Instalar paquetes y llamar librerias
#install.packages("lavaan")
library(lavaan)
## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.
#install.packages("lavaanPlot")
library(lavaanPlot)
#install.packages("readxl")
library(readxl)
Importrar la base de datos
# file.choose()
df <- read_excel ("/Users/ximenabbm/Downloads/Datos_SEM_Eng.xlsx")
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>, …
Parte 1. Experiencias de recuperacion
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 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
'
Parte 2. Análisis Factorial
Confirmatorio
fit <- cfa(modelo,df)
summary(fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 68
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1215.404
## Degrees of freedom 428
## P-value (Chi-square) 0.000
##
## 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) -10613.334
## Loglikelihood unrestricted model (H1) -10005.632
##
## Akaike (AIC) 21362.669
## Bayesian (BIC) 21594.356
## Sample-size adjusted Bayesian (SABIC) 21378.856
##
## 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 0.071
##
## 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 14.786 0.000 1.670 0.857
## RPD03 1.143 0.085 13.420 0.000 1.586 0.798
## RPD05 1.310 0.086 15.269 0.000 1.818 0.877
## RPD06 1.086 0.088 12.282 0.000 1.507 0.745
## RPD07 1.227 0.085 14.451 0.000 1.702 0.843
## RPD08 1.163 0.086 13.487 0.000 1.614 0.801
## RPD09 1.315 0.087 15.175 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 1.120 0.065 17.268 0.000 1.427 0.871
## RRE04 1.024 0.058 17.732 0.000 1.306 0.883
## RRE05 1.055 0.056 18.798 0.000 1.345 0.910
## RRE06 1.243 0.074 16.857 0.000 1.585 0.859
## RRE07 1.115 0.071 15.687 0.000 1.422 0.824
## RRE10 0.815 0.067 12.135 0.000 1.038 0.698
## dominio =~
## RMA02 1.000 1.406 0.729
## RMA03 1.155 0.096 12.060 0.000 1.624 0.799
## RMA04 1.179 0.089 13.267 0.000 1.659 0.874
## RMA05 1.141 0.087 13.049 0.000 1.604 0.860
## RMA06 0.647 0.075 8.618 0.000 0.911 0.581
## RMA07 1.104 0.085 13.050 0.000 1.552 0.860
## RMA08 1.109 0.085 12.985 0.000 1.560 0.856
## RMA09 1.030 0.084 12.251 0.000 1.448 0.811
## RMA10 1.056 0.088 12.039 0.000 1.486 0.798
## control =~
## RCO02 1.000 1.631 0.855
## RCO03 0.948 0.049 19.230 0.000 1.546 0.912
## RCO04 0.795 0.044 18.125 0.000 1.297 0.886
## RCO05 0.817 0.043 18.981 0.000 1.332 0.906
## RCO06 0.834 0.046 18.247 0.000 1.360 0.889
## RCO07 0.834 0.046 18.078 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 0.696 0.155 4.477 0.000 0.357 0.357
## control 1.319 0.200 6.584 0.000 0.583 0.583
## relajacion ~~
## dominio 0.969 0.159 6.085 0.000 0.540 0.540
## control 1.483 0.195 7.610 0.000 0.713 0.713
## dominio ~~
## control 1.221 0.202 6.047 0.000 0.532 0.532
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .RPD01 1.168 0.119 9.778 0.000 1.168 0.378
## .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 0.651 0.072 9.005 0.000 0.651 0.242
## .RRE04 0.481 0.055 8.798 0.000 0.481 0.220
## .RRE05 0.373 0.046 8.147 0.000 0.373 0.171
## .RRE06 0.891 0.097 9.162 0.000 0.891 0.262
## .RRE07 0.953 0.100 9.511 0.000 0.953 0.320
## .RRE10 1.136 0.113 10.092 0.000 1.136 0.513
## .RMA02 1.742 0.175 9.934 0.000 1.742 0.468
## .RMA03 1.489 0.155 9.581 0.000 1.489 0.361
## .RMA04 0.854 0.097 8.772 0.000 0.854 0.237
## .RMA05 0.904 0.101 8.981 0.000 0.904 0.260
## .RMA06 1.627 0.158 10.279 0.000 1.627 0.662
## .RMA07 0.846 0.094 8.980 0.000 0.846 0.260
## .RMA08 0.885 0.098 9.035 0.000 0.885 0.267
## .RMA09 1.090 0.115 9.496 0.000 1.090 0.342
## .RMA10 1.258 0.131 9.590 0.000 1.258 0.363
## .RCO02 0.980 0.105 9.375 0.000 0.980 0.269
## .RCO03 0.482 0.057 8.379 0.000 0.482 0.168
## .RCO04 0.463 0.052 8.967 0.000 0.463 0.216
## .RCO05 0.385 0.045 8.536 0.000 0.385 0.178
## .RCO06 0.493 0.055 8.915 0.000 0.493 0.210
## .RCO07 0.516 0.057 8.987 0.000 0.516 0.218
## desapego 1.925 0.275 7.002 0.000 1.000 1.000
## relajacion 1.625 0.207 7.845 0.000 1.000 1.000
## dominio 1.978 0.317 6.241 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)
Parte 2. Energia recuperada
modelo2 <- ' # Variables latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
'
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)
Parte 3. Compromiso laboral
modelo3 <- ' # Variables latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02
'
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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