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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Related Experiment Video

Updated: Oct 22, 2025

Sampling Soils in a Heterogeneous Research Plot
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Sensitive proportion in ranked set sampling.

Azhar Mehmood Abbasi1, Muhammad Yousaf Shad1

  • 1Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.

Plos One
|August 31, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces concomitant-based rank set sampling (CRSS) for estimating sensitive proportions. CRSS offers a more precise, unbiased estimator than simple random sampling (SRS) without added cost.

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Estimating sensitive proportions accurately is crucial in surveys.
  • Simple random sampling (SRS) can be inefficient for sensitive data.
  • Existing methods may compromise respondent confidentiality.

Purpose of the Study:

  • To propose and evaluate the concomitant-based rank set sampling (CRSS) method.
  • To develop a new ratio-based estimator for sensitive proportions using CRSS.
  • To ensure respondent confidentiality while improving estimation precision.

Main Methods:

  • Concomitant-based rank set sampling (CRSS) procedure.
  • Development of a ratio-based estimator incorporating a randomizing device.
  • Numerical integration technique for evaluating estimator performance.
  • Application using real-world data.

Main Results:

  • CRSS provides an unbiased estimator for the population sensitive proportion.
  • CRSS is consistently more precise than SRS estimators.
  • The new ratio-based estimator maintains confidentiality.
  • Numerical and real-data applications validate the proposed methods.

Conclusions:

  • CRSS is an effective and efficient sampling technique for sensitive proportion estimation.
  • The new ratio-based estimator offers a confidential and precise alternative.
  • CRSS improves upon traditional SRS methods in terms of precision and cost-effectiveness.