Los Modelos de Ecuaciones Estructurales (SEM) es una técnica de análisis de estadistíca multivariada, que permite analizar patrones complejos de relaciones entre variables, realizar comparaciones entre e intragrupos, y validar modelos teóricos y empíricos.
Holdzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° de las dos escuelas (Pasteur y Grand-White).
La base de datos estaá incluida como paquete en R, e incluye las siguentes columnas; * sex: Género (1 = male, 2 = female) * x1: Percepción visual * x2: Juego de cubos * x3: Juego con pastillas/espacial * x4: Comprensión de parrafos * 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 enre las habilidades visual (x1, x2, x3), textual (x4, x5, x6) y velocidad (x7, x8, x9) de los adolescentes.
Práctica: * verbigracia: ejemplo * ex libris: sello para libros * aquelarre: reunión de brujas * beodo: borracho * carpe diem:
#install.packages("lavaan")
library(lavaan)## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.#install.packages("lavaanPlot")
library(lavaanPlot)
library(arules)## Cargando paquete requerido: Matrix##
## Adjuntando el paquete: 'arules'## The following object is masked from 'package:lavaan':
##
## inspect## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(readxl)Codigo base del modelo: modelo <- #Regresiones # Variables Latentes # Varianzas y covarianzas # Intercepto
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 covarianzas
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)La Base de datos contiene distintas mediciones sobre la democracia politica e industrializacion en países en desarrollo durante 1960 y 1965.
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. :6.425modelo2 <-' # Regresiones
# Variables Latentes
democracia1960 =~ y1 + y2 + y3 + y4
democracia1965 =~ y5 + y6 + y7 + y8
industria1960 =~ x1 + x2 + x3
# Varianzas y covarianzas
democracia1960 ~~ democracia1965
industria1960 ~~ democracia1960
industria1960 ~~ democracia1965
# 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|)
## democracia1960 =~
## 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
## democracia1965 =~
## 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
## industria1960 =~
## 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|)
## democracia1960 ~~
## democracia1965 4.487 0.911 4.924 0.000
## industria1960 0.660 0.206 3.202 0.001
## democracia1965 ~~
## industria1960 0.774 0.208 3.715 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
## democracia1960 4.845 1.088 4.453 0.000
## democracia1965 4.345 1.051 4.134 0.000
## industria1960 0.448 0.087 5.169 0.000lavaanPlot(sem2, coef=TRUE, cov=TRUE)En conclusion, la industrializacion impulsa una democración más estable.
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.
df3 <- read_excel("C:\\Users\\Chema\\Downloads\\Datos_SEM_Eng.xlsx")
modelo3 <- ' # Regresiones
# Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + 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 Covarianzas
# Intercepto
'
sem3 <- sem(modelo3, data=df3)
summary(sem3)## lavaan 0.6-19 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1033.143
## Degrees of freedom 399
## 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 1.202 0.078 15.376 0.000
## RPD03 1.140 0.082 13.855 0.000
## RPD05 1.307 0.082 15.888 0.000
## RPD06 1.022 0.087 11.721 0.000
## RPD07 1.211 0.082 14.772 0.000
## RPD09 1.286 0.084 15.315 0.000
## RPD10 1.315 0.085 15.449 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.119 0.065 17.262 0.000
## RRE04 1.024 0.058 17.728 0.000
## RRE05 1.055 0.056 18.792 0.000
## RRE06 1.243 0.074 16.864 0.000
## RRE07 1.115 0.071 15.692 0.000
## RRE10 0.814 0.067 12.123 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 13.051 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 19.234 0.000
## RCO04 0.795 0.044 18.133 0.000
## RCO05 0.816 0.043 18.985 0.000
## RCO06 0.834 0.046 18.249 0.000
## RCO07 0.834 0.046 18.081 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## desapego ~~
## relajacion 1.152 0.165 6.985 0.000
## maestria 0.709 0.158 4.496 0.000
## control 1.345 0.203 6.631 0.000
## relajacion ~~
## maestria 0.969 0.159 6.086 0.000
## control 1.483 0.195 7.611 0.000
## maestria ~~
## control 1.221 0.202 6.047 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .RPD01 1.113 0.115 9.651 0.000
## .RPD02 0.934 0.104 8.990 0.000
## .RPD03 1.379 0.144 9.607 0.000
## .RPD05 0.910 0.105 8.652 0.000
## .RPD06 2.021 0.201 10.039 0.000
## .RPD07 1.169 0.126 9.287 0.000
## .RPD09 1.093 0.121 9.025 0.000
## .RPD10 1.090 0.122 8.948 0.000
## .RRE02 0.624 0.067 9.266 0.000
## .RRE03 0.651 0.072 9.003 0.000
## .RRE04 0.481 0.055 8.794 0.000
## .RRE05 0.373 0.046 8.143 0.000
## .RRE06 0.890 0.097 9.155 0.000
## .RRE07 0.952 0.100 9.506 0.000
## .RRE10 1.137 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 8.979 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 0.979 0.104 9.375 0.000
## .RCO03 0.482 0.058 8.381 0.000
## .RCO04 0.463 0.052 8.967 0.000
## .RCO05 0.385 0.045 8.538 0.000
## .RCO06 0.493 0.055 8.916 0.000
## .RCO07 0.516 0.057 8.989 0.000
## desapego 1.980 0.277 7.142 0.000
## relajacion 1.625 0.207 7.844 0.000
## maestria 1.978 0.317 6.242 0.000
## control 2.661 0.335 7.931 0.000lavaanPlot(sem3, coef = TRUE, cov = TRUE)modelo4 <- ' # Regresiones
# Variables
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianzas
# 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)modelo5 <- ' # Regresiones
# Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + 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
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02
# Varianzas y Covarianzas
# Intercepto
'
sem5 <- sem(modelo5, data=df3)
summary(sem5)## lavaan 0.6-19 ended normally after 102 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 118
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2104.654
## Degrees of freedom 917
## 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 1.202 0.078 15.418 0.000
## RPD03 1.141 0.082 13.915 0.000
## RPD05 1.308 0.082 15.948 0.000
## RPD06 1.017 0.087 11.671 0.000
## RPD07 1.211 0.082 14.801 0.000
## RPD09 1.284 0.084 15.323 0.000
## RPD10 1.312 0.085 15.433 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.279 0.000
## RRE04 1.022 0.058 17.624 0.000
## RRE05 1.054 0.056 18.730 0.000
## RRE06 1.245 0.074 16.870 0.000
## RRE07 1.119 0.071 15.760 0.000
## RRE10 0.816 0.067 12.154 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 8.635 0.000
## RMA07 1.103 0.085 13.056 0.000
## RMA08 1.110 0.085 12.998 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 19.123 0.000
## RCO04 0.794 0.044 18.062 0.000
## RCO05 0.815 0.043 18.912 0.000
## RCO06 0.838 0.045 18.421 0.000
## RCO07 0.837 0.046 18.200 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.561 0.000
## EN04 0.996 0.043 22.936 0.000
## EN05 0.994 0.042 23.907 0.000
## EN06 0.981 0.041 23.935 0.000
## EN07 1.044 0.045 23.117 0.000
## EN08 1.031 0.042 24.450 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.977 0.027 35.866 0.000
## EVI03 0.991 0.048 20.696 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.912 0.034 26.464 0.000
## EDE03 0.576 0.037 15.708 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.655 0.052 12.558 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## desapego ~~
## relajacion 1.151 0.165 6.983 0.000
## maestria 0.709 0.158 4.495 0.000
## control 1.346 0.203 6.632 0.000
## energia 1.429 0.208 6.881 0.000
## vigor 1.109 0.190 5.823 0.000
## dedicacion 1.155 0.210 5.511 0.000
## absorcion 0.901 0.184 4.891 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 1.039 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 4.780 0.000
## absorcion 0.883 0.187 4.725 0.000
## control ~~
## energia 1.988 0.252 7.876 0.000
## vigor 1.493 0.225 6.641 0.000
## dedicacion 1.539 0.246 6.249 0.000
## absorcion 1.221 0.216 5.647 0.000
## energia ~~
## vigor 2.046 0.249 8.225 0.000
## dedicacion 1.855 0.260 7.143 0.000
## absorcion 1.382 0.223 6.189 0.000
## vigor ~~
## dedicacion 2.771 0.294 9.435 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 1.109 0.115 9.655 0.000
## .RPD02 0.930 0.103 8.995 0.000
## .RPD03 1.368 0.142 9.603 0.000
## .RPD05 0.901 0.104 8.645 0.000
## .RPD06 2.038 0.203 10.052 0.000
## .RPD07 1.167 0.126 9.296 0.000
## .RPD09 1.098 0.121 9.048 0.000
## .RPD10 1.102 0.123 8.986 0.000
## .RRE02 0.626 0.068 9.272 0.000
## .RRE03 0.647 0.072 8.990 0.000
## .RRE04 0.490 0.055 8.837 0.000
## .RRE05 0.377 0.046 8.176 0.000
## .RRE06 0.886 0.097 9.149 0.000
## .RRE07 0.940 0.099 9.487 0.000
## .RRE10 1.133 0.112 10.089 0.000
## .RMA02 1.742 0.175 9.938 0.000
## .RMA03 1.501 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 8.992 0.000
## .RMA08 0.883 0.098 9.041 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 9.398 0.000
## .RCO03 0.496 0.058 8.496 0.000
## .RCO04 0.470 0.052 9.027 0.000
## .RCO05 0.392 0.046 8.620 0.000
## .RCO06 0.476 0.054 8.871 0.000
## .RCO07 0.504 0.056 8.970 0.000
## .EN01 0.688 0.071 9.661 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 8.945 0.000
## .EN06 0.368 0.041 8.934 0.000
## .EN07 0.502 0.054 9.211 0.000
## .EN08 0.358 0.041 8.715 0.000
## .EVI01 0.176 0.036 4.904 0.000
## .EVI02 0.244 0.038 6.346 0.000
## .EVI03 1.219 0.124 9.825 0.000
## .EDE01 0.386 0.064 6.026 0.000
## .EDE02 0.494 0.065 7.611 0.000
## .EDE03 0.849 0.086 9.918 0.000
## .EAB01 0.375 0.122 3.067 0.002
## .EAB02 1.150 0.120 9.587 0.000
## desapego 1.983 0.277 7.152 0.000
## relajacion 1.624 0.207 7.837 0.000
## maestria 1.979 0.317 6.243 0.000
## control 2.660 0.335 7.930 0.000
## energia 2.824 0.327 8.624 0.000
## vigor 2.860 0.289 9.904 0.000
## dedicacion 3.467 0.367 9.451 0.000
## absorcion 2.698 0.312 8.656 0.000lavaanPlot(sem5, coef = TRUE, cov = TRUE)En conclusion las experiencias de recuperacion pueden entenderse como un conjunto de cuatro dominios: desapego, relajacion, maestria y control. Cada uno de ellos constribuye significativamente en la variable latente. La energia recuperada es unidimensional y sus variables tambien contribuyen significativamente. De manera global, tanto la energía como las experiencias de recuperación contribuyen significativamente en el engagement laboral, destacando la relación de la dedicación con la absorción del trabajo.