EJ
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* yes you should be able to add another column (variable); just double-click the data and use your preferred spreadsheet editor, press save, and the JASP file ought to be updated. * wrt replicating your analyses on a different data set, have you tri…
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Completely correct! E.J.
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Hi MSB, The correction for multiplicity is usually always through the prior odds. This is even the case in parameter estimation, where you have to spread out your prior mass across more options (imagine a discrete parameter space with an increasing …
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Well I am prodding people, as you are prodding me. But it will be more effective if you prod on our GitHub page, because then everybody in the team gets to see it
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It means that the data have changed the odds in favor of models that include the predictor by a factor of 19. When BFincl = .30, you can interpret this as BFexcl = 1/.30 = 3.33, some change in the odds but nothing to get worked up about. E.J.
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Hi MSB, * The "U" in BF10,U stands for "uncorrected" (the upcoming version will mention this explicitly in the table footnote) -- so yes, they are the same. * Yes, posterior odds = prior odd * BF * Yes, the correction for multip…
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Hi Jonas, In principle, the question of the presence of an effect is independent of the strength/size of that effect. I would argue that the prior odds can still be based on outcomes for earlier experiments, but then in terms of Bayes factors that …
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Hi Jonas, It is indeed a little confusing, because "prior" means different things. On the level of models, the prior p(H1) means "what is the relative plausibility of H1?"; on the level of parameters within a model it means &qu…
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BF inclusion is similar to a regular BF except that it compares two classes of models, one class with the factor of interest and one without. So you start with prior probabilities on the models; comparing the prior probability with vs without the fa…
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My advice: 1. Have an online appendix in which you present the annotated JASP output (and/or put the annotated .jasp file on the OSF). 2. In the main text, for ANOVA, use "best model on top" and describe the column of BF01s (this is genera…
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Hi Jonas, With the BF in hand (say 3 in favor of the null), you need to determine your own prior model odds (say 3 in favor of the alternative). Multiplying these numbers yield the posterior odds (in this case, 3 * 1/3 = 1). Then you can transform …
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Ah, so with the SD's weighted by N-1 instead of N (https://stats.stackexchange.com/questions/66956/whats-the-difference-between-hedges-g-and-cohens-d)? I think that this kind of bias-correction is a frequentist concern, as least as far as inference…
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I'll post this on the GutHub page. E.J.
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Hi Alon, This is more the expertise of Richard Morey, so I'll attend him to this, as well as Quentin Gronau. For what it is worth, I think you are right. The Bayesian ANOVA in JASP is really a Bayesian linear mixed model. Perhaps there is a post on…
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Hi G, A few remarks: 1. The sequential plot stays at BF=1 until about the 10th participant. I assume the first 10 were all from the same condition? Might be good to state explicitly (JASP assumes the participant came in in the order of the rows). 2…
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We'll clarify this in the table heading for the next release; I've made it an issue on our GitHub page.
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U stands for "uncorrected", so it does not include the post-hoc correction term that comes from the prior model probability.
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Thanks for not letting this go. I'll Email the student to request an update. Cheers, E.J.
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Dear xxPolyG, Yes, filtering is a common chore and although you can double-click the data and do your filtering in, say, Calc or Excel, this is somewhat tedious. So we are definitely going to add filtering capacity to JASP. When exactly this will h…
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Hi MMA, Well, the posterior distribution is not exactly a Gaussian (it is close, but for low-N it will have thicker tails). But from the median (=the mean if the distribution is Gaussian) and the 95% CI you can compute the SD. Cheers, E.J.
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Hi Larry, 1. Yes, the priors are all on delta, the standardized effect size (and sd indicates the uncertainty) 2. We are in the process of eliciting a number of other informed prior distributions. 3. For medium-large and large: interesting question…
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Dear MMA, What you are looking for is a "replication Bayes factor" (Verhagen & Wagenmakers, 2014) -- in other words, the change in evidence brought about by the data from the replication, having updated the prior distribution based on…
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From a Sacha Email: "The module doesn't recognize ordered variables yet." We will work on this as soon as Sacha returns from his vacation. It should not be too difficult. Cheers, E.J.
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Hi Lucas, I will take this up with our Lavaan expert, Sacha Epskamp! Cheers, E.J.
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Hi Shuang, Maybe the term "nuisance" is confusing. In the next version of JASP, we are replacing it with "include in null model" or something similar. The question when to do this is a substantive one, and the considerations are…
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Hi Ivan, Yes, you are right, it's 1.543. The choice of whether or not to look at the other effects depends on theoretical considerations I guess. The BF of 2.7 is obtained by comparing prior odds to posterior odds, so this includes the other models…
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Hi jjarryjasp, In later versions, we entered the text "dependent" on the y-axis. However, that is clearly not much better. The upcoming version should have the name of the actual dependent variable there (something you can adjust by editi…
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Hi Scott, For the t-tests, JASP now has "informed priors" that allow great flexibility in their specification. In particular, they can be centered away from zero (https://arxiv.org/abs/1704.02479). We are working to make the ANOVA and ANCO…
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We should include that information in a footnote. The "U" stands for "uncorrected", so it is the BF from a t-test on the two pertinent conditions. Cheers, E.J.
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The BF inclusion averages across all models under consideration. It looks at all models that include the factor of interest, and pits them against all models that exclude that factor. You then look at the change from prior inclusion odds (summed pri…