Actividad 3. Aplicación de modelos de ecuaciones estructurales (actividad individual)
Genaro Rodríguez Alcántara - A00833172
2025-02-19
SEM
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. 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)
- sex: Género (1=male, 2=female)
- x1: Percepción visual
- x2: Juego de cubos
- x3: Juego con pastillas/espacial
- x4: Comprensión de párrafos
- x5: Completar oraciones
- x6: Signficado de palabras
- x7: Sumas aceleradas
- x8: Conteo acelerado de puntos
- x9: Discriminación acelerada de mayúsculas rectas y curvas
Se busca identificar las relaciones entre las habilidades visual (x1,x2,x3), textual (x4,x5,x6) y velocidad (x7,x8,x9) de los adolescentes.
Librerias
library(lavaanPlot)
library(lavaan)
library(readxl)Importar base de datos
df1 <- HolzingerSwineford1939Importar base de datos
- Regresión (~) Variable que depende de otras
- Variables Latentes (=~) no se observa, se infiere
- Variables y Covarianzas (~~) Relaciones entre variables altentes y observada (Varianza: Entre si miisma, Covarianza: Entre otras)
- Intercepto (~1) Valor esperado cuando las demas 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 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. 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
Importar 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():
## 400 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 0.171 7.905 0.000 2.980 0.760
## y3 1.044 0.137 7.597 0.000 2.298 0.705
## y4 1.300 0.145 8.974 0.000 2.860 0.860
## Dem1965 =~
## y5 1.000 2.084 0.803
## y6 1.258 0.216 5.814 0.000 2.623 0.783
## y7 1.282 0.172 7.459 0.000 2.673 0.819
## y8 1.310 0.201 6.529 0.000 2.730 0.847
## Ind1960 =~
## x1 1.000 0.669 0.920
## x2 2.182 0.149 14.621 0.000 1.461 0.973
## x3 1.819 0.141 12.916 0.000 1.218 0.872
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Dem1965 ~
## Dem1960 0.873 0.086 10.148 0.000 0.922 0.922
## Dem1960 ~
## Ind1960 1.565 0.119 13.144 0.000 0.476 0.476
## Dem1965 ~
## Ind1960 1.268 0.179 7.091 0.000 0.407 0.407
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Dem1960 ~~
## Ind1960 -0.041 0.097 -0.423 0.672 -0.031 -0.031
## .Dem1965 ~~
## Ind1960 -0.371 0.089 -4.148 0.000 -0.853 -0.853
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Dem1960 3.875 0.835 4.639 0.000 0.800 0.800
## .Dem1965 0.422 0.154 2.735 0.006 0.097 0.097
## Ind1960 0.448 0.076 5.906 0.000 1.000 1.000
## .y1 1.942 0.398 4.881 0.000 1.942 0.286
## .y2 6.490 1.362 4.765 0.000 6.490 0.422
## .y3 5.340 1.075 4.968 0.000 5.340 0.503
## .y4 2.887 0.597 4.833 0.000 2.887 0.261
## .y5 2.390 0.574 4.164 0.000 2.390 0.355
## .y6 4.343 0.915 4.748 0.000 4.343 0.387
## .y7 3.510 0.595 5.896 0.000 3.510 0.329
## .y8 2.940 0.815 3.607 0.000 2.940 0.283
## .x1 0.082 0.019 4.278 0.000 0.082 0.154
## .x2 0.118 0.072 1.641 0.101 0.118 0.053
## .x3 0.467 0.083 5.655 0.000 0.467 0.240lavaanPlot(cfa2, coef=TRUE, cov=TRUE)Activdad 3: Aplicación de modelos de ecuaciones estructurales
Entender la base de datos
df3 <- read_excel("/Users/genarorodriguezalcantara/Desktop/Tec/Generacion de escenarios futuros con analítica (Gpo 101)/PIB/M1 - Actividad 3/Datos_SEM_Eng.xlsx")
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. :7.000head(df3)## # A tibble: 6 × 51
## ID GEN EXPER EDAD RPD01 RPD02 RPD03 RPD05 RPD06 RPD07 RPD08 RPD09 RPD10
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 22 45 5 1 3 2 3 1 3 2 4
## 2 2 1 22 44 4 4 6 5 3 2 3 4 4
## 3 3 1 30 52 7 7 7 7 7 6 7 7 7
## 4 4 1 17 41 5 5 1 1 3 5 3 2 2
## 5 5 1 23 51 7 6 7 6 7 6 7 6 6
## 6 6 0 31 52 3 4 5 4 3 5 4 4 4
## # ℹ 38 more variables: RRE02 <dbl>, RRE03 <dbl>, RRE04 <dbl>, RRE05 <dbl>,
## # RRE06 <dbl>, RRE07 <dbl>, RRE10 <dbl>, RMA02 <dbl>, RMA03 <dbl>,
## # RMA04 <dbl>, RMA05 <dbl>, RMA06 <dbl>, RMA07 <dbl>, RMA08 <dbl>,
## # RMA09 <dbl>, RMA10 <dbl>, RCO02 <dbl>, RCO03 <dbl>, RCO04 <dbl>,
## # RCO05 <dbl>, RCO06 <dbl>, RCO07 <dbl>, EN01 <dbl>, EN02 <dbl>, EN04 <dbl>,
## # EN05 <dbl>, EN06 <dbl>, EN07 <dbl>, EN08 <dbl>, EVI01 <dbl>, EVI02 <dbl>,
## # EVI03 <dbl>, EDE01 <dbl>, EDE02 <dbl>, EDE03 <dbl>, EAB01 <dbl>, …Parte 1. Experiencias de Recuperación
modelo3_1 <- ' # Regresiones
# Variables latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
# Intercepto
' Generar el análisis factorial confirmatorio (CFA)
cfa3_1 <- sem(modelo3_1, data=df3)
summary(cfa3_1, standardized=TRUE, 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|) Std.lv Std.all
## desapego =~
## RPD01 1.000 1.386 0.788
## RPD02 1.206 0.082 14.780 0.000 1.672 0.858
## RPD03 1.143 0.085 13.374 0.000 1.584 0.797
## RPD05 1.312 0.086 15.244 0.000 1.818 0.878
## RPD06 1.088 0.089 12.266 0.000 1.507 0.745
## RPD07 1.229 0.085 14.440 0.000 1.703 0.844
## RPD08 1.164 0.087 13.447 0.000 1.613 0.800
## RPD09 1.317 0.087 15.153 0.000 1.826 0.874
## RPD10 1.346 0.088 15.258 0.000 1.866 0.878
## relajacion =~
## RRE02 1.000 1.274 0.849
## RRE03 1.120 0.065 17.227 0.000 1.427 0.870
## RRE04 1.025 0.058 17.713 0.000 1.306 0.883
## RRE05 1.055 0.056 18.758 0.000 1.344 0.910
## RRE06 1.245 0.074 16.869 0.000 1.586 0.860
## RRE07 1.117 0.071 15.689 0.000 1.423 0.825
## RRE10 0.815 0.067 12.120 0.000 1.038 0.698
## dominio =~
## RMA02 1.000 1.407 0.730
## RMA03 1.155 0.096 12.079 0.000 1.626 0.800
## RMA04 1.178 0.089 13.274 0.000 1.658 0.873
## RMA05 1.141 0.087 13.072 0.000 1.606 0.861
## RMA06 0.645 0.075 8.597 0.000 0.908 0.579
## RMA07 1.103 0.084 13.061 0.000 1.552 0.860
## RMA08 1.109 0.085 12.994 0.000 1.560 0.856
## RMA09 1.028 0.084 12.246 0.000 1.447 0.810
## RMA10 1.055 0.088 12.044 0.000 1.485 0.798
## control =~
## RCO02 1.000 1.630 0.854
## RCO03 0.948 0.049 19.182 0.000 1.545 0.912
## RCO04 0.796 0.044 18.110 0.000 1.297 0.886
## RCO05 0.818 0.043 18.990 0.000 1.333 0.907
## RCO06 0.834 0.046 18.216 0.000 1.360 0.888
## RCO07 0.835 0.046 18.057 0.000 1.361 0.884
## recuperacion =~
## desapego 1.000 0.713 0.713
## relajacion 1.149 0.131 8.787 0.000 0.892 0.892
## dominio 0.858 0.129 6.666 0.000 0.603 0.603
## control 1.341 0.156 8.605 0.000 0.813 0.813
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .desapego 0.943 0.152 6.207 0.000 0.491 0.491
## .relajacion 0.333 0.089 3.757 0.000 0.205 0.205
## .dominio 1.260 0.212 5.942 0.000 0.636 0.636
## .control 0.900 0.159 5.666 0.000 0.339 0.339
## .RPD01 1.172 0.120 9.782 0.000 1.172 0.379
## .RPD02 0.999 0.108 9.228 0.000 0.999 0.263
## .RPD03 1.441 0.148 9.733 0.000 1.441 0.365
## .RPD05 0.987 0.110 8.964 0.000 0.987 0.230
## .RPD06 1.817 0.182 9.967 0.000 1.817 0.444
## .RPD07 1.173 0.125 9.383 0.000 1.173 0.288
## .RPD08 1.460 0.150 9.714 0.000 1.460 0.360
## .RPD09 1.032 0.114 9.021 0.000 1.032 0.236
## .RPD10 1.034 0.115 8.955 0.000 1.034 0.229
## .RRE02 0.626 0.068 9.274 0.000 0.626 0.278
## .RRE03 0.653 0.073 9.011 0.000 0.653 0.243
## .RRE04 0.481 0.055 8.794 0.000 0.481 0.220
## .RRE05 0.374 0.046 8.153 0.000 0.374 0.172
## .RRE06 0.886 0.097 9.149 0.000 0.886 0.260
## .RRE07 0.950 0.100 9.505 0.000 0.950 0.319
## .RRE10 1.137 0.113 10.093 0.000 1.137 0.513
## .RMA02 1.740 0.175 9.931 0.000 1.740 0.468
## .RMA03 1.485 0.155 9.575 0.000 1.485 0.360
## .RMA04 0.855 0.097 8.772 0.000 0.855 0.237
## .RMA05 0.899 0.100 8.967 0.000 0.899 0.259
## .RMA06 1.631 0.159 10.281 0.000 1.631 0.664
## .RMA07 0.845 0.094 8.977 0.000 0.845 0.260
## .RMA08 0.886 0.098 9.034 0.000 0.886 0.267
## .RMA09 1.094 0.115 9.500 0.000 1.094 0.343
## .RMA10 1.259 0.131 9.590 0.000 1.259 0.363
## .RCO02 0.983 0.105 9.379 0.000 0.983 0.270
## .RCO03 0.484 0.058 8.391 0.000 0.484 0.169
## .RCO04 0.462 0.052 8.963 0.000 0.462 0.215
## .RCO05 0.382 0.045 8.513 0.000 0.382 0.177
## .RCO06 0.494 0.055 8.917 0.000 0.494 0.211
## .RCO07 0.515 0.057 8.985 0.000 0.515 0.218
## recuperacion 0.978 0.202 4.833 0.000 1.000 1.000lavaanPlot(cfa3_1, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa3_1, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 1221.031
## Degrees of freedom 430
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7522.157
## Degrees of freedom 465
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.888
## Tucker-Lewis Index (TLI) 0.879
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10616.148
## Loglikelihood unrestricted model (H1) -10005.632
##
## Akaike (AIC) 21364.296
## Bayesian (BIC) 21589.169
## Sample-size adjusted Bayesian (SABIC) 21380.007
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.097
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.998
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.075
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.206 0.082 14.780 0.000
## RPD03 1.143 0.085 13.374 0.000
## RPD05 1.312 0.086 15.244 0.000
## RPD06 1.088 0.089 12.266 0.000
## RPD07 1.229 0.085 14.440 0.000
## RPD08 1.164 0.087 13.447 0.000
## RPD09 1.317 0.087 15.153 0.000
## RPD10 1.346 0.088 15.258 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.120 0.065 17.227 0.000
## RRE04 1.025 0.058 17.713 0.000
## RRE05 1.055 0.056 18.758 0.000
## RRE06 1.245 0.074 16.869 0.000
## RRE07 1.117 0.071 15.689 0.000
## RRE10 0.815 0.067 12.120 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.155 0.096 12.079 0.000
## RMA04 1.178 0.089 13.274 0.000
## RMA05 1.141 0.087 13.072 0.000
## RMA06 0.645 0.075 8.597 0.000
## RMA07 1.103 0.084 13.061 0.000
## RMA08 1.109 0.085 12.994 0.000
## RMA09 1.028 0.084 12.246 0.000
## RMA10 1.055 0.088 12.044 0.000
## control =~
## RCO02 1.000
## RCO03 0.948 0.049 19.182 0.000
## RCO04 0.796 0.044 18.110 0.000
## RCO05 0.818 0.043 18.990 0.000
## RCO06 0.834 0.046 18.216 0.000
## RCO07 0.835 0.046 18.057 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.149 0.131 8.787 0.000
## dominio 0.858 0.129 6.666 0.000
## control 1.341 0.156 8.605 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.943 0.152 6.207 0.000
## .relajacion 0.333 0.089 3.757 0.000
## .dominio 1.260 0.212 5.942 0.000
## .control 0.900 0.159 5.666 0.000
## .RPD01 1.172 0.120 9.782 0.000
## .RPD02 0.999 0.108 9.228 0.000
## .RPD03 1.441 0.148 9.733 0.000
## .RPD05 0.987 0.110 8.964 0.000
## .RPD06 1.817 0.182 9.967 0.000
## .RPD07 1.173 0.125 9.383 0.000
## .RPD08 1.460 0.150 9.714 0.000
## .RPD09 1.032 0.114 9.021 0.000
## .RPD10 1.034 0.115 8.955 0.000
## .RRE02 0.626 0.068 9.274 0.000
## .RRE03 0.653 0.073 9.011 0.000
## .RRE04 0.481 0.055 8.794 0.000
## .RRE05 0.374 0.046 8.153 0.000
## .RRE06 0.886 0.097 9.149 0.000
## .RRE07 0.950 0.100 9.505 0.000
## .RRE10 1.137 0.113 10.093 0.000
## .RMA02 1.740 0.175 9.931 0.000
## .RMA03 1.485 0.155 9.575 0.000
## .RMA04 0.855 0.097 8.772 0.000
## .RMA05 0.899 0.100 8.967 0.000
## .RMA06 1.631 0.159 10.281 0.000
## .RMA07 0.845 0.094 8.977 0.000
## .RMA08 0.886 0.098 9.034 0.000
## .RMA09 1.094 0.115 9.500 0.000
## .RMA10 1.259 0.131 9.590 0.000
## .RCO02 0.983 0.105 9.379 0.000
## .RCO03 0.484 0.058 8.391 0.000
## .RCO04 0.462 0.052 8.963 0.000
## .RCO05 0.382 0.045 8.513 0.000
## .RCO06 0.494 0.055 8.917 0.000
## .RCO07 0.515 0.057 8.985 0.000
## recuperacion 0.978 0.202 4.833 0.000# Revisar los valores de comparative Fit Index (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90Parte 2. Energía Recuperada
modelo3_2 <- ' # Regresiones
# Variables latentes
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
# Varianzas y Covarianza
energia ~~ energia
# Intercepto
' Generar el análisis factorial confirmatorio (CFA)
cfa3_2 <- sem(modelo3_2, data=df3, se="bootstrap")
summary(cfa3_2, standardized=TRUE, 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 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
## energia =~
## EN01 1.000 1.674 0.893
## EN02 1.029 0.038 27.142 0.000 1.723 0.933
## EN04 0.999 0.044 22.606 0.000 1.672 0.924
## EN05 0.999 0.044 22.769 0.000 1.672 0.939
## EN06 0.986 0.039 25.506 0.000 1.651 0.940
## EN07 1.049 0.042 24.756 0.000 1.755 0.928
## EN08 1.036 0.039 26.276 0.000 1.734 0.946
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## energia 2.801 0.269 10.432 0.000 1.000 1.000
## .EN01 0.711 0.123 5.772 0.000 0.711 0.202
## .EN02 0.444 0.063 7.048 0.000 0.444 0.130
## .EN04 0.481 0.112 4.306 0.000 0.481 0.147
## .EN05 0.375 0.076 4.921 0.000 0.375 0.118
## .EN06 0.359 0.058 6.137 0.000 0.359 0.116
## .EN07 0.499 0.105 4.756 0.000 0.499 0.139
## .EN08 0.353 0.072 4.921 0.000 0.353 0.105lavaanPlot(cfa3_2, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa3_2, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 47.222
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2324.436
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.986
## Tucker-Lewis Index (TLI) 0.978
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2017.154
## Loglikelihood unrestricted model (H1) -1993.543
##
## Akaike (AIC) 4062.308
## Bayesian (BIC) 4110.008
## Sample-size adjusted Bayesian (SABIC) 4065.641
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.103
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.136
## P-value H_0: RMSEA <= 0.050 0.004
## P-value H_0: RMSEA >= 0.080 0.892
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.012
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## energia =~
## EN01 1.000
## EN02 1.029 0.038 27.142 0.000
## EN04 0.999 0.044 22.606 0.000
## EN05 0.999 0.044 22.769 0.000
## EN06 0.986 0.039 25.506 0.000
## EN07 1.049 0.042 24.756 0.000
## EN08 1.036 0.039 26.276 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## energia 2.801 0.269 10.432 0.000
## .EN01 0.711 0.123 5.772 0.000
## .EN02 0.444 0.063 7.048 0.000
## .EN04 0.481 0.112 4.306 0.000
## .EN05 0.375 0.076 4.921 0.000
## .EN06 0.359 0.058 6.137 0.000
## .EN07 0.499 0.105 4.756 0.000
## .EN08 0.353 0.072 4.921 0.000# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90Parte 3. Analisis de Engagement Laboral
modelo3_3 <- ' # Regresiones
# Variables latentes
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianzas y Covarianza
vigor ~~ vigor
dedicacion ~~ dedicacion
vigor ~~ absorcion + dedicacion
dedicacion ~~ absorcion
# Intercepto
' Generar el análisis factorial confirmatorio (CFA)
cfa3_3 <- sem(modelo3_3, data=df3, se="bootstrap")## Warning: lavaan->lav_model_nvcov_bootstrap():
## 12 bootstrap runs resulted in nonadmissible solutions.summary(cfa3_3, standardized=TRUE, 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 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
## vigor =~
## EVI01 1.000 1.684 0.967
## EVI02 0.986 0.027 36.089 0.000 1.660 0.962
## EVI03 0.995 0.054 18.552 0.000 1.675 0.835
## dedicacion =~
## EDE01 1.000 1.857 0.946
## EDE02 0.914 0.044 20.898 0.000 1.698 0.924
## EDE03 0.583 0.080 7.287 0.000 1.082 0.765
## absorcion =~
## EAB01 1.000 1.610 0.918
## EAB02 0.708 0.102 6.961 0.000 1.140 0.750
## EAB03 0.732 0.104 7.011 0.000 1.179 0.669
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## vigor ~~
## absorcion 2.125 0.340 6.248 0.000 0.784 0.784
## dedicacion 2.754 0.351 7.839 0.000 0.881 0.881
## dedicacion ~~
## absorcion 2.728 0.391 6.969 0.000 0.913 0.913
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## vigor 2.836 0.328 8.654 0.000 1.000 1.000
## dedicacion 3.448 0.435 7.928 0.000 1.000 1.000
## .EVI01 0.200 0.054 3.692 0.000 0.200 0.066
## .EVI02 0.220 0.051 4.342 0.000 0.220 0.074
## .EVI03 1.220 0.224 5.448 0.000 1.220 0.303
## .EDE01 0.405 0.113 3.569 0.000 0.405 0.105
## .EDE02 0.495 0.114 4.324 0.000 0.495 0.146
## .EDE03 0.829 0.148 5.583 0.000 0.829 0.415
## .EAB01 0.481 0.168 2.860 0.004 0.481 0.157
## .EAB02 1.010 0.201 5.035 0.000 1.010 0.437
## .EAB03 1.711 0.357 4.798 0.000 1.711 0.552
## absorcion 2.592 0.412 6.292 0.000 1.000 1.000lavaanPlot(cfa3_3, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa3_3, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 271.168
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2254.214
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.889
## Tucker-Lewis Index (TLI) 0.833
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2965.082
## Loglikelihood unrestricted model (H1) -2829.498
##
## Akaike (AIC) 5972.164
## Bayesian (BIC) 6043.715
## Sample-size adjusted Bayesian (SABIC) 5977.163
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.215
## 90 Percent confidence interval - lower 0.192
## 90 Percent confidence interval - upper 0.238
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## vigor =~
## EVI01 1.000
## EVI02 0.986 0.027 36.089 0.000
## EVI03 0.995 0.054 18.552 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.914 0.044 20.898 0.000
## EDE03 0.583 0.080 7.287 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.708 0.102 6.961 0.000
## EAB03 0.732 0.104 7.011 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## absorcion 2.125 0.340 6.248 0.000
## dedicacion 2.754 0.351 7.839 0.000
## dedicacion ~~
## absorcion 2.728 0.391 6.969 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## vigor 2.836 0.328 8.654 0.000
## dedicacion 3.448 0.435 7.928 0.000
## .EVI01 0.200 0.054 3.692 0.000
## .EVI02 0.220 0.051 4.342 0.000
## .EVI03 1.220 0.224 5.448 0.000
## .EDE01 0.405 0.113 3.569 0.000
## .EDE02 0.495 0.114 4.324 0.000
## .EDE03 0.829 0.148 5.583 0.000
## .EAB01 0.481 0.168 2.860 0.004
## .EAB02 1.010 0.201 5.035 0.000
## .EAB03 1.711 0.357 4.798 0.000
## absorcion 2.592 0.412 6.292 0.000# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90Parte 4. Analisis de Engagement Laboral
modelo3_4 <- ' # Regresiones
# Variables latentes
desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
recuperacion =~ desapego + relajacion + dominio + control
energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
vigor =~ EVI01 + EVI02 + EVI03
dedicacion =~ EDE01 + EDE02 + EDE03
absorcion =~ EAB01 + EAB02 + EAB03
# Varianzas y Covarianza
desapego ~~ desapego
relajacion ~~ relajacion
dominio ~~ dominio
control ~~ control
energia ~~ energia
vigor ~~ vigor
dedicacion ~~ dedicacion
vigor ~~ absorcion + dedicacion
dedicacion ~~ absorcion
recuperacion ~~ absorcion + dedicacion + energia + vigor
energia ~~ vigor + dedicacion + absorcion
# Intercepto
' Generar el análisis factorial confirmatorio (CFA)
cfa3_4 <- sem(modelo3_4, data=df3, se="bootstrap")## Warning: lavaan->lav_model_nvcov_bootstrap():
## 14 bootstrap runs resulted in nonadmissible solutions.summary(cfa3_4, standardized=TRUE, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1020
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 13350.303
## Degrees of freedom 1081
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.884
## Tucker-Lewis Index (TLI) 0.877
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15426.580
## Loglikelihood unrestricted model (H1) -14203.926
##
## Akaike (AIC) 31069.161
## Bayesian (BIC) 31437.135
## Sample-size adjusted Bayesian (SABIC) 31094.870
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.083
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.369
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors 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
## desapego =~
## RPD01 1.000 1.387 0.789
## RPD02 1.209 0.077 15.651 0.000 1.677 0.861
## RPD03 1.144 0.072 15.943 0.000 1.586 0.798
## RPD05 1.313 0.084 15.704 0.000 1.822 0.879
## RPD06 1.083 0.102 10.598 0.000 1.502 0.743
## RPD07 1.229 0.088 13.963 0.000 1.705 0.845
## RPD08 1.157 0.103 11.190 0.000 1.605 0.796
## RPD09 1.316 0.101 12.982 0.000 1.825 0.873
## RPD10 1.343 0.098 13.692 0.000 1.863 0.877
## relajacion =~
## RRE02 1.000 1.275 0.850
## RRE03 1.121 0.074 15.133 0.000 1.429 0.872
## RRE04 1.020 0.065 15.758 0.000 1.301 0.880
## RRE05 1.051 0.064 16.486 0.000 1.341 0.908
## RRE06 1.245 0.101 12.285 0.000 1.588 0.861
## RRE07 1.122 0.088 12.722 0.000 1.430 0.829
## RRE10 0.815 0.090 9.067 0.000 1.039 0.698
## dominio =~
## RMA02 1.000 1.407 0.729
## RMA03 1.152 0.072 15.977 0.000 1.621 0.798
## RMA04 1.178 0.086 13.668 0.000 1.658 0.873
## RMA05 1.141 0.083 13.769 0.000 1.604 0.860
## RMA06 0.648 0.095 6.846 0.000 0.911 0.581
## RMA07 1.104 0.089 12.408 0.000 1.553 0.861
## RMA08 1.110 0.101 10.979 0.000 1.562 0.857
## RMA09 1.030 0.099 10.357 0.000 1.449 0.811
## RMA10 1.056 0.087 12.172 0.000 1.486 0.798
## control =~
## RCO02 1.000 1.631 0.855
## RCO03 0.946 0.044 21.268 0.000 1.543 0.910
## RCO04 0.794 0.055 14.546 0.000 1.295 0.884
## RCO05 0.815 0.055 14.738 0.000 1.329 0.904
## RCO06 0.837 0.050 16.816 0.000 1.365 0.892
## RCO07 0.837 0.053 15.657 0.000 1.365 0.887
## recuperacion =~
## desapego 1.000 0.711 0.711
## relajacion 1.071 0.134 8.002 0.000 0.828 0.828
## dominio 0.900 0.143 6.281 0.000 0.631 0.631
## control 1.421 0.155 9.180 0.000 0.859 0.859
## energia =~
## EN01 1.000 1.680 0.897
## EN02 1.026 0.037 27.614 0.000 1.724 0.934
## EN04 0.996 0.044 22.709 0.000 1.674 0.924
## EN05 0.994 0.043 22.955 0.000 1.670 0.938
## EN06 0.981 0.038 25.734 0.000 1.649 0.939
## EN07 1.044 0.042 24.919 0.000 1.754 0.927
## EN08 1.031 0.039 26.320 0.000 1.732 0.945
## vigor =~
## EVI01 1.000 1.691 0.970
## EVI02 0.978 0.027 36.567 0.000 1.654 0.959
## EVI03 0.990 0.053 18.566 0.000 1.675 0.835
## dedicacion =~
## EDE01 1.000 1.860 0.947
## EDE02 0.913 0.044 20.694 0.000 1.697 0.923
## EDE03 0.580 0.081 7.194 0.000 1.079 0.763
## absorcion =~
## EAB01 1.000 1.611 0.919
## EAB02 0.707 0.102 6.924 0.000 1.140 0.750
## EAB03 0.730 0.105 6.968 0.000 1.176 0.668
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## vigor ~~
## absorcion 2.132 0.341 6.247 0.000 0.783 0.783
## dedicacion 2.767 0.350 7.894 0.000 0.880 0.880
## dedicacion ~~
## absorcion 2.731 0.393 6.946 0.000 0.912 0.912
## recuperacion ~~
## absorcion 0.796 0.195 4.074 0.000 0.501 0.501
## dedicacion 1.049 0.218 4.803 0.000 0.572 0.572
## energia 1.367 0.197 6.944 0.000 0.825 0.825
## vigor 1.007 0.185 5.437 0.000 0.604 0.604
## energia ~~
## vigor 2.045 0.255 8.007 0.000 0.720 0.720
## dedicacion 1.852 0.293 6.319 0.000 0.593 0.593
## absorcion 1.340 0.276 4.855 0.000 0.495 0.495
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .desapego 0.951 0.162 5.886 0.000 0.495 0.495
## .relajacion 0.510 0.110 4.621 0.000 0.314 0.314
## .dominio 1.191 0.224 5.313 0.000 0.602 0.602
## .control 0.699 0.156 4.484 0.000 0.263 0.263
## energia 2.823 0.268 10.551 0.000 1.000 1.000
## vigor 2.859 0.327 8.749 0.000 1.000 1.000
## dedicacion 3.458 0.434 7.963 0.000 1.000 1.000
## .RPD01 1.169 0.156 7.501 0.000 1.169 0.378
## .RPD02 0.984 0.158 6.240 0.000 0.984 0.259
## .RPD03 1.435 0.214 6.713 0.000 1.435 0.363
## .RPD05 0.973 0.140 6.949 0.000 0.973 0.227
## .RPD06 1.835 0.248 7.407 0.000 1.835 0.449
## .RPD07 1.166 0.175 6.666 0.000 1.166 0.286
## .RPD08 1.485 0.242 6.141 0.000 1.485 0.366
## .RPD09 1.036 0.216 4.798 0.000 1.036 0.237
## .RPD10 1.044 0.199 5.237 0.000 1.044 0.231
## .RRE02 0.623 0.110 5.677 0.000 0.623 0.277
## .RRE03 0.646 0.120 5.397 0.000 0.646 0.240
## .RRE04 0.494 0.129 3.818 0.000 0.494 0.226
## .RRE05 0.384 0.128 3.009 0.003 0.384 0.176
## .RRE06 0.882 0.124 7.139 0.000 0.882 0.259
## .RRE07 0.929 0.236 3.930 0.000 0.929 0.312
## .RRE10 1.134 0.187 6.050 0.000 1.134 0.512
## .RMA02 1.742 0.237 7.353 0.000 1.742 0.468
## .RMA03 1.500 0.272 5.513 0.000 1.500 0.363
## .RMA04 0.857 0.117 7.322 0.000 0.857 0.238
## .RMA05 0.904 0.186 4.847 0.000 0.904 0.260
## .RMA06 1.626 0.180 9.057 0.000 1.626 0.662
## .RMA07 0.843 0.131 6.433 0.000 0.843 0.259
## .RMA08 0.881 0.155 5.695 0.000 0.881 0.265
## .RMA09 1.089 0.183 5.942 0.000 1.089 0.342
## .RMA10 1.256 0.224 5.601 0.000 1.256 0.363
## .RCO02 0.980 0.144 6.781 0.000 0.980 0.269
## .RCO03 0.493 0.106 4.653 0.000 0.493 0.171
## .RCO04 0.468 0.105 4.459 0.000 0.468 0.218
## .RCO05 0.393 0.071 5.524 0.000 0.393 0.182
## .RCO06 0.479 0.109 4.383 0.000 0.479 0.204
## .RCO07 0.504 0.088 5.719 0.000 0.504 0.213
## .EN01 0.689 0.120 5.731 0.000 0.689 0.196
## .EN02 0.439 0.060 7.289 0.000 0.439 0.129
## .EN04 0.476 0.112 4.240 0.000 0.476 0.145
## .EN05 0.381 0.077 4.968 0.000 0.381 0.120
## .EN06 0.367 0.060 6.136 0.000 0.367 0.119
## .EN07 0.502 0.103 4.878 0.000 0.502 0.140
## .EN08 0.358 0.072 4.995 0.000 0.358 0.107
## .EVI01 0.177 0.049 3.626 0.000 0.177 0.058
## .EVI02 0.242 0.054 4.473 0.000 0.242 0.081
## .EVI03 1.222 0.221 5.536 0.000 1.222 0.303
## .EDE01 0.395 0.114 3.482 0.000 0.395 0.103
## .EDE02 0.498 0.115 4.320 0.000 0.498 0.147
## .EDE03 0.836 0.151 5.549 0.000 0.836 0.418
## .EAB01 0.478 0.168 2.837 0.005 0.478 0.155
## .EAB02 1.010 0.200 5.059 0.000 1.010 0.437
## .EAB03 1.718 0.358 4.804 0.000 1.718 0.554
## recuperacion 0.972 0.201 4.847 0.000 1.000 1.000
## absorcion 2.595 0.412 6.297 0.000 1.000 1.000lavaanPlot(cfa3_4, coef=TRUE, cov=TRUE)Evaluar el Modelo
summary(cfa3_4, fit.measures=TRUE)## lavaan 0.6-19 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
##
## Number of observations 223
##
## Model Test User Model:
##
## Test statistic 2445.310
## Degrees of freedom 1020
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 13350.303
## Degrees of freedom 1081
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.884
## Tucker-Lewis Index (TLI) 0.877
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15426.580
## Loglikelihood unrestricted model (H1) -14203.926
##
## Akaike (AIC) 31069.161
## Bayesian (BIC) 31437.135
## Sample-size adjusted Bayesian (SABIC) 31094.870
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.083
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.369
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## desapego =~
## RPD01 1.000
## RPD02 1.209 0.077 15.651 0.000
## RPD03 1.144 0.072 15.943 0.000
## RPD05 1.313 0.084 15.704 0.000
## RPD06 1.083 0.102 10.598 0.000
## RPD07 1.229 0.088 13.963 0.000
## RPD08 1.157 0.103 11.190 0.000
## RPD09 1.316 0.101 12.982 0.000
## RPD10 1.343 0.098 13.692 0.000
## relajacion =~
## RRE02 1.000
## RRE03 1.121 0.074 15.133 0.000
## RRE04 1.020 0.065 15.758 0.000
## RRE05 1.051 0.064 16.486 0.000
## RRE06 1.245 0.101 12.285 0.000
## RRE07 1.122 0.088 12.722 0.000
## RRE10 0.815 0.090 9.067 0.000
## dominio =~
## RMA02 1.000
## RMA03 1.152 0.072 15.977 0.000
## RMA04 1.178 0.086 13.668 0.000
## RMA05 1.141 0.083 13.769 0.000
## RMA06 0.648 0.095 6.846 0.000
## RMA07 1.104 0.089 12.408 0.000
## RMA08 1.110 0.101 10.979 0.000
## RMA09 1.030 0.099 10.357 0.000
## RMA10 1.056 0.087 12.172 0.000
## control =~
## RCO02 1.000
## RCO03 0.946 0.044 21.268 0.000
## RCO04 0.794 0.055 14.546 0.000
## RCO05 0.815 0.055 14.738 0.000
## RCO06 0.837 0.050 16.816 0.000
## RCO07 0.837 0.053 15.657 0.000
## recuperacion =~
## desapego 1.000
## relajacion 1.071 0.134 8.002 0.000
## dominio 0.900 0.143 6.281 0.000
## control 1.421 0.155 9.180 0.000
## energia =~
## EN01 1.000
## EN02 1.026 0.037 27.614 0.000
## EN04 0.996 0.044 22.709 0.000
## EN05 0.994 0.043 22.955 0.000
## EN06 0.981 0.038 25.734 0.000
## EN07 1.044 0.042 24.919 0.000
## EN08 1.031 0.039 26.320 0.000
## vigor =~
## EVI01 1.000
## EVI02 0.978 0.027 36.567 0.000
## EVI03 0.990 0.053 18.566 0.000
## dedicacion =~
## EDE01 1.000
## EDE02 0.913 0.044 20.694 0.000
## EDE03 0.580 0.081 7.194 0.000
## absorcion =~
## EAB01 1.000
## EAB02 0.707 0.102 6.924 0.000
## EAB03 0.730 0.105 6.968 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## vigor ~~
## absorcion 2.132 0.341 6.247 0.000
## dedicacion 2.767 0.350 7.894 0.000
## dedicacion ~~
## absorcion 2.731 0.393 6.946 0.000
## recuperacion ~~
## absorcion 0.796 0.195 4.074 0.000
## dedicacion 1.049 0.218 4.803 0.000
## energia 1.367 0.197 6.944 0.000
## vigor 1.007 0.185 5.437 0.000
## energia ~~
## vigor 2.045 0.255 8.007 0.000
## dedicacion 1.852 0.293 6.319 0.000
## absorcion 1.340 0.276 4.855 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .desapego 0.951 0.162 5.886 0.000
## .relajacion 0.510 0.110 4.621 0.000
## .dominio 1.191 0.224 5.313 0.000
## .control 0.699 0.156 4.484 0.000
## energia 2.823 0.268 10.551 0.000
## vigor 2.859 0.327 8.749 0.000
## dedicacion 3.458 0.434 7.963 0.000
## .RPD01 1.169 0.156 7.501 0.000
## .RPD02 0.984 0.158 6.240 0.000
## .RPD03 1.435 0.214 6.713 0.000
## .RPD05 0.973 0.140 6.949 0.000
## .RPD06 1.835 0.248 7.407 0.000
## .RPD07 1.166 0.175 6.666 0.000
## .RPD08 1.485 0.242 6.141 0.000
## .RPD09 1.036 0.216 4.798 0.000
## .RPD10 1.044 0.199 5.237 0.000
## .RRE02 0.623 0.110 5.677 0.000
## .RRE03 0.646 0.120 5.397 0.000
## .RRE04 0.494 0.129 3.818 0.000
## .RRE05 0.384 0.128 3.009 0.003
## .RRE06 0.882 0.124 7.139 0.000
## .RRE07 0.929 0.236 3.930 0.000
## .RRE10 1.134 0.187 6.050 0.000
## .RMA02 1.742 0.237 7.353 0.000
## .RMA03 1.500 0.272 5.513 0.000
## .RMA04 0.857 0.117 7.322 0.000
## .RMA05 0.904 0.186 4.847 0.000
## .RMA06 1.626 0.180 9.057 0.000
## .RMA07 0.843 0.131 6.433 0.000
## .RMA08 0.881 0.155 5.695 0.000
## .RMA09 1.089 0.183 5.942 0.000
## .RMA10 1.256 0.224 5.601 0.000
## .RCO02 0.980 0.144 6.781 0.000
## .RCO03 0.493 0.106 4.653 0.000
## .RCO04 0.468 0.105 4.459 0.000
## .RCO05 0.393 0.071 5.524 0.000
## .RCO06 0.479 0.109 4.383 0.000
## .RCO07 0.504 0.088 5.719 0.000
## .EN01 0.689 0.120 5.731 0.000
## .EN02 0.439 0.060 7.289 0.000
## .EN04 0.476 0.112 4.240 0.000
## .EN05 0.381 0.077 4.968 0.000
## .EN06 0.367 0.060 6.136 0.000
## .EN07 0.502 0.103 4.878 0.000
## .EN08 0.358 0.072 4.995 0.000
## .EVI01 0.177 0.049 3.626 0.000
## .EVI02 0.242 0.054 4.473 0.000
## .EVI03 1.222 0.221 5.536 0.000
## .EDE01 0.395 0.114 3.482 0.000
## .EDE02 0.498 0.115 4.320 0.000
## .EDE03 0.836 0.151 5.549 0.000
## .EAB01 0.478 0.168 2.837 0.005
## .EAB02 1.010 0.200 5.059 0.000
## .EAB03 1.718 0.358 4.804 0.000
## recuperacion 0.972 0.201 4.847 0.000
## absorcion 2.595 0.412 6.297 0.000# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90Conclusión General
Los resultados obtenidos en cada etapa del ejercicio permiten confirmar que los SEM son herramientas poderosas para modelar relaciones entre variables latentes en diferentes contextos. En particular: • Se verificaron las relaciones entre habilidades cognitivas en adolescentes. • Se identificó la influencia de la industrialización en la democratización de países en desarrollo. • Se modelaron experiencias de recuperación laboral y engagement, lo que aporta información valiosa para comprender cómo estos factores influyen en el bienestar laboral.
El análisis de los índices de ajuste (CFI, TLI) permitió validar o ajustar los modelos según su desempeño estadístico. En general, los resultados obtenidos respaldan la aplicabilidad de los SEM para entender estructuras de datos complejas y extraer conclusiones sobre relaciones entre variables latentes.
---
title: "Actividad 3. Aplicación de modelos de ecuaciones estructurales (actividad individual)"
author: "Genaro Rodríguez Alcántara - A00833172"
date: "2025-02-19"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: cerulean
---
![](/Users/genarorodriguezalcantara/Desktop/Tec/Generacion de escenarios futuros con analítica (Gpo 101)/PIB/M1 - Actividad 3/giphy.gif)

# <span style="color: brown;">SEM</span>

## <span style="color: brown;">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: brown;">Ejemplo 1. 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)

* sex: Género (1=male, 2=female)
* x1: Percepción visual
* x2: Juego de cubos
* x3: Juego con pastillas/espacial
* x4: Comprensión de párrafos
* x5: Completar oraciones
* x6: Signficado de palabras
* x7: Sumas aceleradas
* x8: Conteo acelerado de puntos
* x9: Discriminación acelerada de mayúsculas rectas y curvas

Se busca identificar las relaciones entre las habilidades visual (x1,x2,x3), textual (x4,x5,x6) y velocidad (x7,x8,x9) de los adolescentes.

### <span style="color: brown;">Librerias</span>
```{r message=FALSE, warning=FALSE}
library(lavaanPlot)
library(lavaan)
library(readxl)
```

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

### <span style="color: brown;">Importar base de datos</span>
1. Regresión (~) Variable que depende de otras
2. Variables Latentes (=~) no se observa, se infiere
3. Variables y Covarianzas (~~) Relaciones entre variables altentes y observada (Varianza: Entre si miisma, Covarianza: Entre otras)
4. Intercepto (~1) Valor esperado cuando las demas variables son cero.

### <span style="color: brown;">Estructurar el modelo</span>
```{r message=FALSE, warning=FALSE}
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: brown;">Generar Análisis Factorial Confirmatorio (CFA)</span>
```{r message=FALSE, warning=FALSE}
cfa1 <- sem(modelo1, data=df1)
summary(cfa1)
lavaanPlot(cfa1, coef=TRUE, cov=TRUE)
```

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

### <span style="color: brown;">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: brown;">Importar base de datos</span>
```{r}
df2 <- PoliticalDemocracy
```

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

### <span style="color: brown;">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: brown;">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: brown;">Activdad 3: Aplicación de modelos de ecuaciones estructurales</span>

### <span style="color: brown;">Entender la base de datos</span>
```{r}
df3 <- read_excel("/Users/genarorodriguezalcantara/Desktop/Tec/Generacion de escenarios futuros con analítica (Gpo 101)/PIB/M1 - Actividad 3/Datos_SEM_Eng.xlsx")
summary(df3)
head(df3)
```

### <span style="color: brown;">Parte 1. Experiencias de Recuperación</span>
```{r}
modelo3_1 <- ' # Regresiones
             # Variables latentes
             desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
             relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
             dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
             control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
             recuperacion =~ desapego + relajacion + dominio + control
             # Varianzas y Covarianza
             desapego ~~ desapego
             relajacion ~~ relajacion
             dominio ~~ dominio
             control ~~ control
             # Intercepto
          ' 
```

### <span style="color: brown;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r}
cfa3_1 <- sem(modelo3_1, data=df3)
summary(cfa3_1, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_1, coef=TRUE, cov=TRUE)
```

### <span style="color: brown;">Evaluar el Modelo</span>
```{r}
summary(cfa3_1, fit.measures=TRUE)
# Revisar los valores de comparative Fit Index (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: brown;">Parte 2. Energía Recuperada</span>
```{r}
modelo3_2 <- ' # Regresiones
             # Variables latentes
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08
             # Varianzas y Covarianza
             energia ~~ energia
             # Intercepto
          ' 
```

### <span style="color: brown;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r}
cfa3_2 <- sem(modelo3_2, data=df3, se="bootstrap")
summary(cfa3_2, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_2, coef=TRUE, cov=TRUE)
```

### <span style="color: brown;">Evaluar el Modelo</span>
```{r}
summary(cfa3_2, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: brown;">Parte 3. Analisis de Engagement Laboral</span>
```{r}
modelo3_3 <- ' # Regresiones
             # Variables latentes
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             # Intercepto
          ' 
```

### <span style="color: brown;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r}
cfa3_3 <- sem(modelo3_3, data=df3, se="bootstrap")
summary(cfa3_3, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_3, coef=TRUE, cov=TRUE)
```

### <span style="color: brown;">Evaluar el Modelo</span>
```{r}
summary(cfa3_3, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

### <span style="color: brown;">Parte 4. Analisis de Engagement Laboral</span>
```{r}
modelo3_4 <- ' # Regresiones
             # Variables latentes
             desapego =~ RPD01 + RPD02 + RPD03 + RPD05 + RPD06 + RPD07 + RPD08 + RPD09 + RPD10
             relajacion =~ RRE02 + RRE03 + RRE04 + RRE05 + RRE06 + RRE07 + RRE10
             dominio =~ RMA02 + RMA03 + RMA04 + RMA05 + RMA06 + RMA07 + RMA08 + RMA09 + RMA10
             control =~ RCO02 + RCO03 + RCO04 + RCO05 + RCO06 + RCO07
             recuperacion =~ desapego + relajacion + dominio + control
             energia =~ EN01 + EN02 + EN04 + EN05 + EN06 + EN07 + EN08 
             vigor =~ EVI01 + EVI02 + EVI03
             dedicacion =~ EDE01 + EDE02 + EDE03
             absorcion =~ EAB01 + EAB02 + EAB03
             # Varianzas y Covarianza
             desapego ~~ desapego
             relajacion ~~ relajacion
             dominio ~~ dominio
             control ~~ control
             energia ~~ energia
             vigor ~~ vigor
             dedicacion ~~ dedicacion
             vigor ~~ absorcion + dedicacion
             dedicacion ~~ absorcion
             recuperacion ~~ absorcion + dedicacion + energia + vigor
             energia ~~ vigor + dedicacion + absorcion
             # Intercepto
          ' 
```

### <span style="color: brown;">Generar el análisis factorial confirmatorio (CFA)</span>
```{r}
cfa3_4 <- sem(modelo3_4, data=df3, se="bootstrap")
summary(cfa3_4, standardized=TRUE, fit.measures=TRUE)
lavaanPlot(cfa3_4, coef=TRUE, cov=TRUE)
```

### <span style="color: brown;">Evaluar el Modelo</span>
```{r}
summary(cfa3_4, fit.measures=TRUE)
# Revisar los valores de comparative Fit Inndex (CFI) y Tucker-Lewis Ibdex (TLI)
# Eccelente si es >= 0.95, Aceptable enntre 0.9 y 0-95, Deficiente < 0.90
```

## <span style="color: brown;">Conclusión General</span>
Los resultados obtenidos en cada etapa del ejercicio permiten confirmar que los SEM son herramientas poderosas para modelar relaciones entre variables latentes en diferentes contextos. En particular:
	•	Se verificaron las relaciones entre habilidades cognitivas en adolescentes.
	•	Se identificó la influencia de la industrialización en la democratización de países en desarrollo.
	•	Se modelaron experiencias de recuperación laboral y engagement, lo que aporta información valiosa para comprender cómo estos factores influyen en el bienestar laboral.

El análisis de los índices de ajuste (CFI, TLI) permitió validar o ajustar los modelos según su desempeño estadístico. En general, los resultados obtenidos respaldan la aplicabilidad de los SEM para entender estructuras de datos complejas y extraer conclusiones sobre relaciones entre variables latentes.