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## Comments

Hi Dirk,

There are two ways to do this. First, there is the Verhagen & Wagenmakers method, where you "simply" use the posterior from the first experiment as a prior for the second experiment. Unfortunately, the updating and specification process are non-trivial for ANOVA models. Second, you can use the Ly method and obtain a BF by adding the data together. This will yield the same result as updating one batch at a time. The Ly method does assume that the data are exchangeable, so the replication is as exact as can be. If you Google "replication Bayes factor" on my website you should find the relevant papers.

Cheers,

E.J.

Hi Erik Jan,

Thank you for the very fast reply. This means I might be able to resubmit before Christmas . I read your paper and this is where I got a bit confused. These are the Bayes factors that I obtained. Note that the factor always denotes how much evidence there is against the interaction being a better model than the two main effects model:

Exp 1: BF10 = 0.41 -> 2.44

Exp 2: BF10 = 0.281 -> 3.56

EXP 1+2: BF10 = 0.194 -> 5.15

If I understand your paper correct I can obtain the bayes factor of experiment 2 where experiment1 is included as a prior as follows: 5.15/2.44 = 2.11.

So if I did everything correct, this means that the bayes factor now becomes smaller despite including exp 1 as a prior than it is without a prior. Maybe I am misunderstanding something but this feels a bit counterintuitive to me, because exp 1, although not very reliable, already demonstrated some evidence against the interaction

Thanks again,

Dirk

Hi Dirk,

Exp 1 provided some evidence against the interaction; consequently, in the model that includes the interaction, the corresponding posterior distribution will have more mass near zero than the prior did. In other words, the interaction --if it exists-- is now known to be relatively small. Effectively, after seeing the data from Exp 1, the interaction model now makes predictions that are relatively similar to the two-main-effects model. When models start to make similar predictions, the evidence decreases.

Cheers,

E.J.

Hi Erik,

Thanks this is very clear. Enjoy the Christmas break!

Cheers,

Dirk

Hello everyone,

We'd like to use the results of the Repeated measures ANOVA of a block in the prior of the repeated measures ANOVA of another block. The second block is the same than the first one but it is based on a different set of stimuli : same participants, same dependent variables, same protocol.

JASP is already really helpful for the analysis of our data especially because it provides bayesian versions of commonly used frequentist tools (like the ANOVA) and is therefore easier to introduce to our 'p-value fans' readers in Human Interface Interaction. In our quantitative study, we would love to exploit the bayesian framework at ts best by injecting a bit of knowledge from block 1 in the analysis of block 2. After some intensive searching, we found no practical way to parameterize the second ANOVA to take into account this a priori knowledge.

It seems that it requires too much Bayesian skills and JASP is not yet implemented with these tools. So is it really a dead end for an informative prior ?

Thank you,

Olivier

Hi Olivier,

There actually exists a workaround, based on transitivity. See https://psyarxiv.com/u8m2s/

Cheers,

E.J.

Thank you Eric Jan for your quick answer, I'll read your paper with great attention.

It's a bit off-topic here, but regarding the challenge to publish bayesian stats in a rather frequentist community, I read a smooth way to report Bayesian analysis as an "expansion" of traditional stats reports. P-value is reported after an ANOVA let's say, and right under, lies the bayesian approach. The ensuing discussion is actually based on the later analysis...

After the reading of the paper and its examples (that are very clear and informative), I think my original question was a bit misleading.

Our purpose is less "to quantify the evidence from the data of a direct replication attempt given data already acquired form an original study" than accumulating the results of 2 similar experimental blocks to produce a more refined/robust conclusion about the measured effect. In my non specialist view, these 2 goals appear to be different.

Another way to see my question, is that I'm looking for an alternative solution (if any) to a mixed model for repeated measure or an ANOVA with the averaged values of each condition from the 2 blocks.

Anyway, I do thank you and your colleagues for the pedagogy you spread about bayesian stats, it is really helpful to me. Here my question seems too specific (and my statistic background too messy) for a definitve answer on a forum. ?