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Home » Data » Sampling and Weighting Webinar June 2015 » De-normalization; original probability weights; level of clustering
Re: De-normalization; original probability weights; level of clustering [message #5567 is a reply to message #5498] Mon, 08 June 2015 15:02 Go to previous messageGo to previous message
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Registered: March 2013
Senior Member

1 - The weights for a particular survey sum up to a funny number - often (depending on the particular weight) something like the total number of households interviewed. Thus, if you took two survey rounds in a row from the same country and the sample size (sum of weights) changed from round to round, you would implicitly be weighting the observations in the larger sample (each) more than the ones in the smaller sample. One way to think about it - the DHS implicitly lays a "population" weight over a "probability weight" by making the weights sum to some particular number. De-normalizing strips off the population weight implicitly provided by DHS and then lays new, meaningful population weights onto the underlying probability of selection. Does that help? You could get some insight by summing up the weights for the two survey rounds and seeing how much they differ.

2 - No, not really - although you could, I think we have confirmed, just divide each weight by the "sum of within-survey weights" thus making each survey weight sum up to 1... that might not be pure probability weights, but it is close.

3 - If you cluster at PSU, it allows for covariances at lower levels too (i.e. at household and woman). So you can always cluster at higher levels and still get the correction at the lower levels.
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