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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Re: Normality Testing |

Date |
Wed, 11 Feb 2004 12:33:29 -0000 |

I can't see your variable to comment but these results don't surprise me. If you sysuse auto foreach v of var price-gear { qui swilk `v' if foreign di "`v' {col 20}" %4.3f r(p) } you will get this: price 0.004 mpg 0.495 rep78 0.293 headroom 0.940 trunk 0.809 weight 0.026 length 0.813 turn 0.996 displacement 0.083 gear_ratio 0.013 If you then follow up, as you did, with say -qnorm- then -- even with a sample size this low, 22, chosen to be of the same order as your example -- you will see that a low P-value can correspond to variables which look as if they should be transformed and variables which, to be sure, don't look exactly normal but would probably not be problematic for -anova-. In short "looks as if it isn't normal" is not the same as "looks as if it would be problematic". In any case I would put more emphasis on choosing response scale on scientific or substantive grounds than because of this normality assumption (which, additionally, is about errors, not responses). The manual entry [R] diagnostic plots points to Rupert Miller's book, which is excellent reading for this area. One of many merits of -glm- is that it lets you decouple the question of response scale and error distribution. Nick n.j.cox@durham.ac.uk Karamjit Shad > Prior to carrying out an anova I tested my data for normality > and some of > the data was non-normal. Ladder suggested a log > transformation would be > suitable. I then checked the transformed data using swilk and > the data is > still non-normal. However sfrancia indicates that it is normal. > . swilk igg60 if group==3 > > Shapiro-Wilk W test for normal data > Variable | Obs W V z Prob>z > -------------+------------------------------------------------- > igg60 | 30 0.74827 8.001 4.300 0.00001 > > . swilk ligg60 if group==3 > > Shapiro-Wilk W test for normal data > Variable | Obs W V z Prob>z > -------------+------------------------------------------------- > ligg60 | 30 0.91745 2.624 1.995 0.02305 > > . sfrancia ligg60 if group==3 > > Shapiro-Francia W' test for normal data > Variable | Obs W' V' z Prob>z > -------------+------------------------------------------------- > ligg60 | 30 0.93170 2.398 1.600 0.05479 > > a qnorm plot shows the data to "gently" oscillate about the normal > distribution but nothing that would worry me too much. > My question is what test should I use for testing for > normality in this > situation - or should I just use a non-parametric analysis. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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