Non Parametric Independent Test effect size
Hi everyone!
I'm performing a research using JASP, I already have the results but I'm struggling in how to interpret the effect size:
I just want to ask about the effec size metric used in the bayesian version of mann whitney's U,
is it the cohen's D? and in that case:
since mann withney's test was meant to be used with non-parametric data, why does the effect size is measured with cohens'D and no with serial rank correlation or another non-parametric effect size metric?
and how is the best way to interpret that effect size (big, small, medium)?
:)
Thanks in advance!
Comments
AKAIK there is no effect size for the Bayesian Mann-Whitney test - the W is the test statistic.
In the non-Bayesian, the rank-biserial correlation is indeed given.
Hi tourette95,
As a result of the approach used to enable Bayesian inference for the Mann-Whitney test, we obtain a posterior distribution for the standardized effect size cohen's d, but on the latent level. You can read more about this in the corresponding paper (https://www.tandfonline.com/doi/full/10.1080/02664763.2019.1709053), although it can be a bit technical. I am planning on writing a blog post that explains some of the rank-based Bayesian analyses that explains the underlying technique.
I will also add the rank-biserial correlation to the Bayesian output.
Kind regards,
Johnny
Thank you so much!!! :)