EJ
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- EJ
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Yes, that's the one. The go-to reference is the Jamil paper (open access, but here's the sci-hub link anyway: http://sci-hub.cc/10.3758/s13428-016-0739-8) E.J.
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Hi Eduard, This is really a question for Richard, who is in charge of the "BayesFactor" component of this Forum. I'll specifically attend him to your question. Cheers, E.J.
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Hi Peter, "Given that the summary stats module can also handle F-statistics from ANOVA analyses" -- Can it? " the ANOVA option in the summary stats module" -- but there isn't one. Regardless, if you have the t-statistic, there i…
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I don't think it's a problem at all. Did you see this paper by Etz and Lakens about not every study needing to provide picture-perfect results? Besides, I think you should only be applauded for being transparent. And my guess is that this will happe…
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Hi Eduard, I'm not sure what tests would be most effected by a violation of assumptions. It feels a little like comparing apples and oranges, but perhaps it can be done. Yes I meant that blog post -- or the next one, http://bayesfactor.blogspot.nl/2…
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Hi Eduard, I assume the interest is in the interaction? In general BFs are less enthusiastic because they look at both sides of the coin --H0 and H1-- instead of just focusing on H0. Indeed, multiplying BFs is not allowed, as it uses the prior agai…
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Hi Paul, This sounds like a fantastic data set. In, say, Bayesian ANOVA it is not an issue if you have more participants in condition A than in condition B, but when we take the mean of the participants then it is an issue that some means are based…
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7e-8 is a really small number! E.J.
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The reason for in the increased support in the inclusion method may be due to the fact that some models (like the null model, or the model with only one factor) perform very poorly. I am not so sure that this effect is of interest to you. Cheers E.J.
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Hi Markus, When you have few models, I am in favor of including the entire tables, perhaps as a supplement. Cheers, E.J.
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I was a bit caught up and did not respond too quickly. I have now responded to a similar question you just asked. Sorry about my tardiness. I try to answer quickly but sometimes diapers and deadlines get in the way. Cheers, E.J.
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Hi Markus, Well, I would just be transparent. Sometimes you do get these conflicts and, in my opinion, they urge caution. If you had a specific contrast in mind then you ought to test that (I believe Richard has a blog post showing how this can be …
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Hi Markus, In JASP we use the marginality principle, which means that when an interaction is present, so are the constituent main effects. The most straightforward test is to compare the full model against the model with only the main effects. When…
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Hi Sau-Chin, If you want to test whether the effect increases, I think I'd suggest a linear contrast or perhaps just an ordinal constraint. I think Richard once wrote a blogpost on how to do that with the BayesFactor R package. Basically you take th…
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Yes, the default settings are meant to serve as an "objective" specification that can be used across a wide range of different scenarios. Cheers, E.J.
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Hi Pieter, With respect to assigning probability to a spike: I don't have an issue with it, for the following reasons: 1. What the BF assesses is not whether H0 is true. The BF compares the predictive performance of two models (H0 and H1 here). Bot…
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Hi Markus, Some analyses require a numerical approximation. The quality of the approximation is indicated in the output tables as a "%error". If I'm not mistaken the current version of JASP allows you to improve the quality of the approxi…
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I'm not sure, we'll have to ask Richard. I'll forward this to him. E.J.
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In your final analysis, you did not test the model with the interaction only. JASP does not allow you to run such an analysis without including the constituent main effects. Instead, what the final analysis does is put the two main effects in as nui…
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Hi Tom. Sorry for the tardy reply. Here are some remarks that I hope will help: 1. You can either say the BF is 32 in favor of model A versus model B, or you can say that the BF is 1/32 = 0.03 in favor of model B versus model A. These statements ar…
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To chime in: 1. Yes, a narrower prior indicates more confidence. The prior distribution under H1 reflects your certainty about the value of the parameter assuming the effect exists. So the width does not speak to your certainty/belief that H1 is tru…
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Hi HannaG, That's a great suggestion, we could add this to the output. By the way, it is easiest to post such suggestions as "feature requests" on the GitHub page. You only need to set up an account once, and it makes it much easier to giv…
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Thanks! E.J.
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Yes, that would be correct. E.J.
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Yes. I would add that you used a default test (and specify whether it was one-sided or two-sided), and indicate the value of the parameter. For instance: "A two-sided Bayes factor.....under the default alternative hypothesis....conditions (i.e.…
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Hi Andy, I'll put this on the GitHub page and will get back to you. Cheers, E.J.
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Thanks, I'll pass it on to our team E.J.
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Thanks, I'll add this to the GitHub feature requests page! E.J.
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Hi Anne, Quentin Gronau has just done the math for the t-test, and we're looking to submit the paper soonish. For ANOVA this is more difficult, but I did just come across this paper that may be relevant....let me look it up: https://arxiv.org/abs/16…