Standardized beta weights for a multilevel regression How can one obtain standardized (fixed effect) regression weights from a multilevel regression?
And, as an "add-on": What is the easiest way to obtain these standardized weights from a mer-object (from the lmer function of the lme4package in R)?
 A: For standard linear models regressed with lm() you can either scale() your predictors data or just use this simple formula:
lm.results = lm(mydata$Y ~ mydata$x1)

sd.y = sd(mydata$Y)
sd.x1 = sd(mydata$x1)
x1.Beta = coef(lm.results)["mydata$x1"] * (sd.x1 / sd.y)

A: For a quick way to get at the standardized beta coefficients directly from any lm (or glm) model in R, try using lm.beta(model) from the QuantPsyc package. For example:
library("MASS")
glmModel = glm(dependentResponseVar ~ predictor1 + predictor2, data=myData)
summary(glmModel)

library(QuantPsyc)
lm.beta(glmModel)

A: Simply scale your explanatory variables to having mean of zero and variance of one before you put them in the model.  Then the coefficients will all be comparable.  The mixed effects nature of the model doesn't impact on this issue.
The best way to do it, and least likely to go wrong, is to use scale() before you fit the model.
A: Assuming you have set the output of your lmer model to lmer.results, fixef(lmer.results) will return the overall fixed effects coefficients.
