jamovi

RE: "Since you have a strong interaction, then the main effects (specially for sex) could be marginal (secondary) to the interaction. This is known as the principle of marginality. In your example you may not be able to claim that women in general perform better than men, only those women who w...

Unfortunately jamovi can't 'split file.' To accomplish what you want, you can either do what you've already done (separate files), or you can use filtering to temporarily remove one sex or the other, or you can use Computed Variables to create two separate additional columns: DV_ForMalesOnly and DVF...

You wrote "There´s an interaction effect where being female and walking yields a higher test score (than being female and biking and being man and biking or walking respectively." That may be true, but it isn't a description of an interaction. The interaction is that the change in the mean...

Or: If your large data set has a manageable number of variables, you could just write a command in the jamovi filter that has the effect of filtering-in only complete rows. Then you could export the data as a csv file (any rows not-filtered-in will be dropped), and then open the csv in jamovi.

It may not be possible to do this strictly within jamovi. In R you could execute the expression: data <- data[complete.cases(data), ] (The above assumes that you already have a data frame named: data) Using jamovi's Rj+ you could execute: df<- data[complete.cases(data), ] but then you would need to ...

If you use large integers (e.g., weights: 666666666, 333333333) won't that suffice?

Yes. Thanks for the correction.

I've created an R function for producing a particular style of editable, rich data plot. In the not-too-distant future I would to turn it into jamovi module. It could be a stand-alone module. Or in principle it could be something that somehow gets added onto jamovi's existing ANOVA, repeated-measure...

I think a rather complex approach would be to assume that each of the two functions is indeed sigmoidal, find and apply a transformation that appears to mostly linearize the functions, then use multiple regression to ascertain whether the slope for the orientation-by-stimulus interaction is signific...

Also note that, like an effect size, the standard error of measurement (SEm) does not change systematically with sample size. So adding or subtracting trials will not tend to make the SEm more 'good' or less 'good.'