Dear Olalekan,
You asked about accounting for the survey design when analysing DHS
data. You should account for the stratification and the clustering
whenever you calculate standard errors- if you do not, the standard
errors will not be correct. I always include the weighting information
since I usually want to know if associations are likely to be true of
the population the survey is designed to represent (with weights) and
not just the survey sample (without weights). Once you have accounted
for the stratification and clustering you don't really lose anything by
also accounting for the weights since you are already working with
robust standard errors.
I don't know of a reason why it would be inappropriate to use sample
weights in a regression analysis- but that may well be ignorance on my
part.
Hope this helps,
Emma
>>> Olalekan Uthman <[email protected]> 30/07/07 15:49 >>>
Dear Stata users,
I am trying to run a logistic regression using Measure
DHS survey data. Quoting from DHS statistics guide:
"Use of sample weights is inappropriate for estimating
relationships, such as regression and correlation
coefficients.
I am not sure whether to ignore the psu and strata
also when running the logistic regression.
Can I just use
xi: logistic bmi i.edu
instead of:
svyset psu [pw=weight], strata(strata)
xi: svy: logistic bmi i.edu
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