Sample Size Determination - BF10 or BFinclusion
Hi everyone!
We are working on a study where we plan to use a Bayesian procedure to determine our sample size. Specifically, we intend to stop data collection when the Bayes Factor (BF) exceeds 5.
We have two independent variables, each predicting a different main effect, with no interaction. We wonder whether it is more appropriate to look at BF10 or BFinclusion in this case.
If we understood correctly:
- BF10 reflects the Bayes Factor comparing a specific model to the Null model.
- BFinclusion reflects the evidence for including a specific factor (or main effect) across all models compared to the models excluding that factor.
Given the focus on our two main effects, would it be more effective to base our sample size determination on BF10 or BFinclusion?
All help will be greatly appreciated!
Imbar
Comments
Dear Imbar,
Well this depends. There is something to be said for both. But if the interest is really in each of the two main effects separately, I would be tempted to monitor BF10 for the model with only main effect A versus the null model, and BF10 for the model with only main effect B versus the null model. Of course this will complicate your stopping rule, but I guess you could continue until both BF10's are greater then 5 (say)
EJ