AnΓ‘lisis Factorial Exploratorio

ConfiguraciΓ³n del entorno

library(tidyverse)
library(EFAtools)
library(psych)
setwd("D:/IntellijIDEA_Projects/R_Tesis_2023/Datos")
getwd()
## [1] "D:/IntellijIDEA_Projects/R_Tesis_2023/Datos"

Datos

# Cargar datos
load("Sedano_2024_Datos.RData")
# Seleccionar variables
fa_dats <- datsF %>%
    select(q_17:q_18)
# correlaciones policΓ³ricas
fa_dats
## # A tibble: 174 Γ— 34
## # Rowwise: 
##     q_17   q_7  q_26  q_28  q_30   q_9  q_25   q_2  q_11   q_4  q_13   q_6  q_31
##    <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
##  1     4     2     4     4     4     4     4     5     4     4     2     2     2
##  2     5     2     4     5     5     4     5     2     4     5     2     5     5
##  3     4     5     4     4     5     5     5     2     5     2     5     1     3
##  4     1     3     3     2     4     2     2     1     3     4     1     4     4
##  5     5     4     5     5     4     4     4     5     4     4     4     4     4
##  6     3     2     5     4     4     4     5     5     4     4     2     5     4
##  7     4     4     4     4     4     5     4     4     4     4     3     3     3
##  8     5     1     2     5     5     5     5     2     4     4     1     2     1
##  9     5     1     5     5     5     5     3     5     5     5     4     1     3
## 10     5     2     5     5     4     5     2     2     4     5     2     5     5
## # β„Ή 164 more rows
## # β„Ή 21 more variables: q_3 <int>, q_14 <int>, q_21 <int>, q_23 <int>,
## #   q_12 <int>, q_22 <int>, q_19 <int>, q_15 <int>, q_33 <int>, q_16 <int>,
## #   q_34 <int>, q_5 <int>, q_8 <int>, q_29 <int>, q_10 <int>, q_27 <int>,
## #   q_24 <int>, q_1 <int>, q_32 <int>, q_20 <int>, q_18 <int>
poly_matrix <- fa_dats %>%
    polychoric()#utiliza psych
poly_rho <- poly_matrix$rho

NΓΊmero de factores a extraer

EFAtools::PARALLEL(fa_dats)
## β„Ή 'x' was not a correlation matrix. Correlations are found from entered raw data.
## Parallel Analysis performed using 1000 simulated random data sets
## Eigenvalues were found using PCA, SMC, and EFA
## 
## Decision rule used: means
## 
## ── Number of factors to retain according to ────────────────────────────────────
## 
## β—Œ PCA-determined eigenvalues:  4
## β—Œ SMC-determined eigenvalues:  11
## β—Œ EFA-determined eigenvalues:  7

## Requerimientos para el anΓ‘lisis

# Bartlett test
EFAtools::BARTLETT(fa_dats)
## β„Ή 'x' was not a correlation matrix. Correlations are found from entered raw data.
## 
## βœ” The Bartlett's test of sphericity was significant at an alpha level of .05.
##   These data are probably suitable for factor analysis.
## 
##   πœ’Β²(561) = 4303.27, p < .001
# Kaiser-Meyer-Olkin
EFAtools::KMO(fa_dats)
## β„Ή 'x' was not a correlation matrix. Correlations are found from entered raw data.
## 
## ── Kaiser-Meyer-Olkin criterion (KMO) ──────────────────────────────────────────
## 
## βœ” The overall KMO value for your data is meritorious.
##   These data are probably suitable for factor analysis.
## 
##   Overall: 0.867
## 
##   For each variable:
##  q_17   q_7  q_26  q_28  q_30   q_9  q_25   q_2  q_11   q_4  q_13   q_6  q_31 
## 0.892 0.873 0.868 0.888 0.891 0.893 0.927 0.879 0.921 0.870 0.859 0.896 0.850 
##   q_3  q_14  q_21  q_23  q_12  q_22  q_19  q_15  q_33  q_16  q_34   q_5   q_8 
## 0.841 0.853 0.781 0.873 0.826 0.804 0.857 0.847 0.902 0.864 0.881 0.833 0.837 
##  q_29  q_10  q_27  q_24   q_1  q_32  q_20  q_18 
## 0.795 0.839 0.875 0.928 0.831 0.816 0.874 0.853