Scale Coarseness as a Methodological Artifact

Abstract

Scale coarseness is a pervasive yet ignored methodological artifact that attenuates observed correlation coefficients in relation to population coefficients. The authors describe how to disattenuate correlations that are biased by scale coarseness in primary-level as well as meta-analytic studies and derive the sampling error variance for the corrected correlation. Results of two Monte Carlo simulations reveal that the correction procedure is accurate and show the extent to which coarseness biases the correlation coefficient under various conditions (i.e., value of the population correlation, number of item scale points, and number of scale items). The authors also offer a Web-based computer program that disattenuates correlations at the primary-study level and computes the sampling error variance as well as confidence intervals for the corrected correlation. Using this program, which implements the correction in primary-level studies, and incorporating the suggested correction in meta-analytic reviews will lead to more accurate estimates of construct-level correlation coefficients.

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