nested Kruskal Wallis

Dear Jude

I'm trying to reconcile where you say "it doesn't meet the assumption of homogeneity of variances" with "sample size within fields is small and unequal". With small sample sizes you can easily generate dummy samples with widely differing variances even where the process generating the data has a single variance. It's difficult to say without more information, but I'd be tempted just to use a linear mixed model to analyse the data. 

regards

Mike D

"Jude Phillips" <jude.phillips at gmail.com> 19/11/2008 22:41 >>>

Hi,

I'm trying to analyse a dataset on the size of beetles collected in
different crop types.  Crop type is the fixed effect, and field is a
random factor, nested in crop.  Although my data is normal after
transformation, it doesn't meet the assumption of homogeneity of
variances.  In addition, sample size within fields is small and
unequal (n=2-10).  So, I'm trying to figure out if I can run a nested
Kruskal Wallis type analysis, since this is more robust to both
problems than an ANOVA, even with permutation.  I've been told it
might be possible to run such an analysis within the coin package, but
I can't figure out how to do this -methods for blocked designs are
mentioned in the documentation, but not nested designs.

I'd appreciate any help or thoughts!

Cheers, Jude Phillips

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