Binomial glms with very small numbers

The advisability of using "glm" with mortality depends not on the 
size of sample groups but on the assumption of independence:  Whether 
you have 3 individuals per group or 30 or 1, is it plausible to assume 
that all individuals represented in your data.frame have independent 
chances of survival give the potentially explanatory variables?  If the 
answer is "yes", then "glm" is appropriate.  If the answer is "no", then 
some other tool may be preferable.  However, "glm" is quick and easy in 
R, and I might start with that, even if I felt the assumption of 
independence was violated.  If I found nothing there, I would not likely 
find anything with techniques that handled more appropriately the 
violations of independence. 

      Similarly, I can't see how it would matter whether potentially 
explanatory variables were continuous or categorical, as long as a 
categorical variable were appropriately coded as a factor (or 
"character", which is then treated as a factor) if it has more than 2 
levels. 

      Hope this helps. 
      spencer graves

V&R describes binomial GLMs with mortality out of 20 budworms.

Is it appropriate to use the same approach with mortality out of
numbers as low as 3?  I feel reticent to do so with data that is not
very continuous.  There are one continuous and one categorical
independent variables.

Would it be more appropriate to treat the response as an ordered
factor with four levels?  If so, what family would one use?

TIA