EJ
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 EJ
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Could you post this issue on our GitHub page? This would help us a lot! (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/). Cheers, E.J.

Dear Bfzldh, If you post this as an issue on our GitHub page we can address it! (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/)). As far as we are concerned, the attractive feature of the GUI is not primarily dat…

Hi Jan, The effect is massive, so it is a plausible result; The ANOVA routine in JASP is taken from the BayesFactor package. What this package does is explained in a paper by Rouder et al. 2012 (Journal of Mathematical Psychology) and this one: http…

Hi Matue, Yes it is. We are in the process of developing Bayesian procedures to make these assessments (or lessen the importance of violating these assumptions, for instance through analyses based on ranks) E.J.

Hi Izymil, Well, there are so many papers that take a ttest to compare the mean of two groups concerning dependent measure X. In the case X is the variance, but that is fine, it could have been anything else. Cheers. E.J.

It is set so that it gives the same result as the default ttest in case of a oneway ANOVA with two levels. The reason that the 0.707 is not the same as the 0.5 is because the ANOVA is parameterized slightly differently. Yes, this is very confusing…

Dear Thalia, First, if you set the *model prior* to Uniform this assigns equal prior plausibility to each model (i.e., each unique combination of predictors). This is standard practice, but the problem is that, implicitly, this setting leads to a pr…

We haven't paid much attention to power (yet). As far as effect sizes are concerned, yes, we'll discuss this. Note that such feature requests can best be posted on our GitHub page (for details see https://jaspstats.org/2018/03/29/requestfeaturere…

This seems a little fishy. I am always wary of these kinds of shortcuts, or at the very least I'd like to see a comparison to a more principled way of doing things. We're going to implement contrasts at some point in the future though. E.J.

Yes, we are well aware of the issue. Currently we use the R defaults, but the user needs more control over these. The reason why we haven't acted yet is that we are aiming for a more comprehensive solution. However, because progress is slower than e…

We are very close to making it straightforward to add such functionality in the future. For now, you could post this issue on our GitHub page. This way your suggestion is not forgotten, is brought to the attention of others (who may support it) and …

I'll pass this on, but our GitHub issue list is alive and well... E.J.

Ah, I'm not sure, but I'll pass this on E.J.

Dear Agata, We are working to make the R code more easily accessible, and we are also completing a tutorial paper. The JASP functionality is based entirely on the BAS R package by Merlise Clyde! Cheers, E.J.

This is just a wild guess, but I think that BEST uses a tlikelihood, not a normal likelihood. This *may* explain the difference

Hi Sarah ANOVA is the same as linear regression when "treatment" (or whatever condition label you have) is a discrete factor. But the linear regression routine does need to realize that "treatment" is factor, not a continuous nu…

We don't have Bayesian polynomial contrasts in there just yet, but it is on our list. You can push us to do this by posting the issue on our GitHub page (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/). E.J.

Hi Kevin, Well, we don't have that in there just yet (although I like the idea of having a measure of association that is very general) but I guess most people would use linear regression and use polynomial predictors...so include both x, x^2 as pr…

Not yet! And I would definitely turn to time series analysis. The Fourier spectrum should be particularly helpful, I think. E.J.

Your calculator takes log to mean log10 (base 10), whereas we use "log" in R, which means "ln" (the natural logarithm with base e). Cheers, E.J.

I've forwarded this to our team. If you'd like to follow this up it would be best to post the issue on our GitHub page (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/). Cheers, E.J.

BF01 is just 1/BF10, so the same information is presented

If your conclusion depends crucially on whether you use Cohens's d or Hedge's g, this suggests that considerable caution is in order. But JASP provides both (I just saw we had a fix in our Hedges g  a new JASP version comes out end up this month a…

Posted on GitHub and being worked on / implemented, I believe E.J.

Looks good. I'd be consistent and use "Bayes factors" instead of "Bayes Factors". E.J.

I'll also ask that this will be made clear in the output or the help file. E.J.

I think you've since reported this on our GitHub page and we're implementing it... E.J.

Also, when in doubt, you can compare output from R to that of JASP. Cheers, E.J.

There's a 2009 paper in Psychonomic Bulletin & Review by Rouder & Morey: @ARTICLE{RouderEtAl2009Ttest, AUTHOR = {Rouder, J. N. and Speckman, P. L. and Sun, D. and Morey, R. D. and Iverson, G.}, TITLE = {{B}ayesian $t$ Tests for Accep…

Hi Joe, population effect size delta = population mu / population sd. So it's the population equivalent of Cohen's d (which is a summary based on the sample, not an inference) Cheers, E.J.