I am pooling Pakistan Demographic Health Survey of 1990-91 and 2012-13. I am looking at the trends in unintended fertility over time. Do i need to re-normalize the weights before pooling, even when I am taking survey year as a covariate?

Second, my analytical sample consist of those women who had birth in last five years and among them I am restricting my analysis to most recent birth. In that case when I am renormalizing weight, number of women aged 15-49 interviewed should be the total that were interviewed or that subsample.]]>

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In the first case, you can just compute the two statistics separately in each dataset. In the second case, you would have to append the two datasets, and in theory should re-normalize using the guidelines described in other threads. However, some DHS people have suggested that since sample sizes are very similar from round to round within a country (at least usually), adjusting the weights shouldn't matter. Also, if you can assume that the population demographics aren't change much, then another option would be to force each survey's weights to sum up to 1 (so, get your sample, sum up the weights within each survey, and gen new_weight = old_weight/sum_of_weights where sum_of_weights is defined separately for each survey). That would preserve the probability of selection correction within each survey, and make the two surveys each add equal total weight to the final regression.

That make sense?]]>