We agree that multi-level analysis should include weights at each level. Until Stata 14, multi-level models in Stata could not use weights at all. Beginning with Stata 14, weights (multi-level weights) are allowed and should be used (as in MLwin). The problem is that DHS does not have cluster-level weights. The clusters are sampled with probability proportional to size, within strata. The sizes of the clusters, usually census enumeration areas, are part of the sampling frame, usually the most recent census. The sampling frame is not public information. DHS only has access to it within the country and is not allowed to make a copy of it. Statistical offices typically do not want to share it.

It is conceivable that there is some way to approximate the cluster-level weights or at least to get away from the invalid assumption that the first stage sampling probabilities are the same for all clusters. We will look into this. Thanks for raising the issue.

This means that V005 is the normalized version of the sampling weight. The main purpose of the normalization process is to avoid the large values for the number of weighted cases in the tables in DHS survey final reports. This applies for all the DHS standard weights, including weights for households, such as HV005, and individuals. The V005 was calculated by multiplying the individual sampling weight by a normalization factor at the national level. The normalization factor is the total number of completed cases divided by the total number of weighted cases. In case of the V005, it is the total number of completed women divided by the weighted total number of completed women. In case of the HV005, it is the total number of completed households divided by the weighted total number of completed households. Therefore the standard weights in the DHS data files are relative weights. Relative weights can be used to estimate means, proportions, rates and ratios because the normalization factor is cancelled out when used in both numerator and denominator, so it has no effect on the calculated indicator values. However, the standard weights are not valid for estimating totals. Also the normalized weight is not valid for pooled data, even for data pooled for women and men in the same survey, because the normalization factor is country and sex specific.

Unfortunately, there is nothing new. With the confidentiality requests from the DHS protocol, we cannot provide the selection probabilities (we cannot keep them) of different levels which are the components of the multi-level weights. So the question is not a technical one, but rather confidential obligations.
Thanks
Ruilin

Individuals in households are not sampled. In the survey design, clusters are sampled and then households are sampled within clusters. After the household has been selected, all eligible respondents (based on age, sex, and de facto residence) are selected.

Ideally we would provide separate sampling fractions (or their inverse, the weights) for the clusters and then the households. At this time it is not possible to do a full multilevel model because we can only provide the product (hv005, etc.) after an adjustment for nonresponse. As has been stated in other responses, for privacy reasons we do not save the more detailed information.

The DHS analysis group uses Stata, sometimes R. We do not use SPSS and we cannot help you with the syntax of multilevel commands in SPSS. Regarding weights, we do not have separate weights for the different levels, only the net weight, which is proportional to hv005 or v005. We intend to develop recommendations for how to partition this into cluster-level and household-level weights but are not yet prepared to suggest anything specific.

Dear user,

To be able to use multilevel modeling with DHS data and the svy command, a weight must be applied for each level. Since we only have one weight, we can make the assumption that all individuals in the household have the same weight. The Stata code to run a multilevel model for a binary outcome is below using the melogit command. However, you can use other mixed model commands if your outcome had a different distribution. The svyset should be the same.

gen wt=v005/1000000
gen wt2=1
svyset v001, weight(wt) strata(v023) , singleunit(centered) || _n, weight(wt2)

*this is a random intercept model
svy: melogit outcome var1 var2 || v001:

*random intercept and random slop for var3
svy: melogit outcome var1 var2 || v001: var3
*for this model you can also add the covariance(unstructured) option. Please read the stata documentation for the melogit command on this
* you can also check the Stata documentation for svyset, they discuss how to construct this for multistage sample design. 

Hope this helps.

Best,

Shireen Assaf
Technical Specialist
The DHS Program

Hello,
Unfortunately I do not have any R code for this. However, please study the survey package in R and you may find an answer.
Thank you.
Best,
Shireen