| Re: variables for svyset in Stata (Bangladesh 2011) [message #3739 is a reply to message #3738] |
Tue, 03 February 2015 22:58   |
mmr-UMICH
Messages: 21
Registered: February 2015
Location: A2, MI
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Member |
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Thank you, Trevor.
I will cut and paste some lines whenever requires from original message and try to clarify concretely the reasons behind that error/warnings and its possible solution:
**now analyzing using poisson for the lower 2 wealth quintiles**
svy: poisson csect v190 if v190<3
The above svy: command is not recommended [and not correctly handle the domain concept] as "if v190<3" in svy: subsets the data (i.e., also deleting design information [aside: full sample design information is important for correctly calculating the sampling errors]) prior to run the poisson regression.
This svy: command uses such a subset data and as a result analysis sample (i.e. "estimation sample" in Stata wording) lacks one strata and 101 PSUs (see below output (cut and paste):
-----------start----
(running poisson on estimation sample)
Survey: Poisson regression
Number of strata = 19 Number of obs = 3645
Number of PSUs = 499 Population size = 3867.0893
Design df = 480
-------- end ------
We have to create a variable, say: mydomain = 1 if v190 < 3, otherwise, mydomain = 0, then use svy command:
svy, subpop(mydomain): poisson csect v190
I hope this run will not encounter such issue and also does not require singleunit(centered)* svyset option. And the output will show the same # of obs and population size, but changed others stats such as # of strata, PSUs and degrees of freedom (df).
I verified that strata 5 and 11 have 5 and 23 PSUs respectively; so this svy, subpop(): that form "analytic" domain/subpopulation/subgroup will not be an issue of singleton-strata from full sample data.
*note that singleunit(method) is kind of practically recommended for "analytic" subgroup and/or subclass analysis which sometimes encounter singleton-strata. This specification also appropriately calculates the degrees of freedom, which is prim important for statistical inferences, e.g, confidence intervals and p-values estimation.
Thank you all again.
Moshiur Rahman
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