Relative weights are used to interpret a regression equation when the researcher is interested in the relative importance of the predictor variables. Like other statistics, relative weights are influenced by sampling and measurement error. To assess the influence of sampling error, a bootstrapping approach to computing confidence intervals around relative weights is described. To assess the influence of measurement error, a Monte Carlo study was conducted in which relative weights were computed before and after correcting correlation matrices for unreliability. The influence of measurement error was generally small but can be substantial in some cases. Correcting for unreliability tended to make the biggest difference when criterion correlations were small, predictor reliabilities were low, and the number of predictors was large.
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