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Stratified Sampling Method01:16

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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. 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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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
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THE GRAPHICAL STRUCTURE OF RESPONDENT-DRIVEN SAMPLING.

Forrest W Crawford1

  • 1Yale School of Public Health, New Haven, CT, USA.

Sociological Methodology
|October 15, 2019
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) helps study hidden populations by analyzing social networks. This new method uses recruitment data to estimate the complete social network structure, improving sampling accuracy.

Keywords:
hidden populationlink tracingmissing datanetwork inferencerespondent-driven samplingsocial network

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Area of Science:

  • Social Sciences
  • Statistics
  • Epidemiology

Background:

  • Respondent-driven sampling (RDS) is crucial for studying hard-to-reach populations.
  • Current RDS methods struggle with incomplete network data, affecting sampling probability accuracy.

Purpose of the Study:

  • To develop a novel method for estimating hidden social network structures from RDS data.
  • To improve the accuracy of sampling probabilities in RDS studies.

Main Methods:

  • A continuous-time model of RDS recruitment was developed.
  • The model incorporates recruitment event timelines, coupon usage, and network degrees.
  • The model interprets RDS data as an exponential random graph model.

Main Results:

  • The study demonstrates that RDS recruitment data contains information about social network structure.
  • A computationally efficient method for estimating the hidden network was developed and validated.
  • The technique was successfully applied to a real-world RDS study of injection drug users.

Conclusions:

  • The proposed method enhances the understanding of social networks in hidden populations.
  • This approach offers a more robust way to analyze RDS data and improve population estimates.