Abstract
The Spearman rank correlation method separately assigns ranks to sample values of each of two variables, X and Y, and substitutes squared differences between the ranks into a computational formula derived from Pearson correlation. The present note examines an alternate procedure which separately transforms the X and Y values to standard scores, ranks the combined standard scores in a single sequence, and calculates the Pearson correlation between the ranks corresponding to the initial scores. A simulation study reveals that significance tests of correlation based on this method effectively control Type I and Type II error probabilities and are slightly more powerful than the Spearman method for normal and various nonnormal distributions and for sample sizes ranging from 8 to 30. This note discusses some advantages of the modified procedure.
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