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Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

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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. Among the various sampling methods used by...
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Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Probability Sampling Method for a Hidden Population Using Respondent-Driven Sampling: Simulation for Cancer

Minsoo Jung1

  • 1Department of Health Science, Dongduk Women's University, Seoul, South Korea

Asian Pacific Journal of Cancer Prevention : APJCP
|June 25, 2015
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) offers a probability sampling method for hidden populations, overcoming low response rates and dishonest answers common in traditional surveys. Computer simulations confirm RDS stabilizes, ensuring sample data independence from initial seeds with sufficient sample size.

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Area of Science:

  • Social Sciences
  • Public Health
  • Statistics

Background:

  • Hidden populations present significant methodological challenges for surveys due to low response rates and potential dishonesty.
  • Traditional methods like snowball sampling have limitations in achieving representative samples from these hard-to-reach groups.
  • Social stigma can deter participation and honest reporting in sensitive population surveys.

Purpose of the Study:

  • To evaluate the efficacy of respondent-driven sampling (RDS) for surveying hidden populations, specifically cancer survivors, using computer simulations.
  • To assess the stability and independence of RDS samples from initial seed selection.
  • To determine if RDS can provide a more reliable alternative to existing survey methods for hidden populations.

Main Methods:

  • Respondent-driven sampling (RDS), a chain-referral sampling technique based on Markov chain principles and social network data.
  • Computer simulations were employed to model RDS processes and analyze sample characteristics.
  • The study focused on a simulated hidden population of cancer survivors to test RDS applicability.

Main Results:

  • RDS chain-referral sampling effects diminish as sample size increases, leading to stabilization.
  • Sample data becomes independent of the initial seeds once a sufficient sample size is achieved.
  • The simulation results indicate that RDS can yield reliable data even with convenience sampling for initial seeds.

Conclusions:

  • Respondent-driven sampling (RDS) is a viable and effective probability sampling method for hidden populations.
  • RDS overcomes key limitations of traditional methods, offering improved accuracy and reliability.
  • The study recommends the utilization of RDS for various domestic survey applications involving hidden populations.