I've spent a long time generating and analyzing a dataset and now it comes down to the statistical analysis... and I'm not sure how best to proceed.
In my dataset, I have rats sorted into one of 4 treatment groups. Within each treatment group, data was recorded from each rat at 10 consecutive timepoints. At each timepoint, 23 datapoints (the dependent variable) were collected from each rat, corresponding to 23 discrete depths within the rat cerebral cortex.
So basically, I have three factors to assess in terms of effect on the DV: treatment group, timepoint, and cortical depth.
My intention is to run an ANOVA to determine the significance of effect of each of these factors and/or the interaction effects between factors. I'm not sure what exact paradigm I should use.
I was advised that because I am using timepoint data, that I should use a repeated measures ANOVA. But since treatment group is clearly a fixed variable, does that mean I would need to use a mixed model approach (as I would have both fixed and random variables)?
Furthermore, I am uncertain as to whether to treat depth is a fixed variable or a random variable (like time). Because I am sampling 23 depths from each animal, it seems to me that this is analogous to sampling 10 timepoints from each animal, making depth a random variable. But I'm not at all certain here.
To make matters more complicated, my data was not really collected at discrete timepoints per se. I have averaged the data across designated time intervals to generate discrete values, but in actuality, the data was collected continuously. Taking this into account, perhaps ANCOVA would be appropriate? Or possibly just scrapping the ANOVA idea and going for regression?
To cover all bases, I am currently using Matlab for the data analysis.
Edit: See comment below for more specific experimental details.