# Pooling data from two different samples: Does the scale of the sampling weights matter?

Background

A colleague recently came to me with a problem. He was tasked with comparing health service utilization indicators in two secondary datasets:

1. The first dataset is a Demographic and Health Survey (DHS) dataset, which has around 9,000 observations and is a nationally representative sample of women 15-49 in Country X. Individuals were sampled using stratified two-stage cluster sampling. The strata for this sample were the province of residence and whether the cluster was in an urban area or rural area.
2. The survey instrument for the second dataset was modeled after the DHS. The same sampling strategy used by the DHS Program was used here. It has around 2,000 observations and is a representative sample of women 15-49 in 3 of the 11 provinces in Country X. As with the DHS, the strata were the province of residence and whether the cluster was in an urban or rural area.

Both datasets contain sampling weights, but the scales are different. For the first dataset, the party that processes the data generally multiplies the weight by 100,000 to preserve decimal places. The documentation urges users to divide the weight variable that is shipped with the dataset by 100,000 before using. For the second dataset, the party that processes the data did not transform the weight variable further, so that the variable could be used as-is.

Problem

The "scale" of the weight--whether divided by 100,000 first or used as is--doesn't really matter when working within a single survey for point estimates of proportions, means, or parameters, as this kind of transformation only affects the "effective" number of observations (i.e. 1,000/1,000,000 is equivalent to 0.01/10). What I am not sure about is whether the weights necessarily need to be re-scaled when the data are pooled. The DHS documentation for sampling states that when pooling DHS datasets, the weights need to be "de-normalized" before using (ICF International, 2012, p. 28) by multiplying the weight by the target population and dividing this by the number of completed cases (in other words, sum of the weights), for each survey, because the given sampling weights are country-time specific. My inclination is that once the weights are de-normalized, it is not necessary to ensure that they are the same scale, as he is only interested in the differences in proportions between the two datasets. Is this correct, or will having variables of different scales be a problem when doing regression?

Reference

1. ICF International. 2012. Demographic and Health Survey Sampling and Household Listing Manual. Calverton, Maryland, USA: ICF International. http://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf

Once that is done, you can combine the weights using a version of the single frame estimation method (Lohr 2009). Since weights are inverse probabilities of selection, the combined weight should be the inverse of the combined probability of selection: $$w_i^c = 1/\pi_i^c = 1/[1-(1-\pi_1^1)(1-\pi_i^2)] \approx 1/(\pi_i^1 + \pi_i^2) = 1/(1/w_i^1 + 1/w_i^2)$$ for the observations in the three provinces that were sampled twice, while the observations in the remaining provinces just retain their DHS weight.