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When repeated measures design is used which assumption is violated?
assumption of sphericity
Unfortunately, repeated measures ANOVAs are particularly susceptible to violating the assumption of sphericity, which causes the test to become too liberal (i.e., leads to an increase in the Type I error rate; that is, the likelihood of detecting a statistically significant result when there isn’t one).
Is normality important for ANOVA?
ANOVA residuals don’t have to be anywhere close to normal in order to fit the model. However, unless you have an enormous amount of data, near-normality of the residuals is essential for p-values computed from the F-distribution to be meaningful.
Which is an example of a repeated measures ANOVA?
Repeated measures ANOVA analyses (1) changes in mean score over 3 or more time points or (2) differences in mean score under 3 or more conditions. This is the equivalent of a one-way ANOVA but for repeated samples and is an extension of a paired-samples t-test. Repeated measures ANOVA is also known as ‘within-subjects’ ANOVA.
How can the normality assumption be tested in R?
So assuming that there is a point in testing the normality assumption for anova (see 1 and 2) How can it be tested in R? Which doesn’t work, since “residuals” don’t have a method (nor predict, for that matter) for the case of repeated measures anova.
How to check the normality assumption in ANOVA?
The normality assumption can be checked by computing the Shapiro-Wilk test for each time point. If the data is normally distributed, the p-value should be greater than 0.05. Tested data was normally distributed at each time point, as assessed by Shapiro-Wilk’s test (p > 0.05).
Is the rANOVA assumption the same as the ANOVA assumption?
It is often stated that rANOVA has the same assumptions as ANOVA, plus the sphericity. That is the claim in Field’s Discovering statistics as well as in Wikipedia’s article on the subject and Lowry’s text.