- I'm responding to statistical questions about my post - On 16 Dec 2002 01:53:33 -0600, Brian Sandle <[EMAIL PROTECTED]> wrote:
> In talk.politics.drugs Rich Ulrich <[EMAIL PROTECTED]> wrote: [ snip, his and mine] BS > > Are you saying you get exactly the same correlations when you put ranks in > Pearson as you do when you put the same ones in Spearman? Absolutely. BS > > Isn't it better to be `regardful' of non-linear stuff and use the proper > formula? What? What "proper formula"? The raw data may have poor scaling *for our purpose*, where the measured, equal intervals are not constant, *for our purpose*. Ranking works a distortion of whatever scaling is there; sometimes, that is preferable. Where it works pretty well is for *simple* tests, such as the correlation between two variables, or the difference between two groups. Where it works badly, almost assuredly, is for complicated tests -- two-way ANOVAs, partial correlations -- whenever the R-squared is large. RU > > and what we sacrifice > > might be (a) the test where we can rely on p-levels, and BS > > In SPSS, if you were to do rank correlations, and use the Spearman, you > would still have p-levels, i.e. significance. And I think you would still > have p-levels if you did Spearman partials, that is with linear stuff &c. I was not clear. (And you are not reading carefully well.) I intended to draw the distinction, for complicated statistics using, ranks, between computing a p-level, which you can do; and "relying on p-levels", which you can't do, [ snip ] RU > > > Instead, I talk about doing a > > 'rank-transformation' to the data; and whether that is > > desirable or useful or damaging. BS > > By that you are turning scores into ranks? You *have* to do that when the > data is non-linear is my guess. Then you put it into a formula designed to > work with ranks. Otherwise what you get out has no meaning. You can turn scores into ranks if you want robustness for the simple test, and you are willing to pass up the chance for complicated tests. When data need a different transformation, you can do a different transformation, and you might preserve the chance for fancier modeling. "Your guesses" are particularly uninformed on statistics, and mostly seem to be wrong. Computers can ignore the formulas designed to work for ranks when general formulas give *precisely* the same thing -- for example, Pearson for Spearman. Oh, the programs often do have some exact formulas for the p-values for small N (under 10, at least, for Spearman's). Those will be an improvement if there are no ties in the scores. It is a matter of indifference, to any users, just how the computation is performed. Textbooks have special formulas for ANOVA, too, starting with equal N, and moving up to multi-way analyses with unequal N. The basic books will omit the matrix notation that makes it easier for professionals manipulate. The basic formulas are useful for purposes of teaching, even though the computer's internal logic can be implemented as regression. [ snip] > > Perhaps you should give some refs. On what area? Try my stats-FAQ. Look at the tags of other folks who post to the stats-groups, and look at their FAQs, too. And look at the FAQs and pages (or texts) that they recommend.... -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
