JohnnyB
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Hi @TarandeepKang and @MartinM , Yes, somehow this got removed at some point. I added the feature back, so it will available again in JASP 0.19 (probably released somewhere in April). Cheers, Johnny
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Hi Carsten, Ah, sorry! Yes, the analysis in JASP matches the method described in the paper. I see now that it is not included in the citations/helpfile, so I will make sure to add it. Cheers Johnny
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Hi Carsten, You can download JASP - the analysis from that paper is available under "T-Tests" -> "Bayesian Paired Samples T-Test" and "Bayesian One Sample T-Test", depending on your research scenario. Cheers Johnny
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Hi @Dexterama, If you add a continuous covariate in the RM ANOVA, it will appear in the between subjects effects table, in addition to all possible interactions being added (since any interaction effect will affect the interpretation of the main eff…
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Sorry, there were all sorts of things that popped up instead.. it's still on our radar, just not with such high priority (since we do already have Bayesian inference for a rank-based correlation coefficient in Kendall's tau). Kind regards Johnny
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Hi @laiskl , The contrast analyses are only available as two-sided tests for now. The contrast weights determine whether you compute A-B or B-A, which in the case of a two-sided t-test leads to the same results. You could manually take the two-sided…
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Hi @jsmnstn , JASP uses the emmeans package in R - specifically, the effect_size function. The effect sizes are therefore based on the full specified model, so might differ from when you would conduct individual t-tests. If you want, you can post/em…
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Hi @PerPalmgren , When you only specify two variables, the two tests are equivalent - the multivariate normality (which has as many dimensions as specified variables) then becomes the bivariate normality check. I guess that when all variables are re…
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In the meantime I doublechecked the results of JASP with the PMCMRplus package in R, and get the same results for the Conover tests - they do differ from jamovi's, since it seems they use an outdated package (PMCPR instead of PMCMRplus). Kind regar…
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Could you share the JASP file you used? You can send it to j.b.vandoorn<at>uva.nl Without it I do not have a way to analyze your situation.
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Hi @gvt , The friedman test is only appropriate for univariate designs, not for designs that have more than 1 predictor variables, as seems to be the case for you. I understand that makes the Friedman test fairly limited, but for now that is beyond …
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Hi @FranckM , The box should lead to different SE's for within subjects factors - are you perhaps looking at a between subjects factor that is in your RM design? Cheers Johnny
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Hi @kay_P & @FeB , I'll reply to each question of FeB below, because I think they also cover kay's question: Question 1: From what we were able to gather from the Internet, you can always use repeated measures in SPSS or JASP and enter a covaria…
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Hi @EmilieC If your design is balanced (also in terms of continuous covariates), then the marginal means will be at least highly similar to the descriptive means, so then the paired t-tests will give an accurate idea of the marginal means differenc…
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HI @EmilieC , Yes, that one is definitely related, but it's a feature, rather than a bug (and consistent with other analysis software). The blogpost I linked explains this property more clearly, including an example. Cheers Johnny
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Hi @andersony3k , I took a look at your data files and comparison of the different programs - it seems in one you applied a filter and in one you did not, which leads to different results. Removing the filter leads to the same results, which are in …
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Hi @EmilieC , You are right - the interactions have disappeared from the marginal means menu and we are currently working on bringing those back! There is a difference between the frequentist and Bayesian anova in how they handle the posthoc tests (…
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Hi @MaddieP , Thanks! The JASP computation (https://github.com/jasp-stats/jaspAnova/blob/ae3f25859919c9a91a19832ae8f09065399f2c84/R/manova.R#L246) is based on the BioTools R package, but also produces the same results as the HePlots package: > he…
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Thanks @MaddieP , what was the model you used? in terms of (in)dependent variables.
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Hi @MaddieP, Could you please provide a data set where you obtain different results? I just checked against the heplots package (https://search.r-project.org/CRAN/refmans/heplots/html/boxM.html), and get the same results in JASP. Kind regards Johnny
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Hi @alexa , To add to EJ's comments - I would not use too extreme values in the robustness test, since at some point the prior becomes so narrow or wide that the model just becomes non-sensical. EJ's suggestion to double and half the main values wor…
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Hi @MaximusLuminis , In that case it will be a bit tricky, because the Friedman test is generally for one-way RM ANOVA's, not for multiple repeated measures. One option to explore would be to try a rank-transformed ANOVA, where you convert your DV t…
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@samamarc , Great to hear this is clarified now. A blogpost I wrote a while ago might illuminate the issue some more: https://jasp-stats.org/2020/04/14/the-wonderful-world-of-marginal-means/ Cheers Johnny
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Hi @MaximusLuminis , I'm afraid that's a limitation of the Friedman test - it requires complete observations (e.g., see here), so you can only run it for those participants who completed all trials. Do you also have a within subjects measurement? Be…
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Hi @albapy , Sorry for the late reply. You can read about this behavior in this recent discussion: https://forum.cogsci.nl/discussion/comment/27509 and linked blogpost: https://jasp-stats.org/2020/04/14/the-wonderful-world-of-marginal-means/ Basical…
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Hi @maamorim , Great to hear! Just to clarify - if you were to compare the planned contrast analysis (which is also based on the marginal means) to the posthoc analysis, these are identical, except for the p-value correction (which always makes the …
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Hi @maamorim In addition to @andersony3k 's helpful comment, I would like to point you to this blogpost from a while ago, where I outline the follow-up tests for ANOVA. Basically, contrasts and post hoc analyses are based on the estimated marginal …
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Ahh Im sorry I thought we were discussing JASP. In R you could write some sort of for-loop for stepping through all pairwise comparisons - as far as I know there is no function in BayesFactor that does this for you.
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Hi Søren, The Bayesian post hoc tests do this, and you can just take the Bayes factors from there. Unfortunately we do not have different types of contrasts (e.g. Helmert/simple/etc), which is still on our todo list. Kind regards, Johnny
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I think interpreting a main effect in isolation while there is a significant interaction effect is pretty tricky to do, and your case highlights why. Because of the interaction effect, if we look at what our model predicts for someone in condition B…