Split plot and strip plot (or split block) designs are commonly used in the agronomy, however, they don’t stop there. We quite often have limited resources and may add on a factor or two on top of our current trial. This blog post and session will expand on the Split-plot and the Strip-plot (split-block) designs.
We have 3 experimental units with 3 differing sizes. The whole plot, the sub-plot, and the sub-sub-plot. This link contains a PDF document that displays the Split-split plot design and also contains the Statistical model.
Factor A is the Whole plot – with two levels: A1 and A2. A1 and A2 are randomly assigned within a block (or rep). In this illustration we have 2 Blocks (Reps).
The WHOLE plot is now divided into SUB Plots. Factor B, which has 3 levels is randomly assigned to each level of Factor A in the WHOLE plots.
The SUB plot is now divided into SUB-SUB Plots. Factor C, which has 5 levels is randomly assigned to each level of Factor B in the SUB plots.
Let’s build the model for the Split-Split plot design as modeled above:
An extension of the split-split-plot, with a 4th experimental unit. Same as above 4 differing experimental unit sizes, and therefore 4 errors to be aware of.
Split-plot x Split-block (strip-plot)
The combinations do not seem to end. The more we look into these designs, the more I realize that many trials that we currently conduct may not be what we think they are.
In this case we are looking at the Split-block or Strip-plot design and within each row/column combination we are adding a third factor within this experimental unit and will aim to randomly assign them – leading us to a Split-plot x split-block design.
I will update with a picture of a design and the statistical model that accompanies it.
After working through these three examples, which design do you think you truly have?
I propose for the last workshop session in April, that we review Latin Square designs, and the combination of Split-plot and latin squares, as I suspect this will talk to a few researchers 🙂