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When you’re working with many correlated variables, they get too unwieldy to use individually. Principal component analysis allows you to combine these variables while extracting the most possible information they contain. It’s an important variable reduction technique.
And it’s not as simple as averaging them. You lose a lot with a simple approach like that.
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Cross-over trials provide a very powerful approach for comparing two treatment conditions. Research subjects get both treatment conditions, which we will label arbitrarily as A and B. A randomly selected half get A followed by B and the other half gets B followed by A. This is generally referred to as the AB/BA design. Because each subject serves as their own control, you ...
Repeated measures ANOVA doesn’t cut it for many repeated measures situations, but do you always need mixed models instead?
There is another option that for many designs works better than either RM ANOVA or mixed models: marginal models. In this training, you’ll gain a clear, practical understanding of marginal statistical models for repeated measures ...
