Significance Testing of Correlation Using Scores, Ranks, and Modified Ranks

A computer simulation study compared significance tests of correlation coefficients calculated from initial scores, from ranks assigned by the Spearman method, and from three kinds of modified ranks in which N sample values were replaced by N12, N/3, and N14 integers. Tests based on the initial scores are more powerful than those based on the various ranks for normal distributions, whereas the reverse is true for mixed-normal, exponential, and Cauchy distributions. Probabilities of Type I and Type II errors are unaffected by reduction in the number of ranks. Implications of these findings for the notion that rank correlation is a nonparametric correlation method are discussed.