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I have trial level data from a study in which participants responded to a series of stimuli. I have a predictor of interest. For the sake of this example, let's call it the size of the stimuli.

There is a null effect of size on both reaction time and accuracy. So I was asked to compute the Bayes Factor for that predictor.

I don't have a clear way to choose an informed prior, and so I am computing the Bayes Factor across a range of priors. I am using the package rstanarm in R for this.

In the analyses of reaction time, I compute the Bayes Factor for size across a range of priors. In particular, normal distributions centred at 0 with sds ranging from 0.10 to 5. I do this using the stan_lmer() function.

I want to do the same for accuracy, analyzed using stan_glmer(family = "binomial"). My question is if it makes sense to use the same range of priors as I did in the analysis of reaction time?

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  • $\begingroup$ Do you want to specify informative prior? I see that, e.g., N(0, 0.10) or even N(0, 5) are informative as the variance is small. For your question, I do not think that it causes any problems when using the same range of priors. However, you should keep in mind that one prior might be informative for a model but might be vague or weekly informative for the other model. $\endgroup$ – TrungDung Jan 12 at 8:36

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