we have ran an experiment where we compared three interaction techniques for a 3d docking task. So we had two factors: the aforementioned technique type and a factor representing the direction translation (i.e.: if participants had to move an object that appeared close to their viewpoint and move it in depth or vice versa). Each trial was repeated 5 times.
Some of those trials were skipped because of the difficulty. If I run a regular repeated measures anova then each participant that even a single missing value will be dropped from the analysis. This means that I'd have to remove more than half of the participants. By reading around it seems I can use a linear mixed model instead.
My doubt is, can I use a mixed model for this type of situation? I am confused as to whether mixed models are only relevant when you have a between-subjects factor such as the classic treatment/control groups. In my case every participant was subjected to the same conditions. There were no between-subjects factors.
I ran the mixed model analysis by using Technique, Direction and Repetition as Repeated, the ID of each participant as the subject and technique and direction as fixed factors.
Are my assumptions correct or did I do a terrible mistake?
If so, what alternatives do I have when dealing with missing values?