I am analyzing a dataset that has a variable for post-stratification weights. As this is a complex survey, the plan is to use the R
survey package. I have been reading its documentation and feel like able to set a survey design correctly. So far, so good. That said, one aspect is still not clear for me.
Lumley says that
survey assumes weights are sampling weights -- i.e. 1/(prop of selection for that observation):
Survey designs are specified using the svydesign function. The main arguments to the the function are id to specify sampling units (PSUs and optionally later stages), strata to specify strata, weights to specify sampling weights, and fpc to specify finite population size corrections. These arguments should be given as formulas, referring to columns in a data frame given as the data argument. (http://r-survey.r-forge.r-project.org/survey/example-design.html)
My dataset does not include a variable for sampling weights. Its weight is a post-stratification weight accounting for probability of selection, unit non-responses, and post-stratifies the sample to match the age and gender joint distribution. The post-stratification weight is rescaled to sample size -- there are 1,000 observations so
sum(poststratification.wt)=1,000, ranging from ~0.9 to ~5.5. I have closely inspected the data and the info available does not allow me to estimate the probability weights from the scratch.
So my question is: Am I safe, or roughly safe, using the provided post-stratification weight in the
svydesign(weights=) argument? If not, what should I do? (Running a 1,000 survey is out of my budget possibility, hehe).