I have two different groups, Treatment A vs Treatment B, with measurements for each individual in four different time points. That is a 2 x 4 design. The dependent variable is a discrete scale from 1 to 4 where 1 is better than 4. The fact that it is a discrete scale means that computing means and standard deviations doesn't make sense (it can't be 2.7 for instance).
Thus, the problem I have is to understand if I could use a Mixed-Factorial ANOVA to analyze whether there are differences between groups and across individuals. A colleague has suggested me to use an Ordinal Logistic Mixed Model which seems a legit approach as well. However, given the nature of the data it looks to me that a Mixed-Factorial ANOVA would be perfectly fine (it is a numeric and ordered scale, although the scale has few levels).
Furthermore, it seems that there aren't good implementations of Mixed Ordered Logit in standard software like R or SPSS for instance.
I would really appreciate if you could give me your insights about how to tackle this problem and which model is better.
ordinal
package, it has an ANOVA however I don't know how you are going to add random effects. $\endgroup$ordinal
package in R is relatively easy to use, and has pretty much everything one could ask for. It does handle mixed-effects designs. It also allows you to specify if the ordered categories should be assumed to equidistant, symmetrical, or neither. Group separations can be determined with theemmeans
package. One thing to be aware of: (last I checked) absolutely don't usecar::Anova
with models from this package unless you use it with theRVAideMemoire
package. $\endgroup$