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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Skewness estimates with svyset data |

Date |
Tue, 4 Nov 2008 09:29:30 -0500 |

--

-Steve On Nov 4, 2008, at 8:00 AM, Richard Palmer-Jones wrote:

Thanks for all your contributions. I worked out the missing "^3" last night (and the x^3 = (_b[ht])^3 - good old Yule and Kendal) i.e.: nlcom ((_b[ht])^3 - 3*_b[ht2] * _b[ht] + 2*(_b[ht])^3)/(_b[ht2] - _b[ht] * _b[ht])^3/2but I am not convinced it gives sensible results - but then how tojudge?In this dataset (NHANES3) using summarize with weights shows heights are not greatly skewed at any age; but weights are clearly negatively skewed up to the age of 5, and positively skewed thereafter (ditto for BMI). The nlcom calculation is quite close to the estimated skewness for height but for weight, although Pearson r = .5, the absolute sizes are not that close (skewness = 0.51 * nlcom - 0.079, r2 = .29, N=49, both coefs p< .000). The nlcom estimate seriously underestimates skewness after age 5 compared to the summarize estimate (with weights). ? I actually want to compare adult heights, weights, and BMIs in a situation where nutritional status has apparently been improving quite rapidly. Heights, weights & BMIs for 25 year olds are greater than those of 45 year olds (assuming no differntial mortalities, which I doubt). Most programs which compute anthopometry z-scores (zanthro, or WHO's Anthro macros) are for children or adolescents, so I wanted something like a zanthro for adults. One might set the standards using USA or UK heatlh surveys which give heights and weights of adults, but then one might want to compute skewness both to test for normality (they are not) and to use the LMS method (Cole et al. 2008) to develop the standards. Height at each age group for both sexes may not be normal, but as noted above weights are generally not (different tests give different results, but omninorm suggests weight is seriously not normal, and height slightly (p between 0.05 & 0.01) not). I suspect that pro temp I am better off using summarize, and smoothing the skewness estimates (and median and cv, but any further advice welcomed. Thanks again RichardOn Mon, Nov 3, 2008 at 10:11 PM, Maarten buis<maartenbuis@yahoo.co.uk> wrote:--- Richard Palmer-Jones <richard.palmerjones@gmail.com> wrote:Thanks - I did check using summarize with weights, and other tests (sktest), and qnorm/pnrom, and generally skewness is no problem, but for some subsamples it may be. I am jconcerned that stratification is lost by these views.It looks like you are worried about normality assumptions. Thisworriesme for two reasons: First, these assumptions typically refer to the residuals, and not the dependent variable (or in other words the dependent variable is normally distributed conditional on theexplanatory variables). Your reference to subsamples suggests thatyouare not looking at the residuals. Second, when you are doing survey methods, you are automatically using robust/Huber/White/sandwich estimators, so you are effectively bypassing many if not all the normality assumptions. Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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/* * 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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**References**:**Re: st: Skewness estimates with svyset data***From:*"Richard Palmer-Jones" <richard.palmerjones@gmail.com>

**Re: st: Skewness estimates with svyset data***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Skewness estimates with svyset data***From:*"Richard Palmer-Jones" <richard.palmerjones@gmail.com>

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