Determining Significance
One approach to testing whether an observed value of ρ is significantly different from zero (r will always maintain −1 ≤ r ≤ 1) is to calculate the probability that it would be greater than or equal to the observed r, given the null hypothesis, by using a permutation test. An advantage of this approach is that it automatically takes into account the number of tied data values there are in the sample, and the way they are treated in computing the rank correlation.
Another approach parallels the use of the Fisher transformation in the case of the Pearson product-moment correlation coefficient. That is, confidence intervals and hypothesis tests relating to the population value ρ can be carried out using the Fisher transformation:
If F(r) is the Fisher transformation of r, the sample Spearman rank correlation coefficient, and n is the sample size, then
is a z-score for r which approximately follows a standard normal distribution under the null hypothesis of statistical independence (ρ = 0).
One can also test for significance using
which is distributed approximately as Student's t distribution with n − 2 degrees of freedom under the null hypothesis. A justification for this result relies on a permutation argument.
A generalization of the Spearman coefficient is useful in the situation where there are three or more conditions, a number of subjects are all observed in each of them, and it is predicted that the observations will have a particular order. For example, a number of subjects might each be given three trials at the same task, and it is predicted that performance will improve from trial to trial. A test of the significance of the trend between conditions in this situation was developed by E. B. Page and is usually referred to as Page's trend test for ordered alternatives.
Read more about this topic: Spearman's Rank Correlation Coefficient
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