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From |
Philip Ender <ender97@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Re: RE: re: RM ANOVA, was SPSS vs. Stata |

Date |
Tue, 3 Aug 2010 09:46:17 -0700 |

"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov> wrote: Phil, and others For larger data sets with high imbalance I don't think there's much doubt that using a mixed model is more flexible and less biased than rpm anova with complete observations only. But for small sample sizes, using infinite degrees of freedom for the denominators (i.e. chi-square statistics rather than F) also creates bias in the inference. What would be nice is to have some way to calculate approximate denominator degrees of freedom after obtaining the pseud0-F statistics with -xtmixed- and -test-. Al Feiveson ---------- Al- I've had some discussions on this topic with Vince Wiggins. He claims that regardlesss of what SAS is doing there isn't a statistically defensible of determining the denominator degrees of freedom. For linear models rescaling chi2 to F yields a correct F-ratio but without denominator df its difficult to get a p-value. I have looked back at my old BMD manual for the 05V procedure and note that it only produces z-tests and chi2 although it was specifically designed for unbalanced repeated measures. So, while not prefect, I do think the F-ratio I obtained in my -xtmixed- example is less biased than than the one from the complete case analysis in SPSS. Phil -- Phil Ender UCLA Statistical Consulting Group * * 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/

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