Hello,
I am trying to combine two separate multi year surveys (survey A & B). Both of which come from different countries. Each set surveys comes with a different sent of pweights for every year. I was wondering if you all had any experience "Weighting the weights" (you multiply the proportion each survey year by the yearly weights) methods of combining weights. One of the surveys, is NHANES (survey A), which you can easily adjust for the different cycles by multiplying the pweights by the proportion of that each cycle contributes to the overall sample. I was thinking about doing something once I combine all cycles of both surveys. However, I am unsure I can do this given the different PSUs and Clusters of the two surveys.
Sampling of Survey A:
Each sampled person in NHANES is assigned a numerical sample weight that measures the number of people in the population represented by that specific person. Sample weights for NHANES participants incorporate adjustments for unequal selection probabilities and certain types of non-response, as well as an adjustment to independent estimates (called control totals) of population sizes for specific age, sex, and race/ethnicity categories. These adjustments are made at the aggregate level for an NHANES sample, so that estimates computed from that sample are nationally representative. Because not all sampled persons completed all portions of the survey, each individual represented in a public release data file may have several different sample weights assigned, depending on the nature of the non-response adjustments required.Two sets of sample weight variables are included in the demographics data file, an interview weight and an exam weight.
The set of all individuals that have nonzero values for a particular version of sample weights comprise a nationally representative sample, so long as those sample weights are incorporated into statistical analyses. Performing append and merge operations to pull together data elements from many data files may result in missing or zero values for sample weight variables, so it is important to ensure that the data set for a particular analysis does not include individuals with zero or missing values for the desired version of sample weight.
Sampling of Survey B: Multi-stage stratified random. Non-response weighting has been incorporated in the weighting strategy. A household weight has been generated for the general population sample which adjusts for non-contact and refusal of households. The individual weights adjust for the additional non-response among individuals in participating households and additional weights take into account participants’ participation in different elements of the survey.
Any guidance or other resources on this topic would be appreciated!
I am trying to combine two separate multi year surveys (survey A & B). Both of which come from different countries. Each set surveys comes with a different sent of pweights for every year. I was wondering if you all had any experience "Weighting the weights" (you multiply the proportion each survey year by the yearly weights) methods of combining weights. One of the surveys, is NHANES (survey A), which you can easily adjust for the different cycles by multiplying the pweights by the proportion of that each cycle contributes to the overall sample. I was thinking about doing something once I combine all cycles of both surveys. However, I am unsure I can do this given the different PSUs and Clusters of the two surveys.
Sampling of Survey A:
Each sampled person in NHANES is assigned a numerical sample weight that measures the number of people in the population represented by that specific person. Sample weights for NHANES participants incorporate adjustments for unequal selection probabilities and certain types of non-response, as well as an adjustment to independent estimates (called control totals) of population sizes for specific age, sex, and race/ethnicity categories. These adjustments are made at the aggregate level for an NHANES sample, so that estimates computed from that sample are nationally representative. Because not all sampled persons completed all portions of the survey, each individual represented in a public release data file may have several different sample weights assigned, depending on the nature of the non-response adjustments required.Two sets of sample weight variables are included in the demographics data file, an interview weight and an exam weight.
The set of all individuals that have nonzero values for a particular version of sample weights comprise a nationally representative sample, so long as those sample weights are incorporated into statistical analyses. Performing append and merge operations to pull together data elements from many data files may result in missing or zero values for sample weight variables, so it is important to ensure that the data set for a particular analysis does not include individuals with zero or missing values for the desired version of sample weight.
Sampling of Survey B: Multi-stage stratified random. Non-response weighting has been incorporated in the weighting strategy. A household weight has been generated for the general population sample which adjusts for non-contact and refusal of households. The individual weights adjust for the additional non-response among individuals in participating households and additional weights take into account participants’ participation in different elements of the survey.
Any guidance or other resources on this topic would be appreciated!