EJ
About
- Username
- EJ
- Joined
- Visits
- 2,532
- Last Active
- Roles
- Member, Administrator, Moderator
Comments
-
Dear bemeev, In order to help you effectively please post the issue on our GitHub page (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/). This is also helpful for the team, as it creates a permanent record of the…
-
Hi HannaG, What is relevant for the BF is the relative predictive performance of H0 vs H1. Unless d=0, the value of d actually tells you nothing at all about the BF; what you need is d and n. Specifically, when you have a low d but high n, you can …
-
Is this helpful: https://jasp-stats.org/wp-content/uploads/2018/06/interaction_effect.gif
-
OK, we fixed this in the new version. We hope to release that today, or otherwise in the very near future. Thanks for bringing this up! Cheers, E.J.
-
Yes, that explanation sounds right. Sometimes there can also be discrepancies because the model is misspecified (e.g., data are not normally distributed). The more fundamental problem is that "interaction" is vague -- all non-additive patt…
-
Hi dustinfife In the model specification tool, you can select the variables you want to have interact, and drag both over to the box simultaneously. This will create the desired interaction term. Let me know whether it works. Cheers, E.J.
-
Dear E, Well, if there are .sav files on the OSF then you should be able to download them and subsequently open them in JASP. I assume your question is whether you can log in from JASP, locate the .sav files, and then read them in? I don't know whe…
-
Hi Boo, About the mean and variance: The data-generating process should be the same: it's OK for the sample estimates to fluctuate. With respect to multiple replications, I think it is conceptually most strong to compute the Replication BF separat…
-
Dear Ajestudillo, Your interpretation is right (as long as, when you speak of one hypothesis being "favored", you mean to say that the data are more likely under that hypothesis than under the other). And yes,. I always transform the BFs …
-
Yes, that's correct, but note that for this approach to work you'd have to assume that other parameters (means and variances) are the same across experiments. If that's not the case, you could simply run a first analysis, get the posterior for effec…
-
Dear Harrison, I think you've posted this in the wrong subforum. This is the one on BayesFactor and JASP! Cheers, E.J.
-
Dear J, There are various explanations. 1. As you can see from the BIC equation, the (implicit, in most cases) penalty for complexity involves N. Basically, with high N the predictions from H1 become more ambitious: if there is a true effect, with…
-
Also, and in general for cases such as these, we can help you out but our programming team will probably need some more information. In order to help you effectively please post the issue on our GitHub page (for details see https://jasp-stats.org/2…
-
Dear Boo, Are you using a t-test? If so, you could take a look at the following two papers: 1. Informed t-test (https://arxiv.org/abs/1704.02479) 2. Replication Bayes factors (https://psyarxiv.com/u8m2s/) Cheers, E.J.
-
Sorry to hear that, but glad it's fixed now
-
For a correlation between ordinal data it is generally recommended to use Spearman's rho or Kendall's tau. If you view some of the video's on our YouTube channel you might get a better idea on how to do this. Cheers, E.J.
-
Dear rhea, Thanks for reporting this. We can help you out but our programming team will need some more information. In order to help you effectively please post the issue on our GitHub page (for details see https://jasp-stats.org/2018/03/29/reques…
-
Hi Rhea, Can you say a bit more about the structure of your data and the questions you wish to answer? Cheers, E.J.
-
I mean a statement that says something like "Warning: the prior structure is appropriate only for continuous predictors"
-
This must be due to rounding, in which case it is best to use the BFs. (I'll double check, thanks for bringing this up). Cheers, E.J.
-
Off the top of my head, I would assume that the "sync" option should do the trick. If this does not work for you, please let us know on our GitHub page and our programming team is at your disposal. [BTW, thanks for bringing these issues to…
-
Please post this issue on our GitHub page, because it clearly is a bug. See https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/
-
This is for the Bayesian regression analysis, I assume? We've become a bit more strict on what variables can be entered as predictors. The prior structure that is put on the regression coefficients assumes that the variable values are not factors. W…
-
Dear JLeborgne, You could start by looking at the examples discussed in this paper: https://link.springer.com/article/10.3758/s13423-017-1323-7 Cheers, E.J.
-
That's strange. I hope you can post this issue on our GitHub page -- this way the programmers immediately see it and can respond effectively. For details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/
-
I don't have any ideas straight off the bat, but this is a typical issue we'd like to see posted on our GitHub page. This way the programming team can assist you effectively, and hopefully make life easier for other users who will otherwise encounte…
-
Hi Rachellql, The results in the Bayesian analysis change from one run to the next because they are obtained using a numerical approximation routine. Under Advanced Options you can increase the samples that the routine draws in order to obtain a mo…
-
Yeah, well, with these kinds of nonstandard data I would recommend a more complex analysis. First of all, your data are binomial on the subject level, as you mention. Second, the effect of delay will be to reduce performance, so there's an ordinal …
-
Not yet, but we are working to revamp Bayesian ANOVA to do just these sorts of things. E.J.
-
Hi Rick, I'm not sure whether this can be accomplished (my personal probability is lower than 5%). If Richard does not reply, you could DM him; if you would you can post the answer here. Cheers, E.J.