BIC Bayes Factors multilevel model comparisons
Dear EJ and others,
We have run a longitudinal study trying to detect changes over time in several cognitive variables. We have done so using multilevel analyses including random effects for each individual in a frequentist manner. However, our main idea would actually be that the two groups that we compare in our study do not change differently over time. Therefore, a reviewer suggested that the addition of BIC BF comparing a model with/without a group interaction would be useful to "test the null hypotheses" being that the added value of an interaction term in our model is indeed not very much (referring to the Wagenmakers et al., 2007 paper on pvalue problems). This seems like a super easy, yet useful Bayesian addition in our paper, and a clear benefit is that anyone could understand what we've done.
However, I have gone back and forward in my thoughts on the BIC BF method, as it seems like a rather strict method to test if the interaction has any added value in the model. Would you think this would indeed be of added value or is it (too) biased in quantifying evidence for H0?
Thank you so much in advance.