My code has been running for several hours now on a MacBook Pro, and while it says it's running... the progress bar (showProgress=TRUE) for the analysis still says 0%?
I know the more observations, variables, and permutations you have, the longer it will take to run...I just wanted to make sure that having '0%' in the console is normal despite the code running for over four hours now. In the past, when I accidentally mis-specified something, it would still take an hour or so to run just to return an error, so ideally I am trying to avoid wasting time running this for it to eventually terminate from some error of model mis-specification.
it sounds like I should just do the permuteMeasEq function within the multi-group CFA framework.
My goal is to detect uniform & non-uniform DIF...but I know the metric and scalar equivalence are fairly comparable anyways.
As a follow-up question. I have a CFA with three groups. The indicators were collected with 7-point likert scales, which I treat as continuous. Data is moderately non-normally distributed. Actually I would use a Satorra-Bentler Chisquare difference test and additionally look at some delta AFI. I understand that the permutation test gives clearer results to interpret than arbitrary cut-off values. For the chisquare difference test I now have at least three options: Satorra-Bentler, chisquare (ML) difference by permutation test or robust chisquare (MLR) by permutation test. Is any variant clearly preferable and, if so, for what reason?
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It ran within several hours versus the MIMIC approach, which was taking days to run and still not finishing.
I liked that the permutation method also adjusted for Type I errors
My sample is around 2000 people, so I was evaluating measurement invariance based on change in CFI/RMSEA, rather than the results of a LRT, as my understanding was larger samples can lead to over-rejection of measurement invariance tests when based solely on change in chi-square.
For the chisquare difference test I now have at least three options: Satorra-Bentler, chisquare (ML) difference by permutation test or robust chisquare (MLR) by permutation test.
Is any variant clearly preferable and, if so, for what reason?