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# Repeated Measures Bayesian ANOVA

Hello!

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!

• Dear CCtt,

The first entry compares the model against itself, so it is always 1.00. Looking at the table quickly, it seems you have some evidence for the null -- highly ambiguous in the case of Test Delay, but more impressive for the other factors.

Also, note that we recently proposed a different default for repeated measures ANOVA. For details see this blogpost (and a reference to a preprint): https://jasp-stats.org/2022/07/29/bayesian-repeated-measures-anova-an-updated-methodology-implemented-in-jasp/

Cheers,

E.J.

• Thank you EJ!

I did not know about the blogpost, really interesting in terms of analyzing