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What is 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.
How do you split a plot in design?
The first level of randomization is applied to the whole plot and is used to assign experimental units to levels of treatment factor A. 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 requirements for split plot ANOVA?
Your variable of interest should be continuous, be normally distributed, and have a similar spread across your groups. You should have repeated measures from the same units of observation (e.g. subject, store, location) and you should have enough data (more than 5 values in each group).
How are repeated measures ANOVA and linear mixed models different?
Both Repeated Measures ANOVA and Linear Mixed Models assume that the dependent variable is continuous, unbounded, and measured on an interval or ratio scale and that residuals are normally distributed.
What’s the difference between split plot and repeated measures?
Despite the use of the same family of models, there are some important differences between split-plot and repeated measures designs especially in relation to randomization and assumptions. The principle of a split-plot design is that different treatments are assigned to sampling units at different scales.
Which is an example of a split plot?
Example of Split-Plot Design and Analysis: The Oats Experiment An experiment on the yield of three varieties (factor A) and four different levels of manure (factor B) was described by Yates ( Complex Experiments , 1935). The experiment area was divided into s=6 blocks. Each of these was then subdivided into a=3 whole plots.