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What is rake weighting?
Raking. For public opinion surveys, the most prevalent method for weighting is iterative proportional fitting, more commonly referred to as raking. The process will adjust the weights so that gender ratio for the weighted survey sample matches the desired population distribution.
What is raking in survey data?
Raking assigns a weight value to each survey respondent such that the weighted distribution of the sample is in very close agreement with two or more marginal control variables. …
How do I add a survey package to R?
To download the survey analysis package, visit a CRAN site, select “Package sources” from the menu, and select “survey”. Then you can download source for the package and a Reference Manual in PDF. Binary packages for Windows and Mac OS X can be installed from menu options in R.
Which is the rake function in survey package?
The raking procedure can be done with the ‘rake’ function form the ‘survey’ package. This function requieres to pass the previously computed survey design object as its first object ( ‘design’ ). The second argument ( ‘sample.margins’) is a list of formulas describing which variables are going to be used for calibration.
How to calculate the raked weight of a survey?
Here we compare our raked weight using the same methodology as they used in the 7th ESS ( ‘ess.raked.weight’) and the weight included in the 7th ESS dataset ( ‘pspwght’ ). Looking at the first 15 observations we see that they are, in most cases, very close.
How do you calculate survey weight in R?
Now we will use the R ‘survey’ package (Lumley,T., 2010) to calibrate weights using the raking procedure. We will do this twice. First time we will compute the raked weighs using our ‘final.weight’ as an input. These contain information from both the base weights and our adjustment for non-response.
What are the control variables in a raking survey?
For example, in household surveys the control variables are typically sample design and socio-demographic variables. Raking is an iterative process that uses the sample design weight as the starting weight and terminates when the convergence criterion is achieved.