Background: I am an ecologist working with intertidal
community structure data. My data is characterised by high
non-normality and lack of equal variance between samples. I
am currently exploring various non-parametric multivariate
techniques, such as non metric multidimensional scaling, and
the suite of statistic tests applicable to such a technique.
(For those of you not familiar with nMDS, it is an
ordination technique that arranges points in space that are
determined from a similarity matrix derived from say, the
Bray-Curtis method. In actuality, nMDS uses the RANK
similarity, and thus eliminates the need for many
assumptions about the original data set.
My data is comprised of the following: percent cover data
about seaweeds in the intertidal. This is determined by
laying a 8 square quadrats on the beach (at 6 different
sites - 8 quads at each), then sampling for a percent cover
value of each species present. So, I have 8 replicate
samples at each of six sites.
Anyways, my question is the following: Is it preferable to
POOL the 8 samples, or to average the samples? What are the
pros and cons of each scenario? I'm aware there is a loss of
power if you pool the samples, but does that make a
difference in this situation.
Any advice would be greatly appreciated.
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