jamovi

First of all, the sums of squares type should normally be Type III (the default) instead of Type I. But more importantly, to run an ANOVA, you cannot have missing cells in your "Gender" by "College Classification" by "Media Use" design. Apparently you are missing some c...

jReshape (Version 0.2.2 and 0.2.4, with jamovi 2.4.14 for Windows) Wide to Long, Advanced, appears to be broken. In the attached example, attempting to reshape RT and Accuracy columns to Long format produces the following error when it should not: 'lengths(varying)' must all match 'length(times)' Te...

Maybe you don't actually have a complete factorial design. In a similar example, below, there's a 1,1 and a 2,2 condition, but no 1,2 and no 2,1 condition. Therefore, no ANOVA results.

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?