RM ANCOVA - calculating the bayes factor
A reviewer is asking us to perform bayesian analyses to quantify strength of evidence for the null. I am new to Bayesian statistics, but luckily JASP is really straightforward! I do have some questions however. I performed a bayesian RM ANCOVA, with a within subjects factor (under the factor 'time', with 2 levels), a between subjects factor (group randomisation), and a covariate (RCQ).
My first question is, if you want to do the analyses to evaluate the strength of evidence for the null, what bayes factor do you select in the analyses; B10 or B01? For the analyses I did, I selected the B01 since I believe this gives results on how much better the data predicts the null hypothesis compared to the alternative hypothesis?
Then I wanted to calculate the bayes factor. I found an article (Bayes like a Baws: Interpreting Bayesian Repeated Measures in JASP, by Sebatiaan Mathôt) on how to calculate the Bayes (or Baws) factor when you have multiple interaction terms, but I am wondering if this is the correct way to calculate the factor.
I've added the results table below. Based on the aforementioned article, I summed all P(M|data) of the models with the interaction term and divided that by the summed P(M|data) of the 'corresponding' models without the interaction term. So in the example below I performed the following calculation:
Baws factor ((0.055 + 0.017) / ( 0.178 + 0.050)) = 0.32
But is this the correct calculation, or do I need to do it differently?