Parallel Analysis in JASP shows persistent discrepancy with R across samples and items
I am adding this as a new discussion, as there were not any responses to the older discussion on this that I followed up upon (https://forum.cogsci.nl/discussion/5382/parallel-analysis-for-efa-in-jasp#latest)
I have the same issue as described in the discussion above: JASP always suggests a different number of factors compared to R in parallel analysis, irrespective of which fa.parallel() settings I use, i.e., SMC = TRUE does not make a difference. I have tried this for 3 different combinations of questionnaires (different combinations of items from the same data set in this case), and I have repeated the parallel analysis several times.
Could there maybe be a different explanation for why this happens, other than that the difference is due to chance (because the simulated data can be a little different every time) as pointed out by @evankesteren? The factor loadings are identical as expected when I use "minres" for both. And it seems odd to me that there is a consistent considerable difference due to chance.
As an example, R suggests 3 whereas JASP suggests 1 factor here. I can see how both make sense, but this happens for any combination of items.https://forum.cogsci.nl/uploads/477/QA52G3T7T33I.png
Any help is appreciated!