I have been trying to figure out the best way to approach analysis of my data for a while now and I'm struggling to understand if a Poisson regression is correct and, embarrassingly, I'm not sure if my data are independent observations.
I have two data sets:
I have data from rows sown with 100 seeds each (n=6) with counts of seedlings recorded at different stages of seedling development (germinated, emerged etc, 6 stages). The same seeds/seedling were tracked over time. I want to know which stages have significantly different counts of seeds/seedlings. Are these independent observations as they are different outcome variables (germinated, emerged etc)? Note: each time point can only be equal to or less than the preceding time point. Should this be approached with a GLM with Poisson distribution or can it be a simple one-way ANOVA or a repeated measures ANOVA?
My second data set has data from 10 seeds per pot (n=6 pots) for two species, under 3 different treatments. There are four outcome variables (germinated, ungerminated etc) with the seeds proportioned into one of the four outcome variables (if 80% germinate, for example, then 10% could be ungerminated and 10% dead). This means quite a lot of 0's in some cases. I have performed a Poisson regression in R to find significant differences between treatments for each outcome variable and it makes sense but I want to check that this is the best approach and meets the correct assumptions but, I'm confused..