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Related Concept Videos

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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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.
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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.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Systematic Sampling Method01:17

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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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Sampling Plans01:23

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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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Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Krista J Gile1, Mark S Handcock1

  • 1University of Massachusetts, Amherst, MA, USA. University of California at Los Angeles, Los Angeles, CA, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|December 8, 2015
PubMed
Summary
This summary is machine-generated.

Respondent-Driven Sampling (RDS) is used for hard-to-reach populations. This study introduces a new model-assisted approach for more accurate statistical inference from RDS data, improving population mean estimation.

Keywords:
Exponential-family random graph modelHard-to-reach population samplingLink-tracingNetwork samplingSocial networks

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Area of Science:

  • Social Network Analysis
  • Statistical Inference
  • Epidemiology

Background:

  • Respondent-Driven Sampling (RDS) is crucial for studying hidden populations via social networks.
  • Traditional inference methods struggle with RDS due to its complex, partially uncontrolled sampling process.
  • Existing techniques often require intricate modeling or fail to provide accurate sampling weights.

Purpose of the Study:

  • To develop a novel, model-assisted approach for more robust statistical inference from RDS data.
  • To introduce a new class of estimators for population means and their standard errors.
  • To improve upon existing estimation methods for RDS, including adjustments for initial convenience samples.

Main Methods:

  • A model-assisted design-based estimator was developed, incorporating a working network model.
  • New estimators for population means were derived.
  • A bootstrap standard error estimator was developed for the new estimators.

Main Results:

  • The proposed model-assisted approach demonstrated improved performance over existing RDS estimators.
  • The method effectively adjusted for initial convenience samples.
  • The approach was successfully applied to estimate HIV prevalence in a high-risk population.

Conclusions:

  • The model-assisted approach offers a more accurate and practical method for statistical inference in Respondent-Driven Sampling.
  • This technique enhances the estimation of population characteristics, such as disease prevalence.
  • The findings have significant implications for public health research involving hard-to-reach groups.