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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: polychoric for huge data sets |

Date |
Wed, 5 Sep 2012 14:54:55 +0100 |

Experiment supports intuition in suggesting that the number of variables is a bigger deal for -polychoric- than the number of observations, and also that you can get results for 8000 obs and 40 variables in several minutes on a mundane computer. That's tedious interactively but doesn't support the claim that Timea made. Best just to write a do-file and let it run while you are doing something else. Nick On Wed, Sep 5, 2012 at 9:59 AM, Nick Cox <njcoxstata@gmail.com> wrote: > Stas Kolenikov's -polychoric- package promises only principal > component analysis. Depending on how you were brought up, that is > distinct from factor analysis, or a limiting case of factor analysis, > or a subset of factor analysis. > > The problem you report as "just can't handle it" with no details > appears to be one of speed, rather than refusal or inability to > perform. > > That aside, what is "appropriate" is difficult to answer. A recent > thread indicated that many on this list are queasy about means or > t-tests for ordinal data, so that would presumably put factor analysis > or PCA of ordinal data beyond the pale. Nevertheless it remains > popular. > > You presumably have the option of taking a random sample from your > data and subjecting that to both (a) PCA of _ranked_ data (which is > equivalent to PCA based on Spearman correlation) and (b) polychoric > PCA. Then it would be good news for you if the substantive or > scientific conclusions were the same, and a difference you need to > think about otherwise. Here the random sample should be large enough > to be substantial, but small enough to get results in reasonable time. > > Alternatively, you could be ruthless about which of your variables are > most interesting or important. A preliminary correlation analysis > would show which variables could be excluded because they are poorly > correlated with anything else, and which could be excluded because > they are very highly correlated with anything else. Even if you can > get it, a PCA based on 40+ variables is often unwieldy to handle and > even more difficult to interpret than one based on say 10 or so > variables. > > Nick > > On Wed, Sep 5, 2012 at 3:37 AM, Timea Partos > <Timea.Partos@cancervic.org.au> wrote: > >> I need to run a factor analysis on ordinal data. My dataset is huge (7000+ cases with 40+ variables) so I can't run the polychoric.do program written by Stas Kolenikov, because it just can't handle it. >> >> Does anyone know of a fast way to obtain the polychoric correlation matrix for very large data sets? >> >> Alternatively, I was thinking of running the factor analysis using the Spearman rho (rank-order correlations) matrix instead. Would this be appropriate? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: polychoric for huge data sets***From:*Stas Kolenikov <skolenik@gmail.com>

**References**:**st: polychoric for huge data sets***From:*Timea Partos <Timea.Partos@cancervic.org.au>

**Re: st: polychoric for huge data sets***From:*Nick Cox <njcoxstata@gmail.com>

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