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Re: st: Questions about svy commands

From   Steven Samuels <>
Subject   Re: st: Questions about svy commands
Date   Sat, 9 Feb 2008 17:50:51 -0500


For analyzing the children there are two issues:

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? Is there a different weight for each household member, (males and females for example) 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.

2. Do you select just the children for an analysis data set, or do you analyze the entire set and use the -subpop- option? 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: msg00810.html .

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.

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.


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)

What command can I use if I first set my data for

Gaby Guerrero Serdan

Deparment of Economics
Royal Holloway, University of London
Egham, Surrey
England, UK

Tel: +44 7912657259

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