Actividad_3_M1

![]

Teoría

Los Modelos de Ecuacaciones 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 valida modelos teóricos y empíricos.

Ejemplo 1- Estidio de Holzinger y Swineford (1939)

Contexto

Holdzinger y Swineford realzizaron exámenes de habilidad mental a adolescentes de 7° y 8° de dos escules (Pasteur y Grans-White).

La base de datos está incluida como paquete de R, e incluye las siguientes columnas:

• sex: Género(1= male 2=female)
• x1: Percepción Visual
• x2: Juego de cubos
• x3: Juegos con pastillas/espacial
• x4: Comprensión de párrafos
• x5: Completar Oraciones
• x6: Significado de Palabras
• x7: Sumas aceleradas
• x8: Conteo acelerado de puntos
• x9: Discriminacion acelerada de mayúsculas rectas y curvas

Se busca identificar las relaciones entre las habilidades visual (x1, x2, x3), textual ( x4, x5, x6) y velocidad (x7, x8, x9) de los adolescentes.

Práctica: * verbigracia: ejemplo * ex libris: sello para libros * aquelarre: reunion de brujas * beodo: borracho * carpe diem: aprovecha el día

Instalar paquetes y llamar librerías

#install.packages("lavaan")
library(lavaan)

## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.

#Lavaan: Análisis de variables Latntes 
#install.packages("lavaanPlot")
library(lavaanPlot)
#install.packages("readxl")
library(readxl)

Tipos de Fórmulas

1. Regresión (~) Variable que depende de otras
2. Variables Latentes (=~) No se observa, se infiere.
3. Varianzas y Covarianzas (~~) Relaciones entre variables latentes y observadas (Varianza entre si misma, Covarianza entre otras).
4. Intercepto (~1) Valor esperado cuendo las demás variables son cero.

Generar el 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
             #VARIABLE LATENTE
             visual=~ x1+x2+x3
             textual =~ x4+x5+x6
             velocidad=~ x7+x8+x9
             
             # VARIANZAS Y COVARIANZAS
             visual ~~ textual
             textual ~~ velocidad 
             velocidad ~~ visual
             # Intercepto
             '
sem_1 <- sem(modelo1, data=df1)
summary(sem_1)

## 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    0.000

lavaanPlot(sem_1, coef=TRUE, cov=TRUE)

En conclusión, la inteligencia de los adolescentes está compuesta por un grupo de factores que no se reducen a un solo 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.   :6.425

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 ...

modelo2 <- ' #Regresiones
            #VARIABLES LATENTES
            democracia1960 =~ y1 + y2 + y3 + y4
            democracia1965 =~ y5 + y6 + y7 + y8
            industrializacion1960 =~ x1 + x2 + x3
            
            #VARIANZA Y COVARIANZA
            democracia1965  ~~ democracia1960
            democracia1960  ~~ industrializacion1960
            democracia1965  ~~ industrializacion1960
            
            #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
##   industrializacion1960 =~                                    
##     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
##     indstrlzcn1960     0.660    0.206    3.202    0.001
##   democracia1965 ~~                                    
##     indstrlzcn1960     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
##     indstrlzcn1960    0.448    0.087    5.169    0.000

lavaanPlot(sem2, coef=TRUE,cov=TRUE)

En conclusion la industrializacion impulsa la democracia, y una democracia estable tiende a seguir estandolo

Actividad 2. 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 Recuperacion

df3 <- read_excel("C:\\Users\\Lgmm7\\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
            
            #VARIANZAS Y COVARIANZAS

    
            #INTERCEPTO
            ' 
sem3 <- sem(modelo3, data=df3)
summary(sem3)

## lavaan 0.6-19 ended normally after 55 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        66
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              1146.465
##   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             1.203    0.081   14.791    0.000
##     RPD03             1.143    0.085   13.423    0.000
##     RPD05             1.310    0.086   15.276    0.000
##     RPD06             1.086    0.088   12.283    0.000
##     RPD07             1.226    0.085   14.458    0.000
##     RPD08             1.163    0.086   13.491    0.000
##     RPD09             1.315    0.087   15.181    0.000
##     RPD10             1.344    0.088   15.295    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.120    0.065   17.291    0.000
##     RRE04             1.024    0.058   17.746    0.000
##     RRE05             1.054    0.056   18.814    0.000
##     RRE06             1.242    0.074   16.849    0.000
##     RRE07             1.114    0.071   15.685    0.000
##     RRE10             0.814    0.067   12.137    0.000
##   maestria =~                                         
##     RMA02             1.000                           
##     RMA03             1.155    0.096   12.066    0.000
##     RMA04             1.179    0.089   13.268    0.000
##     RMA05             1.141    0.087   13.051    0.000
##     RMA06             0.647    0.075    8.611    0.000
##     RMA07             1.103    0.085   13.047    0.000
##     RMA08             1.109    0.085   12.986    0.000
##     RMA09             1.030    0.084   12.251    0.000
##     RMA10             1.056    0.088   12.042    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.950    0.047   20.103    0.000
##     RCO04             0.790    0.043   18.546    0.000
##     RCO05             0.806    0.042   19.213    0.000
##     RCO06             0.809    0.045   17.826    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego ~~                                         
##     relajacion        1.156    0.165    7.024    0.000
##     maestria          0.696    0.156    4.476    0.000
##     control           1.315    0.201    6.535    0.000
##   relajacion ~~                                       
##     maestria          0.969    0.159    6.085    0.000
##     control           1.483    0.196    7.584    0.000
##   maestria ~~                                         
##     control           1.229    0.204    6.036    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .RPD01             1.167    0.119    9.777    0.000
##    .RPD02             1.006    0.109    9.240    0.000
##    .RPD03             1.435    0.147    9.728    0.000
##    .RPD05             0.988    0.110    8.968    0.000
##    .RPD06             1.818    0.182    9.968    0.000
##    .RPD07             1.177    0.125    9.390    0.000
##    .RPD08             1.454    0.150    9.710    0.000
##    .RPD09             1.035    0.115    9.028    0.000
##    .RPD10             1.034    0.115    8.956    0.000
##    .RRE02             0.623    0.067    9.265    0.000
##    .RRE03             0.649    0.072    8.999    0.000
##    .RRE04             0.481    0.055    8.795    0.000
##    .RRE05             0.373    0.046    8.142    0.000
##    .RRE06             0.894    0.098    9.167    0.000
##    .RRE07             0.954    0.100    9.512    0.000
##    .RRE10             1.136    0.113   10.092    0.000
##    .RMA02             1.742    0.175    9.934    0.000
##    .RMA03             1.487    0.155    9.579    0.000
##    .RMA04             0.854    0.097    8.771    0.000
##    .RMA05             0.904    0.101    8.980    0.000
##    .RMA06             1.628    0.158   10.280    0.000
##    .RMA07             0.847    0.094    8.984    0.000
##    .RMA08             0.885    0.098    9.035    0.000
##    .RMA09             1.090    0.115    9.496    0.000
##    .RMA10             1.257    0.131    9.589    0.000
##    .RCO02             0.924    0.102    9.071    0.000
##    .RCO03             0.422    0.056    7.592    0.000
##    .RCO04             0.452    0.052    8.652    0.000
##    .RCO05             0.395    0.048    8.277    0.000
##    .RCO06             0.566    0.063    8.967    0.000
##     desapego          1.926    0.275    7.005    0.000
##     relajacion        1.626    0.207    7.849    0.000
##     maestria          1.978    0.317    6.242    0.000
##     control           2.716    0.338    8.048    0.000

lavaanPlot(sem3, coef=TRUE,cov=TRUE)

Parte 2. Experiencias de Recuperacion

modelo4 <- ' #Regresiones
            #VARIABLES LATENTES
            energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
            
            
            
            
            
            
            
            #VARIANZAS Y COVARIANZAS

    
            #INTERCEPTO
            ' 
sem4 <- sem(modelo4, data=df3)
summary(sem3)

## lavaan 0.6-19 ended normally after 55 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        66
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              1146.465
##   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             1.203    0.081   14.791    0.000
##     RPD03             1.143    0.085   13.423    0.000
##     RPD05             1.310    0.086   15.276    0.000
##     RPD06             1.086    0.088   12.283    0.000
##     RPD07             1.226    0.085   14.458    0.000
##     RPD08             1.163    0.086   13.491    0.000
##     RPD09             1.315    0.087   15.181    0.000
##     RPD10             1.344    0.088   15.295    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.120    0.065   17.291    0.000
##     RRE04             1.024    0.058   17.746    0.000
##     RRE05             1.054    0.056   18.814    0.000
##     RRE06             1.242    0.074   16.849    0.000
##     RRE07             1.114    0.071   15.685    0.000
##     RRE10             0.814    0.067   12.137    0.000
##   maestria =~                                         
##     RMA02             1.000                           
##     RMA03             1.155    0.096   12.066    0.000
##     RMA04             1.179    0.089   13.268    0.000
##     RMA05             1.141    0.087   13.051    0.000
##     RMA06             0.647    0.075    8.611    0.000
##     RMA07             1.103    0.085   13.047    0.000
##     RMA08             1.109    0.085   12.986    0.000
##     RMA09             1.030    0.084   12.251    0.000
##     RMA10             1.056    0.088   12.042    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.950    0.047   20.103    0.000
##     RCO04             0.790    0.043   18.546    0.000
##     RCO05             0.806    0.042   19.213    0.000
##     RCO06             0.809    0.045   17.826    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego ~~                                         
##     relajacion        1.156    0.165    7.024    0.000
##     maestria          0.696    0.156    4.476    0.000
##     control           1.315    0.201    6.535    0.000
##   relajacion ~~                                       
##     maestria          0.969    0.159    6.085    0.000
##     control           1.483    0.196    7.584    0.000
##   maestria ~~                                         
##     control           1.229    0.204    6.036    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .RPD01             1.167    0.119    9.777    0.000
##    .RPD02             1.006    0.109    9.240    0.000
##    .RPD03             1.435    0.147    9.728    0.000
##    .RPD05             0.988    0.110    8.968    0.000
##    .RPD06             1.818    0.182    9.968    0.000
##    .RPD07             1.177    0.125    9.390    0.000
##    .RPD08             1.454    0.150    9.710    0.000
##    .RPD09             1.035    0.115    9.028    0.000
##    .RPD10             1.034    0.115    8.956    0.000
##    .RRE02             0.623    0.067    9.265    0.000
##    .RRE03             0.649    0.072    8.999    0.000
##    .RRE04             0.481    0.055    8.795    0.000
##    .RRE05             0.373    0.046    8.142    0.000
##    .RRE06             0.894    0.098    9.167    0.000
##    .RRE07             0.954    0.100    9.512    0.000
##    .RRE10             1.136    0.113   10.092    0.000
##    .RMA02             1.742    0.175    9.934    0.000
##    .RMA03             1.487    0.155    9.579    0.000
##    .RMA04             0.854    0.097    8.771    0.000
##    .RMA05             0.904    0.101    8.980    0.000
##    .RMA06             1.628    0.158   10.280    0.000
##    .RMA07             0.847    0.094    8.984    0.000
##    .RMA08             0.885    0.098    9.035    0.000
##    .RMA09             1.090    0.115    9.496    0.000
##    .RMA10             1.257    0.131    9.589    0.000
##    .RCO02             0.924    0.102    9.071    0.000
##    .RCO03             0.422    0.056    7.592    0.000
##    .RCO04             0.452    0.052    8.652    0.000
##    .RCO05             0.395    0.048    8.277    0.000
##    .RCO06             0.566    0.063    8.967    0.000
##     desapego          1.926    0.275    7.005    0.000
##     relajacion        1.626    0.207    7.849    0.000
##     maestria          1.978    0.317    6.242    0.000
##     control           2.716    0.338    8.048    0.000

lavaanPlot(sem4, coef=TRUE,cov=TRUE)

Parte 3. Engagment

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
            
            
            #PARTE 2
            energia  =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
            
            
            #PARTE 3
            vigor =~ EVI01 + EVI02 + EVI03
            decoracion =~ EDE01 + EDE02 + EDE03
            absorcion =~ EAB01 + EAB02
            
            #VARIANZAS Y COVARIANZAS

    
            #INTERCEPTO
            ' 
sem5 <- sem(modelo5, data=df3)
summary(sem5)

## lavaan 0.6-19 ended normally after 99 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       118
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              2225.147
##   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             1.204    0.081   14.857    0.000
##     RPD03             1.144    0.085   13.494    0.000
##     RPD05             1.310    0.085   15.358    0.000
##     RPD06             1.080    0.088   12.240    0.000
##     RPD07             1.226    0.084   14.506    0.000
##     RPD08             1.157    0.086   13.447    0.000
##     RPD09             1.313    0.086   15.209    0.000
##     RPD10             1.341    0.088   15.305    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.121    0.065   17.300    0.000
##     RRE04             1.022    0.058   17.635    0.000
##     RRE05             1.054    0.056   18.745    0.000
##     RRE06             1.244    0.074   16.858    0.000
##     RRE07             1.119    0.071   15.759    0.000
##     RRE10             0.817    0.067   12.169    0.000
##   maestria =~                                         
##     RMA02             1.000                           
##     RMA03             1.152    0.096   12.040    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    8.632    0.000
##     RMA07             1.103    0.085   13.054    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   12.054    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.947    0.047   20.035    0.000
##     RCO04             0.788    0.043   18.543    0.000
##     RCO05             0.805    0.042   19.214    0.000
##     RCO06             0.813    0.045   18.072    0.000
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.026    0.044   23.546    0.000
##     EN04              0.996    0.043   22.921    0.000
##     EN05              0.994    0.042   23.894    0.000
##     EN06              0.981    0.041   23.928    0.000
##     EN07              1.044    0.045   23.116    0.000
##     EN08              1.031    0.042   24.445    0.000
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.978    0.027   35.865    0.000
##     EVI03             0.991    0.048   20.692    0.000
##   decoracion =~                                       
##     EDE01             1.000                           
##     EDE02             0.913    0.035   26.447    0.000
##     EDE03             0.577    0.037   15.725    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.656    0.052   12.570    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego ~~                                         
##     relajacion        1.155    0.165    7.023    0.000
##     maestria          0.697    0.156    4.477    0.000
##     control           1.318    0.202    6.540    0.000
##     energia           1.387    0.204    6.786    0.000
##     vigor             1.051    0.187    5.635    0.000
##     decoracion        1.095    0.205    5.335    0.000
##     absorcion         0.861    0.181    4.755    0.000
##   relajacion ~~                                       
##     maestria          0.970    0.159    6.093    0.000
##     control           1.484    0.196    7.585    0.000
##     energia           1.372    0.188    7.290    0.000
##     vigor             0.957    0.168    5.690    0.000
##     decoracion        1.038    0.187    5.552    0.000
##     absorcion         0.766    0.164    4.682    0.000
##   maestria ~~                                         
##     control           1.231    0.204    6.039    0.000
##     energia           1.326    0.209    6.355    0.000
##     vigor             1.008    0.191    5.290    0.000
##     decoracion        0.989    0.207    4.778    0.000
##     absorcion         0.883    0.187    4.725    0.000
##   control ~~                                          
##     energia           1.976    0.253    7.822    0.000
##     vigor             1.487    0.226    6.577    0.000
##     decoracion        1.516    0.247    6.131    0.000
##     absorcion         1.218    0.218    5.582    0.000
##   energia ~~                                          
##     vigor             2.046    0.249    8.224    0.000
##     decoracion        1.854    0.260    7.141    0.000
##     absorcion         1.382    0.223    6.189    0.000
##   vigor ~~                                            
##     decoracion        2.769    0.294    9.432    0.000
##     absorcion         2.191    0.251    8.744    0.000
##   decoracion ~~                                       
##     absorcion         2.796    0.296    9.441    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .RPD01             1.162    0.119    9.777    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             1.837    0.184    9.983    0.000
##    .RPD07             1.172    0.125    9.392    0.000
##    .RPD08             1.475    0.152    9.735    0.000
##    .RPD09             1.038    0.115    9.046    0.000
##    .RPD10             1.043    0.116    8.987    0.000
##    .RRE02             0.625    0.067    9.272    0.000
##    .RRE03             0.646    0.072    8.988    0.000
##    .RRE04             0.490    0.055    8.840    0.000
##    .RRE05             0.377    0.046    8.178    0.000
##    .RRE06             0.890    0.097    9.159    0.000
##    .RRE07             0.941    0.099    9.491    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    9.599    0.000
##    .RMA04             0.854    0.097    8.786    0.000
##    .RMA05             0.907    0.101    9.001    0.000
##    .RMA06             1.625    0.158   10.280    0.000
##    .RMA07             0.847    0.094    8.995    0.000
##    .RMA08             0.883    0.098    9.042    0.000
##    .RMA09             1.086    0.114    9.498    0.000
##    .RMA10             1.254    0.131    9.593    0.000
##    .RCO02             0.920    0.101    9.088    0.000
##    .RCO03             0.436    0.056    7.750    0.000
##    .RCO04             0.456    0.052    8.704    0.000
##    .RCO05             0.398    0.048    8.343    0.000
##    .RCO06             0.544    0.061    8.910    0.000
##    .EN01              0.689    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              0.381    0.043    8.944    0.000
##    .EN06              0.368    0.041    8.931    0.000
##    .EN07              0.501    0.054    9.207    0.000
##    .EN08              0.358    0.041    8.710    0.000
##    .EVI01             0.176    0.036    4.908    0.000
##    .EVI02             0.244    0.038    6.338    0.000
##    .EVI03             1.219    0.124    9.824    0.000
##    .EDE01             0.389    0.064    6.054    0.000
##    .EDE02             0.493    0.065    7.595    0.000
##    .EDE03             0.847    0.085    9.914    0.000
##    .EAB01             0.377    0.122    3.086    0.002
##    .EAB02             1.150    0.120    9.589    0.000
##     desapego          1.931    0.275    7.019    0.000
##     relajacion        1.624    0.207    7.841    0.000
##     maestria          1.979    0.317    6.243    0.000
##     control           2.720    0.338    8.059    0.000
##     energia           2.823    0.327    8.622    0.000
##     vigor             2.860    0.289    9.903    0.000
##     decoracion        3.464    0.367    9.443    0.000
##     absorcion         2.696    0.312    8.654    0.000

lavaanPlot(sem5, coef=TRUE,cov=TRUE)

Conclusiones

Conclusion modelo 1: Las experiencias de recuperacion pueden entenderse como un conjunto de 4 dominios: desapego, relajacion, maestria, y control. Cada uno de ellos contribuye significativamente en la variable latente.

Conclusion modelo 2: La Energía recuperada es unidimensional, y sus vairables tambien contribuyen significativamente. De manera global, tanto la energia como las experiencias de recuperacion contribuyen significativamente en el engagment laboral, destacando la relacion de la dedicacion con la absorcion del trabajo.