Question

How to get standardized coefficients in linear regression?

Answer 1

$\huge\underline\green{\mathcal Answer :-}$

The Steps

Remember all those Z-scores you had to calculate in Intro Stats? It wasn’t the useless exercise you thought it was at the time.

Converting a variable to a Z-score is standardizing.

In other words, do these steps for Y, your outcome variable, and every X, your predictors:

1. Calculate the mean and standard deviation.

2. Create a new standardized version of each variable. To get it, create a new variable in which you subtract the mean from the original value, then divide that by the standard error.

3. Use those standardized versions in the regression.

Could this take a while? Yup.

But if that’s what the journal requires you report, just do it.

A nice advantage, is you can apply it, at least partially, even in regression models that can’t usually accommodate standardized regression coefficients.

For example, in a logistic regression it doesn’t make sense to standardize Y because it’s categorical. But you can standardize all your Xs to get rid of their units.

You can then interpret your odds ratios in terms of one standard deviation increases in each X, rather than one-unit increases.