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What is a split-plot design?
The split-plot design is an experimental design that is used when a factorial treatment structure has two levels of experimental units. The whole plot is split into subplots, and the second level of randomization is used to assign the subplot experimental units to levels of treatment factor B.
What are the advantages and disadvantages of split-plot designs?
Advantages and Disadvantages Compared to completely randomized designs, split-plot designs have the following advantages: Cheaper to run. In the above example, implementing a new irrigation method for each subplot would be extremely expensive. More efficient statistically, with increased precision.
What is the difference of Split and strip plot design?
Although similar sounding, strip plots are not the same as split-plot designs. The main difference between split-block and split-plot experiments is the application of a second factor. In other words, the first, whole-plot factor is completely crossed with a second factor.
Why use split plot Anova?
In statistics, a mixed-design analysis of variance model, also known as a split-plot ANOVA, is used to test for differences between two or more independent groups whilst subjecting participants to repeated measures.
What are the disadvantages of split plot design?
This type of design does have many disadvantages, including:
- Implementing the design can be difficult, and requires advanced knowledge of a specific discipline (e.g. agriculture, factory production, or epidemiology).
- Software packages that assist with the design are hard to find, although SAS and JMP have options.
Is the split plot confounded with a whole plot?
It is important to note that since the whole-plot treatment in the split-plot design is confounded with whole plots and the split-plot treatment is not confounded, if possible, it is better to assign the factor we are most interested in to split plots.
Which is the second approach to split plot design?
As mentioned earlier analysis of split-plot designs using the second approach is based mainly on the randomization restrictions.
How are split plots used in statistical analysis?
In the statistical analysis of split-plot designs, we must take into account the presence of two different sizes of experimental units used to test the effect of whole plot treatment and split-plot treatment. Factor A effects are estimated using the whole plots and factor B and the A*B interaction effects are estimated using the split plots.
What makes a split plot an experimental error?
(If you recall, we mentioned that any interaction between the Blocks and the treatment factor is considered part of the experimental error). Similarly, in the split-plot section of the analysis of variance, all the interactions which include the Block term are pooled to form the error term of the split-plot section.