Actividad 3
Mariano Bautista A01656904
2025-02-19
Ejemplo en clase
imagen_path <- file.choose()
#Cargar la imagen con knitr::include_graphics
knitr::include_graphics(imagen_path)Teoría
Los Modelos de ecuaciones estructurales (ESM) es una técnica 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 7º y 8º de dos escuelas (Pasteur y Grand-White)
La siguiente base de datos estan 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 parrafos * x5: Completar oraciones * x6: Significado de palabras * x7: Sumas aceleradas * x8: Conteo acelerado de puntos * x9: Discriminacion acelerada de mayusculas rectas y curvas
se busca identificar laas relaciones entre los habitantes visual (x1,x2,x3), textual (x4,x5,x6) y velocidad
librerias
#install.packages("lavaan")
library(lavaan)## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.#install.packages("lavaanPlot")
library(lavaanPlot)Importar 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
## 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 si misma, Covarianza es 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
# Varianzas y Covarianzas
visual ~~ visual
textual ~~ textual
velocidad ~~velocidad
visual~~ textual+velocidad
textual~~velocidad
# Intercepto
'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)Ejercicio 1. Democracia politica e industrializacion
Contexto
la base de datos contiene distintas mendiciones sobre la democracia politica e indutrializacion en paises en desarrollo durante 1960 y 1965.
la tabla incliye lo sisguinetes 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 furza laboral emn la industria en 1960
Importar 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. :6.425head(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 si misma, Covarianza es entre otras)
- Intercepto (~1) Valor esperado cuando las demás variables son cero
modelo2 <- ' # Regresiones
# Variables Latentes
primero =~ y1 + y2 + y3 + y4
segundo =~ y5 + y6 + y7 + y8
demo =~ x1 + x2 + x3
# Varianzas y Covarianzas
# Intercepto
'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|)
## primero =~
## 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
## segundo =~
## 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
## demo =~
## 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|)
## primero ~~
## segundo 4.487 0.911 4.924 0.000
## demo 0.660 0.206 3.202 0.001
## segundo ~~
## demo 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .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 0.000
## primero 4.845 1.088 4.453 0.000
## segundo 4.345 1.051 4.134 0.000
## demo 0.448 0.087 5.169 0.000lavaanPlot(cfa2, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa2, fit.measures=TRUE)## 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
##
## Model Test Baseline Model:
##
## Test statistic 730.654
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.953
## Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1564.959
## Loglikelihood unrestricted model (H1) -1528.728
##
## Akaike (AIC) 3179.918
## Bayesian (BIC) 3237.855
## Sample-size adjusted Bayesian (SABIC) 3159.062
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.101
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.139
## P-value H_0: RMSEA <= 0.050 0.021
## P-value H_0: RMSEA >= 0.080 0.827
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## primero =~
## 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
## segundo =~
## 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
## demo =~
## 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|)
## primero ~~
## segundo 4.487 0.911 4.924 0.000
## demo 0.660 0.206 3.202 0.001
## segundo ~~
## demo 0.774 0.208 3.715 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .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 0.000
## primero 4.845 1.088 4.453 0.000
## segundo 4.345 1.051 4.134 0.000
## demo 0.448 0.087 5.169 0.000#Revisar los valores de Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI)
#Excelente si es >= 0.95, aceptable entre 0.90 y 0.95, deficiente < 0.90cargar base de datos
# Instalar y cargar el paquete necesario (si no lo has hecho previamente)
library(readxl)
# Seleccionar el archivo usando un cuadro de diálogo
ruta_archivo <- file.choose()
# Leer el archivo Excel seleccionado
df3 <- read_xlsx(ruta_archivo)
# Si es necesario, convertir a data frame
df3 <- as.data.frame(df3)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)## 'data.frame': 223 obs. of 51 variables:
## $ ID : num 1 2 3 4 5 6 7 8 9 10 ...
## $ GEN : num 1 1 1 1 1 0 0 1 1 1 ...
## $ EXPER: num 22 22 30 17 23 31 26 30 15 15 ...
## $ EDAD : num 45 44 52 41 51 52 53 48 40 38 ...
## $ RPD01: num 5 4 7 5 7 3 5 6 4 2 ...
## $ RPD02: num 1 4 7 5 6 4 5 7 4 3 ...
## $ RPD03: num 3 6 7 1 7 5 4 6 4 2 ...
## $ RPD05: num 2 5 7 1 6 4 4 7 4 3 ...
## $ RPD06: num 3 3 7 3 7 3 5 2 6 7 ...
## $ RPD07: num 1 2 6 5 6 5 6 5 4 1 ...
## $ RPD08: num 3 3 7 3 7 4 6 2 5 3 ...
## $ RPD09: num 2 4 7 2 6 4 7 4 4 2 ...
## $ RPD10: num 4 4 7 2 6 4 7 1 6 2 ...
## $ RRE02: num 6 6 7 6 7 5 7 5 6 7 ...
## $ RRE03: num 6 6 7 6 7 4 7 4 4 7 ...
## $ RRE04: num 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE05: num 6 6 7 6 7 5 7 4 6 7 ...
## $ RRE06: num 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE07: num 6 6 7 6 7 4 7 4 6 7 ...
## $ RRE10: num 6 6 7 6 7 4 7 4 6 7 ...
## $ RMA02: num 4 6 4 3 4 7 5 2 6 7 ...
## $ RMA03: num 5 6 5 4 4 7 5 1 2 7 ...
## $ RMA04: num 5 5 6 4 4 5 5 1 4 7 ...
## $ RMA05: num 5 5 6 4 4 6 5 3 4 7 ...
## $ RMA06: num 6 6 7 6 5 4 5 7 6 7 ...
## $ RMA07: num 4 6 6 5 4 5 7 4 6 7 ...
## $ RMA08: num 5 6 4 4 4 6 6 4 2 7 ...
## $ RMA09: num 3 5 4 3 5 4 5 2 4 7 ...
## $ RMA10: num 7 5 5 4 5 5 6 4 3 7 ...
## $ RCO02: num 7 7 7 5 7 6 7 7 3 7 ...
## $ RCO03: num 7 7 7 5 7 5 7 7 3 7 ...
## $ RCO04: num 7 7 7 6 7 4 7 7 3 7 ...
## $ RCO05: num 7 7 7 6 7 4 7 7 3 7 ...
## $ RCO06: num 7 7 7 6 7 4 7 7 4 7 ...
## $ RCO07: num 5 7 7 6 7 4 7 7 7 7 ...
## $ EN01 : num 6 6 7 4 6 4 7 7 4 7 ...
## $ EN02 : num 7 6 7 4 6 4 7 7 4 7 ...
## $ EN04 : num 6 6 7 4 6 4 7 6 4 7 ...
## $ EN05 : num 5 5 7 5 6 5 7 6 4 7 ...
## $ EN06 : num 5 5 7 5 6 3 7 5 5 7 ...
## $ EN07 : num 5 5 7 2 6 4 7 4 4 7 ...
## $ EN08 : num 6 5 7 5 6 4 7 4 4 7 ...
## $ EVI01: num 6 5 7 5 6 4 7 6 6 0 ...
## $ EVI02: num 6 5 7 6 6 4 6 5 5 1 ...
## $ EVI03: num 6 6 6 7 6 4 6 6 7 0 ...
## $ EDE01: num 6 6 6 5 7 6 7 7 7 1 ...
## $ EDE02: num 7 6 7 6 7 5 7 7 7 5 ...
## $ EDE03: num 7 7 7 7 7 5 7 7 7 6 ...
## $ EAB01: num 7 7 7 6 7 5 7 7 7 0 ...
## $ EAB02: num 7 7 7 6 7 5 7 2 5 1 ...
## $ EAB03: num 6 5 6 5 6 5 7 3 5 0 ...head(df3)## ID GEN EXPER EDAD RPD01 RPD02 RPD03 RPD05 RPD06 RPD07 RPD08 RPD09 RPD10 RRE02
## 1 1 1 22 45 5 1 3 2 3 1 3 2 4 6
## 2 2 1 22 44 4 4 6 5 3 2 3 4 4 6
## 3 3 1 30 52 7 7 7 7 7 6 7 7 7 7
## 4 4 1 17 41 5 5 1 1 3 5 3 2 2 6
## 5 5 1 23 51 7 6 7 6 7 6 7 6 6 7
## 6 6 0 31 52 3 4 5 4 3 5 4 4 4 5
## RRE03 RRE04 RRE05 RRE06 RRE07 RRE10 RMA02 RMA03 RMA04 RMA05 RMA06 RMA07 RMA08
## 1 6 6 6 6 6 6 4 5 5 5 6 4 5
## 2 6 6 6 6 6 6 6 6 5 5 6 6 6
## 3 7 7 7 7 7 7 4 5 6 6 7 6 4
## 4 6 6 6 6 6 6 3 4 4 4 6 5 4
## 5 7 7 7 7 7 7 4 4 4 4 5 4 4
## 6 4 4 5 4 4 4 7 7 5 6 4 5 6
## RMA09 RMA10 RCO02 RCO03 RCO04 RCO05 RCO06 RCO07 EN01 EN02 EN04 EN05 EN06 EN07
## 1 3 7 7 7 7 7 7 5 6 7 6 5 5 5
## 2 5 5 7 7 7 7 7 7 6 6 6 5 5 5
## 3 4 5 7 7 7 7 7 7 7 7 7 7 7 7
## 4 3 4 5 5 6 6 6 6 4 4 4 5 5 2
## 5 5 5 7 7 7 7 7 7 6 6 6 6 6 6
## 6 4 5 6 5 4 4 4 4 4 4 4 5 3 4
## EN08 EVI01 EVI02 EVI03 EDE01 EDE02 EDE03 EAB01 EAB02 EAB03
## 1 6 6 6 6 6 7 7 7 7 6
## 2 5 5 5 6 6 6 7 7 7 5
## 3 7 7 7 6 6 7 7 7 7 6
## 4 5 5 6 7 5 6 7 6 6 5
## 5 6 6 6 6 7 7 7 7 7 6
## 6 4 4 4 4 6 5 5 5 5 5Parte 3. Engagement Laboral
modelo31 <- ' # Regresiones
#Variables latentes
desapego =~ RPD01 + RPD02 +RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio=~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control=~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion +dominio + control
# Varianzas y Covarianzas
desapego~~desapego
relajacion~~relajacion
dominio~~dominio
control~~control
# Intercepto
'Generar el Analisis
cfa3<- sem(modelo31, data=df3)
summary(cfa3)## 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, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa3, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 430
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7522.157
## Degrees of freedom 465
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.888
## Tucker-Lewis Index (TLI) 0.879
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10616.148
## Loglikelihood unrestricted model (H1) -10005.632
##
## Akaike (AIC) 21364.296
## Bayesian (BIC) 21589.169
## Sample-size adjusted Bayesian (SABIC) 21380.007
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.097
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.998
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.075
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.206 0.082 14.780 0.000
## RPD03 1.143 0.085 13.374 0.000
## RPD05 1.312 0.086 15.244 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.440 0.000
## RPD08 1.164 0.087 13.447 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.258 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.227 0.000
## RRE04 1.025 0.058 17.713 0.000
## RRE05 1.055 0.056 18.758 0.000
## RRE06 1.245 0.074 16.869 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.120 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.155 0.096 12.079 0.000
## RMA04 1.178 0.089 13.274 0.000
## RMA05 1.141 0.087 13.072 0.000
## RMA06 0.645 0.075 8.597 0.000
## RMA07 1.103 0.084 13.061 0.000
## RMA08 1.109 0.085 12.994 0.000
## RMA09 1.028 0.084 12.246 0.000
## RMA10 1.055 0.088 12.044 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.182 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.990 0.000
## RCO06 0.834 0.046 18.216 0.000
## RCO07 0.835 0.046 18.057 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.149 0.131 8.787 0.000
## dominio 0.858 0.129 6.666 0.000
## control 1.341 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## .relajacion 0.333 0.089 3.757 0.000
## .dominio 1.260 0.212 5.942 0.000
## .control 0.900 0.159 5.666 0.000
## .RPD01 1.172 0.120 9.782 0.000
## .RPD02 0.999 0.108 9.228 0.000
## .RPD03 1.441 0.148 9.733 0.000
## .RPD05 0.987 0.110 8.964 0.000
## .RPD06 1.817 0.182 9.967 0.000
## .RPD07 1.173 0.125 9.383 0.000
## .RPD08 1.460 0.150 9.714 0.000
## .RPD09 1.032 0.114 9.021 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.653 0.073 9.011 0.000
## .RRE04 0.481 0.055 8.794 0.000
## .RRE05 0.374 0.046 8.153 0.000
## .RRE06 0.886 0.097 9.149 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA02 1.740 0.175 9.931 0.000
## .RMA03 1.485 0.155 9.575 0.000
## .RMA04 0.855 0.097 8.772 0.000
## .RMA05 0.899 0.100 8.967 0.000
## .RMA06 1.631 0.159 10.281 0.000
## .RMA07 0.845 0.094 8.977 0.000
## .RMA08 0.886 0.098 9.034 0.000
## .RMA09 1.094 0.115 9.500 0.000
## .RMA10 1.259 0.131 9.590 0.000
## .RCO02 0.983 0.105 9.379 0.000
## .RCO03 0.484 0.058 8.391 0.000
## .RCO04 0.462 0.052 8.963 0.000
## .RCO05 0.382 0.045 8.513 0.000
## .RCO06 0.494 0.055 8.917 0.000
## .RCO07 0.515 0.057 8.985 0.000
## recuperacion 0.978 0.202 4.833 0.000# Revisar los valores de Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI)195
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente< 0.90Parte 3. Engagement Laboral
modelo32 <- ' # Regresiones
#Variables latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianzas
energia~~energia
# Intercepto
'Generar el Analisis
cfa32 <- sem(modelo32, data=df3)
summary(cfa32)## 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(cfa32, coef=TRUE, cov=TRUE)Evaluar el modelo
summary(cfa32, 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 Tucker-Lewis Index (TLI)195
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente< 0.90Parte 3. Engagement Laboral
modelo33 <- ' # 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 analisis Factorial Confirmatorio
cfa33 <- sem(modelo33, data=df3)
summary(cfa33)## 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(cfa33, coef=TRUE, cov=TRUE)Evaluar completo
summary(cfa33, 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 Tucker-Lewis Index (TLI)195
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente< 0.90Parte 4. Modelo completo
modelo34 <- ' # Regresiones
# Variables Latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~ vigor
dedicacion ~~ dedicacion
absorcion ~~ absorcion
vigor ~~ dedicacion + absorcion
dedicacion ~~ absorcion
# Intercepto
'Generar analisis factorial confirmatorio
cfa34 <- sem(modelo34, data=df3)## Warning: lavaan->lav_lavaan_step11_estoptim():
## Model estimation FAILED! Returning starting values.summary(cfa34)## lavaan 0.6-19 did NOT end normally after 10000 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
##
## 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 NA
## RPD03 1.144 NA
## RPD05 1.314 NA
## RPD06 1.083 NA
## RPD07 1.229 NA
## RPD08 1.157 NA
## RPD09 1.316 NA
## RPD10 1.343 NA
## relajacion =~
## RRE02 1.000
## RRE03 1.121 NA
## RRE04 1.020 NA
## RRE05 1.051 NA
## RRE06 1.245 NA
## RRE07 1.122 NA
## RRE10 0.815 NA
## dominio =~
## RMA02 1.000
## RMA03 1.152 NA
## RMA04 1.178 NA
## RMA05 1.140 NA
## RMA06 0.647 NA
## RMA07 1.104 NA
## RMA08 1.110 NA
## RMA09 1.030 NA
## RMA10 1.057 NA
## control =~
## RCO02 1.000
## RCO03 0.946 NA
## RCO04 0.794 NA
## RCO05 0.815 NA
## RCO06 0.837 NA
## RCO07 0.837 NA
## recuperacion =~
## desapego 1.000
## relajacion 1.072 NA
## dominio 0.900 NA
## control 1.420 NA
## energia =~
## EN01 1.000
## EN02 1.025 NA
## EN04 0.997 NA
## EN05 0.994 NA
## EN06 0.982 NA
## EN07 1.044 NA
## EN08 1.030 NA
## vigor =~
## EVI01 1.000
## EVI02 0.984 NA
## EVI03 0.995 NA
## dedicacion =~
## EDE01 1.000
## EDE02 0.912 NA
## EDE03 0.580 NA
## absorcion =~
## EAB01 1.000
## EAB02 0.708 NA
## EAB03 0.730 NA
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## relajacion ~
## relajacion -0.493 NA
## vigor ~
## vigor 13.358 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .vigor ~~
## dedicacion 2.826 NA
## absorcion 2.293 NA
## dedicacion ~~
## absorcion 2.939 NA
## recuperacion ~~
## energia 1.367 NA
## dedicacion 0.083 NA
## absorcion 0.012 NA
## energia ~~
## dedicacion -0.105 NA
## absorcion -0.249 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 NA
## .dominio 1.192 NA
## .control 0.701 NA
## energia 2.823 NA
## dedicacion 3.581 NA
## absorcion 2.852 NA
## .RPD01 1.169 NA
## .RPD02 0.984 NA
## .RPD03 1.435 NA
## .RPD05 0.973 NA
## .RPD06 1.834 NA
## .RPD07 1.166 NA
## .RPD08 1.484 NA
## .RPD09 1.037 NA
## .RPD10 1.045 NA
## .RRE02 0.624 NA
## .RRE03 0.646 NA
## .RRE04 0.494 NA
## .RRE05 0.384 NA
## .RRE06 0.882 NA
## .RRE07 0.928 NA
## .RRE10 1.134 NA
## .RMA02 1.741 NA
## .RMA03 1.500 NA
## .RMA04 0.858 NA
## .RMA05 0.904 NA
## .RMA06 1.627 NA
## .RMA07 0.843 NA
## .RMA08 0.881 NA
## .RMA09 1.089 NA
## .RMA10 1.256 NA
## .RCO02 0.980 NA
## .RCO03 0.492 NA
## .RCO04 0.468 NA
## .RCO05 0.393 NA
## .RCO06 0.480 NA
## .RCO07 0.504 NA
## .EN01 0.689 NA
## .EN02 0.445 NA
## .EN04 0.472 NA
## .EN05 0.381 NA
## .EN06 0.362 NA
## .EN07 0.502 NA
## .EN08 0.361 NA
## .EVI01 0.196 NA
## .EVI02 0.227 NA
## .EVI03 1.212 NA
## .EDE01 0.394 NA
## .EDE02 0.500 NA
## .EDE03 0.836 NA
## .EAB01 0.478 NA
## .EAB02 1.009 NA
## .EAB03 1.718 NA
## .relajacion 0.508 NA
## recuperacion 0.972 NA
## .vigor 2.839 NAlavaanPlot(cfa34, coef=TRUE, cov=TRUE)Evaluar Modelo
summary(cfa34, fit.measures=TRUE)## Warning: lavaan->lav_object_summary():
## fit measures not available if model did not converge## lavaan 0.6-19 did NOT end normally after 10000 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
##
## 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 NA
## RPD03 1.144 NA
## RPD05 1.314 NA
## RPD06 1.083 NA
## RPD07 1.229 NA
## RPD08 1.157 NA
## RPD09 1.316 NA
## RPD10 1.343 NA
## relajacion =~
## RRE02 1.000
## RRE03 1.121 NA
## RRE04 1.020 NA
## RRE05 1.051 NA
## RRE06 1.245 NA
## RRE07 1.122 NA
## RRE10 0.815 NA
## dominio =~
## RMA02 1.000
## RMA03 1.152 NA
## RMA04 1.178 NA
## RMA05 1.140 NA
## RMA06 0.647 NA
## RMA07 1.104 NA
## RMA08 1.110 NA
## RMA09 1.030 NA
## RMA10 1.057 NA
## control =~
## RCO02 1.000
## RCO03 0.946 NA
## RCO04 0.794 NA
## RCO05 0.815 NA
## RCO06 0.837 NA
## RCO07 0.837 NA
## recuperacion =~
## desapego 1.000
## relajacion 1.072 NA
## dominio 0.900 NA
## control 1.420 NA
## energia =~
## EN01 1.000
## EN02 1.025 NA
## EN04 0.997 NA
## EN05 0.994 NA
## EN06 0.982 NA
## EN07 1.044 NA
## EN08 1.030 NA
## vigor =~
## EVI01 1.000
## EVI02 0.984 NA
## EVI03 0.995 NA
## dedicacion =~
## EDE01 1.000
## EDE02 0.912 NA
## EDE03 0.580 NA
## absorcion =~
## EAB01 1.000
## EAB02 0.708 NA
## EAB03 0.730 NA
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## relajacion ~
## relajacion -0.493 NA
## vigor ~
## vigor 13.358 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .vigor ~~
## dedicacion 2.826 NA
## absorcion 2.293 NA
## dedicacion ~~
## absorcion 2.939 NA
## recuperacion ~~
## energia 1.367 NA
## dedicacion 0.083 NA
## absorcion 0.012 NA
## energia ~~
## dedicacion -0.105 NA
## absorcion -0.249 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 NA
## .dominio 1.192 NA
## .control 0.701 NA
## energia 2.823 NA
## dedicacion 3.581 NA
## absorcion 2.852 NA
## .RPD01 1.169 NA
## .RPD02 0.984 NA
## .RPD03 1.435 NA
## .RPD05 0.973 NA
## .RPD06 1.834 NA
## .RPD07 1.166 NA
## .RPD08 1.484 NA
## .RPD09 1.037 NA
## .RPD10 1.045 NA
## .RRE02 0.624 NA
## .RRE03 0.646 NA
## .RRE04 0.494 NA
## .RRE05 0.384 NA
## .RRE06 0.882 NA
## .RRE07 0.928 NA
## .RRE10 1.134 NA
## .RMA02 1.741 NA
## .RMA03 1.500 NA
## .RMA04 0.858 NA
## .RMA05 0.904 NA
## .RMA06 1.627 NA
## .RMA07 0.843 NA
## .RMA08 0.881 NA
## .RMA09 1.089 NA
## .RMA10 1.256 NA
## .RCO02 0.980 NA
## .RCO03 0.492 NA
## .RCO04 0.468 NA
## .RCO05 0.393 NA
## .RCO06 0.480 NA
## .RCO07 0.504 NA
## .EN01 0.689 NA
## .EN02 0.445 NA
## .EN04 0.472 NA
## .EN05 0.381 NA
## .EN06 0.362 NA
## .EN07 0.502 NA
## .EN08 0.361 NA
## .EVI01 0.196 NA
## .EVI02 0.227 NA
## .EVI03 1.212 NA
## .EDE01 0.394 NA
## .EDE02 0.500 NA
## .EDE03 0.836 NA
## .EAB01 0.478 NA
## .EAB02 1.009 NA
## .EAB03 1.718 NA
## .relajacion 0.508 NA
## recuperacion 0.972 NA
## .vigor 2.839 NA# Revisar los valores de Comparative Fit Index (CFI) y Tucker-Lewis Index (TLI)195
#Excelente si es >= 0.95, Aceptable entre 0.90 y 0.95, Deficiente< 0.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