num [1:223] 5 5 6 4 4 5 5 1 4 7 ...
## $ RMA05: num [1:223] 5 5 6 4 4 6 5 3 4 7 ...
## $ RMA06: num [1:223] 6 6 7 6 5 4 5 7 6 7 ...
## $ RMA07: num [1:223] 4 6 6 5 4 5 7 4 6 7 ...
## $ RMA08: num [1:223] 5 6 4 4 4 6 6 4 2 7 ...
## $ RMA09: num [1:223] 3 5 4 3 5 4 5 2 4 7 ...
## $ RMA10: num [1:223] 7 5 5 4 5 5 6 4 3 7 ...
## $ RCO02: num [1:223] 7 7 7 5 7 6 7 7 3 7 ...
## $ RCO03: num [1:223] 7 7 7 5 7 5 7 7 3 7 ...
## $ RCO04: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
## $ RCO05: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
## $ RCO06: num [1:223] 7 7 7 6 7 4 7 7 4 7 ...
## $ RCO07: num [1:223] 5 7 7 6 7 4 7 7 7 7 ...
## $ EN01 : num [1:223] 6 6 7 4 6 4 7 7 4 7 ...
## $ EN02 : num [1:223] 7 6 7 4 6 4 7 7 4 7 ...
## $ EN04 : num [1:223] 6 6 7 4 6 4 7 6 4 7 ...
## $ EN05 : num [1:223] 5 5 7 5 6 5 7 6 4 7 ...
## $ EN06 : num [1:223] 5 5 7 5 6 3 7 5 5 7 ...
## $ EN07 : num [1:223] 5 5 7 2 6 4 7 4 4 7 ...
## $ EN08 : num [1:223] 6 5 7 5 6 4 7 4 4 7 ...
## $ EVI01: num [1:223] 6 5 7 5 6 4 7 6 6 0 ...
## $ EVI02: num [1:223] 6 5 7 6 6 4 6 5 5 1 ...
## $ EVI03: num [1:223] 6 6 6 7 6 4 6 6 7 0 ...
## $ EDE01: num [1:223] 6 6 6 5 7 6 7 7 7 1 ...
## $ EDE02: num [1:223] 7 6 7 6 7 5 7 7 7 5 ...
## $ EDE03: num [1:223] 7 7 7 7 7 5 7 7 7 6 ...
## $ EAB01: num [1:223] 7 7 7 6 7 5 7 7 7 0 ...
## $ EAB02: num [1:223] 7 7 7 6 7 5 7 2 5 1 ...
## $ EAB03: num [1:223] 6 5 6 5 6 5 7 3 5 0 ...head(df3)## # A tibble: 6 × 51
## ID GEN EXPER EDAD RPD01 RPD02 RPD03 RPD05 RPD06 RPD07 RPD08 RPD09 RPD10
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 22 45 5 1 3 2 3 1 3 2 4
## 2 2 1 22 44 4 4 6 5 3 2 3 4 4
## 3 3 1 30 52 7 7 7 7 7 6 7 7 7
## 4 4 1 17 41 5 5 1 1 3 5 3 2 2
## 5 5 1 23 51 7 6 7 6 7 6 7 6 6
## 6 6 0 31 52 3 4 5 4 3 5 4 4 4
## # ℹ 38 more variables: RRE02 <dbl>, RRE03 <dbl>, RRE04 <dbl>, RRE05 <dbl>,
## # RRE06 <dbl>, RRE07 <dbl>, RRE10 <dbl>, RMA02 <dbl>, RMA03 <dbl>,
## # RMA04 <dbl>, RMA05 <dbl>, RMA06 <dbl>, RMA07 <dbl>, RMA08 <dbl>,
## # RMA09 <dbl>, RMA10 <dbl>, RCO02 <dbl>, RCO03 <dbl>, RCO04 <dbl>,
## # RCO05 <dbl>, RCO06 <dbl>, RCO07 <dbl>, EN01 <dbl>, EN02 <dbl>, EN04 <dbl>,
## # EN05 <dbl>, EN06 <dbl>, EN07 <dbl>, EN08 <dbl>, EVI01 <dbl>, EVI02 <dbl>,
## # EVI03 <dbl>, EDE01 <dbl>, EDE02 <dbl>, EDE03 <dbl>, EAB01 <dbl>, …##Parte 1.
modelo31 <- ' #Regresiones
#Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA03 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
#Varianzas y Covarianzas
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
#Intercepto
' ##Generar analisis factorial confirmatorio
cfa31 <- sem(modelo31, data=df3)
summary(cfa31)## lavaan 0.6-19 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1132.779
## Degrees of freedom 401
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.206 0.082 14.780 0.000
## RPD03 1.143 0.085 13.375 0.000
## RPD05 1.312 0.086 15.245 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.441 0.000
## RPD08 1.163 0.087 13.446 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.257 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.221 0.000
## RRE04 1.025 0.058 17.713 0.000
## RRE05 1.055 0.056 18.758 0.000
## RRE06 1.245 0.074 16.867 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.119 0.000
## dominio =~
## RMA03 1.000
## RMA04 1.048 0.071 14.760 0.000
## RMA05 1.009 0.070 14.375 0.000
## RMA06 0.579 0.064 9.060 0.000
## RMA07 0.986 0.068 14.557 0.000
## RMA08 0.993 0.068 14.512 0.000
## RMA09 0.923 0.068 13.551 0.000
## RMA10 0.934 0.072 13.029 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.180 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.989 0.000
## RCO06 0.834 0.046 18.213 0.000
## RCO07 0.835 0.046 18.056 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.150 0.131 8.786 0.000
## dominio 0.974 0.142 6.863 0.000
## control 1.342 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.945 0.152 6.212 0.000
## .relajacion 0.333 0.088 3.766 0.000
## .dominio 1.582 0.246 6.442 0.000
## .control 0.899 0.159 5.666 0.000
## .RPD01 1.172 0.120 9.782 0.000
## .RPD02 0.999 0.108 9.228 0.000
## .RPD03 1.441 0.148 9.733 0.000
## .RPD05 0.987 0.110 8.963 0.000
## .RPD06 1.817 0.182 9.967 0.000
## .RPD07 1.173 0.125 9.383 0.000
## .RPD08 1.460 0.150 9.714 0.000
## .RPD09 1.033 0.114 9.022 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.654 0.073 9.013 0.000
## .RRE04 0.480 0.055 8.793 0.000
## .RRE05 0.374 0.046 8.151 0.000
## .RRE06 0.886 0.097 9.150 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA03 1.619 0.168 9.653 0.000
## .RMA04 0.851 0.098 8.651 0.000
## .RMA05 0.924 0.103 8.933 0.000
## .RMA06 1.614 0.157 10.255 0.000
## .RMA07 0.819 0.093 8.808 0.000
## .RMA08 0.847 0.096 8.841 0.000
## .RMA09 1.050 0.112 9.364 0.000
## .RMA10 1.279 0.134 9.558 0.000
## .RCO02 0.983 0.105 9.380 0.000
## .RCO03 0.484 0.058 8.392 0.000
## .RCO04 0.462 0.052 8.962 0.000
## .RCO05 0.382 0.045 8.513 0.000
## .RCO06 0.494 0.055 8.917 0.000
## .RCO07 0.515 0.057 8.985 0.000
## recuperacion 0.976 0.202 4.831 0.000lavaanPlot(cfa1, coef=TRUE, cov=TRUE)##Evaluar el modelo
summary (cfa31, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1132.779
## Degrees of freedom 401
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7278.538
## Degrees of freedom 435
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.893
## Tucker-Lewis Index (TLI) 0.884
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10230.913
## Loglikelihood unrestricted model (H1) -9664.524
##
## Akaike (AIC) 20589.826
## Bayesian (BIC) 20807.885
## Sample-size adjusted Bayesian (SABIC) 20605.061
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.090
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.097
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.997
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.075
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.206 0.082 14.780 0.000
## RPD03 1.143 0.085 13.375 0.000
## RPD05 1.312 0.086 15.245 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.441 0.000
## RPD08 1.163 0.087 13.446 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.257 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.221 0.000
## RRE04 1.025 0.058 17.713 0.000
## RRE05 1.055 0.056 18.758 0.000
## RRE06 1.245 0.074 16.867 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.119 0.000
## dominio =~
## RMA03 1.000
## RMA04 1.048 0.071 14.760 0.000
## RMA05 1.009 0.070 14.375 0.000
## RMA06 0.579 0.064 9.060 0.000
## RMA07 0.986 0.068 14.557 0.000
## RMA08 0.993 0.068 14.512 0.000
## RMA09 0.923 0.068 13.551 0.000
## RMA10 0.934 0.072 13.029 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.180 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.989 0.000
## RCO06 0.834 0.046 18.213 0.000
## RCO07 0.835 0.046 18.056 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.150 0.131 8.786 0.000
## dominio 0.974 0.142 6.863 0.000
## control 1.342 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.945 0.152 6.212 0.000
## .relajacion 0.333 0.088 3.766 0.000
## .dominio 1.582 0.246 6.442 0.000
## .control 0.899 0.159 5.666 0.000
## .RPD01 1.172 0.120 9.782 0.000
## .RPD02 0.999 0.108 9.228 0.000
## .RPD03 1.441 0.148 9.733 0.000
## .RPD05 0.987 0.110 8.963 0.000
## .RPD06 1.817 0.182 9.967 0.000
## .RPD07 1.173 0.125 9.383 0.000
## .RPD08 1.460 0.150 9.714 0.000
## .RPD09 1.033 0.114 9.022 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.654 0.073 9.013 0.000
## .RRE04 0.480 0.055 8.793 0.000
## .RRE05 0.374 0.046 8.151 0.000
## .RRE06 0.886 0.097 9.150 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA03 1.619 0.168 9.653 0.000
## .RMA04 0.851 0.098 8.651 0.000
## .RMA05 0.924 0.103 8.933 0.000
## .RMA06 1.614 0.157 10.255 0.000
## .RMA07 0.819 0.093 8.808 0.000
## .RMA08 0.847 0.096 8.841 0.000
## .RMA09 1.050 0.112 9.364 0.000
## .RMA10 1.279 0.134 9.558 0.000
## .RCO02 0.983 0.105 9.380 0.000
## .RCO03 0.484 0.058 8.392 0.000
## .RCO04 0.462 0.052 8.962 0.000
## .RCO05 0.382 0.045 8.513 0.000
## .RCO06 0.494 0.055 8.917 0.000
## .RCO07 0.515 0.057 8.985 0.000
## recuperacion 0.976 0.202 4.831 0.000##Parte 2. Energia Recuperada
modelo32 <- ' #Regresiones
#Variables Latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
#Varianzas y Covarianzas
energia ~~ energia
#Intercepto
'##Generar el Analisis Factorial confirmatorio Modelo 2
cfa32 <- sem(modelo32, data=df3)
summary(cfa32)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000lavaanPlot(cfa1, coef=TRUE, cov=TRUE)##Evaluar el modelo 2
summary (cfa32, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2324.436
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.986
## Tucker-Lewis Index (TLI) 0.978
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2017.154
## Loglikelihood unrestricted model (H1) -1993.543
##
## Akaike (AIC) 4062.308
## Bayesian (BIC) 4110.008
## Sample-size adjusted Bayesian (SABIC) 4065.641
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.103
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.136
## P-value H_0: RMSEA <= 0.050 0.004
## P-value H_0: RMSEA >= 0.080 0.892
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.012
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000##Parte 3. Engagment Laboral
modelo33 <- ' #Regresiones
#Variables Latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
#Varianzas y Covarianzas
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
#Intercepto
'##Generar el Analisis Factorial confirmatorio Modelo 3
cfa33 <- sem(modelo33, data=df3)
summary(cfa32)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000lavaanPlot(cfa33, coef=TRUE, cov=TRUE)##Evaluar el modelo 3
summary (cfa32, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2324.436
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.986
## Tucker-Lewis Index (TLI) 0.978
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2017.154
## Loglikelihood unrestricted model (H1) -1993.543
##
## Akaike (AIC) 4062.308
## Bayesian (BIC) 4110.008
## Sample-size adjusted Bayesian (SABIC) 4065.641
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.103
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.136
## P-value H_0: RMSEA <= 0.050 0.004
## P-value H_0: RMSEA >= 0.080 0.892
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.012
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000##Parte 4. Modelo Completo
modelo34 <- ' #Regresiones
#Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA03 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
#Varianzas y Covarianzas
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
recuperacion ~~ energia + vigor + dedicacion + absorcion
energia ~~ vigor + dedicacion + absorcion
#Intercepto
'##Generar el Analisis Factorial confirmatorio Modelo 4
cfa34 <- sem(modelo34, data=df3)
summary(cfa32)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000lavaanPlot(cfa34, coef=TRUE, cov=TRUE)##Evaluar el modelo 4
summary (cfa34, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 106
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2336.479
## Degrees of freedom 975
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 13086.220
## Degrees of freedom 1035
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.887
## Tucker-Lewis Index (TLI) 0.880
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15041.289
## Loglikelihood unrestricted model (H1) -13873.049
##
## Akaike (AIC) 30294.577
## Bayesian (BIC) 30655.737
## Sample-size adjusted Bayesian (SABIC) 30319.810
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.083
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.368
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.069
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.209 0.081 14.858 0.000
## RPD03 1.144 0.085 13.414 0.000
## RPD05 1.314 0.086 15.311 0.000
## RPD06 1.083 0.089 12.218 0.000
## RPD07 1.229 0.085 14.481 0.000
## RPD08 1.157 0.086 13.376 0.000
## RPD09 1.315 0.087 15.162 0.000
## RPD10 1.343 0.088 15.246 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.299 0.000
## RRE04 1.020 0.058 17.611 0.000
## RRE05 1.051 0.056 18.689 0.000
## RRE06 1.245 0.074 16.915 0.000
## RRE07 1.122 0.071 15.848 0.000
## RRE10 0.815 0.067 12.146 0.000
## dominio =~
## RMA03 1.000
## RMA04 1.051 0.072 14.680 0.000
## RMA05 1.011 0.071 14.294 0.000
## RMA06 0.583 0.064 9.076 0.000
## RMA07 0.989 0.068 14.497 0.000
## RMA08 0.997 0.069 14.464 0.000
## RMA09 0.927 0.069 13.515 0.000
## RMA10 0.937 0.072 12.992 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.155 0.000
## RCO04 0.794 0.044 18.082 0.000
## RCO05 0.815 0.043 18.911 0.000
## RCO06 0.837 0.046 18.392 0.000
## RCO07 0.837 0.046 18.198 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.072 0.121 8.856 0.000
## dominio 1.018 0.142 7.177 0.000
## control 1.422 0.157 9.064 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.557 0.000
## EN04 0.996 0.043 22.911 0.000
## EN05 0.994 0.042 23.892 0.000
## EN06 0.981 0.041 23.941 0.000
## EN07 1.044 0.045 23.102 0.000
## EN08 1.031 0.042 24.448 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.895 0.000
## EVI03 0.990 0.048 20.657 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.221 0.000
## EDE03 0.580 0.037 15.850 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.915 0.000
## EAB03 0.730 0.063 11.619 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.767 0.293 9.427 0.000
## absorcion 2.132 0.248 8.613 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.316 0.000
## recuperacion ~~
## energia 1.366 0.197 6.936 0.000
## vigor 1.007 0.165 6.098 0.000
## dedicacion 1.049 0.179 5.856 0.000
## absorcion 0.796 0.151 5.284 0.000
## energia ~~
## vigor 2.045 0.249 8.223 0.000
## dedicacion 1.852 0.259 7.139 0.000
## absorcion 1.340 0.220 6.091 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.952 0.149 6.402 0.000
## .relajacion 0.510 0.085 6.022 0.000
## .dominio 1.486 0.231 6.441 0.000
## .control 0.698 0.125 5.583 0.000
## energia 2.823 0.327 8.622 0.000
## vigor 2.859 0.289 9.900 0.000
## dedicacion 3.458 0.367 9.425 0.000
## absorcion 2.595 0.301 8.628 0.000
## .RPD01 1.169 0.120 9.781 0.000
## .RPD02 0.984 0.107 9.204 0.000
## .RPD03 1.434 0.147 9.730 0.000
## .RPD05 0.973 0.109 8.939 0.000
## .RPD06 1.835 0.184 9.979 0.000
## .RPD07 1.166 0.124 9.378 0.000
## .RPD08 1.485 0.152 9.739 0.000
## .RPD09 1.036 0.115 9.035 0.000
## .RPD10 1.045 0.116 8.982 0.000
## .RRE02 0.624 0.067 9.254 0.000
## .RRE03 0.647 0.072 8.975 0.000
## .RRE04 0.494 0.056 8.837 0.000
## .RRE05 0.384 0.047 8.203 0.000
## .RRE06 0.882 0.097 9.126 0.000
## .RRE07 0.929 0.098 9.458 0.000
## .RRE10 1.134 0.112 10.086 0.000
## .RMA03 1.634 0.169 9.670 0.000
## .RMA04 0.854 0.098 8.669 0.000
## .RMA05 0.929 0.104 8.953 0.000
## .RMA06 1.609 0.157 10.254 0.000
## .RMA07 0.818 0.093 8.812 0.000
## .RMA08 0.843 0.095 8.837 0.000
## .RMA09 1.045 0.112 9.363 0.000
## .RMA10 1.277 0.134 9.560 0.000
## .RCO02 0.980 0.104 9.395 0.000
## .RCO03 0.493 0.058 8.474 0.000
## .RCO04 0.468 0.052 9.018 0.000
## .RCO05 0.393 0.046 8.620 0.000
## .RCO06 0.479 0.054 8.884 0.000
## .RCO07 0.504 0.056 8.970 0.000
## .EN01 0.689 0.071 9.661 0.000
## .EN02 0.439 0.048 9.066 0.000
## .EN04 0.476 0.051 9.266 0.000
## .EN05 0.381 0.043 8.944 0.000
## .EN06 0.367 0.041 8.925 0.000
## .EN07 0.502 0.055 9.211 0.000
## .EN08 0.358 0.041 8.708 0.000
## .EVI01 0.177 0.036 4.919 0.000
## .EVI02 0.242 0.038 6.299 0.000
## .EVI03 1.222 0.124 9.826 0.000
## .EDE01 0.395 0.065 6.059 0.000
## .EDE02 0.498 0.066 7.580 0.000
## .EDE03 0.836 0.085 9.887 0.000
## .EAB01 0.478 0.099 4.805 0.000
## .EAB02 1.010 0.109 9.283 0.000
## .EAB03 1.718 0.176 9.778 0.000
## recuperacion 0.971 0.198 4.893 0.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