Hi everyone,
I'm trying to fit a multiple regression model and have run into some
questions regarding the appropriate procedure to use. I am trying to
compare fish assemblages (species richness, total abundance, etc.) to
metrics of habitat quality. I swam transects are recorded all fish
observed, then I measured the structural complexity and live coral cover
over each transect. I am interested in weighting which of these two
metrics has the largest influence on structuring fish assemblages.
My strategy was to use a multiple linear regression. Since the data were
in two different measurement units, I scaled the variables to a mean of 0
and std. dev. of 1. This should allow me to compare the sizes of the beta
coefficients to determine the relative (but not absolute) importance of
each habitat variable on the fish assemblage, correct?
My model was lm(Species Richness~Complexity+Coral Cover). I had run a
full model and found no evidence of interactions, so I ran it without the
interaction present.
It turns out coral cover was not significant in any regression. I have
been told that the test I used was incorrect and that the appropriate
procedure is a stepwise regression, which would, undoubtedly, provide me
with Complexity as a significant variable and remove Coral Cover. This
seems to me to be the exact same interpretation as the above model. So,
since I'm very new to all of this, I am wondering how to tell whether one
model is 'incorrect' or 'inappropriate' given that they yield almost
identical results? What are the advantages of a stepwise regression over
a standard multiple regression like I have run?