I have a single ordinal outcome called RATING, and a series of continuous covariates (P1, P2, P3, etc). I have used Kendall's Tau statistic to assess the concordance/correlation between RATING and each covariate as per below.
#load Kendall package library(Kendall) #create example data RATING<-sample(1:4,200,replace=TRUE) P1<-RATING+rnorm(200,0,2.5) P2<-RATING+rnorm(200,0,3) P3<-RATING+rnorm(200,0,3.5) #Calculate Kendall's Tau for each predictor Kendall(RATING,P1) Kendall(RATING,P2) Kendall(RATING,P3)
The resulting Tau statistics will differ in size and significance obviously. My question though is whether there a way to determine which are significantly different from others? For example, can I use the results of these tests to say P1 is significantly more strongly correlated with RATING than P3, or that P3 is significantly less correlated with RATING than all other covariates?
I've found discussion of a method to compare Pearson's R values here How to compare the strength of two Pearson correlations? but I believe it assumes the compared R values are from independent samples, its not clear if it can be applied to Kendall's tau values, and its not clear whether its suitable for multiple comparisons.
Any suggestions greatly appreciated