I have a experimental setup (plant germination). I don't have concrete data yet. I am looking at how many plants germinate in given conditions, broken down as follows:
1) Two different temperatures.
2) For each of the two temperatures, I have 4 salinity concentrations.
3) For each salinity concentrations I have 2 levels of habitat from which the seeds were collected.
4) For each habitat type I have specific locations from which the seeds were collected.
The measure of interest (response) is the proportion of seeds that germinated at various time intervals. I know some people approached this via n-way ANOVA (judging from the literature). But it seems to be that a mixed model may be (more) appropriate.
1) What would be a better approach and why?
2) If one used a mixed model, which of the variables (location, habitat type, salinity) would you include as a random effect?
3) Given that these are proportion or percentage data, what would be the best approach?