I agree with permutation test, however if the test was conducted in the same population before and after the application of treatment, test of difference could be applied. If the distribution of the difference is approximately normal, z - test could be used on one hand. On the other hand, if the distribution of the difference violates normality assumption, them go for permutation test or fit regression model and test for normality assumption.
It has widely been agreed upon that regression model is more powerful to z - test and this will show true distribution of the data. If the normality assumption is violated, appropriate transformation may be sought and applied appropriately.
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Mubasiru Asafe Lamidi
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-----Original Message-----
From: A UK-based worldwide e-mail broadcast system mailing list [mailto:[log in to unmask]] On Behalf Of Phillip Good
Sent: Tuesday, April 01, 2014 1:59 PM
To: [log in to unmask]
Subject: Re: Help with ANCOVA
Use a permutation test
-------- Original Message --------
Subject: Help with ANCOVA
From: Graham Stirrat Clarke <[log in to unmask]>
Date: Tue, April 01, 2014 6:17 am
To: [log in to unmask]
Dear All,
A colleague has a before and after treatment dataset to analyse. He has the pre-variable, the post-variable and a treatment variable. He
has used ANCOVA. However looking at his data both the pre-variable
and post-variable have a far from normal distribution (looks like an exponential, with lots of 0 and 1 values dropping down smoothly to 12 – there are no negative values). I’m not convinced he can use ANCOVA in these circumstances or am I wrong?
One thing I’ve noticed is that the difference between pre and post values does follow an approximately normal distribution. Could we analyse the differences instead? Would repeated measures ANOVA or one of its derivatives be acceptable?
Any help or advice you could offer would be appreciated.
Thank you,
Graham
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