jamovi

Hi,

It should be (and instead of &):

Did you by any chance updated jamovi in the meantime? We recently updated the effect sizes in the t-tests to use more appropriate types of effect sizes for different tests. In this update we changed the effect size of the mann-whitney U test from cohen's d to a rank biserial correlation (which is mo...

So the EFA doesn't use the initial eigenvalues to compare against in EFA (it does in PCA), but instead used the eigenvalues of the common factor solution (see https://github.com/jamovi/jmv/blob/679274a708d42166a6220588daab66226565ddf6/R/pca.b.R#L444). Hence the difference in number of factors.

Hi, So it's hard to answer all your questions cause I'm not an expert on EFA. We use the psych R package for the actual underlying calculations (see https://github.com/jamovi/jmv/blob/master/R/pca.b.R#L52-L55), so for the technical details it's best to read the psych documentation. I can answer ques...

Hi,

The EFA uses listwise deletion.

Cheers

So I assume in terms of R output you mean the residual standard error?
If so, I can easily add that. Can you make an issue on our github so I won't forget: https://github.com/jamovi/jamovi/issues

Oke, I fixed it so that there's a more informative footnote in the table when this happens (https://github.com/jamovi/jmv/pull/256)

So apparently there's some singularity issue, that's not being caught be the jamovi analysis. If you use the following R code (similar to what we use in the background): data <- data.frame( 'id' = 1:15, 'x1' = c(4, 13, 15, 12, 12, 2, 19, 10, 22, 13, 10, 22, 10, 14, 22), 'x2' = c(55, 40, 26, 6, 20, 3...

Just a quick update that I'm looking into it!

Hi Debbie,
Yup, it indeed means that the factors are allowed to correlate in jamovi.