Thank you!
I do account for the weights using fweights in my regression, but the
weights increase the impact of observations, and thereby impacting the
t-statistics making the effect that all explanatory variables are
significant. Is there a way of accounting for that effect on t-stats?
thanks,
Mikhail
----- Original Message -----
From: "Copeland, Laurel" <[email protected]>
To: <[email protected]>
Sent: Friday, May 23, 2003 3:05 PM
Subject: st: RE: statistical significance in a data set with weighted
observations
> The data can be weighted to reflect the sampling design. The sampling
> design is complex to give you a sample that is representative of the
> underlying population, and to allow inferential statistics. The complex
> sampling lets you get a good sample of a large population of unlisted
> smaller units (e.g., all US residents), based on a complete list of larger
> units (e.g., US census tracts). The weight is the inverse of the
> probability of getting sampled. In your sample, individual units had
> differing probabilities of being sampled, so they have differing weights.
> The calculated size of the population that is represented by your sample
> will be produced by Stata -svy-- commands. To analyze such a sample
> properly, you must include the PSU, strata, and weights in your analysis,
if
> they exist. Without the weights, the estimates you get will be biased.
> Sometimes weights are used to allow post-stratification (for matching to a
> known distribution) or to deal with non-response.
> -Laurel
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]]
> Sent: Friday, May 23, 2003 2:52 PM
> To: [email protected]
> Subject: st: statistical significance in a data set with weighted
> observations
>
> Dear Stata Users,
>
> I have encountered this small problem and since I am not sure about how to
> address it myself I've decided to ask you all. Thank you in advance for
any
> advice you might have for me.
>
> I am working with a dataset that has weights for all observations, and
these
> weights exhibit large variation, from 1 to over 500. When I run a
> nonweighted estimation my t-statistics are relatively small, but when
> weights are introduced, the t-statistics jump. Is there a way of
determining
> the true statistical significance of coefficients in this case?
>
> Thanks again for any help you might have,
>
> MM
>
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> *
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*
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