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st: subpops vs. over & lincom t vs. regress t in svyset data

From   Michael Costello <>
To   statalist <>
Subject   st: subpops vs. over & lincom t vs. regress t in svyset data
Date   Tue, 3 Jan 2012 14:14:43 -0500

Happy New Year Statlisters!

I'm working with many many similar survey weighted datasets of
international education data.  Often I am tasked with creating tables
of statistics (means, variances, counts, t-statistics, effect size,
etc.) for many subpopulations and over several phases (baseline,
midterm, final).

We had been calculating our statistics using -svy: varname,
over(subpops)- rather than using many -svy, subpop(subpops): mean
varname- functions in quick succession, as the returned values were
equal.  In a more recent database, the values are not equal, and I'm
wondering why that is.  The subpopulation I was working with was
gender (female=1, male=0).  Could the discrepancies be due to the
handful of observations with gender = . (missing), or is there some
other difference in the calculations?  It appears that using the
-subpop- option treats those observations as non-existent.  How does
-over- treat them?

I'm also trying to find out the difference between the t-statistic
that is printed when I do a -lincom- function and the t-statistic that
is printed when I do a regress function.  For example:

svy: regress score gender
svy: mean score, over(gender)
lincom [score]Male - [score]Female

I believe that the regression function uses a pooled standard error
SE, while the -lincom- uses an unpooled calculation, but I was hoping
for some confirmation on that.

Thanks so much for all your help and advice!  You folks are always so
helpful and informative.

Michael Costello

"To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of."  -Sir Ronald Aylmer Fisher,

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