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Abstract Details

Activity Number: 625
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #305093
Title: New Model-Optimized Sampling Techniques
Author(s): Sung Joon Hong*+ and Sun Woong Kim
Companies: Dongguk University and Dongguk University
Address: Pildong, 3 Ga 26 Jung Gu, Seoul 100-715, , South Korea
Keywords: superpopulation ; general polynomial model ; model optimization
Abstract:

The work of Kim, Heeringa, and Solenberger (2006) provided a theoretical basis of what is called model-optimized sampling methods for yielding sampling designs that give large variance reductions as well as the stability of the variance estimates. Their methods were based on a simple linear regression superpopulation model. Hong et al. (2009) suggested modified sampling methods based on the same model. However, in many real populations, using more complicated superpopulation models would be better with respect to the efficiency. For this, we suggest model-optimized sampling methods using general polynomial superpopulation models. We illustrate the benefits of our new approaches by comparing the efficiencies between the different models.


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