I am trying to create a coefficient plot from multiple logistic regression models, which all have the same predictors, but different sample sizes. This is a pre-test to a multilevel model. My question is two fold:

Given, I want to compare the effect sizes of the same predictor in the different models, I assume I need to use standardized coefficients. How does one calculate standardized coefficients in a logit model?

Is there an easy way to estimate such coefficients in R? For instance with OLS, I could rely on the "lm.beta" function from the QuantPsyc package. I am wondering, is there a functional equivalent for a glm logit? I could not find an immediate solution myself.

**Contents**hide

#### Best Answer

If the predictors you used in the different regression models are measured in the same way it is no longer necessary to normalize the coefficients, since the predictors are already on the same scale the coefficients are as well.

If not, first try to rescale the predictor variables.

### Similar Posts:

- Solved – standardized coefficients from glm logit
- Solved – Relative importance of predictors – Standardized coefficients in Ordinal Logistic Regression
- Solved – How to get the standardized beta coefficients from glm.nb regression in R
- Solved – How to interpret logistic regression coefficient
- Solved – Interpretation regression intercept when only numerical predictors are standardized