"Besides this problem, it still has all the other biases caused by
multistage clustering."
What biases, exactly? I know of none inherent in multi-stage with
well-constructed weights. Consult a good book, such as S Lohr,
Sampling Design and Analysis.
-Steve
2009/8/21 千早 ケンジ <[email protected]>:
> Dear Statalist Members
>
> It is my first post to this list. My name is Guilherme Kenji Chihaya and I
> am currently a graduate student in Tohoku University, Japan. I am using a
> data set called Life Histories and Social Change in Contemporary China. It
> is a survey in which the rural and urban populations of China were treated
> as different populations and each one was sampled for about 4000 samples
> using multistage sampling with clustering.
>
> The proportion of China's rural population was about 70% by the time of the
> survey, however, this survey is designed so that it accounts for 50% of the
> whole sample if you try to analyse the two samples together. Besides this
> problem, it still has all the other biases caused by multistage clustering.
>
> I wonder if it is possible to use svyset to correct all these biases so that
> I can analyse the two samples as one single dataset. The codebook for the
> survey is incomplete, it states that the data is supposed to be analysed
> using Stata complex samples functionality but the part about how to use it
> is unfinished in the available version. There are variables identifying the
> primary sample unit and the stratum used for sampling. However, I don't know
> how to deal with the rural-urban disproportionality.
>
Steven Samuels
[email protected]
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774
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