Hi
I am currently attempting to look at the effects of several nominal
independent variables (all categorical e.g Sun 1, 2 ,3 - Time 1,2,3 day
1,2,3)upon one nominal dependent variable( behavior coded eg.
55=upright sit). I was hoping to examine relationships with PCA or CA
and then to relate the strongest structures to environmental variables
using CCA. Unfortunatly my data has a kurtosis value of >5 ( majority
of frequencies at extremes)and therefore is not normalally distributed.
Could anyone give me some advice regarding which ordination techniques
are best for non-normal data. I have run spearmans rank correlations
and there are definate trends there. I have also performed cluster
analysis to group individual animals according to behavior and hoped to
use MANOVA to evaluate how well differentiated clusters are and to use
DFA to find which variables contribute most strongly to clustering. I
know that these techniques generally rely on the assumption of
normality however how strict is this? There seems to be barely anything
I can use to analyse my non-normal data, surely this cant be right??? I
thought about transforming the data but as I am monitoring behaviours
(105 in all) approximatly half of which barely happen (e.g frequency 1
or 2) and the rest which happen with great frequency (e.g frequency
560) I cannot see how this would help.
Any advice would be greatly appreciated!
Claire
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