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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