title:         Scaling regression inputs by dividing by two standard deviations
authors:       Andrew Gelman
entrydate:     2006-06-10 13:43:33
keywords:      regression, standardization, $z$-score
abstract:      Interpretation of regression coefficients is sensitive to the scale of the inputs.  One method often used to place input variables on a common scale is to divide each variable by its standard deviation.  Here we propose dividing each variable by {em two} standard deviations, so that the generic comparison is with inputs equal to the mean $pm 1$ standard deviation.  The resulting coefficients are then directly comparable for untransformed binary predictors.  We have implemented the procedure as a function in R.  We illustrate the method with a simple public-opinion analysis that is typical of regressions in social science.

http://polmeth.wustl.edu/retrieve.php?id=599

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