Dear R-Users, I am currently looking for a way to test the equality of two correlations that are related in a very special way. Let me describe the situation with an example.
- There are 100 respondents, and there are 2 points in time, t=1 and t=2. - For each of the respondents and at each of the time points, I have information on 10 X-variables and on 10 Y-variables. - Based on this information, I calculate two correlations for each respondent: cor(X[t=1],X[t=2]) and cor(Y[t=1],Y[t=2]), with X and Y being the vectors of the corresponding 10 variables. - Now I get the average correlations over the whole sample using Fishers Z-transformation, i.e. I have mean(cor(X[t=1],X[t=2])) and mean(cor(X[t=1],X[t=2])) and want to know if the mean correlations are significantly different! I haven't found any test that deals with exactly my situation. Therefore, I "simply" apply a paired t-test based on the individual z-correlations. From my point of view this should be ok, because of the z's normality. However, I am unsure if there is a better way to test the hypothesis that I am interested in? I'd be grateful for any comment or hint. Thank you very much, Ralph ----- Ralph Wirth University Erlangen-Nuremberg, Chair of Statistics GfK Group, Department of Methods and Product Development -- View this message in context: http://www.nabble.com/Test-for-equality-of-complicatedly-related-average-correlations-tp19346312p19346312.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
