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.'

FYI: Standard error of the mean is an estimate of the variability of a sample mean, if many samples were drawn from the same population. It's not a reliability index. (Perhaps you're thinking of 'standard error of measurement' rather than the standard error of the mean.) It sounds like you want to a...

Looks like a bug to me. If I toggle back and forth a dozen times--between the filter being on versus off--the "variables" being "described" do change arbitrarily.

There is controversy as to whether it is correct or not. On grounds that the ANOVA assumes equal variances, some stats programs use the same pooled error term for each test in the set of post hoc tests. In such a situation, individual t tests would usually not produce the same result as ANOVA post h...

Personally, I would use the ANOVA posthocs output table to hand-calculate each Cohen's d effect size. Specifically, I would convert each 'SE of differences' to an 'SD of differences' by by multiplying the 'SE of differences' by the square root of the number of differences. I would then divide each m...