What Is Split Split Plot Design

A simple explanation of a split-plot design, including a formal definition and a visual example.

7 Split-Plot Designs In this chapter we are going to learn something about experimental designs that contain experimental units of different sizes, with different randomizations. These so-called split-plot designs are maybe the most misunderstood designs in practice therefore, they are often analyzed in a wrong way.

In a split-plot design, we typically have two factors such as genotype and treatment, as I mentioned in my previous post, where genotypes are set up as the main plot and treatments as the sub-plot. But, when we have three factors, we can use an experimental design called a split-split plot design.

The restriction on randomization mentioned in the split-plot designs can be extended to more than one factor. For the case where the restriction is on two factors the resulting design is called a split-split-plot design. These designs usually have three different sizes or types of experimental units.

The split-split plot design is an extension of the split-plot design to accommodate a third factor one factor in the main plot, another in the split plot and a third factor in the split-split plot. The example shown below illustrates this experiment layout. 8.1 Details for split-split plot designs The statistical model structure this design

Unfortunately, the value of these designs for industrial experimentation has not been fully appreciated. In this paper, we review recent developments and provide guidelines for the use of split-plot designs in industrial applications.

What is a split-plot design? How does it compare to a completely randomized design? Step-by-step example with images. Advantages and disadvantages.

Split-split Plot Arrangement The split-split plot arrangement is especially suited for three or more factor experiments where different levels of precision are required for the factors evaluated.

A split-plot design is a designed experiment that includes at least one hard-to-change factor that is difficult to completely randomize because of time or cost constraints. In a split-plot experiment, levels of the hard-to-change factor are held constant for several experimental runs, which are collectively treated as a whole plot. The easy-to-change factors are varied during these runs, each

Split-plot design is also a partially nested design and it is very similar to a repeated measure design with respect to the analysis, EXCEPT FOR the design originated from the AgricultureBiology field of study. In many situations, split-plot design makes a lot of sense rather the most efficient completely randomized design.