Repeated Measures Bayesian ANOVA
I enjoyed your Bayes JASP workshop last year very much, thank you again for presenting this interesting approach. Since last year I am trying to use JASP and report Bayes factors in my papers as well.
I conducted a bayesian repeated measures ANOVA, but am insecure how to report it properly.
As a classical ANOVA showed evidence for the Null hypothesis, I calculated the BF01 accordingly (compare to best model). The Null Model seems to be fitting best here (see attached file).
However, I read in an article Keysers et al. (2020) that a BF around 1 means there is no evidence at al.
"If the Bayes factor calculated as ℒgroup/ℒnullis >1, there is evidence for the effect of group. If BF < 1, i.e., the null model outperforms the more complex group model, there is evidence for the absence of an effect of group. If BF ≈ 1 we have absence of evidence. This Bayes factor can be interpreted using the same bounds discussed in Fig. 2 and Extended Data Fig. 1."
As the Bayes Factor B01 is 1.000 in my analysis, is there no interpretation at all possible, neither for H0 nor for H1? I am simply not sure how to report this finding exactly in my paper. This would be my approach:
To ensure that our null hypothesis did not arise by chance, we performed a Bayesian repeated measure in favor of the null hypothesis. We found strong evidence in favor of the null hypothesis, as the null model was the best fitiing model (BF01 = 1.000).
Thank you so much!