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I want to know if this is a reasonable approach. I conducted the same experiment on two different species with small sample sizes (7-25 per species) in 4 years at 6 different sites. I measured damage to plants in two treatments.

There is unequal sampling across years and sites. Species, site and year differ in mean damage levels. The data are highly non-normal.

what I did:

  1. I got the residuals of a model damage= species +site(species) as a random effect + year
  2. Then I used a Wilcoxon rank sum test to test differences between treatments.

Does this seem reasonable?

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My first guess is no. In what way are your data "highly non-normal"? Are they counts, or binary classifications? If they're just skewed or something, bootstrapping may be a possibility. –  gung Sep 3 '12 at 21:11
I'm confused as to what you tested with Wilcoxon - differences in residuals? If so, why? Wouldn't a nonlinear multilevel model answer the question more directly? –  Peter Flom Sep 30 '12 at 12:43

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