Hi, I am turning to you with a (hopefully simple?) stats question. I would
like to test equality of two correlation coefficients in a setting with
three variables X,Y,Z, i.e. equality of r(X,Y) and r(Z,Y). I have found a
formula to transform the "2 dependent correlations difference" to
t-distribution with N-3 df:
t = (rxy - rzy)* SQRT[{(n - 3)(1 + rxz)}/ {2(1 - rxy^2 - rxz^2 - rzy^2 +
2rxy*rxz*rzy)}]
(Blalock, H., 1972. Social Statistics. NY: McGraw-Hill. Page 406-7). Am
actually not sure whether this is exact or approximate (even given normality
assumption, the Fisher's Z-transform which this is - I assume - based on, is
approximate, right?).
But to make it a bit more complicated, Shapiro-Wilks test of normality gives
p=0.022 for variable X. Therefore assuming normality may not be safe
(justifiable) at all? What do I do then? Do I report this test as
"assymptotically valid", or do I run some other test?Any ideas? Many thanks in advance, Jaroslav -- View this message in context: http://www.nabble.com/testing-equality-of-two-dependent-correlations-%2B-normality-issue-tp24465768p24465768.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
