R Notebook

library(tidyverse)
library(here)

FedericoModel <- 
  "rel_ma_ma =~ i1 + i2 
   cal_esco =~ i3 + i4 + i5
   rel_ma_al =~ i6 + i7
   cal_esco =~ rel_ma_ma
   rel_ma_al =~ cal_esco + rel_ma_ma"

Adquiriendo los datos

federico_raw <- read_csv(here("SEM lavaan/data_sem", "feder.csv"))

Organizando los datos

Análisis descriptivo

Análisis inferencial

Modelo de regresión lineal múltiple

Construimos el modelo

Revisamos el modelo

summary(federico_model_LM)

Call:
lm(formula = rel_ma_al ~ cal_esco + rel_ma_ma + factor(genero), 
    data = federico_LM)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.81032 -0.41849  0.00157  0.37661  1.79397 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.37887    0.09591  14.376  < 2e-16 ***
cal_esco         0.12722    0.03910   3.254  0.00119 ** 
rel_ma_ma        0.41213    0.03587  11.491  < 2e-16 ***
factor(genero)m -0.01987    0.04323  -0.460  0.64595    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5553 on 796 degrees of freedom
Multiple R-squared:  0.3058,    Adjusted R-squared:  0.3031 
F-statistic: 116.9 on 3 and 796 DF,  p-value: < 2.2e-16