R Notebook

library(tidyverse)
library(here)

SandraModel <- 
  "autoritario =~ i1 , i2 + i3 +i4
   democratico =~ i5 + i6 + i7 +i8
   permisivo =~ i9 + i10 + i11 +i12
   sobreprotector =~ i13 + i14 + i15 +i16
   integral =~ i17 + i18 + i19 +i20
   inatencion =~ i21 + i22 + i23 + i24 + i25
   hiperac_imp =~ i26 + i27 + i28 + i29
   EEF =~ autoritario + democratico + permisivo + sobreprotector + integral
   TDAH =~ inatencion + hiperac_imp
   TDAH =~ EEF"

Adquiriendo los datos

sandra_raw <- read_csv(here("SEM lavaan/data_sem", "sandra.csv"))

Cronbach

autoritario

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i1, i2, i3, i4))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i1, i2, i3, i4))

 

 lower alpha upper     95% confidence boundaries
0.85 0.87 0.88 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
      1    2    3    4    5    6    7 miss
i1 0.02 0.07 0.17 0.34 0.27 0.10 0.03    0
i2 0.00 0.03 0.19 0.37 0.29 0.10 0.01    0
i3 0.00 0.04 0.19 0.35 0.31 0.10 0.01    0
i4 0.02 0.12 0.32 0.33 0.16 0.03 0.01    0

democratico

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i5, i6, i7, i8))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i5, i6, i7, i8))

 

 lower alpha upper     95% confidence boundaries
0.82 0.84 0.85 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
      1    2    3    4    5    6    7 miss
i5 0.01 0.07 0.22 0.38 0.24 0.06 0.01    0
i6 0.01 0.06 0.20 0.34 0.28 0.09 0.02    0
i7 0.02 0.12 0.28 0.38 0.17 0.03 0.01    0
i8 0.01 0.11 0.28 0.34 0.20 0.06 0.01    0

permisivo

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i9, i10, i11, i12))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i9, i10, i11, i12))

 

 lower alpha upper     95% confidence boundaries
0.83 0.85 0.86 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
       1    2    3    4    5    6    7 miss
i9  0.01 0.07 0.24 0.38 0.22 0.07 0.01    0
i10 0.01 0.08 0.20 0.32 0.27 0.10 0.03    0
i11 0.00 0.06 0.23 0.41 0.22 0.06 0.01    0
i12 0.00 0.01 0.09 0.30 0.38 0.18 0.04    0

sobreprotector

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i13, i14, i15, i16))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i13, i14, i15, i16))

 

 lower alpha upper     95% confidence boundaries
0.84 0.85 0.87 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
       1    2    3    4    5    6    7 miss
i13 0.01 0.07 0.30 0.40 0.18 0.03 0.00    0
i14 0.02 0.09 0.28 0.34 0.21 0.05 0.01    0
i15 0.02 0.12 0.29 0.33 0.19 0.05 0.00    0
i16 0.02 0.09 0.28 0.34 0.22 0.05 0.00    0

integral

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i17, i18, i19, i20))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i17, i18, i19, i20))

 

 lower alpha upper     95% confidence boundaries
0.85 0.86 0.88 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
       1    2    3    4    5    6    7 miss
i17 0.01 0.06 0.21 0.38 0.25 0.07 0.01    0
i18 0.00 0.08 0.25 0.36 0.24 0.05 0.01    0
i19 0.02 0.10 0.23 0.32 0.21 0.09 0.03    0
i20 0.00 0.06 0.24 0.39 0.24 0.06 0.01    0

Inatención

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i23, i24, i25, i26))

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i23, i24, i25, i26))

 

 lower alpha upper     95% confidence boundaries
0.73 0.75 0.78 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
       1    2    3    4    5    6    7 miss
i23 0.01 0.05 0.24 0.37 0.26 0.07 0.00    0
i24 0.01 0.09 0.25 0.36 0.23 0.06 0.01    0
i25 0.01 0.06 0.25 0.33 0.25 0.09 0.01    0
i26 0.01 0.10 0.25 0.34 0.23 0.06 0.01    0

hiperac_imp

Por medio del Cronbach Alpha.

alpha(sandra_raw %>% 
        select(i27, i28, i29)) 

Reliability analysis   
Call: alpha(x = sandra_raw %>% select(i27, i28, i29))

 

 lower alpha upper     95% confidence boundaries
0.79 0.81 0.83 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
       1    2    3    4    5    6    7 miss
i27 0.01 0.06 0.22 0.37 0.24 0.09 0.01    0
i28 0.02 0.09 0.32 0.36 0.18 0.03 0.00    0
i29 0.01 0.05 0.21 0.34 0.27 0.10 0.02    0

Organizando los datos

Análisis descriptivo

Análisis inferencial

Modelo de regresión lineal múltiple

Construimos el modelo

sandra_model_LM <- 
  lm(TDAH ~ EEF + genero, 
     data = sandra_LM)

Revisamos el modelo

summary(sandra_model_LM)

Call:
lm(formula = TDAH ~ EEF + genero, data = sandra_LM)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.35667 -0.48016 -0.00596  0.47643  2.21192 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.95534    0.12390  15.782   <2e-16 ***
EEF          0.50712    0.03046  16.647   <2e-16 ***
generom      0.02170    0.04670   0.465    0.642    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6815 on 997 degrees of freedom
Multiple R-squared:  0.2186,    Adjusted R-squared:  0.217 
F-statistic: 139.4 on 2 and 997 DF,  p-value: < 2.2e-16