I'm wondering if you could quickly advise on the best statistical analysis for my data.
I have a designed experiment with 12 plots within one larger area. This area was blocked into 6 to overcome issues of natural gradients present there, so there are 2 plots in each block.
Treatment 1 is a two level treatment split between these 12 plots i.e. 1 plot in every block receives one level of the treatment ("High") and the other receives another level ("Low"). Nested within this, each plot is split into two (24 subplots), and given a second two level Treatment 2 (levels "A" and "B"). Every single plot receiving Treatment 1 has a Treatment Two Subplot receiving level "A", and a subplot receiving level "B".
As such, I have a two-way factorial design, with 4 types of treatment combination: High/A, High/B, Low/A and Low/B.
This experiment was run for three years, and species richness measured as a response (a simple count of the number of species present in each Treatment 2 subplot) each year.
I am interested to see what the effects of the two levels of Treatment Two are on Richness change with time, in the context of the level of Treatment 1 received.
I am not sure if I need:
- A two-way mixed ANOVA, with within subjects effects (Year?) and between subjects effects (Treatment 1 and Treatment 2?)
- A mixed effect model, with Treatment 1 and Treatment 2 as fixed effects, and Year and Plot as random effects. (Is Year a random effect? I am unsure...). Should subplot also be a random effect here?
- A mixed effect model with a time series.
- I am also unsure as to what to do with block. Block doesn't have levels of treatment, it was just an attempt to reduce the incidence of similarity between plots as a result of the natural gradients in the study area...
- In addition, because the richness data is a count of species, i'm not sure if this affects my choice of analysis...
If you have any thoughts on any of these points, I would be glad to hear them.