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Re: st: How to compare models after svy

From   Stas Kolenikov <>
To   "" <>
Subject   Re: st: How to compare models after svy
Date   Tue, 21 May 2013 09:17:21 -0400

What you are used to in logistic regression modeling, and what you
refer as deviance differences, are essentially likelihood ratio tests
(compare the likelihoods of two models, transform them to asymptotic
chi-square). This does not work for complex survey data (or at least
not in a way that could make it directly usable), so what you would
want to do is to use Wald tests available after any estimation command
with -test- (as opposed to the logistic-specific fit test commands or
-lrtest-, whichever you were using). With -svy-, these tests would
produce an F-test, which is roughly chi-square divided by its
(estimated) degrees of freedom. In turn, the estimated degrees of
freedom, as a first approximation, is the number of primary sampling
units minus the number of strata; and -svy- uses a more complicated
approximation based on the eigenvalues / generalized design effects of
the parameter estimates covariance matrix.

If you have never heard about Wald tests (or, for that matter, of
likelihood ratio tests), take a look at Buse (1982), Note that it only
applies to the "standard" i.i.d. data. The generalized design effects
are introduced and discussed in (I believe) Rao and Scott (1981),, reviewed over
again in Rao and Thomas (2003),

-- Stas Kolenikov, PhD, PStat (SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer

On Tue, May 21, 2013 at 7:52 AM, Orian Brook <> wrote:
> Hi all
> I'm hoping for advice on comparing models after using -svy: logit-, as the
> postestimation commands normally used after logit don't work.
> I had been working without using the - svy - prefix as I'm controlling for
> most of the criteria that were used in sampling, and in early iterations of
> the model the results were very similar. With my final model version, the
> results given by using or not using -svy - are still similar, but the
> independent variables I'm interested in are slightly more significant and
> with slightly stronger effect sizes, so I'm in favour of using the prefix!
> However, in my final version I'm using two interactions (both
> categorical-categorical), where not all the interaction effects are
> significant. With -svy- I can compare the change in deviance to the change
> in degrees of freedom to confirm that the model with interactions is a
> better fit. Again, using the -svy- prefix, not all of the interaction
> effects are significant and I want to check whether overall the model with
> interactions is a better fit, but I'm not given the log likelihood or
> deviance statistic. I'm given an f-test but I thought this was only
> appropriate for a least squares model?
> Thanks all
> Orian
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