EJ
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- EJ
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Comments
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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…
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Hi Markus, For the Cauchy, the prior width r equals the interquartile range. So if r=0.707, there is a 50% chance that the true value of effect size lies in the interval from -0.707 to +0.707. I encourage you to read the papers on Bayesian inferenc…
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Hi Tom, It's my turn to be lost. We have 2.936 x 10^12 for the full model and 9.154 x 10^10 for the main effects only model. This means that the support for the full model over the main effects only model is (2.936 x 10^12) / (9.154 x 10^10) = (2.…
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About that 2.6: "The model that receives the most support against the Null model is the two main eects model, Disgust + Fright. Adding the interaction decreases the degree of this support by a factor of 3.240=1.245 = 2.6. This is the Bayes fact…
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Hi Gabriel, Thanks. Yes we do have nonparametric tests (e.g., Mann-Whitney U, Wilcoxon, Spearman, Kendall), and we are working to include more. We are also working to develop and include Bayesian versions of these, and the latest JASP release inclu…
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Weird. Ah well, try this one: https://dl.dropboxusercontent.com/u/1018886/Temp/TheJaspBook.pdf
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It's in Part II here: https://osf.io/m6bi8/ E.J.
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You can look at Part II (https://osf.io/m6bi8/) for some examples. I think this is OK. But I would certainly plot the data! E.J.
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It's work in progress. In the meantime, you can check out Part II here: https://osf.io/m6bi8/ The first two chapters of the manual are here: https://osf.io/r73y9/ E.J.