So I've been reading West et al's book on Linear Mixed Models as I'm trying to figure out how I should analyze my data, and I was wondering if I've correctly understood what I've ready so far. So, I have repeated measures on a response variable: for each subject, a measure under the control condition and under a "treatment." The subjects also differ in sex, which we predict to be an important factor in the response to treatment. Importantly, some subjects (not all) are measured multiple times, because they went through "treatment" multiple times. So, in those cases, I have a new control measure and treatment measure for each individual.
What I think I is a LMM with 3 fixed effects: my response variable, sex, and treatment condition. In addition, I think I will need 1 random effect: subject identification (to correct for the non-independence of some of my data points because some subjects were included twice).
First of all, am I missing anything? The book lost me a bit when it started including all of these interactions in their models... I'm also working with very small sample sizes (because I work with endangered and long-lived animals), so I worry about trying to cram too many variables into the model. Second, do my data need to follow any particular distribution? Or do the residuals need to follow some particular distribution?
Thanks for taking the time to read...