EJ
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- EJ
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Comments
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Hi Friedrich This is under development and we should have it out in the next release Cheers, E.J.
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Hi Lior, A change from 2.5 to 12, based on about 80 additional participants, is not such a large jump. What is remarkable about this sequential plot is not the "jump", but rather the fact that up to 420 or so participants you have such sta…
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Dear Ferengki, We offer various normality tests (depending on the analysis; more importantly, we offer QQ plots); reliability tests are in the menu (for the moment they are under Descriptives -> Reliability Analysis). What exactly do you mean wit…
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Hi JohnAtl, We are currently working on including linear mixed models, but we're not quite there yet. With 90 trials (I assume 45 per condition), I would be amazed if your conclusions depend on the unequal data sizes. So I am going to be rebellious …
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Hi Anna, If you have a massive BF you can tolerate a high error rate. Does it matter for your conclusions whether the BF is one trillion or two trillion in favor of the alternative hypothesis? Probably not. BFs this high will have a lot of action in…
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interesting problem!
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This also works for the correlation test and the AB test. E.J.
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Hi Lior, It would be informative if you could show the sequential plot so we can assess the size of the jump; Very large jumps suggest that the observations that gave rise to it may be an outlier An evidential threshold of 3 is not a lot. Under equa…
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I'll ask the team member who knows more about this... E.J.
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Dear Narcilili, Yes, that's an approximation error. Note that your method works because the prior model probabilities are uniform. In general, the BF column compares the model in the top row ("0") to each of the models in the rows below (&…
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Hi Vicente, I'm not an expert on frequentist methods for multiplicity correction. Have you Googled this? It does seem reasonable -- a little ad-hoc, but then again, it is a frequentist procedure :-) and there may be a rationale. Cheers, E.J.
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Dear Niklas, In general, confidence intervals are numerically close to credible intervals (with a flatish prior or relatively informative data, for most models). But conceptually the procedures are very far apart. See for instance https://learnbayes…
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I'll ask our expert...
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Hi Anna, We have this under development at the moment! Will take a while but we are really happy with the analytical result. E.J.
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Hi B, If your job is data management, I would stick to Excel (or OpenOffice Calc). JASP has some data editing functionality (compute columns, filtering, etc) but it is not a full-fledge editor; JASP's main strength is statistical inference. Cheers, …
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Hi KenC, We try to avoid MCMC as much as we can. For many of our tests, we have analytic solutions, or only require a one-dimensional integral. When we do use numerical methods, the error percentage gives an indication of the accuracy of the approxi…
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Dear Tali, Hmm, the complication here is that "N" is involved both in N-C and in IC-N. So it seems to me that you would like some kind of contrast that tests the symmetric staircase pattern against an asymmetric staircase pattern. Julia Ha…
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Dear Philip, I guess the priors are implied. BIC is an approximation to the Bayes factor under a specific prior assumption for the model parameters (I believe a multivariate normal mean centered at the MLE but with unit information to set the prior…
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Update: if you present the R code, Richard can look with the BayesFactor package at what you did! Cheers, E.J.
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I'll ask our EFA expert. Sorry for the tardy response, this slipped through the cracks...more to follow Cheers, E.J.
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I'm not aware of a Gates' delta. It's the population mean divided by the population sd, so the population version of Cohen's d (i.e., Cohen's delta). E.J.
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Hi Niklas, I have the strong sense that something went wrong with the model specification, but I'll gladly pass this on to our ANOVA expert...more to follow. Cheers, E.J.
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I've attended the experts to your question...
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Dear BrittJane, In general, I think commenting is the more responsible option. This is part of statistics that is still in flux. At the same time, it is a nice robustness check. Cheers, E.J.
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Hi Lior, Hmm I'm not so sure. You are right about the 28% corresponding to a BF change of 4, but I would have guessed it indicates that the BF10 can fluctuate from approximately 12 to 16. I'll double-check... Cheers, E.J.
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Correct. The sample effect is slightly positive, so changing to a one-sided positive hypothesis is helpful for H1, but changing to a one-sided negative hypothesis is harmful for H1 (because the sample effect is in the opposite direction of what H- p…
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Hi Niklas, Well, I have not Googled this, but it seems to me that when you add a covariate, you are changing the main effects. Suppose you test IQ scores of dogs to those of cats, but you have reason to believe that "number of pets per day"…
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Dear Guillaume, The "a" means it is an alphabetical (text) variable. So at least one of the entries has text, and this is also why you can't change the type -- JASP cannot guess what number the string should have. Cheers, E.J.
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Hi scd21, A useful thesis on the topic (by one of the members of the JASP team) is here: https://psyarxiv.com/s56mk I would report the entire table. The uncorrected BFs are just the regular t-tests; the penalty for conducting multiple tests comes in…
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Dear Niklas, This is strange behavior! Apparently lVisC is in almost all models with appreciable posterior probability, but its model-averaged posterior includes zero. Under usual circumstances, this would not happen, so I'm going to guess that the …