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Speeding up categorical ESEM analysis |
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Message/Author |
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I am trying to run an ESEM on 171 categorical variables, which is very slow (does not converge in several days). Aside from specifying NOSERROR and NOCHISQUARE, is there anything I could do to speed it up? I tried feeding Mplus a spearman correlation matrix hoping that would speed things up, but that does apparently not work for ESEM. Presuming nothing works, would it somehow be possible to treat the variables as continuous (half are true-false items, and half are severely skewed items with 0-3 response options), even though assumptions are violated? Sincerely, Erik |
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Try ESTIMATOR=ULS; If the ordered categorical variables have either floor or ceiling effects, you should treat them as categorical. |
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Thank you for the quick reply. I thought 'estimator=uls' only worked on a pure EFA. Since this is a combined EFA-CFA, does uls still work? |
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I think it is. Try it. That's the best way to find out. |
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No luck with ULS. What about treating the variables as continuous and using estimation = MLR? |
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If you used ULS and NOCHI and NOSERR, I wonder if you are extracting too many factors. If not, I can't think of anything else but to approximate as continuous outcomes, but that may be slow too. |
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