Mike wrote on Mon, 8 May 2006 19:43:45 -0700:
??>> I wonder why you would actually want to use the rank
??>> correlation method? <
MM> Spearman's correlation is most appropriate when you want a
MM> correlation measure for two variables that are ordinal
MM> categorical measures (instead of numerical measures). For
MM> example, it may not make sense to arbitrarily assign
MM> numerical values (1,2,3,4,5) to ordinal responses on a
MM> survey questionnaire (Strongly Disagree, Disagree,
MM> Indifferent, Agree, Strongly Agree)
??>> I always thought it was a remnant of the days when more
??>> exact calculation was tedious but I'd be glad to be
??>> enlightened. <
MM> Pearson's correlation (Excel's CORREL worksheet function)
MM> summarizes a linear relationship, so Spearman's correlation
MM> could be used to summarize a nonlinear relationship between
MM> two numerical variables. Also, Spearman's is not influenced
MM> as much by outliers. But, if you truly have a linear
MM> relationship between two numerical variables, you lose
MM> information if you convert the numbers to ranks before
MM> computing correlation.
Thanks Mike, that does put things in a better perspective
especially non-linearity and the effect of outliers.
James Silverton.