Bayesian RM ANOVA (mixed factorial design) with small sample sizes
In my experiment, a reaction time task with 2 conditions, I have tested 3 groups (N=22, N=23, N=21).
I am using a mixed factorial design and have run a Bayesian Repeated Measures ANOVA to find out whether performance in the task depends on condition and group (Repeated Measures Cells = Condition 1, Condition 2; Between-Subject Factors = Group).
My questions are:
1) Can I use this analysis with such sample sizes?
2) Right now, I have kept the default prior of 0.5 for "r scale fixed effects" as there isn’t previous research in this area using this task and/or conditions . I assume that by using the default prior, JASP assumes that I'm testing for medium-size effects of 'condition' and 'group'?
Here is the main output table from JASP:
3) Am I right to assume that there is strong evidence for a main effect of condition, but that the BF of around 1 for group can be regarded as noise / data are insensitive? Furthermore, there is no evidence for or against an interaction (for interaction = 0.9; against = 1.11)? Given that the main effect of group does not add much improvement compared to the null model, I think I need to go with the model with just 'condition' as a main effect. However, when I select "Compare to best model", then there is the main effects model (condition + group) on the very top. Does that mean the best model is actually the one with both main effects despite the fact that 'group' doesn't add much improvement?
Here is the "Compare to best model" table:
Thanks a lot for any advice!