What is a split 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 is split plot design in research?
A split-plot design is an experimental design in which the levels of one or more experimental factors are held constant for a batch of several consecutive experimental runs, which is called a whole plot. A split-plot design results in correlated responses for the experimental runs in the same whole plot.
What is split split plot design and give example?
A typical example of a split-plot design is an irrigation experiment where irrigation levels are applied to large areas, and factors like varieties and fertilizers are assigned to smaller areas within particular irrigation treatments.
What is split plot design in agriculture?
A split plot design is a special case of a factorial treatment structure. ▪ It is used when some factors are harder (or more expensive) to vary than others. ▪ Basically a split plot design consists of two experiments with different experimental units of different “size”.
How many factors are involved in split plot design?
(c) With three factors, the design is split-split plot. The housing unit is the whole plot experimental unit, each subject to a different temperature. Temperature is assigned to housing using CRD. Within each whole plot, the design shown in b is performed.
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 between split plot and factorial design?
Many factorial experiments have one or more restrictions on randomization. In a split-plot design, the experimenter is interested in studying the effects of two fixed factors (including the two-factor interaction).
What is split plot design advantages?
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 are the advantages and disadvantages of split-plot designs?
What are the reasons for choosing a split-plot design in an industrial experimentation?
The subplot effects and subplot-main plot interaction are estimated using with the same subplot error. Two considerations important in choosing an experimental design are feasibility and efficiency. In industrial experimentation a split-plot design is often convenient and the only practical possibility.
How is split plot design calculated?
The split-plot error sum of squares can be calculated by subtraction: ssE2 = sstot – ssR – ssA – ssE1 – ssB – ss(AB). The split-plot error mean square msE2 = ssE2 / a(r –1)(b –1) is used as the error estimate in testing the significance of split-plot factor(B) and interaction(AB).
What is an advantage of using a split-plot design over a two factor factorial in a completely randomized design?
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.