Thank you very much, Adam. I have to get a bit more familiar with the model you propose in order to understand if it applies to my problem as well.
My question is not really "does time show a different effect" but "which one of two measures is more reliable": My respondents have completed exactly the same questionnaire twice (t=1 and t=2). The questionnaire consisted of two ways of measuring attribute importance, and the "better" method of measuring these importances is the one that gives the same importances for each respondent in t=1 and t=2. In other words: I want to examine test-retest reliability of the two measures. Naturally, if X(t=1,t=2)-correlation is higher for a specific respondent than the Y(t=1,t=2)-corralation, than for this respondent the method that yields the X-importances is more reliable. All I want to do is to see if this holds for the whole sample as well... Anyway, thank you again, I will think of your approach. Ralph Adam D. I. Kramer-3 wrote: > > Hi Ralph, > > I had the same problem you do a few months ago, and realized that > the question I had (does time show a different effect for X than Y) was > not > best modeled as differences between correlations across individuals, but > as > whether time interacts with condition. > > I answered this question with >> library(nlme) >> lme(obs ~ cond*time, random=~cond*time|subj) > > ...where obs is the responses on the X or Y variable, cond is a factor of > either X or Y, and subj is your subject variable. This fits a heirarchical > linear model to the data. The relationship between X and time is sig. > diff. > from the relationship between Y and time if the cond:time fixed effect is > true. > > This approach makes better use of your data, because when you correlate > the > observations, you're effectively "losing" variability (because > correlations > are doubly standardized) as well as degrees of freedom (you have 9 df > within > each individual, but each correlation is only one number). > > --Adam > > On Sat, 6 Sep 2008, Ralph79 wrote: > >> >> 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. >> > > ______________________________________________ > 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. > > ----- 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-tp19346312p19355825.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.
