SEM
Héctor Guadalupe de la Garza Treviño - A01177960
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)
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° de dos escuelas (Pasteur y Grand White).
La base de datos esta 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 mayusculas 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 librerias
library(lavaan) # Analisis de variables latentes
library(lavaanPlot)Importar 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.
- Variables latentes (=~) No se observa, se infiere.
- Variazas y Covarianzas (~~) Relaciones entre variables latentes y observadas (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
# 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 Tucker-Lewis Index (TLI)
# Excelente si es >= 0.95, aceptable entre 0.90 y 0.95, Deficiente si es < 0.90Conclusión: Modelo aceptable
Ejercicio 1. Democratizar política e industrialización
Contexto
La base de datos contiene distintas mediciones sobre la demoracia política e industrialización en paises 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 politica 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 politica 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
1. Importar la base de datos
df2 <- PoliticalDemocracy2. Entender 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 3.8922703. Estructurar el modelo
modelo2 <- '
# Definir variables latentes de democratización en 1960 y 1965
Dem1960 =~ y1 + y2 + y3 + y4
Dem1965 =~ y5 + y6 + y7 + y8
# Definir variable latente de industrialización
Ind1960 =~ x1 + x2 + x3
# Relacionar democratización de 1960 con 1965
Dem1965 ~ Dem1960
# Relacionar industrialización con democratización
Dem1960 ~ Ind1960
Dem1965 ~ Ind1960
# Especificar varianzas y covarianzas
Dem1960 ~~ Dem1960
Dem1965 ~~ Dem1965
Ind1960 ~~ Ind1960
Dem1960 ~~ Ind1960
Dem1965 ~~ Ind1960
'4. Generar el análisis factorial confirmatorio (CFA)
cfa2 <- sem(modelo2, data=df2, se="bootstrap")## Warning: lavaan->lav_model_nvcov_bootstrap():
## 409 bootstrap runs resulted in nonadmissible solutions.summary(cfa2, standardized=TRUE, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 72.462
## Degrees of freedom 39
## P-value (Chi-square) 0.001
##
## 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) 0.950
## Tucker-Lewis Index (TLI) 0.930
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1564.959
## Loglikelihood unrestricted model (H1) -1528.728
##
## Akaike (AIC) 3183.918
## Bayesian (BIC) 3246.490
## Sample-size adjusted Bayesian (SABIC) 3161.394
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.107
## 90 Percent confidence interval - lower 0.068
## 90 Percent confidence interval - upper 0.145
## P-value H_0: RMSEA <= 0.050 0.013
## P-value H_0: RMSEA >= 0.080 0.880
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Dem1960 =~
## y1 1.000 2.201 0.845
## y2 1.354 0.164 8.232 0.000 2.980 0.760
## y3 1.044 0.128 8.157 0.000 2.298 0.705
## y4 1.300 0.138 9.384 0.000 2.860 0.860
## Dem1965 =~
## y5 1.000 2.084 0.803
## y6 1.258 0.213 5.911 0.000 2.623 0.783
## y7 1.282 0.168 7.645 0.000 2.673 0.819
## y8 1.310 0.200 6.546 0.000 2.730 0.847
## Ind1960 =~
## x1 1.000 0.669 0.920
## x2 2.182 0.145 15.019 0.000 1.461 0.973
## x3 1.819 0.137 13.286 0.000 1.218 0.872
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Dem1965 ~
## Dem1960 0.873 0.090 9.751 0.000 0.922 0.922
## Dem1960 ~
## Ind1960 1.565 0.116 13.465 0.000 0.476 0.476
## Dem1965 ~
## Ind1960 1.268 0.170 7.474 0.000 0.407 0.407
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Dem1960 ~~
## Ind1960 -0.041 0.096 -0.425 0.671 -0.031 -0.031
## .Dem1965 ~~
## Ind1960 -0.371 0.089 -4.183 0.000 -0.853 -0.853
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Dem1960 3.875 0.797 4.861 0.000 0.800 0.800
## .Dem1965 0.422 0.149 2.841 0.004 0.097 0.097
## Ind1960 0.448 0.071 6.300 0.000 1.000 1.000
## .y1 1.942 0.400 4.860 0.000 1.942 0.286
## .y2 6.490 1.313 4.942 0.000 6.490 0.422
## .y3 5.340 1.070 4.991 0.000 5.340 0.503
## .y4 2.887 0.638 4.522 0.000 2.887 0.261
## .y5 2.390 0.553 4.324 0.000 2.390 0.355
## .y6 4.343 0.849 5.114 0.000 4.343 0.387
## .y7 3.510 0.596 5.887 0.000 3.510 0.329
## .y8 2.940 0.779 3.773 0.000 2.940 0.283
## .x1 0.082 0.019 4.414 0.000 0.082 0.154
## .x2 0.118 0.073 1.618 0.106 0.118 0.053
## .x3 0.467 0.082 5.710 0.000 0.467 0.240lavaanPlot(cfa2, coef=TRUE, cov=TRUE)5. Evaluar el modelo
summary(cfa2, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Number of observations 75
##
## Model Test User Model:
##
## Test statistic 72.462
## Degrees of freedom 39
## P-value (Chi-square) 0.001
##
## 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) 0.950
## Tucker-Lewis Index (TLI) 0.930
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1564.959
## Loglikelihood unrestricted model (H1) -1528.728
##
## Akaike (AIC) 3183.918
## Bayesian (BIC) 3246.490
## Sample-size adjusted Bayesian (SABIC) 3161.394
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.107
## 90 Percent confidence interval - lower 0.068
## 90 Percent confidence interval - upper 0.145
## P-value H_0: RMSEA <= 0.050 0.013
## P-value H_0: RMSEA >= 0.080 0.880
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Dem1960 =~
## y1 1.000
## y2 1.354 0.164 8.232 0.000
## y3 1.044 0.128 8.157 0.000
## y4 1.300 0.138 9.384 0.000
## Dem1965 =~
## y5 1.000
## y6 1.258 0.213 5.911 0.000
## y7 1.282 0.168 7.645 0.000
## y8 1.310 0.200 6.546 0.000
## Ind1960 =~
## x1 1.000
## x2 2.182 0.145 15.019 0.000
## x3 1.819 0.137 13.286 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Dem1965 ~
## Dem1960 0.873 0.090 9.751 0.000
## Dem1960 ~
## Ind1960 1.565 0.116 13.465 0.000
## Dem1965 ~
## Ind1960 1.268 0.170 7.474 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Dem1960 ~~
## Ind1960 -0.041 0.096 -0.425 0.671
## .Dem1965 ~~
## Ind1960 -0.371 0.089 -4.183 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Dem1960 3.875 0.797 4.861 0.000
## .Dem1965 0.422 0.149 2.841 0.004
## Ind1960 0.448 0.071 6.300 0.000
## .y1 1.942 0.400 4.860 0.000
## .y2 6.490 1.313 4.942 0.000
## .y3 5.340 1.070 4.991 0.000
## .y4 2.887 0.638 4.522 0.000
## .y5 2.390 0.553 4.324 0.000
## .y6 4.343 0.849 5.114 0.000
## .y7 3.510 0.596 5.887 0.000
## .y8 2.940 0.779 3.773 0.000
## .x1 0.082 0.019 4.414 0.000
## .x2 0.118 0.073 1.618 0.106
## .x3 0.467 0.082 5.710 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 si es < 0.90User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.950 Tucker-Lewis Index (TLI) 0.930
El modelo indica que:
- Ind1960 influye fuertemente en Dem1960 y Dem1965.
- Dem1960 es un predictor clave de Dem1965.
- Existe una relación negativa inesperada entre Ind1960 y Dem1965, lo que podría indicar un problema de modelado o la necesidad de incluir más variables.
Actividad 3. Bienestar de los trabajadores
1. Descargar librerias
library(readxl)2. Importar la base de datos
df3 <- read_excel("/Users/hectordelagarzatrevino/Library/CloudStorage/GoogleDrive-a01177960@tec.mx/My Drive/LIT/8. Octavo semestre/Generación de escenarios futuros con analítica/Modulo 1/Actividades/Actividad 3/Datos_SEM_Eng.xlsx")3. 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
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 Covarianza
desapego =~ desapego
relajacion =~ relajacion
dominio =~ dominio
control =~ control
# Intercepto
'1. Generar el análisis factorial confirmatorio (CFA)
cfa31 <- sem(modelo31, data=df3)## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.summary(cfa31)## lavaan 0.6-19 ended normally after 310 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 70
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 426
## 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 NA
## RPD03 1.143 NA
## RPD05 1.312 NA
## RPD06 1.088 NA
## RPD07 1.229 NA
## RPD08 1.163 NA
## RPD09 1.317 NA
## RPD10 1.346 NA
## relajacion =~
## RRE02 1.000
## RRE03 1.120 NA
## RRE04 1.025 NA
## RRE05 1.055 NA
## RRE06 1.245 NA
## RRE07 1.117 NA
## RRE10 0.815 NA
## dominio =~
## RMA02 1.000
## RMA03 1.155 NA
## RMA04 1.178 NA
## RMA05 1.141 NA
## RMA06 0.645 NA
## RMA07 1.103 NA
## RMA08 1.109 NA
## RMA09 1.028 NA
## RMA10 1.055 NA
## control =~
## RCO02 1.000
## RCO03 0.948 NA
## RCO04 0.796 NA
## RCO05 0.818 NA
## RCO06 0.834 NA
## RCO07 0.835 NA
## recuperacion =~
## desapego 1.000
## relajacion 1.149 NA
## dominio 0.858 NA
## control 1.341 NA
## desapego =~
## desapego 1.300 NA
## relajacion =~
## relajacion 1.223 NA
## dominio =~
## dominio 2.266 NA
## control =~
## control 2.086 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .RPD01 1.172 NA
## .RPD02 0.999 NA
## .RPD03 1.441 NA
## .RPD05 0.987 NA
## .RPD06 1.817 NA
## .RPD07 1.173 NA
## .RPD08 1.460 NA
## .RPD09 1.032 NA
## .RPD10 1.034 NA
## .RRE02 0.626 NA
## .RRE03 0.653 NA
## .RRE04 0.481 NA
## .RRE05 0.374 NA
## .RRE06 0.886 NA
## .RRE07 0.950 NA
## .RRE10 1.137 NA
## .RMA02 1.740 NA
## .RMA03 1.485 NA
## .RMA04 0.855 NA
## .RMA05 0.899 NA
## .RMA06 1.631 NA
## .RMA07 0.845 NA
## .RMA08 0.886 NA
## .RMA09 1.094 NA
## .RMA10 1.259 NA
## .RCO02 0.983 NA
## .RCO03 0.484 NA
## .RCO04 0.462 NA
## .RCO05 0.382 NA
## .RCO06 0.494 NA
## .RCO07 0.515 NA
## .desapego 0.943 NA
## .relajacion 0.333 NA
## .dominio 1.260 NA
## .control 0.900 NA
## recuperacion 0.978 NAlavaanPlot(cfa31, coef=TRUE, cov=TRUE)2. Evaluar el modelo
summary(cfa31, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 310 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 70
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 426
## 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) 0.887
## Tucker-Lewis Index (TLI) 0.877
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10616.148
## Loglikelihood unrestricted model (H1) -10005.632
##
## Akaike (AIC) 21372.296
## Bayesian (BIC) 21610.798
## Sample-size adjusted Bayesian (SABIC) 21388.959
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.098
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.999
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.075
##
## 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 NA
## RPD03 1.143 NA
## RPD05 1.312 NA
## RPD06 1.088 NA
## RPD07 1.229 NA
## RPD08 1.163 NA
## RPD09 1.317 NA
## RPD10 1.346 NA
## relajacion =~
## RRE02 1.000
## RRE03 1.120 NA
## RRE04 1.025 NA
## RRE05 1.055 NA
## RRE06 1.245 NA
## RRE07 1.117 NA
## RRE10 0.815 NA
## dominio =~
## RMA02 1.000
## RMA03 1.155 NA
## RMA04 1.178 NA
## RMA05 1.141 NA
## RMA06 0.645 NA
## RMA07 1.103 NA
## RMA08 1.109 NA
## RMA09 1.028 NA
## RMA10 1.055 NA
## control =~
## RCO02 1.000
## RCO03 0.948 NA
## RCO04 0.796 NA
## RCO05 0.818 NA
## RCO06 0.834 NA
## RCO07 0.835 NA
## recuperacion =~
## desapego 1.000
## relajacion 1.149 NA
## dominio 0.858 NA
## control 1.341 NA
## desapego =~
## desapego 1.300 NA
## relajacion =~
## relajacion 1.223 NA
## dominio =~
## dominio 2.266 NA
## control =~
## control 2.086 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .RPD01 1.172 NA
## .RPD02 0.999 NA
## .RPD03 1.441 NA
## .RPD05 0.987 NA
## .RPD06 1.817 NA
## .RPD07 1.173 NA
## .RPD08 1.460 NA
## .RPD09 1.032 NA
## .RPD10 1.034 NA
## .RRE02 0.626 NA
## .RRE03 0.653 NA
## .RRE04 0.481 NA
## .RRE05 0.374 NA
## .RRE06 0.886 NA
## .RRE07 0.950 NA
## .RRE10 1.137 NA
## .RMA02 1.740 NA
## .RMA03 1.485 NA
## .RMA04 0.855 NA
## .RMA05 0.899 NA
## .RMA06 1.631 NA
## .RMA07 0.845 NA
## .RMA08 0.886 NA
## .RMA09 1.094 NA
## .RMA10 1.259 NA
## .RCO02 0.983 NA
## .RCO03 0.484 NA
## .RCO04 0.462 NA
## .RCO05 0.382 NA
## .RCO06 0.494 NA
## .RCO07 0.515 NA
## .desapego 0.943 NA
## .relajacion 0.333 NA
## .dominio 1.260 NA
## .control 0.900 NA
## recuperacion 0.978 NA# 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 si es < 0.90Conclusion: Modelo deficiente
Parte 2. Energía recuperada
modelo32 <- ' # Regresiones
# Variables latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianza
energia ~~ energia
# Intercepto
'1. Generar el análisis factorial confirmatorio (CFA)
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)2. 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)
# Excelente si es >= 0.95, aceptable entre 0.90 y 0.95, Deficiente si es < 0.90Conclusion: Modelo Excelente
Parte 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
'1. Generar el análisis factorial confirmatorio (CFA)
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)2. Evaluar el modelo
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)
# Excelente si es >= 0.95, aceptable entre 0.90 y 0.95, Deficiente si es < 0.90Conclusión: Modelo deficiente
Parte 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
recuperacion ~~ energia + vigor + dedicacion + absorcion
energia ~~ vigor + dedicacion + absorcion
# Intercepto
'1. Generar el análisis factorial confirmatorio (CFA)
cfa34 <- sem(modelo34, data=df3)## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.summary(cfa34)## lavaan 0.6-19 ended normally after 685 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 112
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1016
## 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 NA
## RPD03 1.144 NA
## RPD05 1.313 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 1.141 NA
## RMA06 0.648 NA
## RMA07 1.104 NA
## RMA08 1.110 NA
## RMA09 1.030 NA
## RMA10 1.056 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 1.071 NA
## dominio 0.900 NA
## control 1.421 NA
## energia =~
## EN01 1.000
## EN02 1.026 NA
## EN04 0.996 NA
## EN05 0.994 NA
## EN06 0.981 NA
## EN07 1.044 NA
## EN08 1.031 NA
## vigor =~
## EVI01 1.000
## EVI02 0.978 NA
## EVI03 0.990 NA
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 NA
## EDE03 0.580 NA
## absorcion =~
## EAB01 1.000
## EAB02 0.707 NA
## EAB03 0.730 NA
## desapego =~
## desapego 5.040 NA
## relajacion =~
## relajacion 1.609 NA
## dominio =~
## dominio 3.464 NA
## control =~
## control 2.210 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.767 NA
## absorcion 2.132 NA
## dedicacion ~~
## absorcion 2.731 NA
## recuperacion ~~
## energia 1.367 NA
## vigor 1.007 NA
## dedicacion 1.049 NA
## absorcion 0.796 NA
## energia ~~
## vigor 2.045 NA
## dedicacion 1.852 NA
## absorcion 1.340 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.823 NA
## vigor 2.859 NA
## dedicacion 3.458 NA
## absorcion 2.595 NA
## .RPD01 1.169 NA
## .RPD02 0.984 NA
## .RPD03 1.435 NA
## .RPD05 0.973 NA
## .RPD06 1.835 NA
## .RPD07 1.166 NA
## .RPD08 1.485 NA
## .RPD09 1.036 NA
## .RPD10 1.044 NA
## .RRE02 0.623 NA
## .RRE03 0.646 NA
## .RRE04 0.494 NA
## .RRE05 0.384 NA
## .RRE06 0.882 NA
## .RRE07 0.929 NA
## .RRE10 1.134 NA
## .RMA02 1.742 NA
## .RMA03 1.500 NA
## .RMA04 0.857 NA
## .RMA05 0.904 NA
## .RMA06 1.626 NA
## .RMA07 0.843 NA
## .RMA08 0.881 NA
## .RMA09 1.089 NA
## .RMA10 1.256 NA
## .RCO02 0.980 NA
## .RCO03 0.493 NA
## .RCO04 0.468 NA
## .RCO05 0.393 NA
## .RCO06 0.479 NA
## .RCO07 0.504 NA
## .EN01 0.689 NA
## .EN02 0.439 NA
## .EN04 0.476 NA
## .EN05 0.381 NA
## .EN06 0.367 NA
## .EN07 0.502 NA
## .EN08 0.358 NA
## .EVI01 0.177 NA
## .EVI02 0.242 NA
## .EVI03 1.222 NA
## .EDE01 0.395 NA
## .EDE02 0.498 NA
## .EDE03 0.836 NA
## .EAB01 0.478 NA
## .EAB02 1.010 NA
## .EAB03 1.718 NA
## .desapego 0.951 NA
## .relajacion 0.510 NA
## .dominio 1.191 NA
## .control 0.699 NA
## recuperacion 0.972 NAlavaanPlot(cfa34, coef=TRUE, cov=TRUE)2. Evaluar el modelo
summary(cfa34, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 685 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 112
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1016
## 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) 0.876
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15426.580
## Loglikelihood unrestricted model (H1) -14203.926
##
## Akaike (AIC) 31077.161
## Bayesian (BIC) 31458.764
## Sample-size adjusted Bayesian (SABIC) 31103.822
##
## 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 0.411
##
## 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 NA
## RPD03 1.144 NA
## RPD05 1.313 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 1.141 NA
## RMA06 0.648 NA
## RMA07 1.104 NA
## RMA08 1.110 NA
## RMA09 1.030 NA
## RMA10 1.056 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 1.071 NA
## dominio 0.900 NA
## control 1.421 NA
## energia =~
## EN01 1.000
## EN02 1.026 NA
## EN04 0.996 NA
## EN05 0.994 NA
## EN06 0.981 NA
## EN07 1.044 NA
## EN08 1.031 NA
## vigor =~
## EVI01 1.000
## EVI02 0.978 NA
## EVI03 0.990 NA
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 NA
## EDE03 0.580 NA
## absorcion =~
## EAB01 1.000
## EAB02 0.707 NA
## EAB03 0.730 NA
## desapego =~
## desapego 5.040 NA
## relajacion =~
## relajacion 1.609 NA
## dominio =~
## dominio 3.464 NA
## control =~
## control 2.210 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## dedicacion 2.767 NA
## absorcion 2.132 NA
## dedicacion ~~
## absorcion 2.731 NA
## recuperacion ~~
## energia 1.367 NA
## vigor 1.007 NA
## dedicacion 1.049 NA
## absorcion 0.796 NA
## energia ~~
## vigor 2.045 NA
## dedicacion 1.852 NA
## absorcion 1.340 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.823 NA
## vigor 2.859 NA
## dedicacion 3.458 NA
## absorcion 2.595 NA
## .RPD01 1.169 NA
## .RPD02 0.984 NA
## .RPD03 1.435 NA
## .RPD05 0.973 NA
## .RPD06 1.835 NA
## .RPD07 1.166 NA
## .RPD08 1.485 NA
## .RPD09 1.036 NA
## .RPD10 1.044 NA
## .RRE02 0.623 NA
## .RRE03 0.646 NA
## .RRE04 0.494 NA
## .RRE05 0.384 NA
## .RRE06 0.882 NA
## .RRE07 0.929 NA
## .RRE10 1.134 NA
## .RMA02 1.742 NA
## .RMA03 1.500 NA
## .RMA04 0.857 NA
## .RMA05 0.904 NA
## .RMA06 1.626 NA
## .RMA07 0.843 NA
## .RMA08 0.881 NA
## .RMA09 1.089 NA
## .RMA10 1.256 NA
## .RCO02 0.980 NA
## .RCO03 0.493 NA
## .RCO04 0.468 NA
## .RCO05 0.393 NA
## .RCO06 0.479 NA
## .RCO07 0.504 NA
## .EN01 0.689 NA
## .EN02 0.439 NA
## .EN04 0.476 NA
## .EN05 0.381 NA
## .EN06 0.367 NA
## .EN07 0.502 NA
## .EN08 0.358 NA
## .EVI01 0.177 NA
## .EVI02 0.242 NA
## .EVI03 1.222 NA
## .EDE01 0.395 NA
## .EDE02 0.498 NA
## .EDE03 0.836 NA
## .EAB01 0.478 NA
## .EAB02 1.010 NA
## .EAB03 1.718 NA
## .desapego 0.951 NA
## .relajacion 0.510 NA
## .dominio 1.191 NA
## .control 0.699 NA
## recuperacion 0.972 NA# 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 si es < 0.90Conclusión: Modelo deficiente
---
title: "SEM"
author: "Héctor Guadalupe de la Garza Treviño - A01177960"
date: "2025-02-19"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: journal
---

![](/Users/hectordelagarzatrevino/Library/CloudStorage/GoogleDrive-a01177960@tec.mx/My Drive/LIT/8. Octavo semestre/Generación de escenarios futuros con analítica/Modulo 1/Actividades/Actividad 3/liceo-anglo-frances-monterrey-secundaria-clases-768x512.jpg)

# <span style="color: #3749ff;">**Teoría**</span>
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.

# <span style="color: #3749ff;">**Ejemplo 1. Estudio de Holzinger y Swineford (1939)**</span>
Holzinger y Swineford realizaron exámenes de habilidad mental a adolescentes de 7° y 8° de dos escuelas (Pasteur y Grand White).

**La base de datos esta 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 mayusculas 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.

### <span style="color: black;">**Instalar paquetes y llamar librerias**</span>
```{r message=FALSE, warning=FALSE}
library(lavaan) # Analisis de variables latentes
library(lavaanPlot)
```

### <span style="color: black;">**Importar la base de datos**</span>
```{r}
df1 <- HolzingerSwineford1939
```

### <span style="color: black;">**Entender la base de datos**</span>
```{r}
summary(df1)
str(df1)
head(df1)
```

### <span style="color: black;">**Tipos de fórmulas**</span>
1. Regresión (~) Variable que depende de otras.
2. Variables latentes (=~) No se observa, se infiere.
3. Variazas y Covarianzas (~~) Relaciones entre variables latentes y observadas (Varianza: Entre sí misma, Covarianza: Entre otras).
4. Intercepto (~1) Valor esperado cuando las demás variables son cero.

### <span style="color: black;">**Estructurar el modelo**</span>
```{r}
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
           '
```

### <span style="color: black;">**Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**Evaluar el modelo**</span>
```{r}
summary(cfa1, fit.measures=TRUE)
# 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 si es < 0.90
```

Conclusión: **Modelo aceptable**


# <span style="color: #3749ff;">**Ejercicio 1. Democratizar política e industrialización**</span>

## <span style="color: black;">**Contexto**</span>
La base de datos contiene distintas mediciones sobre la demoracia política e industrialización en paises 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 politica 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 politica 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

### <span style="color: black;">**1. Importar la base de datos**</span>
```{r}
df2 <- PoliticalDemocracy
```

### <span style="color: black;">**2. Entender la base de datos**</span>
```{r}
summary(df2)
str(df2)
head(df2)
```

### <span style="color: black;">**3. Estructurar el modelo**</span>
```{r}
modelo2 <- '
  # Definir variables latentes de democratización en 1960 y 1965
  Dem1960 =~ y1 + y2 + y3 + y4
  Dem1965 =~ y5 + y6 + y7 + y8

  # Definir variable latente de industrialización
  Ind1960 =~ x1 + x2 + x3

  # Relacionar democratización de 1960 con 1965
  Dem1965 ~ Dem1960

  # Relacionar industrialización con democratización
  Dem1960 ~ Ind1960
  Dem1965 ~ Ind1960

  # Especificar varianzas y covarianzas
  Dem1960 ~~ Dem1960
  Dem1965 ~~ Dem1965
  Ind1960 ~~ Ind1960
  Dem1960 ~~ Ind1960
  Dem1965 ~~ Ind1960
'
```

### <span style="color: black;">**4. Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa2 <- sem(modelo2, data=df2, se="bootstrap")
summary(cfa2, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa2, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**5. Evaluar el modelo**</span>
```{r}
summary(cfa2, fit.measures=TRUE)
# 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 si es < 0.90
```

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.950
  Tucker-Lewis Index (TLI)                       0.930

El modelo indica que:

* Ind1960 influye fuertemente en Dem1960 y Dem1965.
* Dem1960 es un predictor clave de Dem1965.
* Existe una relación negativa inesperada entre Ind1960 y Dem1965, lo que podría indicar un problema de modelado o la necesidad de incluir más variables.


# <span style="color: #3749ff;">**Actividad 3. Bienestar de los trabajadores**</span>

### <span style="color: black;">**1. Descargar librerias**</span>
```{r message=FALSE, warning=FALSE}
library(readxl)
```

### <span style="color: black;">**2. Importar la base de datos**</span>
```{r}
df3 <- read_excel("/Users/hectordelagarzatrevino/Library/CloudStorage/GoogleDrive-a01177960@tec.mx/My Drive/LIT/8. Octavo semestre/Generación de escenarios futuros con analítica/Modulo 1/Actividades/Actividad 3/Datos_SEM_Eng.xlsx")
```

### <span style="color: black;">**3. Entender la base de datos**</span>
```{r}
summary(df3)
str(df3)
head(df3)
```

# <span style="color: #3749ff;">**Parte 1. Experiencias de recuperación**</span>
```{r}
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 Covarianza
             desapego =~ desapego
             relajacion =~ relajacion
             dominio =~ dominio
             control =~ control
             # Intercepto
           '
```

### <span style="color: black;">**1. Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa31 <- sem(modelo31, data=df3)
summary(cfa31)
lavaanPlot(cfa31, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**2. Evaluar el modelo**</span>
```{r}
summary(cfa31, fit.measures=TRUE)
# 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 si es < 0.90
```

Conclusion: **Modelo deficiente**

# <span style="color: #3749ff;">**Parte 2. Energía recuperada**</span>
```{r}
modelo32 <- ' # Regresiones
             # Variables latentes
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
             # Varianzas y Covarianza
             energia ~~ energia
             # Intercepto
           '
```

### <span style="color: black;">**1. Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa32 <- sem(modelo32, data=df3)
summary(cfa32)
lavaanPlot(cfa32, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**2. Evaluar el modelo**</span>
```{r}
summary(cfa32, fit.measures=TRUE)
# 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 si es < 0.90
```

Conclusion: **Modelo Excelente**

# <span style="color: #3749ff;">**Parte 3. Engagement laboral**</span>
```{r}
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
           '
```

### <span style="color: black;">**1. Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa33 <- sem(modelo33, data=df3)
summary(cfa33)
lavaanPlot(cfa33, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**2. Evaluar el modelo**</span>
```{r}
summary(cfa33, fit.measures=TRUE)
# 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 si es < 0.90
```

Conclusión: **Modelo deficiente**

# <span style="color: #3749ff;">**Parte 4. Modelo completo**</span>
```{r}
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
             recuperacion ~~ energia + vigor + dedicacion + absorcion
             energia ~~ vigor +  dedicacion + absorcion      
             # Intercepto
           '
```

### <span style="color: black;">**1. Generar el análisis factorial confirmatorio (CFA)**</span>
```{r}
cfa34 <- sem(modelo34, data=df3)
summary(cfa34)
lavaanPlot(cfa34, coef=TRUE, cov=TRUE)
```

### <span style="color: black;">**2. Evaluar el modelo**</span>
```{r}
summary(cfa34, fit.measures=TRUE)
# 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 si es < 0.90
```

Conclusión: **Modelo deficiente**