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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).
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.
How to make a split plot in blocks?
Split-plot design Split-plot design Split-plot design Blocks: I A 1 B 2 A 1 B 1 A 1 B 3 A 1 B 2 A 1 B 1 Blocks: II A 2 B 2 A 2 B 1 A 2 B 3 A 2 B 2 A 2 B 1 Blocks: III A 1 B 1 A 1 B 2 A 1 B 3 A 1 B 1 A 1 B 2 Blocks: IV A 2 B 3 A 2 B 2 A 2 B 1 A 2 B 3 A 2 B 2
How is the two period crossover design analysed?
The two period crossover design is analysed with the standard two factor repeated measures |ANOVA – not as one might expect with a three factor ANOVA.
Is the repeated measures ANOVA still in use?
Repeated measures ANOVA is still widely used in many disciplines including the medical sciences, although in recent years the linear mixed effects model has replaced ANOVA’s former predominance for repeated measures analysis.
Why do pen studies use a split plot?
Loughin (2007) considers improved experimental design and analysis for long-term experiments. St-Pierre (2006) explains why pen studies have an implicit split-plot design in which the main plots (pens) receive the treatment of interest, whereas the subplots (cows) receive all the same subplot treatment.
Hence you may find data from a repeated measures design being analyzed with a ‘split plot’ analysis of variance. 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.