Research Questions - predicting DVs will affect IVs.
3 predictor variables (IVs) - all categorical and ordinal. One could be continuous with lots of transforming, the other two are Likert Scale data.
4 outcome variables (DVs) - all categorical and ordinal Likert scale.
All variables are measured across 3 timepoints before, during and after.
The normal distribution was skewed, which I'm attempting to rectify by amending minimal outliers to the mean value. Is there a maximum number that is acceptable to alter by this method? Or minimum to create a normal distribution?
If the above is acceptable, is one-way ANOVA for the one IV that can be continuous the best option? Or best to run Chi-Square for each DV and IV and reduce categories into sets of 2 e.g. depressed/not depressed & healthy/not healthy? However, Andy Field book advises not to use Chi on repeated measures as each person, item, or entity should contribute to only one cell and my DV would be in two.
If a normal distribution is not acceptable as outlined in my previous paragraph, the Friedman test would measure one variable at the 3 timepoints, but not against each other, can I run Friedman first then any suggestions on how to see differences at timepoints?
Any suggestions wonderfully experienced academics, as I'm getting different suggestions from my supervisor and data support groups verses Andy Field's comment above?
Much appreciated
1- even if the data is not normal due to a general consensus that RM is robust to non-normality.
2- I do not recommend using Chi-Square test.
3- My recommendation is to use Three-way RM Multivariate models.
4- Number of Level = 3 Time, Between-Subjects Factor(s) = 3 Factor
Sharon Copsey https://stats.idre.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/
Most people use RM ANOVA in these situations, though there are good reasons to use an ordinal model such as ordinal logistic regression. As it is repeated measures this would require a multilevel ordinal logistic regression - which is quite a step up in complexity from RM ANOVA (so might be beyond what's reasonable for undergraduate or MSc work).
Neither chi-square nor Friedman are a good fit here in my view (Friedman throws away a lot of information as it is really an extension of the sign test to multiple groups).
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