I've collected data on animal visitation at four different points in time. The four time points represent the total animal visitations over a three day period, i.e. 3 days of monitoring at four different times. The first two monitoring times there was no treatment applied to the visitation sites, while on the last two there was an artificial attractant used as a lure. An example of my dataset is below.
SITE TREATMENT TOTAL.VISIT TIME 1 N 2 Sep13 2 N 1 Sep13 3 Y 2 Mar14 4 Y 2 Mar14 . . . 1 N 2 Sep14 2 N 1 Sep14 3 Y 3 Mar15 4 Y 4 Mar14
So, I've got repeated measures at multiple sites, pre- and post-treatment. I'm not interested in the actual sites themselves, but more the effect of the attractant (
TREATMENT) and season (
TIME). Even though the data suggests that (for example) site 1 received the treatment for
Sep13, it did not - its there purely so I can look for differences between sites that would eventually receive the treatment.
My idea (I haven't done anything remotely like this since my undergrad days ~ 10 years ago), was to use a NB GLM (the real data contains lots of zeros, and the conditional variances >> conditional means) to assess the effects of treatment and season in the pre-treatment (
Mar14) data, and then assess them again in the post-treatment (
Mar15). That is, create two "independent" NB GLM models. The idea being that there should be no differences in
TREATMENT for the first monitoring period (maybe difference in
TIME); while for the last two monitoring periods there should (ideally) be a difference in
TREATMENT and not in
Is my method statistically valid, or should I be using some sort of time series or repeated measures design?