EJ
About
- Username
- EJ
- Joined
- Visits
- 2,532
- Last Active
- Roles
- Member, Administrator, Moderator
Comments
-
There's an entire statistical field called "objective Bayes", and their goal is to pick priors that fulfill a number of general desiderata and are fit for use across a wide range of situations. In my experience, within the limits of reason…
-
We have these numbers for some analyses (such as the t-test: both in tables and in figures). But for others it seems to be missing; you've already made a feature request on GitHub? Cheers, E.J.
-
I'd report report the entire model comparison table. Yes you can test the two main effects model against the model that also includes the interaction. It seems to me that this is the most intuitive analysis. Maybe the table will look more compelling…
-
Hi CHei & thanks Aram, You are basically on the right track with all your remarks. 1. Yes you can use the analysis with all sample sizes. 2. I would stick to the default prior. 3. If you select "compare to best model", the resulting …
-
Hi Ivan, It's a little hard to say without the data (descriptives). I could assist more easily if the analysis were done in JASP, but Richard is the expert anyway so I'll bring this post to his attention. I'd be pretty impressed if he knows what's …
-
Hi Kevin. The null is always the absence of an effect (i.e., a point null). In one of the next blog posts on BayesianSpectacles.org, I will explain how you can obtain the test for direction from two directed tests against the point null. EJ
-
The new version (hopefully out today!) will change "dependent" to the label of the DV. Right now you cannot change the axis end points. We are working to make that possible. If you want to up it on the priority list, please make it an issu…
-
Hmm I think we have been focusing on other improvements (new version coming out soon). This is actually more of an issue for our GitHub pages -- perhaps you can post the issue there? This way the programmers will see it and can take the appropriate …
-
Hi Christos, Definitely; we probably will roll that out when we do MANOVA too. If you want to keep the pressure on you can add a GitHub feature request so that this moves up our priority list. Cheers, E.J.
-
That's a good BayesFactor question! For a speedy Email, you might want to Email Richard directly (feel free to post his response here, of Richard agrees). Cheers, E.J.
-
Hi Aram, 1. As far as Bayesian analyses are concerned, we are trying to keep BayesFactors and JASP in sync. 2. There is a confusion in the literature about classical NHST and BFs. My perspective is that p-values are not to be recommended (but you mi…
-
If you add group & time as nuisance, then you are comparing the full model to the two-main effects model. This yields BF=6.25 in favor of adding the interaction. So that's a little better than "no support"
-
That level of evidence is almost completely nondiagnostic. If you have strong reasons to include the two main effects then you can focus on the two main effect model compared to the full model that also includes in the interaction (this is the same …
-
The model with the interaction is the best. To see by how much, it is easiest to select "compare to best model". Cheers, E.J.
-
Hi Francesco, Great example. You summarize the results well. So if you already have the main effects in there, there is some evidence for including the interaction. But at the same time, the null model beats all of the other models. As always, I re…
-
There is Bayesian Design Analysis. See https://osf.io/d4dcu/ E.J.
-
Hi Larry, The RM ANOVA requires numerical approximation. When you increase the number of samples (i.e., draws from the posterior) you increase the quality of the numerical approximation. The downside is that the procedure then takes a little longer…
-
Hi Francesco, You are reporting the BF, which, as you indicate, express the relative likelihood of the data under the models at hand. So that means that you can eliminate the text "assuming that H0 and H1 are equally likely" -- the analys…
-
Sound good, thanks for the paper! Cheers, E.J.
-
Hi Nicolas, The "Baws Factors" should be in the next version of JASP; they are computed by neglecting certain models. So the procedure is the same, but what differs is the set of models that is averaged over. E.J.
-
Well the tests are ready, we need to finish up the paper and include them. It is a big of work, but when we have the paper submitted you are welcome to the code so you could run it before it's implemented in JASP
-
Hi AnaF, We haven't implemented MANCOVA yet, but otherwise you could deal with your mean connectivity DV one variable at a time and run three ANCOVAs. This is only a suggestion after a brief consideration of the problem, there may be other methods …
-
I will leave this one for Richard to address.
-
Hi Nicolas, Generally I'd advocate reporting both analyses. The BF inclusion formula is similar to BF10 in that it quantifies the change from prior odds to posterior odds. But in the BF inclusion case, those odds are computed for all the models tha…
-
Dear Aennkin, JASP respects the principle of marginality, and this means that when the model includes an interaction term, it also includes all of the constituent main effects. For more background and references on this principle see Part II here (…
-
Dear Cesco, It appears we have been experiencing some problems with our downloads. We are already looking into the matter, but I'll nevetheless forward your Email. The current discussion of this issue is on the GitHub pages, so that the programming…
-
Rouder et al., 2015 (the JMP paper) is the key reference on Bayesian repeated measures ANOVA.
-
Thanks for responding, Cesco -- I somehow missed this question
-
Hi Stephen, This is a while ago. There is this paper: Nathoo, F.S. and Masson, E.J. M. (2015), Bayesian Alternatives to Null-Hypothesis Significance Testing for Repeated Measures Designs. Journal of Mathematical Psychology, http://dx.doi.org/10.10…
-
Possibly. I'm having a student write a manual entry on this analysis (and possibly extending it to include other methods as well)