evaluating multiple responses using chi square

Dear Listserv community,

I realize that this is more a statistical theory question, rather an R
application question, but I hope those familiar the theory underlying
manyglm and manylm could help me.

In evaluating the overall response of a community to a treatment, I'm aware
that one class of approaches involves doing univariate analyses for each
species (e.g., ANOVA, t-test, chi square, logistic modeling, etc) and then
"summing" the results across all species and evaluating statistical
significance with a randomization procedure.

My question is has anyone considered using a chi square test instead of
randomization to obtain the significance value? The p value for any test
statistic (F, t) can be converted to a chi square value with 1 df. Because
chi square values are additive (assuming independence), it makes sense to me
that you could simply add up the chi square values for all species and
evaluate the significance of the resulting sum assuming a df equal to the
number of tests (species). Presumably, one could use different tests for
different species, depending on whichever is most appropriate (e.g., anova
for common species that differ in abundance between treatments or chi square
or a logistic model for species that differ in terms of frequency of
occurrence between treatments). If one were confident that the univariate
assumptions held for each species' test, other than the assumption of
independence of responses among species, I'm wondering what if anything is
wrong with such an approach for obtaining a significance value. Perhaps
something similar is being done when the log likelihood value is calculated?
If so, what are the similarities or differences?

Thanks, and I apologize if this question is too basic or has already been
answered.

Steve




J. Stephen Brewer 
Professor 
Department of Biology
PO Box 1848
 University of Mississippi
University, Mississippi 38677-1848
 Brewer web page - http://home.olemiss.edu/~jbrewer/
FAX - 662-915-5144 Phone - 662-202-5877