I am running multiple linear regression with categorical variables and I need confidence interval 95% for standardized regression coefficient. I searched around and found 2 methods:
QuantPsycpackage, with the function
lm.beta. However, using
lm.betaI can only get the standardized coefficients whereas I need with their 95% CI too. Is there a way?
To extract standardized regression coefficient, first standardize all the variables involved, and then run it in linear regression then you'll get estimates for standardized coefficients.
So here is my model:
model1 <- lm(Life_Satisfaction ~ Subjective + Age + Sex + CountryCat11 + CountryCat12 + CountryCat13 + CountryCat14 + CountryCat15 + CountryCat16 + CountryCat17 + CountryCat18 + CountryCat19 + CountryCat20 + CountryCat23 + CountryCat25 + CountryCat28 + CountryCat29 + CountryCat30 + Education_ISCED1 + Education_ISCED2 + Education_ISCED3 + Education_ISCED4 + Education_ISCED5 + Education_ISCED6 + Education_stillinschool + Education_None + Education_other, data=lifesat) lm.beta (model1)
I ran that, but I cannot get the 95% CI.
So I tried the scale method:
model2 <- lm(scale(Life_Satisfaction) ~ scale(Subjective) + scale(Age) + scale(Sex) + scale(CountryCat11) + scale(CountryCat12) + scale(CountryCat13) + scale(CountryCat14) + scale(CountryCat15) + scale(CountryCat16) + scale(CountryCat17) + scale(CountryCat18) + scale(CountryCat19) + scale(CountryCat20) + scale(CountryCat23) + scale(CountryCat25) + scale(CountryCat28) + scale(CountryCat29) + scale(CountryCat30) + scale(Education_ISCED1) + scale(Education_ISCED2) + scale(Education_ISCED3) + scale(Education_ISCED4) + scale(Education_ISCED5) + scale(Education_ISCED6) + scale(Education_stillinschool) + scale(Education_None) + scale(Education_other), data=lifesat) summary(model2)
I ran that, and I got the standardized regression and 95% CI but it was different from the standardized regression results I got from SPSS? Did I do it wrong?