SEM
Rodrigo Arroyo - A01747380
2025-02-19
Modelos de Ecuaciones Estructurales (SEM)
Teoria
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 e intragrupos, y validar modelos teóricos y empíricos.
Ejemplo 1. Estudio de Holzinger y Swineford (1939)
Contexto
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° (secundaria) de dos grandes 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 visuales (x1, x2, x3), habilidad textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolescentes.
Instalar paquetes y llamar librerías
#install.packages("lavaan")
#install.packages("lavaanPlot")library(lavaan)
library(lavaanPlot)Instalar base de datos
df1 <- HolzingerSwineford1939Entendimiento de la Base de Datos
# Resumen estadistico de 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
## # Tipo de datos
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 ...Tipos de Formulas
- Regresion (~) variables que dependen de otras.
- Variables Latentes (=~) No se observa, se infiere.
- Varianzas y Covarianzas (~~) Relaciones entre variables latentes y observadas.
- Varianza: Entre sí misma.
- Covarianza: Entre otras.
- Intercepto (~1) Valor esperado cuando las demás variables son 0.
Estructurar el Modelo
modelo_1 <- ' # Regresiones
# Variables Latentes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7 + x8 + x9
# Varianzas y Covarianza
visual ~~ visual
textual ~~ textual
velocidad ~~ velocidad
visual ~~ textual + velocidad
textual ~~ velocidad
# Intercepto
'Generar Analisis Factorial Confirmatorio (CFA)
cfa1 <- sem(modelo_1, 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 valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo Aceptado!! (Excelente)
Ejercicio 1. Democracia Politica e Industrialización
Contexto
la base de datos contiene distintas mediciones sobre la democracia política e industrialización 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 <- PoliticalDemocracyEntendimiento de la Base de Datos
# Resumen estadistico de 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. :6.425# Tipo de datos
str(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 ...Estructurar el Modelo
modelo_2 <- ' # Regresiones
# Variables Latentes
dato_1960 =~ y1 + y2 + y3 + y4
dato_1965 =~ y5 + y6 + y7 + y8
industrializacion =~ x1 + x2 + x3
# Varianzas y Covarianza
dato_1960 ~~ dato_1960
dato_1965 ~~ dato_1965
industrializacion ~~ industrializacion
dato_1960 ~~ dato_1965 + industrializacion
dato_1965 ~~ industrializacion
# Intercepto
'Generar Analisis Factorial Confirmatorio (CFA)
cfa2 <- sem(modelo_2, 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|)
## dato_1960 =~
## 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
## dato_1965 =~
## 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|)
## dato_1960 ~~
## dato_1965 4.487 0.911 4.924 0.000
## industrializcn 0.660 0.206 3.202 0.001
## dato_1965 ~~
## industrializcn 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## dato_1960 4.845 1.088 4.453 0.000
## dato_1965 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|)
## dato_1960 =~
## 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
## dato_1965 =~
## 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|)
## dato_1960 ~~
## dato_1965 4.487 0.911 4.924 0.000
## industrializcn 0.660 0.206 3.202 0.001
## dato_1965 ~~
## industrializcn 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## dato_1960 4.845 1.088 4.453 0.000
## dato_1965 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 valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo aceptado!! (Bueno)
Actividad 3. Bienestar de los Trabajadores
Importar librerias y paquetes
# install.packages("readxl")library(readxl)Importar la base de datos
df3 <- read_excel("Datos_SEM_Eng.xlsx")Entendimiento de la Base de Datos
# Resumen estadistico de 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. :7.000# Tipo de datos
str(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 ...Estructurar el Modelo 1: Conductas del Trabajador
modelo_3.1 <- ' # 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 + dominio + control
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
# Intercepto
'
# Recuperacion es de segundo ordenGenerar Analisis Factorial Confirmatorio (CFA)
cfa3.1 <- sem(modelo_3.1, data = df3)
summary(cfa3.1)## lavaan 0.6-19 ended normally after 47 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 8.787 0.000
## dominio 0.858 0.129 6.666 0.000
## control 1.341 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## .relajacion 0.333 0.089 3.757 0.000
## .dominio 1.260 0.212 5.942 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
## recuperacion 0.978 0.202 4.833 0.000lavaanPlot(cfa3.1, coef = TRUE, cov = TRUE)Evaluar el Modelo
summary(cfa3.1, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 47 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 8.787 0.000
## dominio 0.858 0.129 6.666 0.000
## control 1.341 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## .relajacion 0.333 0.089 3.757 0.000
## .dominio 1.260 0.212 5.942 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
## recuperacion 0.978 0.202 4.833 0.000#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir
otro
Estructurar el Modelo 2: Energia Recuperada
modelo_3.2 <- ' # Regresiones
# Variables Latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianza
energia ~~ energia
# Intercepto
'Generar Analisis Factorial Confirmatorio (CFA)
cfa3.2 <- sem(modelo_3.2, data = df3)
summary(cfa3.2)## 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(cfa3.2, coef = TRUE, cov = TRUE)Evaluar el Modelo
summary(cfa3.2, 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 valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo Aceptado!! (Excelente)
Estructurar el Modelo 3: Experiencias de Recuperacion
modelo_3.3 <- ' # 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 Analisis Factorial Confirmatorio (CFA)
cfa3.3 <- sem(modelo_3.3, data = df3)
summary(cfa3.3)## 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(cfa3.3, coef = TRUE, cov = TRUE)Evaluar el Modelo
summary(cfa3.3, 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 valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir
otro
Estructurar el Modelo 4: Modelo Completo
modelo_3.4 <- ' # Regresiones
# Variables Latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
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 + dominio + control
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion + energia + recuperacion
dedicacion ~~ absorcion + energia + recuperacion
absorcion ~~ energia + recuperacion
energia ~~ recuperacion
# Intercepto
'Generar Analisis Factorial Confirmatorio (CFA)
cfa3.4 <- sem(modelo_3.4, data = df3)
summary(cfa3.4)## lavaan 0.6-19 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1020
## 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 0.978 0.027 35.896 0.000
## EVI03 0.990 0.048 20.656 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.219 0.000
## EDE03 0.580 0.037 15.851 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.915 0.000
## EAB03 0.730 0.063 11.619 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.558 0.000
## EN04 0.996 0.043 22.912 0.000
## EN05 0.994 0.042 23.892 0.000
## EN06 0.981 0.041 23.944 0.000
## EN07 1.044 0.045 23.105 0.000
## EN08 1.031 0.042 24.449 0.000
## desapego =~
## RPD01 1.000
## RPD02 1.209 0.081 14.858 0.000
## RPD03 1.144 0.085 13.413 0.000
## RPD05 1.313 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 1.316 0.087 15.162 0.000
## RPD10 1.343 0.088 15.247 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.303 0.000
## RRE04 1.020 0.058 17.611 0.000
## RRE05 1.051 0.056 18.690 0.000
## RRE06 1.245 0.074 16.916 0.000
## RRE07 1.122 0.071 15.848 0.000
## RRE10 0.815 0.067 12.147 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.152 0.096 12.038 0.000
## RMA04 1.178 0.089 13.262 0.000
## RMA05 1.141 0.087 13.054 0.000
## RMA06 0.648 0.075 8.623 0.000
## RMA07 1.104 0.085 13.062 0.000
## RMA08 1.110 0.085 13.002 0.000
## RMA09 1.030 0.084 12.257 0.000
## RMA10 1.056 0.088 12.047 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.158 0.000
## RCO04 0.794 0.044 18.081 0.000
## RCO05 0.815 0.043 18.912 0.000
## RCO06 0.837 0.046 18.395 0.000
## RCO07 0.837 0.046 18.199 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.071 0.121 8.858 0.000
## dominio 0.900 0.129 6.965 0.000
## control 1.421 0.157 9.066 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
## energia 2.045 0.249 8.223 0.000
## recuperacion 1.007 0.165 6.098 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.316 0.000
## energia 1.852 0.259 7.139 0.000
## recuperacion 1.049 0.179 5.852 0.000
## absorcion ~~
## energia 1.340 0.220 6.091 0.000
## recuperacion 0.796 0.151 5.281 0.000
## energia ~~
## recuperacion 1.367 0.197 6.938 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 0.149 6.400 0.000
## .relajacion 0.510 0.085 6.021 0.000
## .dominio 1.191 0.200 5.958 0.000
## .control 0.699 0.125 5.583 0.000
## energia 2.823 0.327 8.623 0.000
## vigor 2.859 0.289 9.900 0.000
## dedicacion 3.458 0.367 9.424 0.000
## absorcion 2.595 0.301 8.628 0.000
## .EVI01 0.177 0.036 4.919 0.000
## .EVI02 0.242 0.038 6.298 0.000
## .EVI03 1.222 0.124 9.826 0.000
## .EDE01 0.395 0.065 6.060 0.000
## .EDE02 0.498 0.066 7.579 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
## .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 8.945 0.000
## .EN06 0.367 0.041 8.925 0.000
## .EN07 0.502 0.055 9.210 0.000
## .EN08 0.358 0.041 8.708 0.000
## .RPD01 1.169 0.120 9.782 0.000
## .RPD02 0.984 0.107 9.204 0.000
## .RPD03 1.435 0.147 9.730 0.000
## .RPD05 0.973 0.109 8.940 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 9.034 0.000
## .RPD10 1.044 0.116 8.982 0.000
## .RRE02 0.623 0.067 9.253 0.000
## .RRE03 0.646 0.072 8.974 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
## .RMA02 1.742 0.175 9.935 0.000
## .RMA03 1.500 0.156 9.595 0.000
## .RMA04 0.857 0.098 8.786 0.000
## .RMA05 0.904 0.101 8.985 0.000
## .RMA06 1.626 0.158 10.280 0.000
## .RMA07 0.843 0.094 8.978 0.000
## .RMA08 0.881 0.098 9.029 0.000
## .RMA09 1.089 0.115 9.498 0.000
## .RMA10 1.256 0.131 9.591 0.000
## .RCO02 0.980 0.104 9.394 0.000
## .RCO03 0.493 0.058 8.473 0.000
## .RCO04 0.468 0.052 9.019 0.000
## .RCO05 0.393 0.046 8.620 0.000
## .RCO06 0.479 0.054 8.883 0.000
## .RCO07 0.504 0.056 8.969 0.000
## recuperacion 0.972 0.199 4.896 0.000lavaanPlot(cfa3.4, coef = TRUE, cov = TRUE)Evaluar el Modelo
summary(cfa3.4, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1020
## 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) -15426.580
## Loglikelihood unrestricted model (H1) -14203.926
##
## Akaike (AIC) 31069.161
## Bayesian (BIC) 31437.135
## Sample-size adjusted Bayesian (SABIC) 31094.870
##
## 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 0.369
##
## 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 0.978 0.027 35.896 0.000
## EVI03 0.990 0.048 20.656 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.219 0.000
## EDE03 0.580 0.037 15.851 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.915 0.000
## EAB03 0.730 0.063 11.619 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.558 0.000
## EN04 0.996 0.043 22.912 0.000
## EN05 0.994 0.042 23.892 0.000
## EN06 0.981 0.041 23.944 0.000
## EN07 1.044 0.045 23.105 0.000
## EN08 1.031 0.042 24.449 0.000
## desapego =~
## RPD01 1.000
## RPD02 1.209 0.081 14.858 0.000
## RPD03 1.144 0.085 13.413 0.000
## RPD05 1.313 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 1.316 0.087 15.162 0.000
## RPD10 1.343 0.088 15.247 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.303 0.000
## RRE04 1.020 0.058 17.611 0.000
## RRE05 1.051 0.056 18.690 0.000
## RRE06 1.245 0.074 16.916 0.000
## RRE07 1.122 0.071 15.848 0.000
## RRE10 0.815 0.067 12.147 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.152 0.096 12.038 0.000
## RMA04 1.178 0.089 13.262 0.000
## RMA05 1.141 0.087 13.054 0.000
## RMA06 0.648 0.075 8.623 0.000
## RMA07 1.104 0.085 13.062 0.000
## RMA08 1.110 0.085 13.002 0.000
## RMA09 1.030 0.084 12.257 0.000
## RMA10 1.056 0.088 12.047 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.158 0.000
## RCO04 0.794 0.044 18.081 0.000
## RCO05 0.815 0.043 18.912 0.000
## RCO06 0.837 0.046 18.395 0.000
## RCO07 0.837 0.046 18.199 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.071 0.121 8.858 0.000
## dominio 0.900 0.129 6.965 0.000
## control 1.421 0.157 9.066 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
## energia 2.045 0.249 8.223 0.000
## recuperacion 1.007 0.165 6.098 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.316 0.000
## energia 1.852 0.259 7.139 0.000
## recuperacion 1.049 0.179 5.852 0.000
## absorcion ~~
## energia 1.340 0.220 6.091 0.000
## recuperacion 0.796 0.151 5.281 0.000
## energia ~~
## recuperacion 1.367 0.197 6.938 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 0.149 6.400 0.000
## .relajacion 0.510 0.085 6.021 0.000
## .dominio 1.191 0.200 5.958 0.000
## .control 0.699 0.125 5.583 0.000
## energia 2.823 0.327 8.623 0.000
## vigor 2.859 0.289 9.900 0.000
## dedicacion 3.458 0.367 9.424 0.000
## absorcion 2.595 0.301 8.628 0.000
## .EVI01 0.177 0.036 4.919 0.000
## .EVI02 0.242 0.038 6.298 0.000
## .EVI03 1.222 0.124 9.826 0.000
## .EDE01 0.395 0.065 6.060 0.000
## .EDE02 0.498 0.066 7.579 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
## .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 8.945 0.000
## .EN06 0.367 0.041 8.925 0.000
## .EN07 0.502 0.055 9.210 0.000
## .EN08 0.358 0.041 8.708 0.000
## .RPD01 1.169 0.120 9.782 0.000
## .RPD02 0.984 0.107 9.204 0.000
## .RPD03 1.435 0.147 9.730 0.000
## .RPD05 0.973 0.109 8.940 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 9.034 0.000
## .RPD10 1.044 0.116 8.982 0.000
## .RRE02 0.623 0.067 9.253 0.000
## .RRE03 0.646 0.072 8.974 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
## .RMA02 1.742 0.175 9.935 0.000
## .RMA03 1.500 0.156 9.595 0.000
## .RMA04 0.857 0.098 8.786 0.000
## .RMA05 0.904 0.101 8.985 0.000
## .RMA06 1.626 0.158 10.280 0.000
## .RMA07 0.843 0.094 8.978 0.000
## .RMA08 0.881 0.098 9.029 0.000
## .RMA09 1.089 0.115 9.498 0.000
## .RMA10 1.256 0.131 9.591 0.000
## .RCO02 0.980 0.104 9.394 0.000
## .RCO03 0.493 0.058 8.473 0.000
## .RCO04 0.468 0.052 9.019 0.000
## .RCO05 0.393 0.046 8.620 0.000
## .RCO06 0.479 0.054 8.883 0.000
## .RCO07 0.504 0.056 8.969 0.000
## recuperacion 0.972 0.199 4.896 0.000#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90Conclusión
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir
otro
---
title: "SEM"
author: "Rodrigo Arroyo - A01747380"
date: "2025-02-19"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: journal
---

# <span style = "color: blue;"> **Modelos de Ecuaciones Estructurales (SEM)** </span>

![](niño.gif)

# <span style = "color: blue;"> **Teoria** </span>

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 e intragrupos, y validar modelos teóricos y empíricos.

# <span style = "color: blue;"> **Ejemplo 1. Estudio de Holzinger y Swineford (1939)** </span>
## <span style = "color: blue;"> Contexto </span>

Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° (secundaria) de dos grandes 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 visuales* (x1, x2, x3), *habilidad textual* (x4, x5, x6) y *velocidad* (x7, x8, x9) de los adolescentes.

## <span style = "color: blue;"> Instalar paquetes y llamar librerías </span>
```{r message=FALSE, warning=FALSE}
#install.packages("lavaan")
#install.packages("lavaanPlot")
```

```{r message=FALSE, warning=FALSE}
library(lavaan)
library(lavaanPlot)
```

## <span style = "color: blue;"> Instalar base de datos </span>
```{r message=FALSE, warning=FALSE}
df1 <- HolzingerSwineford1939
```

## <span style = "color: blue;"> Entendimiento de la Base de Datos </span>
```{r message=FALSE, warning=FALSE}
# Resumen estadistico de la base de datos
summary(df1)
# Tipo de datos
str(df1)
```

## <span style = "color: blue;"> Tipos de Formulas </span>

1. **Regresion** (~) variables que dependen de otras.
2. **Variables Latentes** (=~) No se observa, se infiere.
3. **Varianzas y Covarianzas** (~~) Relaciones entre variables latentes y observadas.
  * Varianza: Entre sí misma.  
  * Covarianza: Entre otras.  
4. **Intercepto** (~1) Valor esperado cuando las demás variables son 0.


## <span style = "color: blue;"> Estructurar el Modelo </span>
```{r message=FALSE, warning=FALSE}
modelo_1 <- ' # Regresiones
              # Variables Latentes
                visual =~ x1 + x2 + x3
                textual =~ x4 + x5 + x6
                velocidad =~ x7 + x8 + x9
              # Varianzas y Covarianza
                visual ~~ visual
                textual ~~ textual
                velocidad ~~ velocidad
                visual ~~ textual + velocidad
                textual ~~ velocidad
              # Intercepto
'
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa1 <- sem(modelo_1, data = df1)
summary(cfa1)

lavaanPlot(cfa1, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa1, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo Aceptado!! (Excelente)


# <span style = "color: blue;"> **Ejercicio 1. Democracia Politica e Industrialización** </span>
##  <span style = "color: blue;"> Contexto </span>
la base de datos contiene distintas mediciones sobre la democracia política e industrialización 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  

##  <span style = "color: blue;"> Importar la Base de Datos </span>
```{r message=FALSE, warning=FALSE}
df2 <- PoliticalDemocracy
```


## <span style = "color: blue;"> Entendimiento de la Base de Datos </span>
```{r message=FALSE, warning=FALSE}
# Resumen estadistico de la base de datos
summary(df2)
# Tipo de datos
str(df2)
```

## <span style = "color: blue;"> Estructurar el Modelo </span>
```{r message=FALSE, warning=FALSE}
modelo_2 <- ' # Regresiones
              # Variables Latentes
                dato_1960 =~ y1 + y2 + y3 + y4
                dato_1965 =~ y5 + y6 + y7 + y8
                industrializacion =~ x1 + x2 + x3
              # Varianzas y Covarianza
                dato_1960 ~~ dato_1960
                dato_1965 ~~ dato_1965
                industrializacion ~~ industrializacion
                dato_1960 ~~ dato_1965 + industrializacion
                dato_1965 ~~ industrializacion
              # Intercepto
'
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa2 <- sem(modelo_2, data = df2)
summary(cfa2)

lavaanPlot(cfa2, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa2, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo aceptado!! (Bueno)

# <span style = "color: blue;"> **Actividad 3. Bienestar de los Trabajadores** </span>
## <span style = "color: blue;"> Importar librerias y paquetes </span>
```{r message=FALSE, warning=FALSE}
# install.packages("readxl")
```

```{r message=FALSE, warning=FALSE}
library(readxl)
```

## <span style = "color: blue;"> Importar la base de datos </span>
```{r message=FALSE, warning=FALSE}
df3 <- read_excel("Datos_SEM_Eng.xlsx")
```

## <span style = "color: blue;"> Entendimiento de la Base de Datos </span>
```{r message=FALSE, warning=FALSE}
# Resumen estadistico de la base de datos
summary(df3)
# Tipo de datos
str(df3)
```

## <span style = "color: blue;"> Estructurar el Modelo 1: Conductas del Trabajador </span>
```{r message=FALSE, warning=FALSE}
modelo_3.1 <- ' # 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 + dominio + control
              # Varianzas y Covarianza
                desapego ~~ desapego
                relajacion ~~ relajacion
                dominio ~~ dominio
                control ~~ control
              # Intercepto
'
# Recuperacion es de segundo orden
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa3.1 <- sem(modelo_3.1, data = df3)
summary(cfa3.1)

lavaanPlot(cfa3.1, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa3.1, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir otro

## <span style = "color: blue;"> Estructurar el Modelo 2: Energia Recuperada </span>
```{r message=FALSE, warning=FALSE}
modelo_3.2 <- ' # Regresiones
              # Variables Latentes
                energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
              # Varianzas y Covarianza
                energia ~~ energia
              # Intercepto
'
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa3.2 <- sem(modelo_3.2, data = df3)
summary(cfa3.2)

lavaanPlot(cfa3.2, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa3.2, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo Aceptado!! (Excelente)

## <span style = "color: blue;"> Estructurar el Modelo 3: Experiencias de Recuperacion </span>
```{r message=FALSE, warning=FALSE}
modelo_3.3 <- ' # 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
'
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa3.3 <- sem(modelo_3.3, data = df3)
summary(cfa3.3)

lavaanPlot(cfa3.3, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa3.3, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir otro

## <span style = "color: blue;"> Estructurar el Modelo 4: Modelo Completo </span>
```{r message=FALSE, warning=FALSE}
modelo_3.4 <- ' # Regresiones
              # Variables Latentes
                vigor =~ EVI01 + EVI02 + EVI03
                dedicacion =~ EDE01 + EDE02 + EDE03
                absorcion =~ EAB01 + EAB02 + EAB03
                energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
                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 + dominio + control
              # Varianzas y Covarianza
                desapego ~~ desapego
                relajacion ~~ relajacion
                dominio ~~ dominio
                control ~~ control
                energia ~~ energia
                vigor ~~ vigor
                dedicacion ~~ dedicacion
                absorcion ~~ absorcion
                vigor ~~ dedicacion + absorcion + energia + recuperacion
                dedicacion ~~ absorcion + energia + recuperacion
                absorcion ~~ energia + recuperacion
                energia ~~ recuperacion
              # Intercepto
'
```

## <span style = "color: blue;"> Generar Analisis Factorial Confirmatorio (CFA) </span>
```{r message=FALSE, warning=FALSE}
cfa3.4 <- sem(modelo_3.4, data = df3)
summary(cfa3.4)

lavaanPlot(cfa3.4, coef = TRUE, cov = TRUE)
```

## <span style = "color: blue;"> Evaluar el Modelo </span>
```{r message=FALSE, warning=FALSE}
summary(cfa3.4, fit.measures = TRUE)

#Revisar valores de Comparative FIT Index (CFI) y Tucker-Lewis Index (TLI)
# Excelente si es mayor a 0.95
# Aceptable entre 0.90 y 0.95
# Deficiente menor a 0.90
```

**Conclusión**  
Modelo Deficiente, se puede aceptar sin embargo se recomienda elegir otro

