Is there any downside to using a repeated-measure ANOVA when you have a within-subjects 2-level factor where 1 level is represented by more trials than the other. I know in ANOVA we are comparing means, but does the the tighter standard error on the one level throw off the ANOVA? For example, if my IV is stimulus congruency and I present congruent trials 80 times while only presenting incongruent trials 20 times, can I just run the the ANOVA including all of the trials or should I run the analysis by selecting the same amount of trials for each factor level (i.e. select only 20 of the congruent trials to pair with the 20 incongruent trials)? If this is OK for a strictly within-subjects design, does it cause problems if a balanced or unbalanced between-subjects variable is added to the design?
I think there are ways to do unbalanced repeated measures ANOVA; maybe look at the books "Variance Components" by Searle, Casella, and McCulloch, and "Components of Variance" by Cox and Solomon. It also seems to be in some books about multilevel / hierarchical / mixed effect models (e.g. Raudenbush and Bryk), where it is treated as a special case of a hierarchical / multilevel / mixed effect model.
I'm not sure what programs support unbalanced repeated measures anova ("Unbalanced designs create special difficulties for the analysis of variance."), but most statistical programs support mixed effect models. Is there any reason that would be insufficient?