To Steven Samuel:
First, let me apologize myself for "interfering" into
your fascinating conversation. I wonder if it is your
opinion that the same concepts are prevalent when some
scientists do not take into account sampling weights
when analyzing cluster samples arising from household
survey with multilevel regression techniques, or it is
simply that techniques for dealing with sampling
weights are not available for multilevel regression?
TIA,
Moises Rosas
--- Steven Joel Hirsch Samuels
<[email protected]> wrote:
> > To Steven Samuel
> > Forgive me for interferring your conversation with
> Mr. Richard
> > Williams.
> > However I'm dealing with a dataset consisting of
> 10 subsamples with
> > information collected over a period of 7 years.
> >
> > I was just wondering why you suggest to the ignore
> the study
> > weights, especially if they were
> post-stratified...?
> >
> > Regards,
> > --
> > John Singhammer, Dr.phil, Mphil
> > Dept. of Public Health
> > Olof Palmes All� 17
> > DK8200 Aarhus
> > Tel: +45 8728 4715
> > Mobile phone: +45 2530 5768
> >
>
>
> You are not interfering This is a conversation open
> to all. This is
> a slightly expanded version of what I sent to you
> privately.
>
> How to treat the subpopulation and weights depends
> on the purpose of
> the study. There is a Statalist thread which you
> can look up. First,
> note that the 'subpopulation' Richard's student
> wants to study is not
> a 'subsample'. I have sometimes taken 10 random
> subsamples of a
> single population to study variability between
> samples. This is the
> method of 'interpenetrating replicated subsamples'
> of Mahalanobis
> which was popularized by WE Deming in the
> 1950's(Sample Design in
> Business Research, Wiley, 1960).
>
> To expand on the reason for ignoring the
> subpopulation criterion. If
> Richard's student were to analyze the data as a
> subpopulation, then
> every sample mean have to be considered a ratio
> estimate, effectively
> analyzed with a 'ratio' procedure, which is what the
> 'subpop' option
> in the survey commands does. This is because the
> denominator in mean
> = (sum of X variable)/(no. of people in the
> subpopulation) would be
> considered a random variable. At an extreme, the
> very appearance of a
> subpopulation is a random event and the appropriate
> SE takes this
> into account. However it is likely that Richard's
> student is
> interested in the subpopulation as a way of studying
> a question
> unrelated to the original targt population--see
> below. In
> theoretical terms, she may want to study
> associations, conditional on
> membership in the subpopulation.
>
> To answer your question about weights.
>
> 1. If the purpose of a study is analytic (hypothesis
> testing,
> studying relations between variables) then Richard's
> student may not
> be really interested in the original target
> population. As an
> example, she might never report the weighted counts;
> she would report
> the sample counts for crucial variables. The only
> weights that I
> would suggest, if any, are those which correct for
> non-response and
> unequal probability of selection.
>
> 2. It may be better to consider the study as an
> 'experimental
> design', where population numbers of the
> experimental groups are not
> relevant. In Survey Errors and Survey Costs by R.
> Groves (Wiley
> Books), Groves posts the example of a study of noise
> in the vicinity
> of an airport. A study is to be done dividing the
> area around the
> airport into 'strata', which are zones at equal
> distance from the
> flight path or airport. An equal sample size is
> taken from each zone
> and the goal is to study relation of noise to
> distance. Of course
> most people in the study area will not live in the
> closest zones. A
> weighted analysis would give the closest people
> their population
> weight. This would be okay if the main goal was
> descriptive--to
> estimate the 'average' noise experienced by
> residents around the
> airport. However if you consider this an
> experimental design, then
> you want equal numbers at each dose, or, in fact,
> more at the
> extremes. Thus you would not apply the population
> weights.
>
> You may think this is an extreme case, but I have
> seen just this
> analysis in a published study of the association of
> gestational age
> to birth weight. Low birth weight infants were
> oversampled--they are
> only 5-10% of the population. Yet the analysts did
> the weighted
> analysis, which meant that the association in the
> vicinity of low
> birthweights was badly determined unless the model
> was correct.
>
> This is an ongoing debate among survey
> statisticians, so you will get
> different points of view.
>
>
> On Nov 21, 2007, at 3:08 PM, John Singhammer wrote:
>
>
> > To Steven Samuel
> > Forgive me for interferring your conversation with
> Mr. Richard
> > Williams.
> > However I'm dealing with a dataset consisting of
> 10 subsamples with
> > information collected over a period of 7 years.
> >
> > I was just wondering why you suggest to the ignore
> the study
> > weights, especially if they were
> post-stratified...?
> >
> > Regards,
> > --
> > John Singhammer, Dr.phil, Mphil
> > Dept. of Public Health
> > Olof Palmes All� 17
> > DK8200 Aarhus
> > Tel: +45 8728 4715
> > Mobile phone: +45 2530 5768
> >
>
> Steven Samuels
>
> [email protected]
> 18 Cantine's Island
> Saugerties, NY 12477
> Phone: 845-246-0774
> EFax: 208-498-7441
>
>
>
>
>
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