Actividad 3
Gamaliel Ostos A01277023
2025-02-19
Teoría
Los Modelos de Ecuaciones Estructurales (SEM) es una técnica de análisis de estadística multivariada, que permite analizar patrones complejos de relaciones entre variables, realizar comparaciones entre intragrupos, y validar modelos teóricos y empíricos.
1. Estudio de Holzinger y Swineford (1939)
Contexto
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° de dos escuelas (Pasteur y Grand-White).
La base de datos está incluida como paquete en R, e incluye las siguientes columnas: * sex: Género (1=male, 2=female) * x1: Percepción visual * x2: Juego con cubos * x3: Juego con pastillas (espacial) * x4: Comprensión de párrafos * x5: Completar oraciones * x6: Significado de palabras * x7: Sumas aceleradas * x8: Conteo acelerado de puntos * x9: Discriminación acelerada de mayúsculas rectas y curvas
Se busca identificar las relaciones entre las habilidades visual (x1, x2, x3), textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolescentes.
Instalar paquetes y llamar librerías
#install.packages("lavaan") # Lavent 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 7.500000Tipos de Fórmulas
- Regresión (~) Variable que depende de otras.
- Variables latentes (=~) No se observa, se infiere.
- Varianzas y Covarianzas (~~) Relaciones entre variables latentes y observada (Varianza: Entre sí misma, Covarianza: Entre otras).
- Intercepto (~1) Valor esperado cuando las demás variables son cero.
Estructurar el Modelo
modelo1 <- ' # Regresiones
# Variables Latentes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7 + x8 + x9
# Varianzas y Covarianzas
visual ~~ visual
textual ~~ textual
velocidad ~~ velocidad
visual ~~ textual + velocidad
textual ~~ velocidad
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
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 comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.90 y 0.95, Deficiente < 0.90Conclusión: Aceptable
Ejercicio 1. Democracia Política e Industrialización
Contexto
La base de datos contiene distintas mediciones sobre la democracia política e industrialización en países 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 <- PoliticalDemocracyLibrerías y paquetes
#install.packages("readxl")
library(readxl)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 3.892270Estructurar el Modelo
modelo2 <- ' # Regresiones
# Variables Latentes
industrial =~ x1 + x2 + x3
democracia_60 =~ y1 + y2 + y3 + y4
democracia_65 =~ y5 + y6 + y7 + y8
# Varianzas y Covarianzas
democracia_60 ~~ democracia_60
democracia_65 ~~ democracia_65
industrial ~~ industrial
industrial ~~ democracia_60 + democracia_65
democracia_60 ~~ democracia_65
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
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|)
## industrial =~
## x1 1.000
## x2 2.182 0.139 15.714 0.000
## x3 1.819 0.152 11.956 0.000
## democracia_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
## democracia_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
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## industrial ~~
## democracia_60 0.660 0.206 3.202 0.001
## democracia_65 0.774 0.208 3.715 0.000
## democracia_60 ~~
## democracia_65 4.487 0.911 4.924 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## democracia_60 4.845 1.088 4.453 0.000
## democracia_65 4.345 1.051 4.134 0.000
## industrial 0.448 0.087 5.169 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
## .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 0.000lavaanPlot(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|)
## industrial =~
## x1 1.000
## x2 2.182 0.139 15.714 0.000
## x3 1.819 0.152 11.956 0.000
## democracia_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
## democracia_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
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## industrial ~~
## democracia_60 0.660 0.206 3.202 0.001
## democracia_65 0.774 0.208 3.715 0.000
## democracia_60 ~~
## democracia_65 4.487 0.911 4.924 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## democracia_60 4.845 1.088 4.453 0.000
## democracia_65 4.345 1.051 4.134 0.000
## industrial 0.448 0.087 5.169 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
## .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# Revisar los valores de comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.90 y 0.95, Deficiente < 0.90
# EXCELENTEActividad 3. Bienestar de los Trabajadores
#install.packages("readxl")
library(readxl)Importar la base de datos
df3 <- read_excel("C:\\Users\\gamas\\Downloads\\Datos_SEM_Eng.xlsx")Parte 1. Experiencias de recuperación
modelo31 <- ' # Regresiones
# Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + control
# Varianzas y Covarianza
desapego ~~ desapego
dominio ~~ dominio
control ~~ control
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa31<-sem(modelo31,data=df3)
summary(cfa31)## lavaan 0.6-19 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 430
## 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 13.374 0.000
## RPD05 1.312 0.086 15.244 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.440 0.000
## RPD08 1.164 0.087 13.447 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.258 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.227 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 16.869 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.120 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.155 0.096 12.079 0.000
## RMA04 1.178 0.089 13.274 0.000
## RMA05 1.141 0.087 13.072 0.000
## RMA06 0.645 0.075 8.597 0.000
## RMA07 1.103 0.084 13.061 0.000
## RMA08 1.109 0.085 12.994 0.000
## RMA09 1.028 0.084 12.246 0.000
## RMA10 1.055 0.088 12.044 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.182 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.990 0.000
## RCO06 0.834 0.046 18.216 0.000
## RCO07 0.835 0.046 18.057 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.149 0.131 8.786 0.000
## control 1.341 0.156 8.605 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## dominio ~~
## recuperacion 0.839 0.149 5.638 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## dominio 1.980 0.317 6.246 0.000
## .control 0.900 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 8.964 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 1.032 0.114 9.021 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.653 0.073 9.011 0.000
## .RRE04 0.481 0.055 8.794 0.000
## .RRE05 0.374 0.046 8.153 0.000
## .RRE06 0.886 0.097 9.149 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA02 1.740 0.175 9.931 0.000
## .RMA03 1.485 0.155 9.575 0.000
## .RMA04 0.855 0.097 8.772 0.000
## .RMA05 0.899 0.100 8.967 0.000
## .RMA06 1.631 0.159 10.281 0.000
## .RMA07 0.845 0.094 8.977 0.000
## .RMA08 0.886 0.098 9.034 0.000
## .RMA09 1.094 0.115 9.500 0.000
## .RMA10 1.259 0.131 9.590 0.000
## .RCO02 0.983 0.105 9.379 0.000
## .RCO03 0.484 0.058 8.391 0.000
## .RCO04 0.462 0.052 8.963 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
## .relajacion 0.333 0.089 3.757 0.000
## recuperacion 0.978 0.202 4.833 0.000lavaanPlot(cfa31, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa31, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 430
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7522.157
## Degrees of freedom 465
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.888
## Tucker-Lewis Index (TLI) 0.879
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10616.148
## Loglikelihood unrestricted model (H1) -10005.632
##
## Akaike (AIC) 21364.296
## Bayesian (BIC) 21589.169
## Sample-size adjusted Bayesian (SABIC) 21380.007
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.097
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.998
##
## 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 13.374 0.000
## RPD05 1.312 0.086 15.244 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.440 0.000
## RPD08 1.164 0.087 13.447 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.258 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.227 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 16.869 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.120 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.155 0.096 12.079 0.000
## RMA04 1.178 0.089 13.274 0.000
## RMA05 1.141 0.087 13.072 0.000
## RMA06 0.645 0.075 8.597 0.000
## RMA07 1.103 0.084 13.061 0.000
## RMA08 1.109 0.085 12.994 0.000
## RMA09 1.028 0.084 12.246 0.000
## RMA10 1.055 0.088 12.044 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.182 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.990 0.000
## RCO06 0.834 0.046 18.216 0.000
## RCO07 0.835 0.046 18.057 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.149 0.131 8.786 0.000
## control 1.341 0.156 8.605 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## dominio ~~
## recuperacion 0.839 0.149 5.638 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## dominio 1.980 0.317 6.246 0.000
## .control 0.900 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 8.964 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 1.032 0.114 9.021 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.653 0.073 9.011 0.000
## .RRE04 0.481 0.055 8.794 0.000
## .RRE05 0.374 0.046 8.153 0.000
## .RRE06 0.886 0.097 9.149 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA02 1.740 0.175 9.931 0.000
## .RMA03 1.485 0.155 9.575 0.000
## .RMA04 0.855 0.097 8.772 0.000
## .RMA05 0.899 0.100 8.967 0.000
## .RMA06 1.631 0.159 10.281 0.000
## .RMA07 0.845 0.094 8.977 0.000
## .RMA08 0.886 0.098 9.034 0.000
## .RMA09 1.094 0.115 9.500 0.000
## .RMA10 1.259 0.131 9.590 0.000
## .RCO02 0.983 0.105 9.379 0.000
## .RCO03 0.484 0.058 8.391 0.000
## .RCO04 0.462 0.052 8.963 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
## .relajacion 0.333 0.089 3.757 0.000
## recuperacion 0.978 0.202 4.833 0.000# Revisar los valores de comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.85 y 0.95, Deficiente < 0.90
# ACEPTABLEParte 2. Energía recuperada
modelo32 <- ' # Regresiones
# Variables Latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianza
energia ~~ energia
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
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(cfa32, coef=TRUE, cov=TRUE)Evaluar el Modelo
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# Revisar los valores de comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.85 y 0.95, Deficiente < 0.90
# EXCELENTEParte 3. Engagement laboral
modelo33 <- ' # Regresiones
# Variables Latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianzas y Covarianza
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa33<-sem(modelo33,data=df3)
summary(cfa33)## lavaan 0.6-19 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 271.168
## 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|)
## vigor =~
## EVI01 1.000
## EVI02 0.986 0.028 35.166 0.000
## EVI03 0.995 0.049 20.456 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.914 0.035 26.126 0.000
## EDE03 0.583 0.037 15.913 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.708 0.051 13.891 0.000
## EAB03 0.732 0.063 11.644 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.754 0.293 9.404 0.000
## absorcion 2.125 0.247 8.600 0.000
## dedicacion ~~
## absorcion 2.728 0.293 9.311 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## vigor 2.836 0.289 9.811 0.000
## dedicacion 3.448 0.367 9.399 0.000
## absorcion 2.592 0.301 8.615 0.000
## .EVI01 0.200 0.040 4.947 0.000
## .EVI02 0.220 0.041 5.437 0.000
## .EVI03 1.220 0.125 9.772 0.000
## .EDE01 0.405 0.066 6.130 0.000
## .EDE02 0.495 0.066 7.521 0.000
## .EDE03 0.829 0.084 9.869 0.000
## .EAB01 0.481 0.100 4.816 0.000
## .EAB02 1.010 0.109 9.271 0.000
## .EAB03 1.711 0.175 9.764 0.000lavaanPlot(cfa33, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa33, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 271.168
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2254.214
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.889
## Tucker-Lewis Index (TLI) 0.833
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2965.082
## Loglikelihood unrestricted model (H1) -2829.498
##
## Akaike (AIC) 5972.164
## Bayesian (BIC) 6043.715
## Sample-size adjusted Bayesian (SABIC) 5977.163
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.215
## 90 Percent confidence interval - lower 0.192
## 90 Percent confidence interval - upper 0.238
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## vigor =~
## EVI01 1.000
## EVI02 0.986 0.028 35.166 0.000
## EVI03 0.995 0.049 20.456 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.914 0.035 26.126 0.000
## EDE03 0.583 0.037 15.913 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.708 0.051 13.891 0.000
## EAB03 0.732 0.063 11.644 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.754 0.293 9.404 0.000
## absorcion 2.125 0.247 8.600 0.000
## dedicacion ~~
## absorcion 2.728 0.293 9.311 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## vigor 2.836 0.289 9.811 0.000
## dedicacion 3.448 0.367 9.399 0.000
## absorcion 2.592 0.301 8.615 0.000
## .EVI01 0.200 0.040 4.947 0.000
## .EVI02 0.220 0.041 5.437 0.000
## .EVI03 1.220 0.125 9.772 0.000
## .EDE01 0.405 0.066 6.130 0.000
## .EDE02 0.495 0.066 7.521 0.000
## .EDE03 0.829 0.084 9.869 0.000
## .EAB01 0.481 0.100 4.816 0.000
## .EAB02 1.010 0.109 9.271 0.000
## .EAB03 1.711 0.175 9.764 0.000# Revisar los valores de comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.85 y 0.95, Deficiente < 0.90
# ACEPTABLEParte 4. Engagement laboral
modelo34 <- ' # Regresiones
# Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + control
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
# Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa34<-sem(modelo34,data=df3)
summary(cfa34)## lavaan 0.6-19 ended normally after 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 112
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2439.471
## Degrees of freedom 1016
## 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 1.208 0.081 14.855 0.000
## RPD03 1.144 0.085 13.419 0.000
## RPD05 1.313 0.086 15.312 0.000
## RPD06 1.083 0.089 12.223 0.000
## RPD07 1.229 0.085 14.477 0.000
## RPD08 1.157 0.086 13.385 0.000
## RPD09 1.315 0.087 15.168 0.000
## RPD10 1.343 0.088 15.254 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.274 0.000
## RRE04 1.021 0.058 17.637 0.000
## RRE05 1.052 0.056 18.705 0.000
## RRE06 1.245 0.074 16.896 0.000
## RRE07 1.121 0.071 15.818 0.000
## RRE10 0.815 0.067 12.142 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.151 0.096 12.046 0.000
## RMA04 1.178 0.089 13.275 0.000
## RMA05 1.140 0.087 13.072 0.000
## RMA06 0.647 0.075 8.622 0.000
## RMA07 1.103 0.084 13.073 0.000
## RMA08 1.110 0.085 13.020 0.000
## RMA09 1.029 0.084 12.265 0.000
## RMA10 1.055 0.088 12.052 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.137 0.000
## RCO04 0.794 0.044 18.077 0.000
## RCO05 0.815 0.043 18.923 0.000
## RCO06 0.837 0.046 18.374 0.000
## RCO07 0.837 0.046 18.193 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.070 0.120 8.925 0.000
## control 1.413 0.155 9.106 0.000
## energia =~
## EN01 1.000
## EN02 1.027 0.044 23.512 0.000
## EN04 0.997 0.044 22.896 0.000
## EN05 0.994 0.042 23.866 0.000
## EN06 0.982 0.041 23.908 0.000
## EN07 1.045 0.045 23.091 0.000
## EN08 1.031 0.042 24.415 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.870 0.000
## EVI03 0.991 0.048 20.667 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.275 0.000
## EDE03 0.580 0.037 15.830 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.922 0.000
## EAB03 0.730 0.063 11.645 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.765 0.293 9.425 0.000
## absorcion 2.133 0.248 8.614 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.317 0.000
## dominio ~~
## recuperacion 0.851 0.149 5.702 0.000
## energia 1.326 0.209 6.355 0.000
## vigor 1.009 0.191 5.291 0.000
## dedicacion 0.989 0.207 4.777 0.000
## absorcion 0.865 0.184 4.689 0.000
## recuperacion ~~
## energia 1.357 0.196 6.911 0.000
## vigor 0.996 0.165 6.032 0.000
## dedicacion 1.045 0.180 5.809 0.000
## absorcion 0.779 0.151 5.168 0.000
## energia ~~
## vigor 2.044 0.249 8.221 0.000
## dedicacion 1.851 0.259 7.137 0.000
## absorcion 1.340 0.220 6.091 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.935 0.147 6.355 0.000
## .relajacion 0.493 0.084 5.838 0.000
## dominio 1.981 0.317 6.249 0.000
## .control 0.686 0.127 5.402 0.000
## energia 2.821 0.327 8.616 0.000
## vigor 2.858 0.289 9.897 0.000
## dedicacion 3.457 0.367 9.422 0.000
## absorcion 2.596 0.301 8.637 0.000
## .RPD01 1.168 0.119 9.781 0.000
## .RPD02 0.986 0.107 9.208 0.000
## .RPD03 1.434 0.147 9.730 0.000
## .RPD05 0.974 0.109 8.943 0.000
## .RPD06 1.834 0.184 9.979 0.000
## .RPD07 1.169 0.125 9.382 0.000
## .RPD08 1.483 0.152 9.738 0.000
## .RPD09 1.036 0.115 9.034 0.000
## .RPD10 1.043 0.116 8.980 0.000
## .RRE02 0.624 0.067 9.258 0.000
## .RRE03 0.649 0.072 8.986 0.000
## .RRE04 0.490 0.056 8.825 0.000
## .RRE05 0.381 0.047 8.189 0.000
## .RRE06 0.884 0.097 9.134 0.000
## .RRE07 0.932 0.098 9.466 0.000
## .RRE10 1.135 0.112 10.087 0.000
## .RMA02 1.739 0.175 9.935 0.000
## .RMA03 1.501 0.156 9.598 0.000
## .RMA04 0.858 0.098 8.791 0.000
## .RMA05 0.902 0.100 8.985 0.000
## .RMA06 1.627 0.158 10.281 0.000
## .RMA07 0.844 0.094 8.984 0.000
## .RMA08 0.879 0.097 9.029 0.000
## .RMA09 1.090 0.115 9.501 0.000
## .RMA10 1.258 0.131 9.596 0.000
## .RCO02 0.981 0.104 9.395 0.000
## .RCO03 0.494 0.058 8.479 0.000
## .RCO04 0.468 0.052 9.015 0.000
## .RCO05 0.391 0.045 8.606 0.000
## .RCO06 0.480 0.054 8.887 0.000
## .RCO07 0.504 0.056 8.967 0.000
## .EN01 0.691 0.072 9.665 0.000
## .EN02 0.440 0.048 9.072 0.000
## .EN04 0.475 0.051 9.263 0.000
## .EN05 0.380 0.043 8.943 0.000
## .EN06 0.367 0.041 8.927 0.000
## .EN07 0.501 0.054 9.206 0.000
## .EN08 0.358 0.041 8.709 0.000
## .EVI01 0.178 0.036 4.939 0.000
## .EVI02 0.241 0.038 6.290 0.000
## .EVI03 1.220 0.124 9.823 0.000
## .EDE01 0.397 0.065 6.091 0.000
## .EDE02 0.495 0.065 7.569 0.000
## .EDE03 0.837 0.085 9.891 0.000
## .EAB01 0.477 0.099 4.826 0.000
## .EAB02 1.012 0.109 9.303 0.000
## .EAB03 1.716 0.175 9.784 0.000
## recuperacion 0.989 0.201 4.927 0.000lavaanPlot(cfa34, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa34, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 112
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2439.471
## Degrees of freedom 1016
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 13350.303
## Degrees of freedom 1081
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.884
## Tucker-Lewis Index (TLI) 0.877
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15423.661
## Loglikelihood unrestricted model (H1) -14203.926
##
## Akaike (AIC) 31071.322
## Bayesian (BIC) 31452.926
## Sample-size adjusted Bayesian (SABIC) 31097.984
##
## 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 0.386
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068
##
## 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 1.208 0.081 14.855 0.000
## RPD03 1.144 0.085 13.419 0.000
## RPD05 1.313 0.086 15.312 0.000
## RPD06 1.083 0.089 12.223 0.000
## RPD07 1.229 0.085 14.477 0.000
## RPD08 1.157 0.086 13.385 0.000
## RPD09 1.315 0.087 15.168 0.000
## RPD10 1.343 0.088 15.254 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.274 0.000
## RRE04 1.021 0.058 17.637 0.000
## RRE05 1.052 0.056 18.705 0.000
## RRE06 1.245 0.074 16.896 0.000
## RRE07 1.121 0.071 15.818 0.000
## RRE10 0.815 0.067 12.142 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.151 0.096 12.046 0.000
## RMA04 1.178 0.089 13.275 0.000
## RMA05 1.140 0.087 13.072 0.000
## RMA06 0.647 0.075 8.622 0.000
## RMA07 1.103 0.084 13.073 0.000
## RMA08 1.110 0.085 13.020 0.000
## RMA09 1.029 0.084 12.265 0.000
## RMA10 1.055 0.088 12.052 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.137 0.000
## RCO04 0.794 0.044 18.077 0.000
## RCO05 0.815 0.043 18.923 0.000
## RCO06 0.837 0.046 18.374 0.000
## RCO07 0.837 0.046 18.193 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.070 0.120 8.925 0.000
## control 1.413 0.155 9.106 0.000
## energia =~
## EN01 1.000
## EN02 1.027 0.044 23.512 0.000
## EN04 0.997 0.044 22.896 0.000
## EN05 0.994 0.042 23.866 0.000
## EN06 0.982 0.041 23.908 0.000
## EN07 1.045 0.045 23.091 0.000
## EN08 1.031 0.042 24.415 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.870 0.000
## EVI03 0.991 0.048 20.667 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.275 0.000
## EDE03 0.580 0.037 15.830 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.922 0.000
## EAB03 0.730 0.063 11.645 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.765 0.293 9.425 0.000
## absorcion 2.133 0.248 8.614 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.317 0.000
## dominio ~~
## recuperacion 0.851 0.149 5.702 0.000
## energia 1.326 0.209 6.355 0.000
## vigor 1.009 0.191 5.291 0.000
## dedicacion 0.989 0.207 4.777 0.000
## absorcion 0.865 0.184 4.689 0.000
## recuperacion ~~
## energia 1.357 0.196 6.911 0.000
## vigor 0.996 0.165 6.032 0.000
## dedicacion 1.045 0.180 5.809 0.000
## absorcion 0.779 0.151 5.168 0.000
## energia ~~
## vigor 2.044 0.249 8.221 0.000
## dedicacion 1.851 0.259 7.137 0.000
## absorcion 1.340 0.220 6.091 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.935 0.147 6.355 0.000
## .relajacion 0.493 0.084 5.838 0.000
## dominio 1.981 0.317 6.249 0.000
## .control 0.686 0.127 5.402 0.000
## energia 2.821 0.327 8.616 0.000
## vigor 2.858 0.289 9.897 0.000
## dedicacion 3.457 0.367 9.422 0.000
## absorcion 2.596 0.301 8.637 0.000
## .RPD01 1.168 0.119 9.781 0.000
## .RPD02 0.986 0.107 9.208 0.000
## .RPD03 1.434 0.147 9.730 0.000
## .RPD05 0.974 0.109 8.943 0.000
## .RPD06 1.834 0.184 9.979 0.000
## .RPD07 1.169 0.125 9.382 0.000
## .RPD08 1.483 0.152 9.738 0.000
## .RPD09 1.036 0.115 9.034 0.000
## .RPD10 1.043 0.116 8.980 0.000
## .RRE02 0.624 0.067 9.258 0.000
## .RRE03 0.649 0.072 8.986 0.000
## .RRE04 0.490 0.056 8.825 0.000
## .RRE05 0.381 0.047 8.189 0.000
## .RRE06 0.884 0.097 9.134 0.000
## .RRE07 0.932 0.098 9.466 0.000
## .RRE10 1.135 0.112 10.087 0.000
## .RMA02 1.739 0.175 9.935 0.000
## .RMA03 1.501 0.156 9.598 0.000
## .RMA04 0.858 0.098 8.791 0.000
## .RMA05 0.902 0.100 8.985 0.000
## .RMA06 1.627 0.158 10.281 0.000
## .RMA07 0.844 0.094 8.984 0.000
## .RMA08 0.879 0.097 9.029 0.000
## .RMA09 1.090 0.115 9.501 0.000
## .RMA10 1.258 0.131 9.596 0.000
## .RCO02 0.981 0.104 9.395 0.000
## .RCO03 0.494 0.058 8.479 0.000
## .RCO04 0.468 0.052 9.015 0.000
## .RCO05 0.391 0.045 8.606 0.000
## .RCO06 0.480 0.054 8.887 0.000
## .RCO07 0.504 0.056 8.967 0.000
## .EN01 0.691 0.072 9.665 0.000
## .EN02 0.440 0.048 9.072 0.000
## .EN04 0.475 0.051 9.263 0.000
## .EN05 0.380 0.043 8.943 0.000
## .EN06 0.367 0.041 8.927 0.000
## .EN07 0.501 0.054 9.206 0.000
## .EN08 0.358 0.041 8.709 0.000
## .EVI01 0.178 0.036 4.939 0.000
## .EVI02 0.241 0.038 6.290 0.000
## .EVI03 1.220 0.124 9.823 0.000
## .EDE01 0.397 0.065 6.091 0.000
## .EDE02 0.495 0.065 7.569 0.000
## .EDE03 0.837 0.085 9.891 0.000
## .EAB01 0.477 0.099 4.826 0.000
## .EAB02 1.012 0.109 9.303 0.000
## .EAB03 1.716 0.175 9.784 0.000
## recuperacion 0.989 0.201 4.927 0.000# Revisar los valores de comparativo fit index (CFI) y Tucker-Lewis (TLI)
# Excelente si es >= 0.95, Aceptable si entre 0.85 y 0.95, Deficiente < 0.90
# 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