Dear Steven,
> 1. Are the existing weights appropriate for the
> children? To answer
> this I would need more information about the survey.
> How did children
> get into the sample? As part of selected
> households?
Children got into the sample as part of selected
households. PSUs were selected with linear systematic
pps sampling. Stratification was done at the regional
level and for urban/rural areas.
> > or is the weight the same for all members of the
household?
> Were the data post-stratified in any way? If there
> is just one weight
> for all members of the household, then use that.
I have only one weight for all households (same for
all members in the same household). So, I will use
this.
>
> 2. Do you select just the children for an analysis
> data set, or do
> you analyze the entire set and use the -subpop-
> option?
I have selected children below 5 years old because my
analysis is only related to children <5. I have not
used subpop for my regressions.
The second
> approach is the only one which will provide entirely
> correct standard
> errors, although often there will be little
> difference. Austin
> Nichols showed how to create a data set for use with
> the -subpop-
> option that will be only a little larger than one
> containing only
> children. See:
> http://www.stata.es/statalist/archive/2007-11/
> msg00810.html .
Thanks, will take a look at Austins. Since my analysis
is at the individual level if I keep all the sample.
Then I will have more than 150,000 obs from which I
really need is around 15,000. But I guess that this
wont be a major problem if I increase the memory.
Another issue is that I also run regressions for
different groups within my sub-sample of children. So
I have split children into 4 cohorts and in the areas
where they reside. So my regressions are a sub-sub of
the whole sample. This sounds confusing but the idea
is to use a diffs-diffs estimators. So I compare
cohorts in different areas.
>
> 3. Although -svy- does not work with -areg-, you can
> use -areg- with
> a -weight- option and with the proper PSU as the
> cluster variable.
> You will be unable to use the -strata- option, and
> this could
> potentially lead to estimated standard errors that
> are larger than
> the true ones. It will also artificially increase
> the degrees of
> freedom for error. You can get around these by
> adding dummy
> variables for stratum into your model. If strata are
> defined by your
> �province� variable, then you have effectively done
> that.
They are difined by province but also by urban/rural.
So I would need to include dummies for urban areas as
well, I suppose?
I have not thought about using pweight with areg. I
assume I could also use xi:regress y x
i.dummiesprovince i.cohortbirth urban [pweight=z]
> 4. If there are too many strata to add as dummies
> (and strata are not
> defined by your provinces), ignore the strata in the
> analysis, but
> adjust the degrees of freedom by hand. The proper
> degrees of freedom
> for error will be the listed d.f. minus the number
> of strata. You can
> compute correct confidence intervals, say 95%
> intervals, as follows:
>
> 4.1. Find the error degrees of freedom from the
> -areg- output WITH
> the the -cluster- option. Suppose it is, df1 = 180.
> If you had 80
> strata, the degrees of freedom should be df2 =180 -
> 80 = 100.
>
> 4.2. With 180 degrees of freedeom, the t-multiplier
> for a standard
> error would be 1.973, but this is too small. Compute
> the t-multiplier
> for the correct degrees of freedom and 95% CI as
> invttail
> (100,.025), or 1.9840.
>
> 4.3. You should INCREASE the nominal confidence
> level for -areg-, so
> that the t-multiplier with 180 d.f. is 1.9840. What
> should the level
> be? First find: ttail(180,1.9840), or 0.02439.
> The proper -level-
> is then: 1- 2x.02439=0.951. So you should specify a
> -level-
> statement as �set level 95.12�.
>
> You can find the proper level in one step by:
>
> di 1-2*ttail(df1 , invttail(df2,.025))
> //finds level where df1 is the nominal degrees of
> freedom and df2 is
> the actual degrees of freedom =df1- n. strata.
This is very useful to know. I need to study this
closer.
Thanks for your very explicit and clear answers.
rgds,
Gaby
> -Steven
>
> On Feb 9, 2008, at 7:04 AM, Ana Gabriela Guerrero
> Serdan wrote:
>
> > Dear all,
> >
> > Sorry for these probably obvious questions. Have
> > looked into the archives but I'm still confused
> on
> > the following issues:
> >
> > 1) I am using survey data (two-stages with
> > stratification). I am looking at children less
> than
> > five years old. Can I apply svy set as usual to
> my
> > sub-sample of children as follows?
> >
> > svyset [pweight= expweigh], strata(AI05) psu(
> AI06)
> >
> >
> > 2) I had initially done my analyis with linear
> > ressions without the svyset, controlling for
> > differences in provinces and cohorts, and
> clustering
> > at the district level. I used areg as follows:
> >
> > areg Y X DummiesProvinces, vce(cluster district)
> > absorb(mdate)
> >
> > What command can I use if I first set my data for
> > svyset?
> >
> >
> > Gaby Guerrero Serdan
> >
> > Deparment of Economics
> > Royal Holloway, University of London
> > TW20 OEX
> > Egham, Surrey
> > England, UK
> >
>
http://www.rhul.ac.uk/economics/About-Us/postgrads.html
> > http://www.flickr.com/photos/49939890@N00/show/
> >
> > Tel: +44 7912657259
> >
> >
> >
> >
>
______________________________________________________________________
>
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> > *
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> > *
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>
Gaby Guerrero Serdan
Deparment of Economics
Royal Holloway, University of London
TW20 OEX
Egham, Surrey
England, UK
http://www.rhul.ac.uk/economics/About-Us/postgrads.html
http://www.flickr.com/photos/49939890@N00/show/
Tel: +44 7912657259
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