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Re: Normalizing weight for region/province [message #2413 is a reply to message #2179] Sat, 14 June 2014 03:06 Go to previous messageGo to previous message
jcon is currently offline  jcon
Messages: 3
Registered: May 2014
Location: United States
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Yes, thanks, I think that works. I will also run by some biostat people.

These concepts apply not only for the growing demand for trend analysis (pooling multiple datasets), grouping regions/comparing countries, but also for looking within single country datasets.

For programme work (targeting and evaluation) people want to compare one province to another. First step is to make sure people are looking at confidence intervals when they do this. CIs must be calculated with unweighted n; i'll take a closer look to confirm that. Beyond using confidence intervals how do you compare two provinces. If you keep the weights, you lose your sample size; If you remove the weights the estimates will be off. For most surveys the second stage uses implicit stratification; urban and rural villages from one province are grouped together and then villages are selected PPS. So, at the province level it is a self weighting sample until there are corrections for mistakes in the sampling frame and non-response. As long as those two things aren't really bad, weighted and unweighted estimates at province level will be nearly identical. The most practical way to statistically compare provinces and have the right sample size is probably just to compare with no weights. However, it seems the right statistical methodology would be to renormalize based on the n of the two provinces so that you maintain corrections for sample frame/non-response.
 
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