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Ranked set sampling for efficient estimation of a population proportion.

Haiying Chen1, Elizabeth A Stasny, Douglas A Wolfe

  • 1Department of Public Health Sciences, Wake Forest University, Winston Salem, NC 27157, USA. daw@stat.ohio-state.edu

Statistics in Medicine
|August 16, 2005
PubMed
Summary
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Ranked set sampling (RSS) using logistic regression enhances binary variable ranking for more precise population proportion estimation. This method offers significant improvements over simple random sampling (SRS).

Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Ranked set sampling (RSS) is an efficient sampling technique.
  • Existing RSS methods are well-studied for continuous variables.
  • RSS application for binary variables remains underexplored.

Purpose of the Study:

  • To investigate the utility of logistic regression for ranking binary variables in RSS.
  • To enhance the precision of population proportion estimation using RSS for binary data.

Main Methods:

  • Proposed logistic regression model to aid in ranking binary variables.
  • Applied RSS with logistic regression to estimate population proportion.
  • Utilized National Health and Nutrition Examination Survey III data for illustration.

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Main Results:

  • Logistic regression improved the accuracy of preliminary ranking in RSS.
  • Substantial gains in precision were achieved for population proportion estimation.
  • Demonstrated effectiveness of the proposed method on real-world survey data.

Conclusions:

  • Logistic regression is a valuable tool for enhancing RSS with binary variables.
  • The proposed method offers a more precise alternative to simple random sampling (SRS).
  • This approach has significant implications for survey research involving binary outcomes.