Scale Coarseness as a Methodological Artifact
研究发现尺度粗糙度会低估相关系数，并提出了校正方法，通过蒙特卡洛模拟验证其准确性，还提供了在线程序帮助研究者校正相关、计算标准误和置信区间。
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.