From Monira Parveen at WFP:

Similar study was conducted by IFPRI in Bangladesh on WFP’s 5 armed transfer modalities including cash, food and intensive Nutrition BCC. Dr. Akhter Ahmed, Chief of Party, IFPRI, Bangladesh can be contacted for the methodolody.

Dear Nick:

You have posed a good question. Stratified sampling is frequently done using geographic strata in order to minimize the sample size necessary to make reasonably precise estimates of outcomes in each geographic subdivision; for example, in each province or region. However, another reason to do stratified sampling is to increase the precision obtained in your sample. If the sample is stratified on a variable which is associated with the outcome, taking into account the stratification during data analysis will result in better precision than would be obtained with a non-stratified sample of the same size.

My question is: Why are you stratifying your sample? If you are measuring the effectiveness of the two different interventions (cash and cash plus BCC) against the control. you are not attempting to estimate outcomes in each stratum. If you are stratifying to get better precision, then you need stratify only on factors you know will be associated with the outcome you are measuring. Stratifying on factors not associated with your outcome is a waste of time and resources.

But there are costs and problems with stratification. First, to account for stratification when comparing the outcome variable in each intervention group, you will need to do stratified or multi-variate analysis which greatly complicates data analysis. Second, you must have data on all stratification variables for each sampling unit. Any sampling unit without complete data on all stratification variables cannot be included in the sample because you will not be able to place it in a stratum. Third, if you stratify on too many variables, the number of sampling units in each stratum can become vary small. The stratum is defined as the set of values for all stratification variables. For example, if you stratify on wealth quintile (5 possible values), safe water supply (2 possible values), adequate sanitation (2 possible values), and 3 different levels of disease prevalence (3 possible values) you will have 60 strata (5 x 2 x 2 x 3). This will lead to very small numbers in each stratum which is not a good idea.

So, in short, if you wish to improve the precision of your study, I would select 1 or 2 variables which you think are most strongly related to stunting and stratify on them, if you have the necessary data.

Thanks Tamsin Walters and Monira Parveen.

Could you please guide me the contact address of Dr. Akhter Ahmed. You can sent it to my gmail (nicholustintzaw@gmail.com).

best regards,
Nick

thanks a lot for your very informative comment.

Yes, the objective of stratifying is to measure the effectiveness of two different intervention against the control and also comparing between each arms (cash only vs cash + BCC). We also considering as estimation to reduce 5% stunting prevalence to estimate sample size for each arm and hope that the sample size mentioned in my question have enough power to detect this 5% variance.

There will be also census data collection in all sample area before performing stratification (we are going to allocate three stratum based on three arms). I do agree with your point as considering one or two main key variables which have strong relationship to our outcome. In this points, what will be your recommendation on those key variables.

best regards,
Nick

Dear Nick:
I'm not sure what you mean by "we are going to allocate three stratum based on three arms". It sounds like you may be confusing allocation to treatment arms and stratification of a random sample. If you like, you can e-mail me at bradleyawoodruff@gmail.com for further discussion.