Ridge regression approach to sample balancing with maximum effective base

Article type: Research Article

Authors: Lipovetsky, Stan

Affiliations: GfK Custom Research North America, 8401 Golden Valley Rd., Minneapolis, MN 55427, USA. E-mail: stan.lipovetsky@gfk.com

Abstract: Sample balancing, or raking, or post-stratification, is widely utilized in survey research for weighting sample data for a better correspondence to Census or other known population quotas. Cross-tables of counts are mostly used in the Deming-Stephan iterative proportional fitting to find the weights for adjusting data to known margins. The paper suggests an objective for finding weights with the minimum variance, so with the maximum effective sample size. The model can be expressed as a ridge regression, which is applied to the original data, without its collapsing to cross-tables. The explicit regression solution allows to study the weighting analytically, which helps interpret and improve the sample balance results.

Keywords: Sample balance, post-stratification, raking, weighting, generalized regression, ridge regression

DOI: 10.3233/MAS-2007-2103

Journal: Model Assisted Statistics and Applications, vol. 2, no. 1, pp. 17-26, 2007