SEM

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.Holzinger y Swineford (1939)

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: Signficado de palabras
  • x7: Sumas aceleradas
  • x8: Conteo acelerado de puntos
  • x9: Discriminación acelerada de mayúsculas rectas y curvas

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

Librerias

library(lavaanPlot)
library(lavaan)
library(readxl)

Importar base de datos

df1 <- HolzingerSwineford1939

Importar base de datos

  1. Regresión (~) Variable que depende de otras
  2. Variables Latentes (=~) no se observa, se infiere
  3. Variables y Covarianzas (~~) Relaciones entre variables altentes y observada (Varianza: Entre si miisma, Covarianza: Entre otras)
  4. Intercepto (~1) Valor esperado cuando las demas variables son cero.

Estructurar el modelo

modelo1 <- ' # 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 Análisis Factorial Confirmatorio (CFA)

cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
## lavaan 0.6-19 ended normally after 35 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        21
## 
##   Number of observations                           301
## 
## Model Test User Model:
##                                                       
##   Test statistic                                85.306
##   Degrees of freedom                                24
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   visual =~                                           
##     x1                1.000                           
##     x2                0.554    0.100    5.554    0.000
##     x3                0.729    0.109    6.685    0.000
##   textual =~                                          
##     x4                1.000                           
##     x5                1.113    0.065   17.014    0.000
##     x6                0.926    0.055   16.703    0.000
##   velocidad =~                                        
##     x7                1.000                           
##     x8                1.180    0.165    7.152    0.000
##     x9                1.082    0.151    7.155    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   visual ~~                                           
##     textual           0.408    0.074    5.552    0.000
##     velocidad         0.262    0.056    4.660    0.000
##   textual ~~                                          
##     velocidad         0.173    0.049    3.518    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     visual            0.809    0.145    5.564    0.000
##     textual           0.979    0.112    8.737    0.000
##     velocidad         0.384    0.086    4.451    0.000
##    .x1                0.549    0.114    4.833    0.000
##    .x2                1.134    0.102   11.146    0.000
##    .x3                0.844    0.091    9.317    0.000
##    .x4                0.371    0.048    7.779    0.000
##    .x5                0.446    0.058    7.642    0.000
##    .x6                0.356    0.043    8.277    0.000
##    .x7                0.799    0.081    9.823    0.000
##    .x8                0.488    0.074    6.573    0.000
##    .x9                0.566    0.071    8.003    0.000
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)

Ejercicio 1. Democratizar política e industrialización

Contexto

La base de datos contiene distintas mediciones sobre la demoracia 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 politica 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 politica 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 base de datos

df2 <- PoliticalDemocracy

2. Entender la base de datos

summary(df2)
##        y1               y2               y3               y4        
##  Min.   : 1.250   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 2.900   1st Qu.: 0.000   1st Qu.: 3.767   1st Qu.: 1.581  
##  Median : 5.400   Median : 3.333   Median : 6.667   Median : 3.333  
##  Mean   : 5.465   Mean   : 4.256   Mean   : 6.563   Mean   : 4.453  
##  3rd Qu.: 7.500   3rd Qu.: 8.283   3rd Qu.:10.000   3rd Qu.: 6.667  
##  Max.   :10.000   Max.   :10.000   Max.   :10.000   Max.   :10.000  
##        y5               y6               y7               y8        
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 3.692   1st Qu.: 0.000   1st Qu.: 3.478   1st Qu.: 1.301  
##  Median : 5.000   Median : 2.233   Median : 6.667   Median : 3.333  
##  Mean   : 5.136   Mean   : 2.978   Mean   : 6.196   Mean   : 4.043  
##  3rd Qu.: 7.500   3rd Qu.: 4.207   3rd Qu.:10.000   3rd Qu.: 6.667  
##  Max.   :10.000   Max.   :10.000   Max.   :10.000   Max.   :10.000  
##        x1              x2              x3       
##  Min.   :3.784   Min.   :1.386   Min.   :1.002  
##  1st Qu.:4.477   1st Qu.:3.663   1st Qu.:2.300  
##  Median :5.075   Median :4.963   Median :3.568  
##  Mean   :5.054   Mean   :4.792   Mean   :3.558  
##  3rd Qu.:5.515   3rd Qu.:5.830   3rd Qu.:4.523  
##  Max.   :6.737   Max.   :7.872   Max.   :6.425
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 ...
head(df2)
##      y1       y2       y3       y4       y5       y6       y7       y8       x1
## 1  2.50 0.000000 3.333333 0.000000 1.250000 0.000000 3.726360 3.333333 4.442651
## 2  1.25 0.000000 3.333333 0.000000 6.250000 1.100000 6.666666 0.736999 5.384495
## 3  7.50 8.800000 9.999998 9.199991 8.750000 8.094061 9.999998 8.211809 5.961005
## 4  8.90 8.800000 9.999998 9.199991 8.907948 8.127979 9.999998 4.615086 6.285998
## 5 10.00 3.333333 9.999998 6.666666 7.500000 3.333333 9.999998 6.666666 5.863631
## 6  7.50 3.333333 6.666666 6.666666 6.250000 1.100000 6.666666 0.368500 5.533389
##         x2       x3
## 1 3.637586 2.557615
## 2 5.062595 3.568079
## 3 6.255750 5.224433
## 4 7.567863 6.267495
## 5 6.818924 4.573679
## 6 5.135798 3.892270

3. Estructurar el modelo

modelo2 <- '
  # Definir variables latentes de democratización en 1960 y 1965
  Dem1960 =~ y1 + y2 + y3 + y4
  Dem1965 =~ y5 + y6 + y7 + y8

  # Definir variable latente de industrialización
  Ind1960 =~ x1 + x2 + x3

  # Relacionar democratización de 1960 con 1965
  Dem1965 ~ Dem1960

  # Relacionar industrialización con democratización
  Dem1960 ~ Ind1960
  Dem1965 ~ Ind1960

  # Especificar varianzas y covarianzas
  Dem1960 ~~ Dem1960
  Dem1965 ~~ Dem1965
  Ind1960 ~~ Ind1960
  Dem1960 ~~ Ind1960
  Dem1965 ~~ Ind1960
  '

4. Generar el análisis factorial confirmatorio (CFA)

cfa2 <- sem(modelo2, data=df2, se="bootstrap")
summary(cfa2, standardized=TRUE, fit.measures=TRUE)
## lavaan 0.6-19 ended normally after 38 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        27
## 
##   Number of observations                            75
## 
## Model Test User Model:
##                                                       
##   Test statistic                                72.462
##   Degrees of freedom                                39
##   P-value (Chi-square)                           0.001
## 
## 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)                    0.950
##   Tucker-Lewis Index (TLI)                       0.930
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1564.959
##   Loglikelihood unrestricted model (H1)      -1528.728
##                                                       
##   Akaike (AIC)                                3183.918
##   Bayesian (BIC)                              3246.490
##   Sample-size adjusted Bayesian (SABIC)       3161.394
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.107
##   90 Percent confidence interval - lower         0.068
##   90 Percent confidence interval - upper         0.145
##   P-value H_0: RMSEA <= 0.050                    0.013
##   P-value H_0: RMSEA >= 0.080                    0.880
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.055
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Dem1960 =~                                                            
##     y1                1.000                               2.201    0.845
##     y2                1.354    0.173    7.831    0.000    2.980    0.760
##     y3                1.044    0.136    7.660    0.000    2.298    0.705
##     y4                1.300    0.143    9.063    0.000    2.860    0.860
##   Dem1965 =~                                                            
##     y5                1.000                               2.084    0.803
##     y6                1.258    0.224    5.615    0.000    2.623    0.783
##     y7                1.282    0.177    7.240    0.000    2.673    0.819
##     y8                1.310    0.212    6.177    0.000    2.730    0.847
##   Ind1960 =~                                                            
##     x1                1.000                               0.669    0.920
##     x2                2.182    0.155   14.113    0.000    1.461    0.973
##     x3                1.819    0.143   12.703    0.000    1.218    0.872
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Dem1965 ~                                                             
##     Dem1960           0.873    0.093    9.394    0.000    0.922    0.922
##   Dem1960 ~                                                             
##     Ind1960           1.565    0.116   13.514    0.000    0.476    0.476
##   Dem1965 ~                                                             
##     Ind1960           1.268    0.177    7.163    0.000    0.407    0.407
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .Dem1960 ~~                                                            
##     Ind1960          -0.041    0.095   -0.431    0.666   -0.031   -0.031
##  .Dem1965 ~~                                                            
##     Ind1960          -0.371    0.089   -4.169    0.000   -0.853   -0.853
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Dem1960           3.875    0.801    4.841    0.000    0.800    0.800
##    .Dem1965           0.422    0.154    2.733    0.006    0.097    0.097
##     Ind1960           0.448    0.073    6.138    0.000    1.000    1.000
##    .y1                1.942    0.401    4.844    0.000    1.942    0.286
##    .y2                6.490    1.394    4.656    0.000    6.490    0.422
##    .y3                5.340    1.078    4.953    0.000    5.340    0.503
##    .y4                2.887    0.663    4.354    0.000    2.887    0.261
##    .y5                2.390    0.556    4.300    0.000    2.390    0.355
##    .y6                4.343    0.878    4.949    0.000    4.343    0.387
##    .y7                3.510    0.603    5.820    0.000    3.510    0.329
##    .y8                2.940    0.830    3.544    0.000    2.940    0.283
##    .x1                0.082    0.018    4.519    0.000    0.082    0.154
##    .x2                0.118    0.073    1.630    0.103    0.118    0.053
##    .x3                0.467    0.083    5.619    0.000    0.467    0.240
lavaanPlot(cfa2, coef=TRUE, cov=TRUE)

Activdad 3: Aplicación de modelos de ecuaciones estructurales

Entender la base de datos

df3 <- read_excel("/Users/marcelotam/Desktop/M1_Est/Datos_SEM_Eng.xlsx")
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
head(df3)
## # A tibble: 6 × 51
##      ID   GEN EXPER  EDAD RPD01 RPD02 RPD03 RPD05 RPD06 RPD07 RPD08 RPD09 RPD10
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     1     1    22    45     5     1     3     2     3     1     3     2     4
## 2     2     1    22    44     4     4     6     5     3     2     3     4     4
## 3     3     1    30    52     7     7     7     7     7     6     7     7     7
## 4     4     1    17    41     5     5     1     1     3     5     3     2     2
## 5     5     1    23    51     7     6     7     6     7     6     7     6     6
## 6     6     0    31    52     3     4     5     4     3     5     4     4     4
## # ℹ 38 more variables: RRE02 <dbl>, RRE03 <dbl>, RRE04 <dbl>, RRE05 <dbl>,
## #   RRE06 <dbl>, RRE07 <dbl>, RRE10 <dbl>, RMA02 <dbl>, RMA03 <dbl>,
## #   RMA04 <dbl>, RMA05 <dbl>, RMA06 <dbl>, RMA07 <dbl>, RMA08 <dbl>,
## #   RMA09 <dbl>, RMA10 <dbl>, RCO02 <dbl>, RCO03 <dbl>, RCO04 <dbl>,
## #   RCO05 <dbl>, RCO06 <dbl>, RCO07 <dbl>, EN01 <dbl>, EN02 <dbl>, EN04 <dbl>,
## #   EN05 <dbl>, EN06 <dbl>, EN07 <dbl>, EN08 <dbl>, EVI01 <dbl>, EVI02 <dbl>,
## #   EVI03 <dbl>, EDE01 <dbl>, EDE02 <dbl>, EDE03 <dbl>, EAB01 <dbl>, …

Parte 1. Experiencias de Recuperación

modelo3_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
          '

Generar el análisis factorial confirmatorio (CFA)

cfa3_1 <- sem(modelo3_1, data=df3)
summary(cfa3_1, standardized=TRUE, 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|)   Std.lv  Std.all
##   desapego =~                                                           
##     RPD01             1.000                               1.386    0.788
##     RPD02             1.206    0.082   14.780    0.000    1.672    0.858
##     RPD03             1.143    0.085   13.374    0.000    1.584    0.797
##     RPD05             1.312    0.086   15.244    0.000    1.818    0.878
##     RPD06             1.088    0.089   12.266    0.000    1.507    0.745
##     RPD07             1.229    0.085   14.440    0.000    1.703    0.844
##     RPD08             1.164    0.087   13.447    0.000    1.613    0.800
##     RPD09             1.317    0.087   15.153    0.000    1.826    0.874
##     RPD10             1.346    0.088   15.258    0.000    1.866    0.878
##   relajacion =~                                                         
##     RRE02             1.000                               1.274    0.849
##     RRE03             1.120    0.065   17.227    0.000    1.427    0.870
##     RRE04             1.025    0.058   17.713    0.000    1.306    0.883
##     RRE05             1.055    0.056   18.758    0.000    1.344    0.910
##     RRE06             1.245    0.074   16.869    0.000    1.586    0.860
##     RRE07             1.117    0.071   15.689    0.000    1.423    0.825
##     RRE10             0.815    0.067   12.120    0.000    1.038    0.698
##   dominio =~                                                            
##     RMA02             1.000                               1.407    0.730
##     RMA03             1.155    0.096   12.079    0.000    1.626    0.800
##     RMA04             1.178    0.089   13.274    0.000    1.658    0.873
##     RMA05             1.141    0.087   13.072    0.000    1.606    0.861
##     RMA06             0.645    0.075    8.597    0.000    0.908    0.579
##     RMA07             1.103    0.084   13.061    0.000    1.552    0.860
##     RMA08             1.109    0.085   12.994    0.000    1.560    0.856
##     RMA09             1.028    0.084   12.246    0.000    1.447    0.810
##     RMA10             1.055    0.088   12.044    0.000    1.485    0.798
##   control =~                                                            
##     RCO02             1.000                               1.630    0.854
##     RCO03             0.948    0.049   19.182    0.000    1.545    0.912
##     RCO04             0.796    0.044   18.110    0.000    1.297    0.886
##     RCO05             0.818    0.043   18.990    0.000    1.333    0.907
##     RCO06             0.834    0.046   18.216    0.000    1.360    0.888
##     RCO07             0.835    0.046   18.057    0.000    1.361    0.884
##   recuperacion =~                                                       
##     desapego          1.000                               0.713    0.713
##     relajacion        1.149    0.131    8.787    0.000    0.892    0.892
##     dominio           0.858    0.129    6.666    0.000    0.603    0.603
##     control           1.341    0.156    8.605    0.000    0.813    0.813
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .desapego          0.943    0.152    6.207    0.000    0.491    0.491
##    .relajacion        0.333    0.089    3.757    0.000    0.205    0.205
##    .dominio           1.260    0.212    5.942    0.000    0.636    0.636
##    .control           0.900    0.159    5.666    0.000    0.339    0.339
##    .RPD01             1.172    0.120    9.782    0.000    1.172    0.379
##    .RPD02             0.999    0.108    9.228    0.000    0.999    0.263
##    .RPD03             1.441    0.148    9.733    0.000    1.441    0.365
##    .RPD05             0.987    0.110    8.964    0.000    0.987    0.230
##    .RPD06             1.817    0.182    9.967    0.000    1.817    0.444
##    .RPD07             1.173    0.125    9.383    0.000    1.173    0.288
##    .RPD08             1.460    0.150    9.714    0.000    1.460    0.360
##    .RPD09             1.032    0.114    9.021    0.000    1.032    0.236
##    .RPD10             1.034    0.115    8.955    0.000    1.034    0.229
##    .RRE02             0.626    0.068    9.274    0.000    0.626    0.278
##    .RRE03             0.653    0.073    9.011    0.000    0.653    0.243
##    .RRE04             0.481    0.055    8.794    0.000    0.481    0.220
##    .RRE05             0.374    0.046    8.153    0.000    0.374    0.172
##    .RRE06             0.886    0.097    9.149    0.000    0.886    0.260
##    .RRE07             0.950    0.100    9.505    0.000    0.950    0.319
##    .RRE10             1.137    0.113   10.093    0.000    1.137    0.513
##    .RMA02             1.740    0.175    9.931    0.000    1.740    0.468
##    .RMA03             1.485    0.155    9.575    0.000    1.485    0.360
##    .RMA04             0.855    0.097    8.772    0.000    0.855    0.237
##    .RMA05             0.899    0.100    8.967    0.000    0.899    0.259
##    .RMA06             1.631    0.159   10.281    0.000    1.631    0.664
##    .RMA07             0.845    0.094    8.977    0.000    0.845    0.260
##    .RMA08             0.886    0.098    9.034    0.000    0.886    0.267
##    .RMA09             1.094    0.115    9.500    0.000    1.094    0.343
##    .RMA10             1.259    0.131    9.590    0.000    1.259    0.363
##    .RCO02             0.983    0.105    9.379    0.000    0.983    0.270
##    .RCO03             0.484    0.058    8.391    0.000    0.484    0.169
##    .RCO04             0.462    0.052    8.963    0.000    0.462    0.215
##    .RCO05             0.382    0.045    8.513    0.000    0.382    0.177
##    .RCO06             0.494    0.055    8.917    0.000    0.494    0.211
##    .RCO07             0.515    0.057    8.985    0.000    0.515    0.218
##     recuperacion      0.978    0.202    4.833    0.000    1.000    1.000
lavaanPlot(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 los valores de comparative Fit Index (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90

Parte 2. Energía Recuperada

modelo3_2 <- ' # Regresiones
             # Variables latentes
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
             # Varianzas y Covarianza
             energia ~~ energia
             # Intercepto
          '

Generar el análisis factorial confirmatorio (CFA)

cfa3_2 <- sem(modelo3_2, data=df3, se="bootstrap")
summary(cfa3_2, standardized=TRUE, 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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   energia =~                                                            
##     EN01              1.000                               1.674    0.893
##     EN02              1.029    0.035   29.029    0.000    1.723    0.933
##     EN04              0.999    0.045   22.281    0.000    1.672    0.924
##     EN05              0.999    0.044   22.755    0.000    1.672    0.939
##     EN06              0.986    0.036   27.655    0.000    1.651    0.940
##     EN07              1.049    0.040   26.043    0.000    1.755    0.928
##     EN08              1.036    0.038   27.494    0.000    1.734    0.946
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     energia           2.801    0.263   10.641    0.000    1.000    1.000
##    .EN01              0.711    0.122    5.825    0.000    0.711    0.202
##    .EN02              0.444    0.061    7.328    0.000    0.444    0.130
##    .EN04              0.481    0.113    4.263    0.000    0.481    0.147
##    .EN05              0.375    0.077    4.852    0.000    0.375    0.118
##    .EN06              0.359    0.058    6.159    0.000    0.359    0.116
##    .EN07              0.499    0.102    4.885    0.000    0.499    0.139
##    .EN08              0.353    0.070    5.007    0.000    0.353    0.105
lavaanPlot(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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.029    0.035   29.029    0.000
##     EN04              0.999    0.045   22.281    0.000
##     EN05              0.999    0.044   22.755    0.000
##     EN06              0.986    0.036   27.655    0.000
##     EN07              1.049    0.040   26.043    0.000
##     EN08              1.036    0.038   27.494    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     energia           2.801    0.263   10.641    0.000
##    .EN01              0.711    0.122    5.825    0.000
##    .EN02              0.444    0.061    7.328    0.000
##    .EN04              0.481    0.113    4.263    0.000
##    .EN05              0.375    0.077    4.852    0.000
##    .EN06              0.359    0.058    6.159    0.000
##    .EN07              0.499    0.102    4.885    0.000
##    .EN08              0.353    0.070    5.007    0.000
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90

Parte 3. Analisis de Engagement Laboral

modelo3_3 <- ' # Regresiones
             # Variables latentes
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             # Intercepto
          '

Generar el análisis factorial confirmatorio (CFA)

cfa3_3 <- sem(modelo3_3, data=df3, se="bootstrap")
summary(cfa3_3, standardized=TRUE, 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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   vigor =~                                                              
##     EVI01             1.000                               1.684    0.967
##     EVI02             0.986    0.028   35.716    0.000    1.660    0.962
##     EVI03             0.995    0.053   18.860    0.000    1.675    0.835
##   dedicacion =~                                                         
##     EDE01             1.000                               1.857    0.946
##     EDE02             0.914    0.042   21.534    0.000    1.698    0.924
##     EDE03             0.583    0.080    7.257    0.000    1.082    0.765
##   absorcion =~                                                          
##     EAB01             1.000                               1.610    0.918
##     EAB02             0.708    0.103    6.904    0.000    1.140    0.750
##     EAB03             0.732    0.105    6.983    0.000    1.179    0.669
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   vigor ~~                                                              
##     absorcion         2.125    0.341    6.240    0.000    0.784    0.784
##     dedicacion        2.754    0.354    7.791    0.000    0.881    0.881
##   dedicacion ~~                                                         
##     absorcion         2.728    0.387    7.045    0.000    0.913    0.913
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     vigor             2.836    0.335    8.470    0.000    1.000    1.000
##     dedicacion        3.448    0.428    8.056    0.000    1.000    1.000
##    .EVI01             0.200    0.054    3.698    0.000    0.200    0.066
##    .EVI02             0.220    0.050    4.414    0.000    0.220    0.074
##    .EVI03             1.220    0.223    5.472    0.000    1.220    0.303
##    .EDE01             0.405    0.114    3.550    0.000    0.405    0.105
##    .EDE02             0.495    0.122    4.057    0.000    0.495    0.146
##    .EDE03             0.829    0.148    5.600    0.000    0.829    0.415
##    .EAB01             0.481    0.165    2.915    0.004    0.481    0.157
##    .EAB02             1.010    0.195    5.171    0.000    1.010    0.437
##    .EAB03             1.711    0.343    4.990    0.000    1.711    0.552
##     absorcion         2.592    0.409    6.333    0.000    1.000    1.000
lavaanPlot(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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.986    0.028   35.716    0.000
##     EVI03             0.995    0.053   18.860    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.914    0.042   21.534    0.000
##     EDE03             0.583    0.080    7.257    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.708    0.103    6.904    0.000
##     EAB03             0.732    0.105    6.983    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     absorcion         2.125    0.341    6.240    0.000
##     dedicacion        2.754    0.354    7.791    0.000
##   dedicacion ~~                                       
##     absorcion         2.728    0.387    7.045    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     vigor             2.836    0.335    8.470    0.000
##     dedicacion        3.448    0.428    8.056    0.000
##    .EVI01             0.200    0.054    3.698    0.000
##    .EVI02             0.220    0.050    4.414    0.000
##    .EVI03             1.220    0.223    5.472    0.000
##    .EDE01             0.405    0.114    3.550    0.000
##    .EDE02             0.495    0.122    4.057    0.000
##    .EDE03             0.829    0.148    5.600    0.000
##    .EAB01             0.481    0.165    2.915    0.004
##    .EAB02             1.010    0.195    5.171    0.000
##    .EAB03             1.711    0.343    4.990    0.000
##     absorcion         2.592    0.409    6.333    0.000
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90

Parte 4. Analisis de Engagement Laboral

modelo3_4 <- ' # 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
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08 
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             desapego ~~ desapego
             relajacion ~~ relajacion
             dominio ~~ dominio
             control ~~ control
             energia ~~ energia
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             recuperacion ~~ absorcion + dedicacion + energia + vigor
             energia ~~ vigor + dedicacion + absorcion
             # Intercepto
          '

Generar el análisis factorial confirmatorio (CFA)

cfa3_4 <- sem(modelo3_4, data=df3, se="bootstrap")
summary(cfa3_4, standardized=TRUE, 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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   desapego =~                                                           
##     RPD01             1.000                               1.387    0.789
##     RPD02             1.209    0.074   16.231    0.000    1.677    0.861
##     RPD03             1.144    0.069   16.524    0.000    1.586    0.798
##     RPD05             1.313    0.080   16.450    0.000    1.822    0.879
##     RPD06             1.083    0.102   10.629    0.000    1.502    0.743
##     RPD07             1.229    0.081   15.162    0.000    1.705    0.845
##     RPD08             1.157    0.101   11.448    0.000    1.605    0.796
##     RPD09             1.316    0.093   14.106    0.000    1.825    0.873
##     RPD10             1.343    0.090   14.942    0.000    1.863    0.877
##   relajacion =~                                                         
##     RRE02             1.000                               1.275    0.850
##     RRE03             1.121    0.072   15.468    0.000    1.429    0.872
##     RRE04             1.020    0.062   16.474    0.000    1.301    0.880
##     RRE05             1.051    0.060   17.597    0.000    1.341    0.908
##     RRE06             1.245    0.097   12.864    0.000    1.588    0.861
##     RRE07             1.122    0.086   13.027    0.000    1.430    0.829
##     RRE10             0.815    0.088    9.228    0.000    1.039    0.698
##   dominio =~                                                            
##     RMA02             1.000                               1.407    0.729
##     RMA03             1.152    0.072   16.108    0.000    1.621    0.798
##     RMA04             1.178    0.086   13.641    0.000    1.658    0.873
##     RMA05             1.141    0.083   13.807    0.000    1.604    0.860
##     RMA06             0.648    0.096    6.724    0.000    0.911    0.581
##     RMA07             1.104    0.088   12.512    0.000    1.553    0.861
##     RMA08             1.110    0.101   11.018    0.000    1.562    0.857
##     RMA09             1.030    0.095   10.873    0.000    1.449    0.811
##     RMA10             1.056    0.083   12.697    0.000    1.486    0.798
##   control =~                                                            
##     RCO02             1.000                               1.631    0.855
##     RCO03             0.946    0.046   20.747    0.000    1.543    0.910
##     RCO04             0.794    0.054   14.708    0.000    1.295    0.884
##     RCO05             0.815    0.057   14.367    0.000    1.329    0.904
##     RCO06             0.837    0.052   15.971    0.000    1.365    0.892
##     RCO07             0.837    0.055   15.333    0.000    1.365    0.887
##   recuperacion =~                                                       
##     desapego          1.000                               0.711    0.711
##     relajacion        1.071    0.130    8.229    0.000    0.828    0.828
##     dominio           0.900    0.140    6.446    0.000    0.631    0.631
##     control           1.421    0.157    9.075    0.000    0.859    0.859
##   energia =~                                                            
##     EN01              1.000                               1.680    0.897
##     EN02              1.026    0.035   29.539    0.000    1.724    0.934
##     EN04              0.996    0.045   22.362    0.000    1.674    0.924
##     EN05              0.994    0.043   22.988    0.000    1.670    0.938
##     EN06              0.981    0.035   27.991    0.000    1.649    0.939
##     EN07              1.044    0.040   26.334    0.000    1.754    0.927
##     EN08              1.031    0.037   27.601    0.000    1.732    0.945
##   vigor =~                                                              
##     EVI01             1.000                               1.691    0.970
##     EVI02             0.978    0.027   36.233    0.000    1.654    0.959
##     EVI03             0.990    0.053   18.791    0.000    1.675    0.835
##   dedicacion =~                                                         
##     EDE01             1.000                               1.860    0.947
##     EDE02             0.913    0.043   21.441    0.000    1.697    0.923
##     EDE03             0.580    0.081    7.174    0.000    1.079    0.763
##   absorcion =~                                                          
##     EAB01             1.000                               1.611    0.919
##     EAB02             0.707    0.103    6.871    0.000    1.140    0.750
##     EAB03             0.730    0.105    6.971    0.000    1.176    0.668
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   vigor ~~                                                              
##     absorcion         2.132    0.342    6.235    0.000    0.783    0.783
##     dedicacion        2.767    0.353    7.843    0.000    0.880    0.880
##   dedicacion ~~                                                         
##     absorcion         2.731    0.389    7.022    0.000    0.912    0.912
##   recuperacion ~~                                                       
##     absorcion         0.796    0.196    4.053    0.000    0.501    0.501
##     dedicacion        1.049    0.217    4.823    0.000    0.572    0.572
##     energia           1.367    0.189    7.240    0.000    0.825    0.825
##     vigor             1.007    0.188    5.358    0.000    0.604    0.604
##   energia ~~                                                            
##     vigor             2.045    0.247    8.289    0.000    0.720    0.720
##     dedicacion        1.852    0.276    6.715    0.000    0.593    0.593
##     absorcion         1.340    0.261    5.133    0.000    0.495    0.495
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .desapego          0.951    0.152    6.267    0.000    0.495    0.495
##    .relajacion        0.510    0.105    4.859    0.000    0.314    0.314
##    .dominio           1.191    0.214    5.564    0.000    0.602    0.602
##    .control           0.699    0.150    4.671    0.000    0.263    0.263
##     energia           2.823    0.262   10.778    0.000    1.000    1.000
##     vigor             2.859    0.333    8.585    0.000    1.000    1.000
##     dedicacion        3.458    0.428    8.078    0.000    1.000    1.000
##    .RPD01             1.169    0.157    7.448    0.000    1.169    0.378
##    .RPD02             0.984    0.155    6.332    0.000    0.984    0.259
##    .RPD03             1.435    0.208    6.883    0.000    1.435    0.363
##    .RPD05             0.973    0.147    6.598    0.000    0.973    0.227
##    .RPD06             1.835    0.252    7.284    0.000    1.835    0.449
##    .RPD07             1.166    0.162    7.215    0.000    1.166    0.286
##    .RPD08             1.485    0.236    6.299    0.000    1.485    0.366
##    .RPD09             1.036    0.209    4.967    0.000    1.036    0.237
##    .RPD10             1.044    0.192    5.450    0.000    1.044    0.231
##    .RRE02             0.623    0.105    5.910    0.000    0.623    0.277
##    .RRE03             0.646    0.123    5.239    0.000    0.646    0.240
##    .RRE04             0.494    0.128    3.868    0.000    0.494    0.226
##    .RRE05             0.384    0.125    3.066    0.002    0.384    0.176
##    .RRE06             0.882    0.126    6.988    0.000    0.882    0.259
##    .RRE07             0.929    0.231    4.026    0.000    0.929    0.312
##    .RRE10             1.134    0.188    6.045    0.000    1.134    0.512
##    .RMA02             1.742    0.234    7.436    0.000    1.742    0.468
##    .RMA03             1.500    0.270    5.558    0.000    1.500    0.363
##    .RMA04             0.857    0.116    7.413    0.000    0.857    0.238
##    .RMA05             0.904    0.188    4.817    0.000    0.904    0.260
##    .RMA06             1.626    0.176    9.252    0.000    1.626    0.662
##    .RMA07             0.843    0.133    6.321    0.000    0.843    0.259
##    .RMA08             0.881    0.156    5.651    0.000    0.881    0.265
##    .RMA09             1.089    0.179    6.090    0.000    1.089    0.342
##    .RMA10             1.256    0.225    5.581    0.000    1.256    0.363
##    .RCO02             0.980    0.142    6.889    0.000    0.980    0.269
##    .RCO03             0.493    0.106    4.654    0.000    0.493    0.171
##    .RCO04             0.468    0.105    4.481    0.000    0.468    0.218
##    .RCO05             0.393    0.071    5.539    0.000    0.393    0.182
##    .RCO06             0.479    0.111    4.309    0.000    0.479    0.204
##    .RCO07             0.504    0.093    5.418    0.000    0.504    0.213
##    .EN01              0.689    0.119    5.793    0.000    0.689    0.196
##    .EN02              0.439    0.058    7.533    0.000    0.439    0.129
##    .EN04              0.476    0.113    4.201    0.000    0.476    0.145
##    .EN05              0.381    0.077    4.925    0.000    0.381    0.120
##    .EN06              0.367    0.060    6.160    0.000    0.367    0.119
##    .EN07              0.502    0.100    5.025    0.000    0.502    0.140
##    .EN08              0.358    0.070    5.088    0.000    0.358    0.107
##    .EVI01             0.177    0.050    3.555    0.000    0.177    0.058
##    .EVI02             0.242    0.053    4.580    0.000    0.242    0.081
##    .EVI03             1.222    0.220    5.563    0.000    1.222    0.303
##    .EDE01             0.395    0.114    3.481    0.000    0.395    0.103
##    .EDE02             0.498    0.122    4.074    0.000    0.498    0.147
##    .EDE03             0.836    0.150    5.568    0.000    0.836    0.418
##    .EAB01             0.478    0.166    2.872    0.004    0.478    0.155
##    .EAB02             1.010    0.195    5.172    0.000    1.010    0.437
##    .EAB03             1.718    0.344    5.000    0.000    1.718    0.554
##     recuperacion      0.972    0.194    5.013    0.000    1.000    1.000
##     absorcion         2.595    0.409    6.339    0.000    1.000    1.000
lavaanPlot(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                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   desapego =~                                         
##     RPD01             1.000                           
##     RPD02             1.209    0.074   16.231    0.000
##     RPD03             1.144    0.069   16.524    0.000
##     RPD05             1.313    0.080   16.450    0.000
##     RPD06             1.083    0.102   10.629    0.000
##     RPD07             1.229    0.081   15.162    0.000
##     RPD08             1.157    0.101   11.448    0.000
##     RPD09             1.316    0.093   14.106    0.000
##     RPD10             1.343    0.090   14.942    0.000
##   relajacion =~                                       
##     RRE02             1.000                           
##     RRE03             1.121    0.072   15.468    0.000
##     RRE04             1.020    0.062   16.474    0.000
##     RRE05             1.051    0.060   17.597    0.000
##     RRE06             1.245    0.097   12.864    0.000
##     RRE07             1.122    0.086   13.027    0.000
##     RRE10             0.815    0.088    9.228    0.000
##   dominio =~                                          
##     RMA02             1.000                           
##     RMA03             1.152    0.072   16.108    0.000
##     RMA04             1.178    0.086   13.641    0.000
##     RMA05             1.141    0.083   13.807    0.000
##     RMA06             0.648    0.096    6.724    0.000
##     RMA07             1.104    0.088   12.512    0.000
##     RMA08             1.110    0.101   11.018    0.000
##     RMA09             1.030    0.095   10.873    0.000
##     RMA10             1.056    0.083   12.697    0.000
##   control =~                                          
##     RCO02             1.000                           
##     RCO03             0.946    0.046   20.747    0.000
##     RCO04             0.794    0.054   14.708    0.000
##     RCO05             0.815    0.057   14.367    0.000
##     RCO06             0.837    0.052   15.971    0.000
##     RCO07             0.837    0.055   15.333    0.000
##   recuperacion =~                                     
##     desapego          1.000                           
##     relajacion        1.071    0.130    8.229    0.000
##     dominio           0.900    0.140    6.446    0.000
##     control           1.421    0.157    9.075    0.000
##   energia =~                                          
##     EN01              1.000                           
##     EN02              1.026    0.035   29.539    0.000
##     EN04              0.996    0.045   22.362    0.000
##     EN05              0.994    0.043   22.988    0.000
##     EN06              0.981    0.035   27.991    0.000
##     EN07              1.044    0.040   26.334    0.000
##     EN08              1.031    0.037   27.601    0.000
##   vigor =~                                            
##     EVI01             1.000                           
##     EVI02             0.978    0.027   36.233    0.000
##     EVI03             0.990    0.053   18.791    0.000
##   dedicacion =~                                       
##     EDE01             1.000                           
##     EDE02             0.913    0.043   21.441    0.000
##     EDE03             0.580    0.081    7.174    0.000
##   absorcion =~                                        
##     EAB01             1.000                           
##     EAB02             0.707    0.103    6.871    0.000
##     EAB03             0.730    0.105    6.971    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   vigor ~~                                            
##     absorcion         2.132    0.342    6.235    0.000
##     dedicacion        2.767    0.353    7.843    0.000
##   dedicacion ~~                                       
##     absorcion         2.731    0.389    7.022    0.000
##   recuperacion ~~                                     
##     absorcion         0.796    0.196    4.053    0.000
##     dedicacion        1.049    0.217    4.823    0.000
##     energia           1.367    0.189    7.240    0.000
##     vigor             1.007    0.188    5.358    0.000
##   energia ~~                                          
##     vigor             2.045    0.247    8.289    0.000
##     dedicacion        1.852    0.276    6.715    0.000
##     absorcion         1.340    0.261    5.133    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .desapego          0.951    0.152    6.267    0.000
##    .relajacion        0.510    0.105    4.859    0.000
##    .dominio           1.191    0.214    5.564    0.000
##    .control           0.699    0.150    4.671    0.000
##     energia           2.823    0.262   10.778    0.000
##     vigor             2.859    0.333    8.585    0.000
##     dedicacion        3.458    0.428    8.078    0.000
##    .RPD01             1.169    0.157    7.448    0.000
##    .RPD02             0.984    0.155    6.332    0.000
##    .RPD03             1.435    0.208    6.883    0.000
##    .RPD05             0.973    0.147    6.598    0.000
##    .RPD06             1.835    0.252    7.284    0.000
##    .RPD07             1.166    0.162    7.215    0.000
##    .RPD08             1.485    0.236    6.299    0.000
##    .RPD09             1.036    0.209    4.967    0.000
##    .RPD10             1.044    0.192    5.450    0.000
##    .RRE02             0.623    0.105    5.910    0.000
##    .RRE03             0.646    0.123    5.239    0.000
##    .RRE04             0.494    0.128    3.868    0.000
##    .RRE05             0.384    0.125    3.066    0.002
##    .RRE06             0.882    0.126    6.988    0.000
##    .RRE07             0.929    0.231    4.026    0.000
##    .RRE10             1.134    0.188    6.045    0.000
##    .RMA02             1.742    0.234    7.436    0.000
##    .RMA03             1.500    0.270    5.558    0.000
##    .RMA04             0.857    0.116    7.413    0.000
##    .RMA05             0.904    0.188    4.817    0.000
##    .RMA06             1.626    0.176    9.252    0.000
##    .RMA07             0.843    0.133    6.321    0.000
##    .RMA08             0.881    0.156    5.651    0.000
##    .RMA09             1.089    0.179    6.090    0.000
##    .RMA10             1.256    0.225    5.581    0.000
##    .RCO02             0.980    0.142    6.889    0.000
##    .RCO03             0.493    0.106    4.654    0.000
##    .RCO04             0.468    0.105    4.481    0.000
##    .RCO05             0.393    0.071    5.539    0.000
##    .RCO06             0.479    0.111    4.309    0.000
##    .RCO07             0.504    0.093    5.418    0.000
##    .EN01              0.689    0.119    5.793    0.000
##    .EN02              0.439    0.058    7.533    0.000
##    .EN04              0.476    0.113    4.201    0.000
##    .EN05              0.381    0.077    4.925    0.000
##    .EN06              0.367    0.060    6.160    0.000
##    .EN07              0.502    0.100    5.025    0.000
##    .EN08              0.358    0.070    5.088    0.000
##    .EVI01             0.177    0.050    3.555    0.000
##    .EVI02             0.242    0.053    4.580    0.000
##    .EVI03             1.222    0.220    5.563    0.000
##    .EDE01             0.395    0.114    3.481    0.000
##    .EDE02             0.498    0.122    4.074    0.000
##    .EDE03             0.836    0.150    5.568    0.000
##    .EAB01             0.478    0.166    2.872    0.004
##    .EAB02             1.010    0.195    5.172    0.000
##    .EAB03             1.718    0.344    5.000    0.000
##     recuperacion      0.972    0.194    5.013    0.000
##     absorcion         2.595    0.409    6.339    0.000
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
---
title: "Actividad 03"
author: "Marcelo Tam Arica - A01722140"
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: journal
    highlight: tango
    css: styles.css
---
![](/Users/marcelotam/Desktop/M1_Est/unnamed.gif)

# SEM

## <span style="color: red;">**Teoría**</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: black;">**Ejemplo 1.Holzinger y Swineford (1939)**</span>
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: Signficado de palabras
* x7: Sumas aceleradas
* x8: Conteo acelerado de puntos
* x9: Discriminación acelerada de mayúsculas rectas y curvas

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

### <span style="color: black;">Librerias</span>
```{r message=FALSE, warning=FALSE}
library(lavaanPlot)
library(lavaan)
library(readxl)
```

### <span style="color: black;">Importar base de datos</span>
```{r}
df1 <- HolzingerSwineford1939
```

### <span style="color: black;">Importar base de datos</span>
1. Regresión (~) Variable que depende de otras
2. Variables Latentes (=~) no se observa, se infiere
3. Variables y Covarianzas (~~) Relaciones entre variables altentes y observada (Varianza: Entre si miisma, Covarianza: Entre otras)
4. Intercepto (~1) Valor esperado cuando las demas variables son cero.

### <span style="color: black;">Estructurar el modelo</span>
```{r message=FALSE, warning=FALSE}
modelo1 <- ' # 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: black;">Generar Análisis Factorial Confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)
```

## <span style="color: black;">**Ejercicio 1. Democratizar política e industrialización**</span>

### <span style="color: black;">Contexto</span>
La base de datos contiene distintas mediciones sobre la demoracia 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 politica 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 politica 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: black;">Importar base de datos</span>
```{r}
df2 <- PoliticalDemocracy
```

### <span style="color: black;">2. Entender la base de datos</span>
```{r}
summary(df2)
str(df2)
head(df2)
```

### <span style="color: black;">3. Estructurar el modelo</span>
```{r message=FALSE, warning=FALSE}
modelo2 <- '
  # Definir variables latentes de democratización en 1960 y 1965
  Dem1960 =~ y1 + y2 + y3 + y4
  Dem1965 =~ y5 + y6 + y7 + y8

  # Definir variable latente de industrialización
  Ind1960 =~ x1 + x2 + x3

  # Relacionar democratización de 1960 con 1965
  Dem1965 ~ Dem1960

  # Relacionar industrialización con democratización
  Dem1960 ~ Ind1960
  Dem1965 ~ Ind1960

  # Especificar varianzas y covarianzas
  Dem1960 ~~ Dem1960
  Dem1965 ~~ Dem1965
  Ind1960 ~~ Ind1960
  Dem1960 ~~ Ind1960
  Dem1965 ~~ Ind1960
  '
```

### <span style="color: black;">4. Generar el análisis factorial confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa2 <- sem(modelo2, data=df2, se="bootstrap")
summary(cfa2, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa2, coef=TRUE, cov=TRUE)
```

## <span style="color: blue;">**Activdad 3: Aplicación de modelos de ecuaciones estructurales**</span>

### <span style="color: black;">Entender la base de datos</span>
```{r message=FALSE, warning=FALSE}
df3 <- read_excel("/Users/marcelotam/Desktop/M1_Est/Datos_SEM_Eng.xlsx")
summary(df3)
head(df3)
```

### <span style="color: black;">Parte 1. Experiencias de Recuperación</span>
```{r}
modelo3_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
          ' 
```

### <span style="color: black;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa3_1 <- sem(modelo3_1, data=df3)
summary(cfa3_1, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_1, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">Evaluar el Modelo</span>
```{r message=FALSE, warning=FALSE}
summary(cfa3_1, fit.measures=TRUE)
# Revisar los valores de comparative Fit Index (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: black;">Parte 2. Energía Recuperada</span>
```{r message=FALSE, warning=FALSE}
modelo3_2 <- ' # Regresiones
             # Variables latentes
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
             # Varianzas y Covarianza
             energia ~~ energia
             # Intercepto
          ' 
```

### <span style="color: black;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa3_2 <- sem(modelo3_2, data=df3, se="bootstrap")
summary(cfa3_2, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_2, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">Evaluar el Modelo</span>
```{r message=FALSE, warning=FALSE}
summary(cfa3_2, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: black;">Parte 3. Analisis de Engagement Laboral</span>
```{r message=FALSE, warning=FALSE}
modelo3_3 <- ' # Regresiones
             # Variables latentes
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             # Intercepto
          ' 
```

### <span style="color: black;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa3_3 <- sem(modelo3_3, data=df3, se="bootstrap")
summary(cfa3_3, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_3, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">Evaluar el Modelo</span>
```{r message=FALSE, warning=FALSE}
summary(cfa3_3, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: black;">Parte 4. Analisis de Engagement Laboral</span>
```{r message=FALSE, warning=FALSE}
modelo3_4 <- ' # 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
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08 
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             desapego ~~ desapego
             relajacion ~~ relajacion
             dominio ~~ dominio
             control ~~ control
             energia ~~ energia
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             recuperacion ~~ absorcion + dedicacion + energia + vigor
             energia ~~ vigor + dedicacion + absorcion
             # Intercepto
          ' 
```

### <span style="color: black;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa3_4 <- sem(modelo3_4, data=df3, se="bootstrap")
summary(cfa3_4, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_4, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">Evaluar el Modelo</span>
```{r message=FALSE, warning=FALSE}
summary(cfa3_4, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

