I conducted an experiment to assess the effect of five interventions on blood glucose levels in rats. Therefore, the experiment included five groups, with each group containing five rats. Every day and for each rat, I measured the change in blood glucose levels following the administration of the interventions. Therefore, I generated 25 readings daily (change scores). I repeated the experiment 15 times (over 15 consecutive days) and generated 15 sets of 25 readings. The experiments were always conducted on the same rats.
I want to run a one-way ANOVA to assess the differences in blood glucose levels between the five groups. I can set up my data in 2 ways:
- Calculate the mean change in blood glucose levels for each rat over the 15 days and then run ANOVA. The total sample size in this approach is 25 (5x5).
- Treat replicates as separate entries and run ANOVA. The total sample size in this approach is 375 (5x5x15).
What considerations should I make when selecting my approach? What are the pitfalls? For example, the second approach is more powerful because of the 15-fold greater sample size, but at what cost? Is the first approach invalid because I am collapsing a lot of data and losing some of the properties of the data?