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What is the most suitable statistical test for ordinal data?
The most suitable statistical tests for ordinal data (e.g., Likert scale) are non-parametric tests, such as Mann-Whitney U test (one variable, no assumption on distribution), Wilcoxon signed rank sum test (two variables, normal distribution), Kruskal Wallis test (two or more groups, no assumption on distribution).
Can you average ordinal data?
Using the mean of ordinal data is fine; just be careful not to make interval or ratio statements about your data — even researchers who take a more relaxed view of averaging ordinal data would disagree with that practice.
Repeated Measures with Ordinal Levels The most common form of a repeated measures design occurs when participants are measured over several times or trials, and the Trial variable is thus an ordered variable. I will take as my example an actual study of changes in children’s stress levels as a result of the creation of a new airport.
How are multiple comparisons with repeated measures different?
However, the arithmetic is no different is we compare (Mean1 + Mean2 + Mean3)/3 with (Mean4 + Mean5)/2. In other words, we can compare means of means. If you had two control groups and three treatment groups, that particular contrast might make a lot of sense.
How to compare two groups with multiple measurements?
In each group there are 3 people and some variable were measured with 3-4 repeats. As you can see there are two groups made of few individuals for which few repeated measurements were made. I would like to compare two groups using means calculated for individuals, not measure simple mean for the whole group.
When to use a repeated measures ANOVA in statistics?
A repeated measures ANOVA will not inform you where the differences between groups lie as it is an omnibus statistical test. The same would be true if you were investigating different conditions or treatments rather than time points, as used in this example.