Interpretting main effect * covariate interactions in Bayesian RM-ANOVA.
Hi, I am a new JASP user (by the way, really enjoying it so far, especially on my more archaic hardware). I have a question regarding the JASP output for Bayesian RM-ANOVAs.
My design involves two within-subject variables (A and 
and a covariates (c) within a classic RM-ANOVA the default model specification leads to the following output:
(Main effects and interactions with covariates): A, Ac, B, Bc, AB, AB*c
The same specification within the Bayesian variant leads to A + c and A + B + A* c rather than simply A + Ac? This implies the main effect for both A and covariate c, right? I have to explicitly ask for the interaction Ac? And since it is a covariate would I be looking to compare the models A to A + Ac or to A + c + Ac?
Thank you!
Comments
Hi Dion,
Can you give us some more detail regarding the hypothesis? And maybe you can add a .jasp file or some output table? (if you aren't ready to share your results with the world just create fake labels and/or use half of the data set).
Cheers,
E.J.
Hey E.J.,
Thanks for the quick response. I've attached a JASP file (sorry for the unclear labels, I'm borrowing the data). The first analysis in the JASP file is a standard RM-ANOVA, the second is the Bayesian. The hypothesis would be that the data supports the interaction Information * NVS.
Thank you,
Hi Dion,
I can't see the file -- maybe I'm looking in the wrong spot. You can Email it to me directly, or put it on the OSF and send the link.
Cheers,
E.J.