Dear JMP Community,
I have tried to read related post before on similar topics. It is not covering the questions I have.
Let me explain the background first.
Objective:
I am trying to find a statistical method to perform Variance Decomposition on a specific process.
We have huge number of data with lots of Factors (mixed of categorical and continuous data type) and Responses (continuous data type)..
Finding:
So far, the common method is to use ANOVA (eta square and omega square).
However, we need to consider the assumption violations for ANOVA. In case we do not meet the assumptions, especially those that are critical like variance homogeneity, we are worried about how reliable the eta square is.
Questions:
Is there any other recommended statistical method we could use like a "non-parametric" eta square, or anything that is equivalent to it.
We know the non-parametric "ANOVA" would be Kruskal Wallis, but I'm not sure if JMP can provide the "eta square" under Kruskal Wallis. At least I can't find it.
Another is Welch's ANOVA, however this method seems to work only on One-Way (one factor only), but my case have multiple factors.
Thanks in advance for you advice.
Note: I have a JMP Pro, in case there are available method under JMP Pro.
Thanks,
Chris