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Home » Countries » India » Districts as cluster-level for multi-level model (Weighting for multi-level modelling)
Districts as cluster-level for multi-level model [message #18796] Sun, 23 February 2020 05:28 Go to previous message
dgodha
Messages: 44
Registered: November 2016
Location: India
Member
Hello,

I will appreciate your expert guidance on my query. We usually use 'psu' as the cluster level in DHS data. In my case, the group size is too small if I use 'psu'.
  	
Group Variable |     #Groups    Minimum    Average    Maximum
           psu |     25,063          1        3.1         16

Since NFHS-4 is representative at the district level and we have to anyway create a variable for the cluster-weight, I am wondering if it is possible to use district as the cluster-level. I tried changing my weighting command for psu to district but as you can see in the output, I don't get the p-values and CIs.
*Rescaling of weights
	gen wt=v005/1000000
	
*Level 1 weights using scaling method 1: New weights sum to district sample size
	gen sqw = wt*wt 
	egen sumsqw = sum(sqw), by(sdistri) 
	egen sumw = sum(wt), by(sdistri) 
	gen pwt11 = wt*sumw/sumsqw 

* Survey setting
	gen wt2=1
	svyset sdistri, weight(wt2) strata(v023) , singleunit(centered) || _n, weight(pwt11)

*Output
*******
Number of strata   =     2,509                  Number of obs     =  1,538,126
Number of PSUs     =     2,509                  Population size   =  1,438,715
Subpop. no. obs   =     78,446
Subpop. size      =  73,653.12
Design df         =          0
F(   0,      0)   =          .
Prob > F          =          .


Linearized
 y           Coef.    Std. Err.      t       P>t     [95% Conf. Interval]

_cons     -1.585093   .0192937   -82.16       .            .           .

sdistri      
var(_cons) .1527032   .0153514                             .           .

Note: 5 strata omitted because they contain no subpopulation members.
Note: Strata with single sampling unit centered at overall mean.

I am not sure what is going wrong and will appreciate any understanding.
Thank you
Deepali


Deepali
 
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