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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