| Re: When to use iweight and pweight in stata [message #13197 is a reply to message #13193] |
Mon, 02 October 2017 18:33   |
Reduced-For(u)m
Messages: 292
Registered: March 2013
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Senior Member |
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You are clustering your standard errors in Stata, because it says the number of clusters in your output. You probably set that up with your "svyset" command (not on printout how you set up svyset, but I'm guessing you set the PSU as a "cluster" which is what the DHS recommends).
But I'll bet your SPSS code does not do this. Essentially, "clustering" will allow your confidence intervals/p-values to account for within-group correlations in error terms (because people in the same village are more similar to each other than two total strangers). The result is that you have to inflate your standard errors (make your p-values bigger) to account for the within-cluster similarities among people, otherwise you get p-values that are much too small relative to the "true" value.
So I think the difference is that in Stata you are (correctly) allowing for correlations among error terms and within-cluster heteroskedasticity (via your svyset command that references the PSUs) and this is generating (appropriately) larger standard errors and p-values than your SPSS code which is treating each observation as independent.
I'd look up "clustered standard errors" in SPSS, add that to your code (clustering at the PSU level) and I suspect at that point your p-values will become much closer (that is, the SPSS estimates will look more like the Stata estimates). I don't actually know how to do it in SPSS, but it should be relatively straight forward.
Again - this is not about weighting variables, that only affects point estimates. This is about accounting for clustering in your standard error/p-value calculations.
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