The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Measuring stratification effects for multistage sampling

다단추출 표본설계의 층효율성 연구

  • Taehoon Kim (Busan Public Health Policy Institute) ;
  • KeeJae Lee (Department of Statistics & Data Science, Korea National Open University) ;
  • Inho Park (Department of Statistics & Data Science, Pukyong National University)
  • 김태훈 (부산광역시 공공보건의료지원단) ;
  • 이기재 (한국방송통신대학교 통계데이터사이언스학과) ;
  • 박인호 (부경대학교 데이터정보과학부 통계데이터사이언스 전공)
  • Received : 2023.06.17
  • Accepted : 2023.06.19
  • Published : 2023.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Sampling designs often use stratified sampling, where elements or clusters of the study population are divided into strata and an independent sample is chosen from each stratum. The stratification strategy consists of stratification and sample allocation, which are important issues that are repeatedly considered in survey sampling. Although a stratified multistage sample design is often used in practice, the literature tends to discuss simple sampling in terms of stratum effects or stratum efficiency. This study examines an existing stratum efficiency measure for two-stage sampling and further proposes additional stratum efficiency measures using the design effect model. The proposed measures are used to evaluate the stratification strategy of the sample design for high school students of the 4th Korean National Environmental Health Survey (KoNEHS).

표본설계는 개체 혹은 집락을 층으로 나눈후 층별로 독립적으로 표본추출하는 층화추출을 종종 채택한다. 층화 전략은 크게 층구분과 표본할당으로 구성되는데 이는 조사연구에서 반복적으로 고려되는 중요한 주제이다. 조사연구에서는 층화다단추출 방식의 복합표본설계를 채택하고 있지만 층효과 혹은 층효율성과 관련하여서 표본론 교재들에서 주로 단순추출에 대해서 다루어지고 있다. 본 연구는 이단추출에 대한 기존 층효율성 측도를 살펴보며 설계효과모형을 적용한 추가적인 층효율성 측도들을 제안하였다. 제안된 측도들을 활용하여 제4기 국민환경기초조사의 고등학교 대상 표본설계의 층화전략에 대해 평가하였다.

Keywords

Acknowledgement

Inho Park's work was supported by a Research Grant of Pukyong National University (2021).

References

  1. Chen S and Rust K (2017). An extension of Kish's formula for design effects to two- and three-stage designs with stratification, Journal of Survey Statistics and Methodology, 5, 111-130. https://doi.org/10.1093/jssam/smw036
  2. Feng X (2006). On confidence intervals for proportions with focus on the U.S. National Health and Nutribution Examination Surveys (Master's thesis), Simon Fraser University, Burnaby, BC, Canada.
  3. Gabler S, Hader S, and Lahiri P (1999). A model based justification of Kish's formula for design effects for weighting and clustering, Survey Methodology, 25, 105-106.
  4. Heeringa SG, West BT, and Berglund PA (2010). Applied Survey Data Analysis, Chapman and Hall/CRC, New York.
  5. Kim TH (2023). Stratification effects under multistage sampling (Master's thesis), Pukyong National University, Busan, Korea.
  6. Kish L (1987). Weighting in De f t2, Survey Statistician, June, 26-30.
  7. Krenzke T and Kali J (2016). Review of the FoodAPS 2012 Sample Design. A technical report by Westat prepared for Economic Research Service, U.S. Department of Agriculture.
  8. Lehtonen R and Pahkinen E (2004). Practical Methods for Design and Analysis of Complex Surveys (2nd ed), Chapman and Hall/CRC, New York.
  9. Lohr SL (2010). Sampling: Design and Analysis (2nd ed), Brooks/Cole, Boston.
  10. National Institute of Environmental Research (2021). Sample documentation for cycle 4 of the Korean National Environmental Health Survey (KoNEHS).
  11. Park I (2015). Understanding complex design features via design effect models, The Korean Journal of Applied Statistics, 28, 1217-1225. https://doi.org/10.5351/KJAS.2015.28.6.1217
  12. Park I and Hwang HG (2019). Sample size determination using design effect formula for repeated surveys, The Korean Journal of Applied Statistics, 32, 1-10. https://doi.org/10.5351/KJAS.2019.32.1.001
  13. Rust K and Broene P (2010). Design effects for totals in multi-stage samples, In Proceedings of the Joint Statistical Meetings, Section on Survey Research Methods, American Statistical Association, Vancouver, British Columbia, 2174-2181.
  14. SAS Institute Inc. (2010). SAS/STAT 9.3 User's Guide, Cary, NC: SAS Institute Inc.
  15. Valliant R, Dever JA, and Kreuter F (2019). Practical Tools for Designing and Weighting Survey Samples (2nd ed), Springer, New York.
  16. Wolter KM (2007). Introduction to Variance Estimation (2nd ed), Springer, New York.