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is there a way to compare (standardized) beta coefficients of one sample and regression without generating two models and conducting an anova? Is there a simpler method with e.g. one function?

For example, if I have this model and would want to compare beta coefficients of SE_gesamt and CE_gesamt (only two variables):

library(lm.beta)

fit1 <- lm(Umint_gesamt ~ Alter + Geschlecht_Dummy + SE_gesamt + CE_gesamt + EmoP_gesamt + Emp_gesamt + IN_gesamt + DN_gesamt + SozID_gesamt, data=dataset)
summary(fit1)
lm.beta(fit1)

All the best, Karen

Rui Barradas
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Karen H
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    What kind of comparison do you want? To see if they are significantly different? –  May 12 '18 at 12:43
  • You probably want the `multcomp::glht` function to do a general linear hypothesis test, but how to do it depends on the answer to @dash2's question. – user2554330 May 12 '18 at 15:25
  • Yes, I would like to know if they are significantly different - and not only if they are a significant predictor that adds additional significant variance to the model. – Karen H May 13 '18 at 10:43

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