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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. Would this be sensible to do? Thanks! 


We do not provide parallel analysis for categorical data because we have found it does not work well for categorical data. 


Hi Linda, 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). Thanks! 


1. Convergence is not an issue. The randomly generated correlation matrices are used only to compute eigenvalues. 2. The principal component eigenvalues are used in that we compute the eigenvalues for the correlation matrix not adjusting the diagonal. 


Thanks for the clarifications! 


Hi, Could anyone clarify why parallel analysis would not work fine to determine de dimensionality of a categorical, dichotomous, data matrix? Maybe this is somewhere else, but I could not find it by myself. Thanks, Carlos 


Look at what I have found out: http://www.csie.ntu.edu.tw/~r95038/Try/%A7O%A4H%BD%D7%A4%E5/697.pdf 


And here I found a study on why parallel analysis should be used with caution with dichotomous matrices: http://www.er.uqam.ca/nobel/r17165/RECHERCHE/COMMUNICATIONS/2006/AERA_RASCH_SIG/AERA_RASCH_SIG/SUMMARY.pdf 


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. 


Hi, Is there a reference that could be used to say that the performance of parallel analysis on categorical is poor? I have been suggested to perform parallel analysis to decide upon number of factors to keep in an EFA, but either want to learn how to, or motivate why not. Regards, Örjan 


We looked at parallel analysis for categorical variables using eigenvalues for latent correlations (tetrachoric, polychoric...), but it didn't seem to point to the right number of factors so we decided to not include it in Mplus, but only have it for continuous outcomes. Perhaps it has to do with the correlations not capturing all information in the data with categorical variables. I know of no references on this. 

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