Struggles with Survey Weighting and Regression Modeling

Struggles with Survey Weighting and Regression Modeling (DOI: http://dx.doi.org/10.1214/088342306000000691) was written by Andrew Gelman in 2007. It was published in Statistical Science (vol. 22, no. 2).

The author demonstrates that, under certain conditions, poststratification and survey weighting are the same procedure.

ps.svg

wt.svg

In contrast, the literature on weighted analysis is confused. In some circumstances, it is recommended to not use survey weights when models of survey data. In addition, weighted standard errors are not trivial to estimate.

Survey weighting also runs into problems as the number of stratified cells grows, potentially to the point where a cell has no respondents.

The authors start from poststratification and try to reverse-engineer weighting methods.

First, using regression of the outcome on the stratifying variables:

regress1.svg

regress2.svg

regress3.svg

Reading notes

Note also that the author repeats their argument from here. This is, as before, applicable because of the constraint on weight computation.


CategoryRicottone