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st: Pooled Datasets (DHS) - use of syvset & regional controls


From   Jörg Kattner <40437@student.hhs.se>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: Pooled Datasets (DHS) - use of syvset & regional controls
Date   Fri, 3 Jan 2014 23:04:49 +0000

Dear Stata list serve members,

For a research paper we would like to pool household surveys (DHS) from different years into a single dataset. 

We experience the following two challenges:

1)
In order to account for the complex survey design we think we have to correctly specify the weights, stratification and clusters for each survey. Even though each survey is from the same country, they can differ slightly depending on the year. 
Thus when we pool them, we still want to correctly specify the survey design. However now the question arises how to do it. Before when doing each year by its own, we used code along the following line:

gen weight = v005 / 1000000
egen stratid = group (v024 v025), label
svyset [pweight=weight], psu(v021) strata(stratid)

The main thing that differs between the surveys is the stratification variables. Sometimes there exists already a stratification variable, sometimes we had to create one like above. Also sometimes the variable v024 (region) for example has 6 values in one year and 10 in the next year. Is it even possible to correctly stratify our dataset when we pool different surveys?


2)
Since we also want to control for regional / community effects later on in our regression models (using svy: reg or svy: logit/clogit) it can be problematic if the defined regions and clusters differ between the surveys.

The only solution we see, is performing single regressions for each year/survey. The drawback is that one cannot directly see whether differences in the constant term or the coefficient of maternal education between the different years/surveys are significant. 

Is there any other statistical method that could deal with this dilemma?


Any help is much appreciated. Thanks a lot in advance!


Best regards,
Lukas & Jörg


     
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