Hi!
In my study on kelp I want to see how wave exposure and current (both
continuous variables) affect kelp physical measures along the Norwegian
coast. To assure a balanced design, I stratified my study area into 9
different classes, defined by all combinations of three levels of wave
exposure and three levels of current and randomly selected 27 stations
such that all 9 combinations of the variable levels where represented
three times. Then 10 samples were taken at each station.
Analyzing these data without taking into account the dependencies
between samples within each station will be wrong, due to
pseudoreplication. So I wanted to perform a mixed model, including
station as a random factor. But since, due to the sampling design,
station is strongly confounded with the variables in question, I lose
all my variation to this variable and nothing is left for the two
variables in interest, i.e. wave exposure and current.
So how should I take this dependency into account? Do I have to perform
my analyses on averaged values from each sample, and thereby reduce
power and the possibility of treating wave exposure and current as
continuous variables? I hope not! Can I possibly do some kind of nested
analysis, to specify the sampling structure, without including station
as a random factor?
Any help/comments on this is greatly appreciated!
Gunda
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