I've got a mixture of parametric and non-parametric data, with a repeated measures design; measuring animal behaviour in three different treatments.
For parametric data, it's simple enough calculating mean difference and 95% confidence intervals. I also want to do the same for the medians of non-parametric data.
For non-parametric data, when looking at differences between treatments I have used Wilcoxon matched-pair signed rank tests. My question is, is there a way to calculate median differences between two treatments +95% confidence intervals for the difference?
If not, I've read this page – would the method suggested by Pallant, J. (2007) be a suitable alternative?
Best Answer
First, note that there are no parametric vs. nonparametric data, only parametric vs. nonparametric tests. The distinction relates to the assumptions a test makes on the distribution of the test statistics.
Yes, you can calculate median differences, and you can also calculate confidence intervals for this median. CIs are a Good Thing. CIs for the median are less common and a bit harder to calculate than CIs for the mean. Here Aksakal links to an introduction., However, it's probably easier just to bootstrap it.
The method suggested by Pallant (2007) in the thread you link to is an effect size, not a confidence interval. This can also be informative; in particular, you can compare it between studies. It's just not a CI, and it gives other information than a CI. Use whatever information you want to convey, perhaps even both measures.
(Or do you want a CI for Pollard's effect size? Bootstrap it. When it doubt, bootstrap!)