Xu, Man posted on Wednesday, March 06, 2013 - 4:06 am
I'd like to run parallel anlaysis for some categorical data, but the parallel anlaysis otpion is not available for categorical data.
I was wondering if it makes sense to use biserial/tetrachoric correlation matrix as data input, and analyse that as if it is continuous (but without estimating the thresholds).
To get the biserial/tetrachoric correlation matrix based on the same sample (taking into account of missing data), I would declare all data as categorical and ask for SAMPSTAT output to get correlation matrix.
I have a couple of questions regarding the use of parallel analysis in MPlus:
1) What happens when the ML solution does not converge with the random data? I imagine that this would happen quite often when trying to extract multiple factors from data uncorrelated in the population.
2) Is there a particular reason why you chose not to use the principal component eigenvalues for parallel analysis? The PA procedure seems to work quite well with these eigenvalues, including cases with ordinal data and polychoric correlations (e.g., Garrido, Abad, & Ponsoda, 2012).
We had parallel analysis developed also for tetrachoric and polychoric correlations, but my explorations of it indicated that it didn't work well, so we didn't include it in release versions. The poor performance may have to do with the fact that these correlation matrices behave differently than correlations among observed continuous variables.