ACT3
Rodrigo Angulo A00834667
2025-08-18
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 e intragrupos, y validar modelos teóricos y empíricos.
Ejemplo 1. Estudio de Holzinger y Swineford
Contexto
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7 y 8 de dos escuelas (Pasteur y Grand-White).
sex: Género (1=male, 2=female)
x1: Percepción visual
x2: Juego de 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 y x6) y velocidad (x7, x8 y x9) de los adolecentes.
Practica: * verbigracia: ejemplo * ex libris: sello para libros * aquelarre: reunion de brujas * beodo: borracho * carpe diem: Aprovecha el día
Instalar paquetes y llamar librerias
#install.packages("lavaan")
library(lavaan)
#Lavaan: analisis de Variables Latentes
#install.packages("lavaanPlot")
library(lavaanPlot)
library(readxl)Tipos de formulas
- Regresion (~) Variable que depende de otras
- Variable latente (=~) No se observa, se infiere.
- Varianzas y covarianzas (~~) Relaciones entre variables latentes y observadas (Varianza entre si misma, covarianza entre otras)
- Intercepto (~1) valor esperado cuando las demas variables son cero
Codigo de lo que hace el modelo modelo1 <- ’ #Regresiones #Variables Latentes #Varianzas y covarianza #Intercepto ’
Generar modelo
df1 <- HolzingerSwineford1939
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 ...modelo1 <- ' #Regresiones
#Variables Latentes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7 + x8 + x9
#Varianzas y covarianza
visual ~~ textual
textual ~~ velocidad
velocidad ~~visual
#Intercepto
'
sem1 <- sem(modelo1, data=df1)
summary(sem1)## 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
## textual ~~
## velocidad 0.173 0.049 3.518 0.000
## visual ~~
## velocidad 0.262 0.056 4.660 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .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
## 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 0.000lavaanPlot(sem1, coef=TRUE, cov=TRUE)En conclusión la inteligencia de los adolecentes esta compuesta por un grupo de factores que no se reducen a un sólo número.
Ejercicio 1. Democracia Política e Industrialización
Contexto
La base de datos contiene distintas mediciones sobre la democrecia politica e industralizacion 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 posicion politica en 1960 y3: imparcialidad de elecciones en1960 y4: eficacion de la legislatura electa en 1960 y5: Calificaciones sobre libertad de prensa en 1965 y6: Libertad de la posicion politica en 1965 y7: imparcialidad de elecciones en1965 y8: eficacion de la legislatura electa en 1965 x1: PIB per cápita en 1960 x2: Consumo de energia inanimada per cápita en 1960 *x3: Porcentaje de la fuerza laboral en la industria en 1960
## Generar el modelo
df2 <- PoliticalDemocracy
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 ...modelo2 <- ' #Regresiones
#Variables Latentes
Democracia60 =~ y1 + y2 + y3 + y4
Democracia65 =~ y5 + y6 + y7 + y8
Industria60 =~ x1 + x2 + x3
#Varianzas y covarianza
Democracia60 ~~ Industria60
Democracia65 ~~ Industria60
#Intercepto
'
sem2 <- sem(modelo2, data=df2)
summary(sem2)## 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|)
## Democracia60 =~
## 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
## Democracia65 =~
## 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
## Industria60 =~
## 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|)
## Democracia60 ~~
## Industria60 0.660 0.206 3.202 0.001
## Democracia65 ~~
## Industria60 0.774 0.208 3.715 0.000
## Democracia60 ~~
## Democracia65 4.487 0.911 4.924 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .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
## Democracia60 4.845 1.088 4.453 0.000
## Democracia65 4.345 1.051 4.134 0.000
## Industria60 0.448 0.087 5.169 0.000lavaanPlot(sem2, coef=TRUE, cov=TRUE)En conclusión la industralización impulsa la democracia, y una democracia estable, tiene a segir estándolo.
Actividad 3. Bienestar de Colaboradores
Contexto
Uno de los retos más importantes de las organizaciones es entender el estado y bienestar de los colaboradores, ya que puede impactar directamente en el desempeño y el logro de los objetivos.
Parte 1. Experiencias de Recuperación
df3 <- read_excel("C:\\Users\\admin\\Downloads\\Datos_SEM_Eng.xlsx")
modelo3 <- ' #Regresiones
#Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 +RPD06 +RPD07 +RPD08 +RPD09 +RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
maestria =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06+ RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
#Varianzas y covarianza
#Intercepto
'
sem3 <- sem(modelo3, data=df3)
summary(sem3)## lavaan 0.6-19 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 68
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1215.404
## Degrees of freedom 428
## 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 1.204 0.081 14.786 0.000
## RPD03 1.143 0.085 13.420 0.000
## RPD05 1.310 0.086 15.269 0.000
## RPD06 1.086 0.088 12.282 0.000
## RPD07 1.227 0.085 14.451 0.000
## RPD08 1.163 0.086 13.487 0.000
## RPD09 1.315 0.087 15.175 0.000
## RPD10 1.345 0.088 15.290 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.268 0.000
## RRE04 1.024 0.058 17.732 0.000
## RRE05 1.055 0.056 18.798 0.000
## RRE06 1.243 0.074 16.857 0.000
## RRE07 1.115 0.071 15.687 0.000
## RRE10 0.815 0.067 12.135 0.000
## maestria =~
## RMA02 1.000
## RMA03 1.155 0.096 12.060 0.000
## RMA04 1.179 0.089 13.267 0.000
## RMA05 1.141 0.087 13.049 0.000
## RMA06 0.647 0.075 8.618 0.000
## RMA07 1.104 0.085 13.050 0.000
## RMA08 1.109 0.085 12.985 0.000
## RMA09 1.030 0.084 12.251 0.000
## RMA10 1.056 0.088 12.039 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.230 0.000
## RCO04 0.795 0.044 18.125 0.000
## RCO05 0.817 0.043 18.981 0.000
## RCO06 0.834 0.046 18.247 0.000
## RCO07 0.834 0.046 18.078 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## desapego ~~
## relajacion 1.155 0.164 7.023 0.000
## maestria 0.696 0.155 4.477 0.000
## control 1.319 0.200 6.584 0.000
## relajacion ~~
## maestria 0.969 0.159 6.085 0.000
## control 1.483 0.195 7.610 0.000
## maestria ~~
## control 1.221 0.202 6.047 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .RPD01 1.168 0.119 9.778 0.000
## .RPD02 1.005 0.109 9.240 0.000
## .RPD03 1.434 0.147 9.728 0.000
## .RPD05 0.989 0.110 8.969 0.000
## .RPD06 1.817 0.182 9.968 0.000
## .RPD07 1.177 0.125 9.391 0.000
## .RPD08 1.454 0.150 9.710 0.000
## .RPD09 1.035 0.115 9.028 0.000
## .RPD10 1.033 0.115 8.956 0.000
## .RRE02 0.624 0.067 9.269 0.000
## .RRE03 0.651 0.072 9.005 0.000
## .RRE04 0.481 0.055 8.798 0.000
## .RRE05 0.373 0.046 8.147 0.000
## .RRE06 0.891 0.097 9.162 0.000
## .RRE07 0.953 0.100 9.511 0.000
## .RRE10 1.136 0.113 10.092 0.000
## .RMA02 1.742 0.175 9.934 0.000
## .RMA03 1.489 0.155 9.581 0.000
## .RMA04 0.854 0.097 8.772 0.000
## .RMA05 0.904 0.101 8.981 0.000
## .RMA06 1.627 0.158 10.279 0.000
## .RMA07 0.846 0.094 8.980 0.000
## .RMA08 0.885 0.098 9.035 0.000
## .RMA09 1.090 0.115 9.496 0.000
## .RMA10 1.258 0.131 9.590 0.000
## .RCO02 0.980 0.105 9.375 0.000
## .RCO03 0.482 0.057 8.379 0.000
## .RCO04 0.463 0.052 8.967 0.000
## .RCO05 0.385 0.045 8.536 0.000
## .RCO06 0.493 0.055 8.915 0.000
## .RCO07 0.516 0.057 8.987 0.000
## desapego 1.925 0.275 7.002 0.000
## relajacion 1.625 0.207 7.845 0.000
## maestria 1.978 0.317 6.241 0.000
## control 2.660 0.335 7.930 0.000lavaanPlot(sem3, coef=TRUE, cov=TRUE)Parte 2. Experiencias de Recuperacion
modelo4 <- ' #Regresiones
#Variables Latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
#Varianzas y covarianza
#Intercepto
'
sem4 <- sem(modelo4, data=df3)
summary(sem4)## 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|)
## .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
## energia 2.801 0.327 8.565 0.000lavaanPlot(sem4, coef=TRUE, cov=TRUE)Parte 3. Engagement laboral
modelo5 <- ' #Regresiones
#Variables Latentes
#Parte 1
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 +RPD06 +RPD07 +RPD08 +RPD09 +RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
maestria =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06+ RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
#Parte 2
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
#Parte 3
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02
#Varianzas y covarianza
#Intercepto
'
sem5 <- sem(modelo5, data=df3)
summary(sem5)## lavaan 0.6-19 ended normally after 103 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 120
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2313.998
## Degrees of freedom 961
## 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 1.204 0.081 14.854 0.000
## RPD03 1.144 0.085 13.492 0.000
## RPD05 1.311 0.085 15.353 0.000
## RPD06 1.080 0.088 12.240 0.000
## RPD07 1.226 0.085 14.502 0.000
## RPD08 1.157 0.086 13.445 0.000
## RPD09 1.313 0.086 15.205 0.000
## RPD10 1.341 0.088 15.302 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.282 0.000
## RRE04 1.022 0.058 17.629 0.000
## RRE05 1.054 0.056 18.736 0.000
## RRE06 1.245 0.074 16.864 0.000
## RRE07 1.119 0.071 15.754 0.000
## RRE10 0.817 0.067 12.165 0.000
## maestria =~
## RMA02 1.000
## RMA03 1.152 0.096 12.038 0.000
## RMA04 1.179 0.089 13.273 0.000
## RMA05 1.140 0.087 13.046 0.000
## RMA06 0.648 0.075 8.634 0.000
## RMA07 1.103 0.085 13.056 0.000
## RMA08 1.110 0.085 12.997 0.000
## RMA09 1.031 0.084 12.268 0.000
## RMA10 1.057 0.088 12.052 0.000
## control =~
## RCO02 1.000
## RCO03 0.945 0.049 19.120 0.000
## RCO04 0.794 0.044 18.058 0.000
## RCO05 0.815 0.043 18.910 0.000
## RCO06 0.838 0.045 18.422 0.000
## RCO07 0.837 0.046 18.200 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.552 0.000
## EN04 0.996 0.043 22.929 0.000
## EN05 0.994 0.042 23.900 0.000
## EN06 0.981 0.041 23.931 0.000
## EN07 1.044 0.045 23.110 0.000
## EN08 1.031 0.042 24.444 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.863 0.000
## EVI03 0.991 0.048 20.695 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.912 0.034 26.456 0.000
## EDE03 0.576 0.037 15.716 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.655 0.052 12.563 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## desapego ~~
## relajacion 1.155 0.164 7.022 0.000
## maestria 0.697 0.156 4.477 0.000
## control 1.321 0.201 6.588 0.000
## energia 1.387 0.204 6.785 0.000
## vigor 1.051 0.186 5.635 0.000
## dedicacion 1.096 0.205 5.336 0.000
## absorcion 0.860 0.181 4.755 0.000
## relajacion ~~
## maestria 0.970 0.159 6.093 0.000
## control 1.482 0.195 7.609 0.000
## energia 1.372 0.188 7.290 0.000
## vigor 0.957 0.168 5.690 0.000
## dedicacion 1.038 0.187 5.553 0.000
## absorcion 0.766 0.164 4.682 0.000
## maestria ~~
## control 1.222 0.202 6.050 0.000
## energia 1.326 0.209 6.355 0.000
## vigor 1.008 0.191 5.290 0.000
## dedicacion 0.990 0.207 4.779 0.000
## absorcion 0.883 0.187 4.725 0.000
## control ~~
## energia 1.988 0.252 7.875 0.000
## vigor 1.492 0.225 6.641 0.000
## dedicacion 1.539 0.246 6.248 0.000
## absorcion 1.221 0.216 5.647 0.000
## energia ~~
## vigor 2.046 0.249 8.225 0.000
## dedicacion 1.854 0.260 7.142 0.000
## absorcion 1.382 0.223 6.189 0.000
## vigor ~~
## dedicacion 2.770 0.294 9.434 0.000
## absorcion 2.191 0.251 8.744 0.000
## dedicacion ~~
## absorcion 2.797 0.296 9.442 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .RPD01 1.162 0.119 9.778 0.000
## .RPD02 0.997 0.108 9.236 0.000
## .RPD03 1.422 0.146 9.722 0.000
## .RPD05 0.976 0.109 8.953 0.000
## .RPD06 1.836 0.184 9.983 0.000
## .RPD07 1.173 0.125 9.393 0.000
## .RPD08 1.475 0.151 9.734 0.000
## .RPD09 1.038 0.115 9.046 0.000
## .RPD10 1.043 0.116 8.986 0.000
## .RRE02 0.626 0.067 9.275 0.000
## .RRE03 0.647 0.072 8.994 0.000
## .RRE04 0.490 0.055 8.840 0.000
## .RRE05 0.377 0.046 8.179 0.000
## .RRE06 0.888 0.097 9.156 0.000
## .RRE07 0.941 0.099 9.492 0.000
## .RRE10 1.131 0.112 10.089 0.000
## .RMA02 1.742 0.175 9.938 0.000
## .RMA03 1.500 0.156 9.600 0.000
## .RMA04 0.854 0.097 8.786 0.000
## .RMA05 0.907 0.101 9.001 0.000
## .RMA06 1.624 0.158 10.280 0.000
## .RMA07 0.846 0.094 8.993 0.000
## .RMA08 0.883 0.098 9.042 0.000
## .RMA09 1.086 0.114 9.498 0.000
## .RMA10 1.255 0.131 9.594 0.000
## .RCO02 0.981 0.104 9.399 0.000
## .RCO03 0.496 0.058 8.496 0.000
## .RCO04 0.470 0.052 9.028 0.000
## .RCO05 0.392 0.046 8.620 0.000
## .RCO06 0.475 0.054 8.870 0.000
## .RCO07 0.503 0.056 8.969 0.000
## .EN01 0.689 0.071 9.662 0.000
## .EN02 0.439 0.048 9.070 0.000
## .EN04 0.475 0.051 9.263 0.000
## .EN05 0.380 0.043 8.944 0.000
## .EN06 0.368 0.041 8.933 0.000
## .EN07 0.502 0.054 9.211 0.000
## .EN08 0.358 0.041 8.714 0.000
## .EVI01 0.176 0.036 4.910 0.000
## .EVI02 0.244 0.038 6.341 0.000
## .EVI03 1.219 0.124 9.824 0.000
## .EDE01 0.387 0.064 6.037 0.000
## .EDE02 0.494 0.065 7.606 0.000
## .EDE03 0.848 0.086 9.917 0.000
## .EAB01 0.376 0.122 3.075 0.002
## .EAB02 1.150 0.120 9.588 0.000
## desapego 1.931 0.275 7.018 0.000
## relajacion 1.624 0.207 7.838 0.000
## maestria 1.979 0.317 6.243 0.000
## control 2.659 0.335 7.930 0.000
## energia 2.823 0.327 8.623 0.000
## vigor 2.860 0.289 9.903 0.000
## dedicacion 3.466 0.367 9.448 0.000
## absorcion 2.697 0.312 8.655 0.000lavaanPlot(sem5, coef=TRUE, cov=TRUE)Conclusiones
En conclusion las experiencias de recuperación pueden entenderse como un conjunto de 4 dominios: desapego, relajación, maestria y control. Cada uno de ellos contrivuye significativamente en la variable latente. La energia recuperada es unidimensional, y sus variables tambien contribuyen significativamente. De manera global, tanto la energia como las experiencias de recuperacion contribuyen significativamente en el engagement laboral, destacando la relacion de la dedicacin con la absorcion del trabajo