library(readxl)
library(lavaan)
## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.
library(lavaanPlot)

Importar la base de datos: 

df <- read_excel("/Users/hanniahdz58/Downloads/Datos_SEM_Eng.xlsx")

Entender la base de datos: 

summary(df)
##        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
str(df)
## tibble [223 × 51] (S3: tbl_df/tbl/data.frame)
##  $ ID   : num [1:223] 1 2 3 4 5 6 7 8 9 10 ...
##  $ GEN  : num [1:223] 1 1 1 1 1 0 0 1 1 1 ...
##  $ EXPER: num [1:223] 22 22 30 17 23 31 26 30 15 15 ...
##  $ EDAD : num [1:223] 45 44 52 41 51 52 53 48 40 38 ...
##  $ RPD01: num [1:223] 5 4 7 5 7 3 5 6 4 2 ...
##  $ RPD02: num [1:223] 1 4 7 5 6 4 5 7 4 3 ...
##  $ RPD03: num [1:223] 3 6 7 1 7 5 4 6 4 2 ...
##  $ RPD05: num [1:223] 2 5 7 1 6 4 4 7 4 3 ...
##  $ RPD06: num [1:223] 3 3 7 3 7 3 5 2 6 7 ...
##  $ RPD07: num [1:223] 1 2 6 5 6 5 6 5 4 1 ...
##  $ RPD08: num [1:223] 3 3 7 3 7 4 6 2 5 3 ...
##  $ RPD09: num [1:223] 2 4 7 2 6 4 7 4 4 2 ...
##  $ RPD10: num [1:223] 4 4 7 2 6 4 7 1 6 2 ...
##  $ RRE02: num [1:223] 6 6 7 6 7 5 7 5 6 7 ...
##  $ RRE03: num [1:223] 6 6 7 6 7 4 7 4 4 7 ...
##  $ RRE04: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE05: num [1:223] 6 6 7 6 7 5 7 4 6 7 ...
##  $ RRE06: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE07: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RRE10: num [1:223] 6 6 7 6 7 4 7 4 6 7 ...
##  $ RMA02: num [1:223] 4 6 4 3 4 7 5 2 6 7 ...
##  $ RMA03: num [1:223] 5 6 5 4 4 7 5 1 2 7 ...
##  $ RMA04: num [1:223] 5 5 6 4 4 5 5 1 4 7 ...
##  $ RMA05: num [1:223] 5 5 6 4 4 6 5 3 4 7 ...
##  $ RMA06: num [1:223] 6 6 7 6 5 4 5 7 6 7 ...
##  $ RMA07: num [1:223] 4 6 6 5 4 5 7 4 6 7 ...
##  $ RMA08: num [1:223] 5 6 4 4 4 6 6 4 2 7 ...
##  $ RMA09: num [1:223] 3 5 4 3 5 4 5 2 4 7 ...
##  $ RMA10: num [1:223] 7 5 5 4 5 5 6 4 3 7 ...
##  $ RCO02: num [1:223] 7 7 7 5 7 6 7 7 3 7 ...
##  $ RCO03: num [1:223] 7 7 7 5 7 5 7 7 3 7 ...
##  $ RCO04: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
##  $ RCO05: num [1:223] 7 7 7 6 7 4 7 7 3 7 ...
##  $ RCO06: num [1:223] 7 7 7 6 7 4 7 7 4 7 ...
##  $ RCO07: num [1:223] 5 7 7 6 7 4 7 7 7 7 ...
##  $ EN01 : num [1:223] 6 6 7 4 6 4 7 7 4 7 ...
##  $ EN02 : num [1:223] 7 6 7 4 6 4 7 7 4 7 ...
##  $ EN04 : num [1:223] 6 6 7 4 6 4 7 6 4 7 ...
##  $ EN05 : num [1:223] 5 5 7 5 6 5 7 6 4 7 ...
##  $ EN06 : num [1:223] 5 5 7 5 6 3 7 5 5 7 ...
##  $ EN07 : num [1:223] 5 5 7 2 6 4 7 4 4 7 ...
##  $ EN08 : num [1:223] 6 5 7 5 6 4 7 4 4 7 ...
##  $ EVI01: num [1:223] 6 5 7 5 6 4 7 6 6 0 ...
##  $ EVI02: num [1:223] 6 5 7 6 6 4 6 5 5 1 ...
##  $ EVI03: num [1:223] 6 6 6 7 6 4 6 6 7 0 ...
##  $ EDE01: num [1:223] 6 6 6 5 7 6 7 7 7 1 ...
##  $ EDE02: num [1:223] 7 6 7 6 7 5 7 7 7 5 ...
##  $ EDE03: num [1:223] 7 7 7 7 7 5 7 7 7 6 ...
##  $ EAB01: num [1:223] 7 7 7 6 7 5 7 7 7 0 ...
##  $ EAB02: num [1:223] 7 7 7 6 7 5 7 2 5 1 ...
##  $ EAB03: num [1:223] 6 5 6 5 6 5 7 3 5 0 ...
head(df)
## # 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>, …

Part 1. Experiencias de Recuperación 

modelo <- ' 
# 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
  control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
'

# Análisis Factorial Confirmatorio
fit <- cfa(modelo,df)
summary(fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 57 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        66
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                              1141.201
##   Degrees of freedom                               399
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              7243.291
##   Degrees of freedom                               435
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.891
##   Tucker-Lewis Index (TLI)                       0.881
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -10260.683
##   Loglikelihood unrestricted model (H1)      -9690.083
##                                                       
##   Akaike (AIC)                               20653.367
##   Bayesian (BIC)                             20878.240
##   Sample-size adjusted Bayesian (SABIC)      20669.078
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.091
##   90 Percent confidence interval - lower         0.085
##   90 Percent confidence interval - upper         0.098
##   P-value H_0: RMSEA <= 0.050                    0.000
##   P-value H_0: RMSEA >= 0.080                    0.999
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.071
## 
## 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                               1.388    0.789
##     RPD02             1.204    0.081   14.787    0.000    1.670    0.857
##     RPD03             1.143    0.085   13.420    0.000    1.586    0.798
##     RPD05             1.310    0.086   15.270    0.000    1.818    0.877
##     RPD06             1.086    0.088   12.282    0.000    1.507    0.745
##     RPD07             1.227    0.085   14.452    0.000    1.702    0.843
##     RPD08             1.163    0.086   13.487    0.000    1.614    0.801
##     RPD09             1.315    0.087   15.176    0.000    1.825    0.873
##     RPD10             1.345    0.088   15.290    0.000    1.866    0.878
##   relajacion =~                                                         
##     RRE02             1.000                               1.275    0.850
##     RRE03             1.119    0.065   17.258    0.000    1.427    0.870
##     RRE04             1.025    0.058   17.747    0.000    1.306    0.884
##     RRE05             1.055    0.056   18.811    0.000    1.345    0.911
##     RRE06             1.243    0.074   16.847    0.000    1.584    0.859
##     RRE07             1.115    0.071   15.676    0.000    1.421    0.824
##     RRE10             0.814    0.067   12.127    0.000    1.038    0.698
##   dominio =~                                                            
##     RMA02             1.000                               1.396    0.724
##     RMA03             1.151    0.098   11.779    0.000    1.607    0.791
##     RMA04             1.197    0.091   13.175    0.000    1.671    0.880
##     RMA05             1.146    0.089   12.830    0.000    1.600    0.858
##     RMA06             0.650    0.076    8.527    0.000    0.908    0.579
##     RMA07             1.122    0.086   12.995    0.000    1.567    0.868
##     RMA08             1.127    0.087   12.925    0.000    1.574    0.864
##     RMA09             1.027    0.086   11.968    0.000    1.434    0.803
##   control =~                                                            
##     RCO02             1.000                               1.631    0.855
##     RCO03             0.948    0.049   19.224    0.000    1.546    0.912
##     RCO04             0.795    0.044   18.126    0.000    1.297    0.886
##     RCO05             0.817    0.043   18.979    0.000    1.332    0.906
##     RCO06             0.834    0.046   18.254    0.000    1.361    0.889
##     RCO07             0.834    0.046   18.083    0.000    1.361    0.884
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   desapego ~~                                                           
##     relajacion        1.155    0.164    7.023    0.000    0.653    0.653
##     dominio           0.672    0.154    4.367    0.000    0.347    0.347
##     control           1.319    0.200    6.584    0.000    0.583    0.583
##   relajacion ~~                                                         
##     dominio           0.935    0.157    5.955    0.000    0.525    0.525
##     control           1.483    0.195    7.610    0.000    0.713    0.713
##   dominio ~~                                                            
##     control           1.187    0.200    5.944    0.000    0.521    0.521
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .RPD01             1.167    0.119    9.778    0.000    1.167    0.377
##    .RPD02             1.005    0.109    9.240    0.000    1.005    0.265
##    .RPD03             1.434    0.147    9.728    0.000    1.434    0.363
##    .RPD05             0.989    0.110    8.969    0.000    0.989    0.230
##    .RPD06             1.817    0.182    9.968    0.000    1.817    0.444
##    .RPD07             1.177    0.125    9.391    0.000    1.177    0.289
##    .RPD08             1.454    0.150    9.710    0.000    1.454    0.358
##    .RPD09             1.035    0.115    9.028    0.000    1.035    0.237
##    .RPD10             1.033    0.115    8.956    0.000    1.033    0.229
##    .RRE02             0.624    0.067    9.269    0.000    0.624    0.277
##    .RRE03             0.652    0.072    9.009    0.000    0.652    0.243
##    .RRE04             0.480    0.055    8.790    0.000    0.480    0.219
##    .RRE05             0.372    0.046    8.135    0.000    0.372    0.170
##    .RRE06             0.893    0.097    9.166    0.000    0.893    0.262
##    .RRE07             0.955    0.100    9.513    0.000    0.955    0.321
##    .RRE10             1.137    0.113   10.093    0.000    1.137    0.513
##    .RMA02             1.771    0.179    9.902    0.000    1.771    0.476
##    .RMA03             1.546    0.162    9.559    0.000    1.546    0.375
##    .RMA04             0.814    0.096    8.489    0.000    0.814    0.226
##    .RMA05             0.918    0.103    8.880    0.000    0.918    0.264
##    .RMA06             1.632    0.159   10.259    0.000    1.632    0.665
##    .RMA07             0.800    0.092    8.709    0.000    0.800    0.246
##    .RMA08             0.842    0.096    8.785    0.000    0.842    0.254
##    .RMA09             1.133    0.120    9.471    0.000    1.133    0.355
##    .RCO02             0.980    0.105    9.374    0.000    0.980    0.269
##    .RCO03             0.483    0.058    8.383    0.000    0.483    0.168
##    .RCO04             0.463    0.052    8.967    0.000    0.463    0.216
##    .RCO05             0.385    0.045    8.537    0.000    0.385    0.178
##    .RCO06             0.492    0.055    8.911    0.000    0.492    0.210
##    .RCO07             0.515    0.057    8.985    0.000    0.515    0.218
##     desapego          1.925    0.275    7.002    0.000    1.000    1.000
##     relajacion        1.625    0.207    7.844    0.000    1.000    1.000
##     dominio           1.950    0.316    6.169    0.000    1.000    1.000
##     control           2.660    0.335    7.930    0.000    1.000    1.000
lavaanPlot(model = fit, coefs = TRUE, covs = TRUE)

Part 2. Energía Recuperada 

modelo2 <- ' 
# Variables latentes
  energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08 +
'

# Análisis Factorial Confirmatorio
fit2 <- cfa(modelo2,df)
summary(fit2, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 32 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        14
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                                47.222
##   Degrees of freedom                                14
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              2324.436
##   Degrees of freedom                                21
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.986
##   Tucker-Lewis Index (TLI)                       0.978
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -2017.154
##   Loglikelihood unrestricted model (H1)      -1993.543
##                                                       
##   Akaike (AIC)                                4062.308
##   Bayesian (BIC)                              4110.008
##   Sample-size adjusted Bayesian (SABIC)       4065.641
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.103
##   90 Percent confidence interval - lower         0.072
##   90 Percent confidence interval - upper         0.136
##   P-value H_0: RMSEA <= 0.050                    0.004
##   P-value H_0: RMSEA >= 0.080                    0.892
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.012
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   energia =~                                                            
##     EN01              1.000                               1.674    0.893
##     EN02              1.029    0.044   23.192    0.000    1.723    0.933
##     EN04              0.999    0.044   22.583    0.000    1.672    0.924
##     EN05              0.999    0.042   23.649    0.000    1.672    0.939
##     EN06              0.986    0.042   23.722    0.000    1.651    0.940
##     EN07              1.049    0.046   22.856    0.000    1.755    0.928
##     EN08              1.036    0.043   24.173    0.000    1.734    0.946
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .EN01              0.711    0.074    9.651    0.000    0.711    0.202
##    .EN02              0.444    0.049    9.012    0.000    0.444    0.130
##    .EN04              0.481    0.052    9.214    0.000    0.481    0.147
##    .EN05              0.375    0.042    8.830    0.000    0.375    0.118
##    .EN06              0.359    0.041    8.798    0.000    0.359    0.116
##    .EN07              0.499    0.055    9.129    0.000    0.499    0.139
##    .EN08              0.353    0.041    8.580    0.000    0.353    0.105
##     energia           2.801    0.327    8.565    0.000    1.000    1.000
lavaanPlot(model = fit2, coefs = TRUE, covs = TRUE)

Part 3. Compromiso Laboral 

modelo3 <- ' 
# Variables latentes
  vigor =~ EVI01 + EVI02 + EVI03
  dedicacion =~ EDE01 + EDE02 + EDE03
  absorcion =~ EAB01 + EAB02
'

# Análisis Factorial Confirmatorio
fit3 <- cfa(modelo3,df)
summary(fit3, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        19
## 
##   Number of observations                           223
## 
## Model Test User Model:
##                                                       
##   Test statistic                               203.167
##   Degrees of freedom                                17
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              2072.083
##   Degrees of freedom                                28
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.909
##   Tucker-Lewis Index (TLI)                       0.850
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -2579.528
##   Loglikelihood unrestricted model (H1)      -2477.944
##                                                       
##   Akaike (AIC)                                5197.055
##   Bayesian (BIC)                              5261.792
##   Sample-size adjusted Bayesian (SABIC)       5201.578
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.222
##   90 Percent confidence interval - lower         0.195
##   90 Percent confidence interval - upper         0.249
##   P-value H_0: RMSEA <= 0.050                    0.000
##   P-value H_0: RMSEA >= 0.080                    1.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.061
## 
## 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
##   vigor =~                                                              
##     EVI01             1.000                               1.684    0.966
##     EVI02             0.986    0.028   35.155    0.000    1.660    0.962
##     EVI03             0.995    0.049   20.476    0.000    1.676    0.835
##   dedicacion =~                                                         
##     EDE01             1.000                               1.861    0.948
##     EDE02             0.912    0.035   26.335    0.000    1.697    0.923
##     EDE03             0.578    0.037   15.767    0.000    1.075    0.760
##   absorcion =~                                                          
##     EAB01             1.000                               1.639    0.935
##     EAB02             0.658    0.053   12.526    0.000    1.079    0.710
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   vigor ~~                                                              
##     dedicacion        2.763    0.293    9.419    0.000    0.881    0.881
##     absorcion         2.184    0.250    8.730    0.000    0.791    0.791
##   dedicacion ~~                                                         
##     absorcion         2.796    0.296    9.439    0.000    0.916    0.916
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .EVI01             0.200    0.040    4.964    0.000    0.200    0.066
##    .EVI02             0.221    0.040    5.455    0.000    0.221    0.074
##    .EVI03             1.218    0.125    9.770    0.000    1.218    0.303
##    .EDE01             0.388    0.065    6.004    0.000    0.388    0.101
##    .EDE02             0.498    0.066    7.599    0.000    0.498    0.147
##    .EDE03             0.844    0.085    9.903    0.000    0.844    0.422
##    .EAB01             0.387    0.124    3.118    0.002    0.387    0.126
##    .EAB02             1.145    0.120    9.543    0.000    1.145    0.496
##     vigor             2.836    0.289    9.810    0.000    1.000    1.000
##     dedicacion        3.465    0.367    9.443    0.000    1.000    1.000
##     absorcion         2.686    0.312    8.606    0.000    1.000    1.000
lavaanPlot(model = fit3, coefs = TRUE, covs = TRUE)
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