A Stata program for calibration weighting

John D'Souza
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John D'Souza: National Centre for Social Research, London

United Kingdom Stata Users' Group Meetings 2010 from Stata Users Group

Abstract: Although survey data are sometimes weighted by their selection weights, it is often preferable to use auxiliary information available on the whole population to improve estimation. Calibration weighting (Deville and Sarndal, 1992, Journal of the American Statistical Association 87: 376-382) is one of the most common methods of doing this. This method adjusts the selection weights so that known population totals for the auxiliary variables are reproduced exactly, while ensuring that the calibrated weights are as close as possible to the original sampling weight. The simplest example of calibration is poststratification. This is the special case where the auxiliary variable is a single categorical variable. General calibration extends this to deal with more than one auxiliary variable and allows the user to include both categorical and numerical variables. A typical example might occur in a population survey, where the selection weights could be calibrated to ensure that the sample weighted by the calibration weights has exactly the same distribution as the population on variables such as age, sex, and region. Many packages have routines for calibration. SAS has the macro CALMAR; GenStat has the procedure SVCALIBRATE; and R has the function calibrate. However, no such routine is publicly available in Stata. I will introduce a user-written Stata program for calibration and will also discuss a simple extension to show how it can incorporate a nonresponse correction. I will also briefly discuss the program's strengths and limitations when compared to rival packages.

Date: 2010-09-17
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