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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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[Progress on the application of respondent-driven sampling in population size estimation].

L M Zhu1, X T Zhang1, K F Ma1

  • 1Division of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
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Respondent-driven sampling (RDS) is a versatile method for reaching hidden populations and is increasingly used for general population studies. RDS network size can be weighted for accurate population size estimation.

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Area of Science:

  • Social Sciences
  • Epidemiology
  • Statistical Methods

Background:

  • Respondent-driven sampling (RDS) is a key technique for sampling hidden populations, including transgender women, female sex workers, and men who have sex with men.
  • These populations are often hard to reach due to stigma and legal issues.
  • RDS is now being adapted for use in general population studies.

Purpose of the Study:

  • To review the application of RDS in population size estimation.
  • To explore the potential of RDS for broader research applications.
  • To provide insights into the future development of RDS methodologies.

Main Methods:

  • The study summarizes existing research on RDS application for population size estimation.
  • It discusses the weighting of RDS samples based on network size.
  • The review covers the progression of RDS from hidden to general populations.

Main Results:

  • RDS network size can be weighted to estimate population characteristics and size.
  • The application of RDS is expanding beyond traditionally hidden populations.
  • Continuous improvements are enhancing the utility of RDS in research.

Conclusions:

  • RDS is a valuable tool for population size estimation, especially when accounting for network properties.
  • The adaptability of RDS suggests its growing importance in diverse research settings.
  • Further development of RDS methods will broaden its applicability in social and epidemiological research.