Wow, ViAnn Beadle, you are my hero!!!:))) Now I can uninstall the v.17:)) Really, some things are not written anywhere, and they are so hard to find! I thank you all for your help, it means so much to me!

Ok, I will write this down in detail. I have a sample of 556 patients with prostate cancer. Biopsies were taken before the surgical intervention. I grouped the positive no. of biopsies in 3 groups (Group 1 - 1-2 positive biopsies, Group 2 - 3-4 positive biopsies, Group 3 - 5-10 positive biopsies), so this would be the ordinal, independent variable. Biochemical relapse after intervention, a nominal, dichotomous variable, yes or no (involves a 3 year period).

Task: correlation and get info on how I could predict the relapse chances based on positive fragments group. Problem: Choosing the appropriate tests. I have read a lot in the past week, but it seems there is a problem with correlation between ordinal and nominal, no manual likes to talk about it, especially if the outcome variable is the nominal.

After a looooong studying period I have chosen Cramer's v and lambda, because books say that: Kendall-tau can be used only with 2 ordinals, tau-b if (no. columns=no. rows), tau-c if (no. columns<>no. rows), Pearson r only for scale and normally distributed, Spearman only with 2 ordinals, Gamma and Sommer's d only with 2 ordinals; and I quoted above from a book regarding the ordinal+nominal test that says Cramer's V is appropriate.

As I understand, nominal tests can be used for ordinals, some non-parametric for parametric, only you loose some power of the test (e.g. Wilcoxon for a normally distributed scale). But Cramer's and lambda gave me even this way positive, statistically significant results.:))

Now, I want to do a binomial logistic regression test, but first I have to learn about it more, and how to interpret it.

So Sommers'd for Kendall is like lambda for Cramer's???

It would be awesome to have a list which shows the tests paired in 3 , like: (test which shows existence of correlation )-(test for measure the power of the correlation) - (test for predicting the chances or calculating the outcome variable based on independent)

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