Not in disagreement with this response... But loglinear ANOVA is also an alternative. I've not tried with STATA, but it is a viable alternatibe for data that don't meet the assumptions of traditional ANOVA...
See Chapter 11 in Alan Agresti's Categorical Data Analysis text (Wiley).
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Nick Cox
Sent: Tuesday, October 28, 2008 9:02 AM
To: [email protected]
Subject: st: RE: non-parametric MANOVA
I find some inconsistency in this request. After all, what would a non-parametric MANOVA look like except something like a MANOVA, except that your data have been transformed to ranks? If you are happy to reduce your data to ranks, why cavil at some other transformation, which typically would lose less information?
Further, my visceral instinct is that MANOVA is more robust to non-normality than people fear.
More positively, if this were my problem, I might do
1. MANOVA on original data
2. MANOVA on rank-transformed data
If the conclusions were substantively similar, stop there. Otherwise, consider what specific transformations were advisable.
Nick
[email protected]
Jochen Späth
I want to do a Manova (14 different dependent variables, 2 main factors) and am stuck with the problem that most of my fourteen variables are not normally distributed (and I do not want to transform them in order to get them normal since I have only remote access to the data which complicates things a lot). So, my question is: is there a way to do such a MANOVA in STATA using non-parametric techniques (the -kwallis- command allows only for one factor and one dependent variable as far as I know)?
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