| Re: When to use iweight and pweight in stata [message #13261 is a reply to message #13199] |
Tue, 10 October 2017 10:34  |
Bridgette-DHS
Messages: 3199
Registered: February 2013
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Senior Member |
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A response from DHS Stata Specialist, Tom Pullum:
Below I will insert some Stata lines that you can run after you have opened a KR file. If you want to adjust for weights, clustering, and stratification in a table, the best way (in my opinion!) is with logit (if one of the variables is binary) or mlogit (if both variables have more than two categories). You cannot get a chi square, but you can get the p-value for an F, which will be an equivalent test of the significance of the association. Note that the correspondence is with a likelihood ratio chi-square, rather than a Pearson chi-square. The correspondences between the following approaches with Stata add to my confidence in how Stata handles weights. You could check whether you get the same correspondences with SPSS.
gen stunted=.
replace stunted=0 if hw70<600
replace stunted=1 if hw70<-200
gen age=b8
replace age=. if v008-b3<6
tab stunted age, lrchi2
scalar pvalue=r(p_lr)
scalar list pvalue
logit stunted i.age
scalar pvalue=e(p)
scalar list pvalue
svyset v021 [pweight=v005], strata(v022) singleunit(centered)
svy: logit stunted i.age
scalar pvalue=e(p)
scalar list pvalue
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