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Re: st: Testing whether two estimated survey means are statistically different
From
Austin Nichols <[email protected]>
To
[email protected]
Subject
Re: st: Testing whether two estimated survey means are statistically different
Date
Wed, 19 Oct 2011 11:40:22 -0400
Andrew Wade <[email protected]>:
The hypothesis that the mean for a specific sub-population is not
different from the mean for the whole population is equivalent to
the hypothesis that the mean for a specific sub-population is not
different from the mean for its complement.
I.e. regress on a dummy for the specific sub-population. The test that
the coef on the dummy=0 is the test you want.
On Wed, Oct 19, 2011 at 10:45 AM, Andrew Wade <[email protected]> wrote:
> Hello,
>
> I want to test the hypothesis that the estimated mean of a specific sub-population, is not statistically different from the estimated mean of the whole population.
>
> The means are generated from a complex survey, using two separate operations of the svy: mean command.
>
> My reading of the documentation is that this type of test, when using svy: mean, should be conducted using lincom.
>
> The problem is that lincom can only be applied (it would appear) to the most recent mean estimate.
> However, to estimate the means of interest, I first have to estimate the svy: mean for the population, and separately use the svy:mean, over, command to estimate the mean for the specific sub-population of interest.
>
> Is there a way of 'loading' into the lincom command a previously stored mean and variance estimate, to then test against a separate estimate?
>
> And presumably, the fact that one of the means of interest is a sub-population of the other mean, is a potential problem?
>
> Or should I simply use the manual ttesti command for this test?
>
> The code for this is:
>
> ttesti 6047 293.9012 9256.260402 79889 767.4463 39570.47905
>
> The n values here are the weighted population counts.
> As noted above, the 6047 is a subset of the 79889.
>
> (it is notable here that the CI's for the two means overlap, but that the test doesn't find the two estimates to be statistically difference from each other)
>
> Or should I assume unequal variances...
>
> ttesti 6047 293.9012 9256.260402 79889 767.4463 39570.47905 , unequal
>
> This 2nd ttesti approach does find the estimates to be statistically different to each other, due to a much lower s.e. estimate.
>
> Keen for any suggestions.
>
> Regards,
>
> Andrew
>
>
> Andrew Wade | Principal Consultant
> The Allen Consulting Group
>
> Level 9, 60 Collins Street, Melbourne VIC 3000
> T: 03 8650 6000 | DDL: 03 8650 6043 | F: 03 9654 6363 | M: 0448 367 298
> [email protected] | www.allenconsult.com.au
>
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