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Webinar Details

Survey Weighting: Goals and Methods

Richard Valliant

Tuesday, November 14, 2017
12:00 - 1:30 PM CDT/ 1:00 - 2:30 PM EDT/ 10:00 - 11:30 AM PDT

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About This Course:


Survey data sets usually come with at least one analysis weight for each respondent record in the sample. Analysts interested in calculating population estimates are told to use the same set of weights for all analyses—means, totals, linear and nonlinear models, etc. Analysis weights are designed to: 1. Account for the probabilities used to select units (in cases where random sampling is used); 2. Adjust in cases where it cannot be determined whether some sample units are members of the population under study; 3. Adjust for eligible units that do not respond to the survey to limit the effects of nonresponse bias; 4. Incorporate external data to reduce standard errors of estimates and to compensate when the sample does not correctly cover the desired population. Survey statisticians usually think of weighting in the context of probability samples, where units are selected by some random means from a well-defined population. All four steps above can be applied to probability samples. However, because of the current popularity of volunteer web panels and other kinds of “found” data, how to weight nonprobability samples is also worth considering. This webinar will review different techniques used in weighting, including formation of adjustment classes for unknown eligibility and nonresponse based on combinations of covariates, response propensity estimates, and regression trees. Use of auxiliary frame or population data to reduce variances and correct for coverage errors will be covered. We will also review how some of the same techniques can be used to weight nonprobability samples. Throughout we will give examples of how R and Stata can be used to compute weights.


Learning Objectives:

  • Understand the different steps in weighting and the reasoning behind each
  • Understand how weights are used to correct for coverage errors and nonresponse
  • Understand how weighting approaches differ for probability and nonprobability samples

About the Instructor:

Dr. Richard Valliant
is a Research Professor Emeritus at the Universities of Michigan and Maryland. He has over 40 years of experience in survey sampling, estimation theory, and statistical computing. He was formerly an Associate Director at Westat and a mathematical statistician with the Bureau of Labor Statistics. He has a range of applied experience in survey estimation and sample design on a variety of establishment and household surveys. He is a Fellow of the American Statistical Association and has been an editor of the Journal of the American Statistical Association, the Journal of Official Statistics, and Survey Methodology.