JohnnyB
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 JohnnyB
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Ahh Im sorry I thought we were discussing JASP. In R you could write some sort of forloop for stepping through all pairwise comparisons  as far as I know there is no function in BayesFactor that does this for you.

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

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…

Hi @Whirly123 , I just took a look (always excited for such little puzzles!), and I think something went wrong in the encoding because there is not a perfect correspondence between the two ways of encoding: https://forum.cogsci.nl/uploads/434/P75JY…

Hi Søren, Unfortunately not  the emmeans/contrast functionality is only included in the frequentist ANOVA's in JASP. As far as I know the emmeans package does not include Bayesian analyses, or am I mistaken? We do include Bayesian post hoc tests, w…

Hi @camillacaponi , Without your JASP file I cannot be sure where your analysis went wrong, because when I perform a RM ANOVA on your data, I think I obtain similar results as you report in SPSS. See the post hoc tests below (with Holm and Bonferoni…

Hi @Farina , Your assumption is correct  before we were doing manual ttests (so no particular package), with corresponding df's, so I was also glad we were able to upgrade the procedure. Kind regards Johnny

Hi @Farina , Since version 16, we use the contrast function of the emmeans package (see https://rdrr.io/cran/emmeans/man/contrast.html) to compute the df's/SE for custom contrast analyses, which is an improvement on how it was done before. The diffe…

Hi @autumn_moments5648 , The Bonferroni adjustment can either be an adjustment to the alpha that is used for deciding on statistical significance (e.g., 0.05 / 3 = 0.016666), or you can adjust the observed pvalues themselves by multiplying them by …

Hi @ETcog , Interesting! Could you maybe send me your data set so I can take a closer look? This might be an interesting cognitive task though  how sure are we that there are more than 30 points in the plot? You can send it to j<dot>b<dot…

Hi @Valentinos, The Dunnett test that JASP offers, is the type where all levels are compared to a control group (i.e., "Dunnett’s ManytoOne Comparisons Test"). This is based on the multcomp package (specifically, the glht and mcp functio…

Hi @je77je , Sorry for the delayed response, this flew under my radar! The reason for the discrepancy you see, is that by default, the RM ANOVA post hoc tests use the pooled standard error (you can see that the SE is the same for all 3 comparisons)…

Hi @szaszkob , Unfortunately the design requirements for the Friedman test are pretty strict (it is the same in other software, like R). It also does not allow multiway designs, and can only assess main effects. You could consider an ANOVA on the …

Hi @Daniela3h and @davidwidman , Unfortunately this feature is not included in the contrast analysis yet, but I will look into implementing this relatively soon. In the meantime, you could look into the source provided by David, or try to conduct a …

Hi @PerPalmgren , I am getting the same results as you. The only thing that could have changed is this one https://github.com/jaspstats/jaspdesktop/pull/3140. This is a pretty old PR, but maybe Mark has not updated the analysis (though it looks as…

Hi @MassMobile , Yes, it works through regrouping, although I do not think this should be warranted and actually implemented a check recently to limit the application of the Friedman test, since this could be misleading. In the future we want to imp…

Hi @andersony3k , Yes that would be a great functionality! We are now exploring the possibilty of specifying a more flexible structure for the SE's (with the nlme package) in ANOVA, which would make this functionality very straightforward to impleme…

@PerPalmgren, I am not sure if both discuss the same issue. It seems the post you reference speaks about the main model in ANOVA, whereas above we were discussing the best course for a followup test on a default ANOVA, but I could be mistaken. I fe…

Hi @AlejandroG , Thank you for taking the time to explain your issue. I just looked into the ttest plotting code a bit more, and, just as in the RM ANOVA, we apply the procedure for confidence intervals of group means described in Morey, 2008 (who …

Hi @amelie1711 , Unfortunately this is an issue with 16.2  it is fixed now, but the fix will only be in the next release. So you can best use 16.1 for contrast analysis. Apologies for the inconvenience. Kind regards, Johnny

Hi @AlejandroG , The paired samples ttest uses the standard deviation of the differences to draw the CI's, so the descriptives per condition are not telling the full picture here. Similarly, the RM ANOVA also looks at the sd of the condition differ…

Hi @vicente_inefo , I think this is a problem that stems from binary reasoning about statistical results  if you hold the 0.05 threshold as very holy, and a pvalue below that as plain evidence for a difference, then yes, this Cohen d CI is a bit c…

Hi @BillL2 , Thanks for reporting this, I just asked our programmer if something changed in that particular widget, since it is definitely supposed to support negative values! Kind regards, Johnny

Hi @vicente_inefo , My guess here is that the pvalues are for the t/mean difference value (you can see that the CI for the mean difference does not include 0), which use a slightly different standard error for their standardization and CI computati…

Hi @bananenkuchen , I cannot fully say, based on the information you provide. It seems that the sums of squares are the same, but the F and pvalues are indeed different. Did you use the same sum of squares types for both analyses? Did you make sure…

Hi @erindancey , The line chart as it is given for the RM ANOVA is specific to that analysis, since it uses the full RM ANOVA model for creating the error bars (you can read more about that in the RM ANOVA helpfile). I'm afraid you cannot make the s…

Hi @erindancey I think this is due to different handling of the missing data. If you look at the sample size in each table, you will see that they differ (for week 1 it's 10 and 13 vs 5 and 12). The RM ANOVA excludes cases listwise, which means tha…

Hi @roundcircle , This is probably due to the post hoc tests using the pooled error term, whereas the paired ttests use only the error term of the two groups under consideration. If you untick this option in the RM ANOVA post hoc tests, the values …

Hi @tong , We apply a correction to these confidence intervals, based on Loftus & Masson (1994) and Morey (2008). This is what we write in the help file: By selecting this option, error bars will be displayed in the plot. The error bars can eith…

Hi Izymyl, Thanks for getting back to us, showing the infinity seems like a good solution to avoid confusion. Cheers, Johnny