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Activity Number: 582
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #304775
Title: A Simple Approach to Sample Allocation for Multivariate Stratified Sampling
Author(s): Sun Woong Kim*+ and Eun-Jeong Nam and Young Sung Han
Companies: Dongguk University and Korea Statistics Promotion Institute and Dongguk University
Address: Department of Statistics, 100-715 Seoul, , South Korea
Keywords: nonlinear programming ; compromise ; precision ; sampling variances

In stratified simple random sampling the values of sample sizes in the respective strata should be chosen to reduce sampling variances. For example, if the cost per unit is the same in all strata, Neyman allocation can be used for the purpose. His allocation for minimizing the variance will be the best for one variable, but it will not in general be best for other variables in a survey with many variables (items). Some compromise needs to be reached in the allocation. Huddleston et al. (1970) proposed an algorithm using nonlinear programming (NLP) for compromise allocation. Their classic approach can lead to infeasible NLP problems. Thus, we present a modification of their approach. However, the approach may not be satisfactory because the solution can be less precise than Neyman allocations or proportional allocations for some individual variables. We suggest an alternative approach using NLP based on a different principle. This approach for minimizing the sampling variances of the variables under study is simple to use and always provides solution. We illustrate the new approach by using real survey data.

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