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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 5 Sep 2012 09:05:26 -0500 |

Obviously, -polychoric- computing time is quadratic in the number of variables, but linear (or may be even faster) in the number of observations. There's also the curse of large sample sizes: most of the time, the underlying bivariate normality will be considered violated by -polychoric-, and that may create computational difficulties, such as flat regions, ridges, and multiple local optima. On Wed, Sep 5, 2012 at 8:54 AM, Nick Cox <njcoxstata@gmail.com> wrote: > 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/ -- -- Stas Kolenikov, PhD, PStat (SSC) :: http://stas.kolenikov.name -- Senior Survey Statistician, Abt SRBI :: work email kolenikovs at srbi dot com -- Opinions stated in this email are mine only, and do not reflect the position of my employer * * 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:*Nick Cox <njcoxstata@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>

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

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