SEM
Andrea Ortiz
2025-02-19
Teoría
Los modelos de ecuaciones estructurales 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: Estudio de Holzinger y Swineford(1939)
Contexto
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7º y 8º de dos escuelas (Pasteur y Grand-White).
La base de datos está incluida como paquete en R, e incluye las siguientes columnas
- sex: Género (1=male, 2=female)
- x1: Percepción visual
- x2: Juego con cubos
- x3: Juego con pastillas/espacial
- x4: Comprensión de párrafos
- x5: Completar oraciones
- x6: Significado de palabras
- x7: Sumas aceleradas
- x8: Conteo acelerado de puntos
- x9: Discriminación acelerada de mayúsculas rectas y curvas
Se busca identificar las relaciones entre las habilidades visual (x1,x2,x3), textual (x4,x5,x6) y velocidad (x7,x8,x9) de los adolescentes
Instalar paquetes y llamar librerías
#install.packages("lavaan") #Análisis de variables latentes
library (lavaan)
#install.packages("lavaanPlot")
library (lavaanPlot)Llamar la base de datos
df1 <- HolzingerSwineford1939Entender la base de datos
summary(df1)## id sex ageyr agemo
## Min. : 1.0 Min. :1.000 Min. :11 Min. : 0.000
## 1st Qu.: 82.0 1st Qu.:1.000 1st Qu.:12 1st Qu.: 2.000
## Median :163.0 Median :2.000 Median :13 Median : 5.000
## Mean :176.6 Mean :1.515 Mean :13 Mean : 5.375
## 3rd Qu.:272.0 3rd Qu.:2.000 3rd Qu.:14 3rd Qu.: 8.000
## Max. :351.0 Max. :2.000 Max. :16 Max. :11.000
##
## school grade x1 x2
## Grant-White:145 Min. :7.000 Min. :0.6667 Min. :2.250
## Pasteur :156 1st Qu.:7.000 1st Qu.:4.1667 1st Qu.:5.250
## Median :7.000 Median :5.0000 Median :6.000
## Mean :7.477 Mean :4.9358 Mean :6.088
## 3rd Qu.:8.000 3rd Qu.:5.6667 3rd Qu.:6.750
## Max. :8.000 Max. :8.5000 Max. :9.250
## NA's :1
## x3 x4 x5 x6
## Min. :0.250 Min. :0.000 Min. :1.000 Min. :0.1429
## 1st Qu.:1.375 1st Qu.:2.333 1st Qu.:3.500 1st Qu.:1.4286
## Median :2.125 Median :3.000 Median :4.500 Median :2.0000
## Mean :2.250 Mean :3.061 Mean :4.341 Mean :2.1856
## 3rd Qu.:3.125 3rd Qu.:3.667 3rd Qu.:5.250 3rd Qu.:2.7143
## Max. :4.500 Max. :6.333 Max. :7.000 Max. :6.1429
##
## x7 x8 x9
## Min. :1.304 Min. : 3.050 Min. :2.778
## 1st Qu.:3.478 1st Qu.: 4.850 1st Qu.:4.750
## Median :4.087 Median : 5.500 Median :5.417
## Mean :4.186 Mean : 5.527 Mean :5.374
## 3rd Qu.:4.913 3rd Qu.: 6.100 3rd Qu.:6.083
## Max. :7.435 Max. :10.000 Max. :9.250
## str(df1)## 'data.frame': 301 obs. of 15 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 11 ...
## $ sex : int 1 2 2 1 2 2 1 2 2 2 ...
## $ ageyr : int 13 13 13 13 12 14 12 12 13 12 ...
## $ agemo : int 1 7 1 2 2 1 1 2 0 5 ...
## $ school: Factor w/ 2 levels "Grant-White",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ grade : int 7 7 7 7 7 7 7 7 7 7 ...
## $ x1 : num 3.33 5.33 4.5 5.33 4.83 ...
## $ x2 : num 7.75 5.25 5.25 7.75 4.75 5 6 6.25 5.75 5.25 ...
## $ x3 : num 0.375 2.125 1.875 3 0.875 ...
## $ x4 : num 2.33 1.67 1 2.67 2.67 ...
## $ x5 : num 5.75 3 1.75 4.5 4 3 6 4.25 5.75 5 ...
## $ x6 : num 1.286 1.286 0.429 2.429 2.571 ...
## $ x7 : num 3.39 3.78 3.26 3 3.7 ...
## $ x8 : num 5.75 6.25 3.9 5.3 6.3 6.65 6.2 5.15 4.65 4.55 ...
## $ x9 : num 6.36 7.92 4.42 4.86 5.92 ...head(df1)## id sex ageyr agemo school grade x1 x2 x3 x4 x5 x6
## 1 1 1 13 1 Pasteur 7 3.333333 7.75 0.375 2.333333 5.75 1.2857143
## 2 2 2 13 7 Pasteur 7 5.333333 5.25 2.125 1.666667 3.00 1.2857143
## 3 3 2 13 1 Pasteur 7 4.500000 5.25 1.875 1.000000 1.75 0.4285714
## 4 4 1 13 2 Pasteur 7 5.333333 7.75 3.000 2.666667 4.50 2.4285714
## 5 5 2 12 2 Pasteur 7 4.833333 4.75 0.875 2.666667 4.00 2.5714286
## 6 6 2 14 1 Pasteur 7 5.333333 5.00 2.250 1.000000 3.00 0.8571429
## x7 x8 x9
## 1 3.391304 5.75 6.361111
## 2 3.782609 6.25 7.916667
## 3 3.260870 3.90 4.416667
## 4 3.000000 5.30 4.861111
## 5 3.695652 6.30 5.916667
## 6 4.347826 6.65 7.500000Tipos de fórmulas
- Regresión (~) Variable que depende de otras #Lo checas manual
- Variables latentes (=~) No se observa, se infiere
- Varianzas y covarianzas (~~) Relaciones entre variables latentes y observada (varianza: entre sí misma, covarianza: entre otras)
- Intercepto (~1) Valor esperado cuando las demás variables son cero
Estructurar el modelo
modelo1 <- '# Regresiones
# Variables latentes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7 + x8 + x9
# Varianza y covarianza
visual ~~ visual
textual ~~ textual
velocidad ~~ velocidad
visual ~~ textual + velocidad
textual ~~ velocidad
# Intercepto
'Generar el 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 0.000lavaanPlot(cfa1, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa1, fit.measures = TRUE)## 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
##
## Model Test Baseline Model:
##
## Test statistic 918.852
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.931
## Tucker-Lewis Index (TLI) 0.896
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3737.745
## Loglikelihood unrestricted model (H1) -3695.092
##
## Akaike (AIC) 7517.490
## Bayesian (BIC) 7595.339
## Sample-size adjusted Bayesian (SABIC) 7528.739
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092
## 90 Percent confidence interval - lower 0.071
## 90 Percent confidence interval - upper 0.114
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.840
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065
##
## 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# Si el Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI) son:
# Cercanos o mayores a 0.95 --> Excelente
# Si está entre 0.90 y 0.95 --> Aceptable
# Si es menor a 0.90 --> DeficienteConclusión: Modelo Aceptable
Ejercicio: Democracia política e industrialización
Contexto
La base de datos contiene distintas menciones sobre la democracia política e industrialización en países 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 política 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 política 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
Relaciones entre datos de 1960, 1965, e indicadores macro en 1960
Llamar la base de datos
df2 <- PoliticalDemocracyEntender 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. :6.425str(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 3.892270modelo2 <- '
# Measurement model
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ x3
dem65 ~ x3 + x2
# Correlations
y1 ~~ y5
y2 ~~ y4
y2 ~~ y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y5
'Generar el análisis factorial confirmatorio (CFA)
cfa2 <- sem(modelo2, data = df2)
summary(cfa2)## lavaan 0.6-19 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 33.855
## Degrees of freedom 26
## P-value (Chi-square) 0.139
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## dem60 =~
## y1 1.000
## y2 1.261 0.183 6.882 0.000
## y3 1.034 0.151 6.863 0.000
## y4 1.257 0.145 8.663 0.000
## dem65 =~
## y5 1.000
## y6 1.292 0.188 6.858 0.000
## y7 1.279 0.167 7.652 0.000
## y8 1.323 0.163 8.099 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## dem60 ~
## x3 0.548 0.191 2.875 0.004
## dem65 ~
## x3 0.493 0.213 2.314 0.021
## x2 0.221 0.149 1.483 0.138
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .y1 ~~
## .y5 0.736 0.369 1.994 0.046
## .y2 ~~
## .y4 1.465 0.703 2.085 0.037
## .y6 2.102 0.747 2.814 0.005
## .y3 ~~
## .y7 1.184 0.617 1.918 0.055
## .y4 ~~
## .y8 0.422 0.460 0.918 0.359
## .y5 ~~
## .y6 -0.704 0.376 -1.872 0.061
## .dem60 ~~
## .dem65 3.545 0.795 4.458 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .y1 1.835 0.447 4.109 0.000
## .y2 7.486 1.398 5.355 0.000
## .y3 5.242 0.974 5.384 0.000
## .y4 3.207 0.756 4.240 0.000
## .y5 2.436 0.506 4.817 0.000
## .y6 3.978 0.802 4.963 0.000
## .y7 3.598 0.712 5.052 0.000
## .y8 2.716 0.609 4.457 0.000
## .dem60 4.395 1.000 4.395 0.000
## .dem65 3.245 0.827 3.926 0.000lavaanPlot(cfa2, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa2, fit.measures = TRUE)## lavaan 0.6-19 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 33.855
## Degrees of freedom 26
## P-value (Chi-square) 0.139
##
## Model Test Baseline Model:
##
## Test statistic 498.135
## Degrees of freedom 44
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.983
## Tucker-Lewis Index (TLI) 0.971
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1310.988
## Loglikelihood unrestricted model (H1) -1294.060
##
## Akaike (AIC) 2673.976
## Bayesian (BIC) 2734.231
## Sample-size adjusted Bayesian (SABIC) 2652.286
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.118
## P-value H_0: RMSEA <= 0.050 0.333
## P-value H_0: RMSEA >= 0.080 0.347
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## dem60 =~
## y1 1.000
## y2 1.261 0.183 6.882 0.000
## y3 1.034 0.151 6.863 0.000
## y4 1.257 0.145 8.663 0.000
## dem65 =~
## y5 1.000
## y6 1.292 0.188 6.858 0.000
## y7 1.279 0.167 7.652 0.000
## y8 1.323 0.163 8.099 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## dem60 ~
## x3 0.548 0.191 2.875 0.004
## dem65 ~
## x3 0.493 0.213 2.314 0.021
## x2 0.221 0.149 1.483 0.138
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .y1 ~~
## .y5 0.736 0.369 1.994 0.046
## .y2 ~~
## .y4 1.465 0.703 2.085 0.037
## .y6 2.102 0.747 2.814 0.005
## .y3 ~~
## .y7 1.184 0.617 1.918 0.055
## .y4 ~~
## .y8 0.422 0.460 0.918 0.359
## .y5 ~~
## .y6 -0.704 0.376 -1.872 0.061
## .dem60 ~~
## .dem65 3.545 0.795 4.458 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .y1 1.835 0.447 4.109 0.000
## .y2 7.486 1.398 5.355 0.000
## .y3 5.242 0.974 5.384 0.000
## .y4 3.207 0.756 4.240 0.000
## .y5 2.436 0.506 4.817 0.000
## .y6 3.978 0.802 4.963 0.000
## .y7 3.598 0.712 5.052 0.000
## .y8 2.716 0.609 4.457 0.000
## .dem60 4.395 1.000 4.395 0.000
## .dem65 3.245 0.827 3.926 0.000# El Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI) son mayores a 0.95, por lo que son excelentes y se puede decir que es un buen modelo 
Actividad. Bienestar de los Trabajadores
Instalar paquetes y llamar librerías
#install.packages("readxl")
library(readxl)Importar la base de datos
df3 <- read_excel("~/Downloads/R databases /Datos_SEM_Eng.xlsx")Entender la base de datos
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. :7.000str(df3)## 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(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
# Varianza y covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
# Intercepto
'Generar análisis factorial confirmatorio
cfa3_1 <- sem(modelo3_1, data = df3)
summary(cfa3_1)## 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
##
## 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 0.000lavaanPlot(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 0.000Parte 2: Energía recuperada
modelo3_2 <- '# Regresiones
# Variables latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianza y covarianza
energia ~~ energia
# Intercepto
'Generar análisis factorial confirmatorio
cfa3_2 <- sem(modelo3_2, data = df3)
summary(cfa3_2)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000lavaanPlot(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 Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.044 23.192 0.000
## EN04 0.999 0.044 22.583 0.000
## EN05 0.999 0.042 23.649 0.000
## EN06 0.986 0.042 23.722 0.000
## EN07 1.049 0.046 22.856 0.000
## EN08 1.036 0.043 24.173 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.327 8.565 0.000
## .EN01 0.711 0.074 9.651 0.000
## .EN02 0.444 0.049 9.012 0.000
## .EN04 0.481 0.052 9.214 0.000
## .EN05 0.375 0.042 8.830 0.000
## .EN06 0.359 0.041 8.798 0.000
## .EN07 0.499 0.055 9.129 0.000
## .EN08 0.353 0.041 8.580 0.000Parte 3: Experiencias de recuperacion
modelo3_3 <- '# Regresiones
# Variables latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianza y covarianza
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
# Intercepto
'Generar análisis factorial confirmatorio
cfa3_3 <- sem(modelo3_3, data = df3)
summary(cfa3_3)## 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
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## vigor =~
## EVI01 1.000
## EVI02 0.986 0.028 35.166 0.000
## EVI03 0.995 0.049 20.456 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.914 0.035 26.126 0.000
## EDE03 0.583 0.037 15.913 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.708 0.051 13.891 0.000
## EAB03 0.732 0.063 11.644 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.754 0.293 9.404 0.000
## absorcion 2.125 0.247 8.600 0.000
## dedicacion ~~
## absorcion 2.728 0.293 9.311 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## vigor 2.836 0.289 9.811 0.000
## dedicacion 3.448 0.367 9.399 0.000
## absorcion 2.592 0.301 8.615 0.000
## .EVI01 0.200 0.040 4.947 0.000
## .EVI02 0.220 0.041 5.437 0.000
## .EVI03 1.220 0.125 9.772 0.000
## .EDE01 0.405 0.066 6.130 0.000
## .EDE02 0.495 0.066 7.521 0.000
## .EDE03 0.829 0.084 9.869 0.000
## .EAB01 0.481 0.100 4.816 0.000
## .EAB02 1.010 0.109 9.271 0.000
## .EAB03 1.711 0.175 9.764 0.000lavaanPlot(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 Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## vigor =~
## EVI01 1.000
## EVI02 0.986 0.028 35.166 0.000
## EVI03 0.995 0.049 20.456 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.914 0.035 26.126 0.000
## EDE03 0.583 0.037 15.913 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.708 0.051 13.891 0.000
## EAB03 0.732 0.063 11.644 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.754 0.293 9.404 0.000
## absorcion 2.125 0.247 8.600 0.000
## dedicacion ~~
## absorcion 2.728 0.293 9.311 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## vigor 2.836 0.289 9.811 0.000
## dedicacion 3.448 0.367 9.399 0.000
## absorcion 2.592 0.301 8.615 0.000
## .EVI01 0.200 0.040 4.947 0.000
## .EVI02 0.220 0.041 5.437 0.000
## .EVI03 1.220 0.125 9.772 0.000
## .EDE01 0.405 0.066 6.130 0.000
## .EDE02 0.495 0.066 7.521 0.000
## .EDE03 0.829 0.084 9.869 0.000
## .EAB01 0.481 0.100 4.816 0.000
## .EAB02 1.010 0.109 9.271 0.000
## .EAB03 1.711 0.175 9.764 0.000Parte 4: Modelo completo
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
# Varianza y covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
recuperacion ~~ energia + vigor + dedicacion + absorcion
energia ~~ vigor + dedicacion + absorcion
# Intercepto
'Generar análisis factorial confirmatorio
cfa3_4 <- sem(modelo3_4, data = df3)
summary(cfa3_4)## 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
##
## 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 1.209 0.081 14.858 0.000
## RPD03 1.144 0.085 13.413 0.000
## RPD05 1.313 0.086 15.311 0.000
## RPD06 1.083 0.089 12.218 0.000
## RPD07 1.229 0.085 14.481 0.000
## RPD08 1.157 0.086 13.376 0.000
## RPD09 1.316 0.087 15.162 0.000
## RPD10 1.343 0.088 15.247 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.303 0.000
## RRE04 1.020 0.058 17.611 0.000
## RRE05 1.051 0.056 18.690 0.000
## RRE06 1.245 0.074 16.916 0.000
## RRE07 1.122 0.071 15.848 0.000
## RRE10 0.815 0.067 12.147 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.152 0.096 12.038 0.000
## RMA04 1.178 0.089 13.262 0.000
## RMA05 1.141 0.087 13.054 0.000
## RMA06 0.648 0.075 8.623 0.000
## RMA07 1.104 0.085 13.062 0.000
## RMA08 1.110 0.085 13.002 0.000
## RMA09 1.030 0.084 12.257 0.000
## RMA10 1.056 0.088 12.047 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.158 0.000
## RCO04 0.794 0.044 18.081 0.000
## RCO05 0.815 0.043 18.912 0.000
## RCO06 0.837 0.046 18.395 0.000
## RCO07 0.837 0.046 18.199 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.071 0.121 8.858 0.000
## dominio 0.900 0.129 6.965 0.000
## control 1.421 0.157 9.066 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.558 0.000
## EN04 0.996 0.043 22.912 0.000
## EN05 0.994 0.042 23.892 0.000
## EN06 0.981 0.041 23.944 0.000
## EN07 1.044 0.045 23.105 0.000
## EN08 1.031 0.042 24.449 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.896 0.000
## EVI03 0.990 0.048 20.656 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.219 0.000
## EDE03 0.580 0.037 15.851 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.915 0.000
## EAB03 0.730 0.063 11.619 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.767 0.293 9.427 0.000
## absorcion 2.132 0.248 8.613 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.316 0.000
## recuperacion ~~
## energia 1.367 0.197 6.938 0.000
## vigor 1.007 0.165 6.098 0.000
## dedicacion 1.049 0.179 5.852 0.000
## absorcion 0.796 0.151 5.281 0.000
## energia ~~
## vigor 2.045 0.249 8.223 0.000
## dedicacion 1.852 0.259 7.139 0.000
## absorcion 1.340 0.220 6.091 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 0.149 6.400 0.000
## .relajacion 0.510 0.085 6.021 0.000
## .dominio 1.191 0.200 5.958 0.000
## .control 0.699 0.125 5.583 0.000
## energia 2.823 0.327 8.623 0.000
## vigor 2.859 0.289 9.900 0.000
## dedicacion 3.458 0.367 9.424 0.000
## absorcion 2.595 0.301 8.628 0.000
## .RPD01 1.169 0.120 9.782 0.000
## .RPD02 0.984 0.107 9.204 0.000
## .RPD03 1.435 0.147 9.730 0.000
## .RPD05 0.973 0.109 8.940 0.000
## .RPD06 1.835 0.184 9.979 0.000
## .RPD07 1.166 0.124 9.378 0.000
## .RPD08 1.485 0.152 9.739 0.000
## .RPD09 1.036 0.115 9.034 0.000
## .RPD10 1.044 0.116 8.982 0.000
## .RRE02 0.623 0.067 9.253 0.000
## .RRE03 0.646 0.072 8.974 0.000
## .RRE04 0.494 0.056 8.837 0.000
## .RRE05 0.384 0.047 8.203 0.000
## .RRE06 0.882 0.097 9.126 0.000
## .RRE07 0.929 0.098 9.458 0.000
## .RRE10 1.134 0.112 10.086 0.000
## .RMA02 1.742 0.175 9.935 0.000
## .RMA03 1.500 0.156 9.595 0.000
## .RMA04 0.857 0.098 8.786 0.000
## .RMA05 0.904 0.101 8.985 0.000
## .RMA06 1.626 0.158 10.280 0.000
## .RMA07 0.843 0.094 8.978 0.000
## .RMA08 0.881 0.098 9.029 0.000
## .RMA09 1.089 0.115 9.498 0.000
## .RMA10 1.256 0.131 9.591 0.000
## .RCO02 0.980 0.104 9.394 0.000
## .RCO03 0.493 0.058 8.473 0.000
## .RCO04 0.468 0.052 9.019 0.000
## .RCO05 0.393 0.046 8.620 0.000
## .RCO06 0.479 0.054 8.883 0.000
## .RCO07 0.504 0.056 8.969 0.000
## .EN01 0.689 0.071 9.661 0.000
## .EN02 0.439 0.048 9.066 0.000
## .EN04 0.476 0.051 9.266 0.000
## .EN05 0.381 0.043 8.945 0.000
## .EN06 0.367 0.041 8.925 0.000
## .EN07 0.502 0.055 9.210 0.000
## .EN08 0.358 0.041 8.708 0.000
## .EVI01 0.177 0.036 4.919 0.000
## .EVI02 0.242 0.038 6.298 0.000
## .EVI03 1.222 0.124 9.826 0.000
## .EDE01 0.395 0.065 6.060 0.000
## .EDE02 0.498 0.066 7.579 0.000
## .EDE03 0.836 0.085 9.887 0.000
## .EAB01 0.478 0.099 4.805 0.000
## .EAB02 1.010 0.109 9.283 0.000
## .EAB03 1.718 0.176 9.778 0.000
## recuperacion 0.972 0.199 4.896 0.000lavaanPlot(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 Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.209 0.081 14.858 0.000
## RPD03 1.144 0.085 13.413 0.000
## RPD05 1.313 0.086 15.311 0.000
## RPD06 1.083 0.089 12.218 0.000
## RPD07 1.229 0.085 14.481 0.000
## RPD08 1.157 0.086 13.376 0.000
## RPD09 1.316 0.087 15.162 0.000
## RPD10 1.343 0.088 15.247 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.065 17.303 0.000
## RRE04 1.020 0.058 17.611 0.000
## RRE05 1.051 0.056 18.690 0.000
## RRE06 1.245 0.074 16.916 0.000
## RRE07 1.122 0.071 15.848 0.000
## RRE10 0.815 0.067 12.147 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.152 0.096 12.038 0.000
## RMA04 1.178 0.089 13.262 0.000
## RMA05 1.141 0.087 13.054 0.000
## RMA06 0.648 0.075 8.623 0.000
## RMA07 1.104 0.085 13.062 0.000
## RMA08 1.110 0.085 13.002 0.000
## RMA09 1.030 0.084 12.257 0.000
## RMA10 1.056 0.088 12.047 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.049 19.158 0.000
## RCO04 0.794 0.044 18.081 0.000
## RCO05 0.815 0.043 18.912 0.000
## RCO06 0.837 0.046 18.395 0.000
## RCO07 0.837 0.046 18.199 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.071 0.121 8.858 0.000
## dominio 0.900 0.129 6.965 0.000
## control 1.421 0.157 9.066 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.044 23.558 0.000
## EN04 0.996 0.043 22.912 0.000
## EN05 0.994 0.042 23.892 0.000
## EN06 0.981 0.041 23.944 0.000
## EN07 1.044 0.045 23.105 0.000
## EN08 1.031 0.042 24.449 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 35.896 0.000
## EVI03 0.990 0.048 20.656 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.035 26.219 0.000
## EDE03 0.580 0.037 15.851 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.051 13.915 0.000
## EAB03 0.730 0.063 11.619 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.767 0.293 9.427 0.000
## absorcion 2.132 0.248 8.613 0.000
## dedicacion ~~
## absorcion 2.731 0.293 9.316 0.000
## recuperacion ~~
## energia 1.367 0.197 6.938 0.000
## vigor 1.007 0.165 6.098 0.000
## dedicacion 1.049 0.179 5.852 0.000
## absorcion 0.796 0.151 5.281 0.000
## energia ~~
## vigor 2.045 0.249 8.223 0.000
## dedicacion 1.852 0.259 7.139 0.000
## absorcion 1.340 0.220 6.091 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 0.149 6.400 0.000
## .relajacion 0.510 0.085 6.021 0.000
## .dominio 1.191 0.200 5.958 0.000
## .control 0.699 0.125 5.583 0.000
## energia 2.823 0.327 8.623 0.000
## vigor 2.859 0.289 9.900 0.000
## dedicacion 3.458 0.367 9.424 0.000
## absorcion 2.595 0.301 8.628 0.000
## .RPD01 1.169 0.120 9.782 0.000
## .RPD02 0.984 0.107 9.204 0.000
## .RPD03 1.435 0.147 9.730 0.000
## .RPD05 0.973 0.109 8.940 0.000
## .RPD06 1.835 0.184 9.979 0.000
## .RPD07 1.166 0.124 9.378 0.000
## .RPD08 1.485 0.152 9.739 0.000
## .RPD09 1.036 0.115 9.034 0.000
## .RPD10 1.044 0.116 8.982 0.000
## .RRE02 0.623 0.067 9.253 0.000
## .RRE03 0.646 0.072 8.974 0.000
## .RRE04 0.494 0.056 8.837 0.000
## .RRE05 0.384 0.047 8.203 0.000
## .RRE06 0.882 0.097 9.126 0.000
## .RRE07 0.929 0.098 9.458 0.000
## .RRE10 1.134 0.112 10.086 0.000
## .RMA02 1.742 0.175 9.935 0.000
## .RMA03 1.500 0.156 9.595 0.000
## .RMA04 0.857 0.098 8.786 0.000
## .RMA05 0.904 0.101 8.985 0.000
## .RMA06 1.626 0.158 10.280 0.000
## .RMA07 0.843 0.094 8.978 0.000
## .RMA08 0.881 0.098 9.029 0.000
## .RMA09 1.089 0.115 9.498 0.000
## .RMA10 1.256 0.131 9.591 0.000
## .RCO02 0.980 0.104 9.394 0.000
## .RCO03 0.493 0.058 8.473 0.000
## .RCO04 0.468 0.052 9.019 0.000
## .RCO05 0.393 0.046 8.620 0.000
## .RCO06 0.479 0.054 8.883 0.000
## .RCO07 0.504 0.056 8.969 0.000
## .EN01 0.689 0.071 9.661 0.000
## .EN02 0.439 0.048 9.066 0.000
## .EN04 0.476 0.051 9.266 0.000
## .EN05 0.381 0.043 8.945 0.000
## .EN06 0.367 0.041 8.925 0.000
## .EN07 0.502 0.055 9.210 0.000
## .EN08 0.358 0.041 8.708 0.000
## .EVI01 0.177 0.036 4.919 0.000
## .EVI02 0.242 0.038 6.298 0.000
## .EVI03 1.222 0.124 9.826 0.000
## .EDE01 0.395 0.065 6.060 0.000
## .EDE02 0.498 0.066 7.579 0.000
## .EDE03 0.836 0.085 9.887 0.000
## .EAB01 0.478 0.099 4.805 0.000
## .EAB02 1.010 0.109 9.283 0.000
## .EAB03 1.718 0.176 9.778 0.000
## recuperacion 0.972 0.199 4.896 0.000# En conclusión el modelo no es tan bueno, ya que el Comparative Fit Index (CFI) y el Tucker-Lewis Index (TLI) son de 0.884 y 0.877 respectivamente. Una opción sería intentar con otro tipo de modelo, ya que con este no se podrían obtener resultados verí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