How to interpret interaction in bayesian linar regression [jasp 08.1.2]
I am very new to Bayesian analyses, I saw a presenation about JASP in a conference a few years ago and since then I have used JASP to recheck some of my results using Bayesian analyses (just to be sure).
I am now ready to make the next move, and try to actaully publish a manuscript using only JASP results for Bayesian analyses. However, as I am quite new to this way of doing analyses, I want to make sure I interpret the results in an appropriate manner.
As an example, I put some results here [example2.html].
I would now interpret this as follows:
Model without interaction / model with interaction: BF18.23, indicating strong evidence _against _ the inclusion of the interaction.
Then when I compare do: both main effects / only gender: BF2.77, indicating anecdotal evidence, meaning that based on the current model specifications and data I cannot determine whether or not RA may predict stress.
Could you let me know whether this interpretation is correct?
Second, let's assume for a minute that the BF's were actually reversed for the model with vs. without interaction (i.e., there would indeed be evidence to suggest that the model with interaction is more likely).
How would I then go about interpreting this interaction effect further? Could I just make a plot?