Actividad 3 - Módulo 1
Lisset Hernández A01284611
2025-02-19
Teoría
Los Modelos de Ecuaciones Estructurales (SEM) es una técnica de análisis de estadística multivariada, que permite analizar patrones complejos de relaciones entre variables, realizar comparaciones entre e intragrupos, y validar modelos teóricos y empíricos.
Ejemplo 1. Estudio de Holzinger y Swineford (1939)
Contexto
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7mo y 8vo 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
library(lavaan)
library(lavaanPlot)Importar la base de datos
df1 <- HolzingerSwineford1939summary(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
- 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 variables son 0
Estructurar el modelo
modelo1 <- '# Regresiones
#Variables Latentes
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
velocidad =~ x7 + x8 + x9
#Varianzas y Covarianza
visual ~~ visual
textual ~~ textual
velocidad ~~ velocidad
visual ~~ textual + velocidad
textual ~~ velocidad
#Intercepto
'Generar 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#Revisar los valores de Comparative Fit Index (CFI) y Tuker-Lewis Index (TLI)
#Excelente si son mayores a 0.95
#Aceptable entre 0.90 y 0.95, Deficiente menor a 0.9Conclusión: Modelo Aceptable
Ejercicio 1. Democracia Política e Industrialización
Contexto
La base de datos contiene distintas mediciones sobre la democracia política e industrialización en países en desarrollo durantw 1960 y 1965 La tabla incluye los siguientes datos:
- y1: Calificaciones sobre libertad de prensa en 1960
- y2: Libertad de la oposicioón 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 oposicioón política en 1965
- y7: Imparcialidad de elecciones en 1965
- y8: Eficacia de la legislatura electa en 1965
- x1: PIB per cápica en 1960
- x2: Consumo de energía inanimada per cápita en 1960
- x3: Porcentaje de la fuerza laboral en la industria en 1960
Importar la base de datos
df2 <- PoliticalDemocracysummary(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 3.892270Tipos de fórmulas
- Regresión(~) Variable que depende de otras
- 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 variables son 0
Estructurar el modelo
modelo2 <- '# Regresiones
#Variables Latentes
Periodo1 =~ y1 +y2 +y3 + y4
Periodo2 =~ y5 +y6 + y7 + y8
Indicador =~ x1 + x2 + x3
#Varianzas y Covarianza
Periodo1 ~~ Periodo1
Periodo2 ~~ Periodo2
Indicador ~~ Indicador
Periodo1 ~~ Periodo2 + Indicador
Periodo2 ~~ Indicador
#Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa2 <- sem (modelo2, data=df2)
summary(cfa2)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 72.462
## Degrees of freedom 41
## P-value (Chi-square) 0.002
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Periodo1 =~
## y1 1.000
## y2 1.354 0.175 7.755 0.000
## y3 1.044 0.150 6.961 0.000
## y4 1.300 0.138 9.412 0.000
## Periodo2 =~
## y5 1.000
## y6 1.258 0.164 7.651 0.000
## y7 1.282 0.158 8.137 0.000
## y8 1.310 0.154 8.529 0.000
## Indicador =~
## x1 1.000
## x2 2.182 0.139 15.714 0.000
## x3 1.819 0.152 11.956 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Periodo1 ~~
## Periodo2 4.487 0.911 4.924 0.000
## Indicador 0.660 0.206 3.202 0.001
## Periodo2 ~~
## Indicador 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## Periodo1 4.845 1.088 4.453 0.000
## Periodo2 4.345 1.051 4.134 0.000
## Indicador 0.448 0.087 5.169 0.000
## .y1 1.942 0.395 4.910 0.000
## .y2 6.490 1.185 5.479 0.000
## .y3 5.340 0.943 5.662 0.000
## .y4 2.887 0.610 4.731 0.000
## .y5 2.390 0.447 5.351 0.000
## .y6 4.343 0.796 5.456 0.000
## .y7 3.510 0.668 5.252 0.000
## .y8 2.940 0.586 5.019 0.000
## .x1 0.082 0.020 4.180 0.000
## .x2 0.118 0.070 1.689 0.091
## .x3 0.467 0.090 5.174 0.000lavaanPlot(cfa2, coef=TRUE, cov=TRUE)Ejercicio 3. Bienestar de los Trabajadores
Importar paquetes y llamar líbrerias
library(readxl)Importar la base de datos
df3 <- read_excel("/Users/lishdz/Desktop/8vo/periodo 1/modulo 1 - r/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 + RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
#Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
#Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa3.1 <- sem (modelo3.1, data=df3)
summary(cfa3.1)## 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 0.000#Revisar los valores de Comparative Fit Index (CFI) y Tuker-Lewis Index (TLI)
#Excelente si son mayores a 0.95
#Aceptable entre 0.90 y 0.95, Deficiente menor a 0.9Parte 2. Energía Recuperada
modelo3.2 <- '# Regresiones
#Variables Latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
#Varianzas y Covarianza
energia ~~ energia
#Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
cfa3.2 <- sem (modelo3.2, data=df3)
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 0.000#Revisar los valores de Comparative Fit Index (CFI) y Tuker-Lewis Index (TLI)
#Excelente si son mayores a 0.95
#Aceptable entre 0.90 y 0.95, Deficiente menor a 0.9Parte 3. Engagement Laboral
modelo3.3 <- '# Regresiones
#Variables Latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
#Varianzas y Covarianza
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
#Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
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 0.000#Revisar los valores de Comparative Fit Index (CFI) y Tuker-Lewis Index (TLI)
#Excelente si son mayores a 0.95
#Aceptable entre 0.90 y 0.95, Deficiente menor a 0.9Parte 4. Modelo Completo
modelo3.4 <- '# Regresiones
#Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
#Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
recuperacion ~~ energia + vigor + dedicacion + absorcion
energia ~~ vigor + dedicacion + absorcion
#Intercepto
'Generar el Análisis Factorial Confirmatorio (CFA)
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#Revisar los valores de Comparative Fit Index (CFI) y Tuker-Lewis Index (TLI)
#Excelente si son mayores a 0.95
#Aceptable entre 0.90 y 0.95, Deficiente menor a 0.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