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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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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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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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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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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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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 Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sequential adaptive strategies for sampling rare clustered populations.

Fulvia Mecatti1, Charalambos Sismanidis2, Emanuela Furfaro3

  • 1University of Milano-Bicocca, U7 Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.

Statistical Methods & Applications
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New adaptive sampling strategies improve rare disease detection in population surveys by exploiting spatial clustering. These methods offer flexibility for logistics and budget, outperforming traditional designs in tuberculosis prevalence studies.

Keywords:
AsymptoticsBudget and logistic constraintsInformative designsIntra-cluster variationOver-samplingPoisson samplingPseudo Horvitz-Thompson estimator

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Area of Science:

  • Statistics
  • Epidemiology
  • Survey Methodology

Background:

  • Population-based surveys often face challenges in detecting rare traits due to uneven spatial distribution.
  • Existing sampling designs may not adequately address logistical and budget constraints or exploit spatial clustering for efficiency.

Purpose of the Study:

  • To propose a novel class of adaptive, sequential sampling strategies for surveys of rare traits with spatial heterogeneity.
  • To develop unbiased estimators and a weighting system to account for selection bias.
  • To evaluate the performance of these strategies, particularly in the context of tuberculosis prevalence surveys.

Main Methods:

  • Integration of an adaptive component into sequential selection to intensify detection of positive cases.
  • Development of a class of unbiased estimators, including variance estimation, and a ready-to-implement weighting system.
  • Application and simulation studies using tuberculosis prevalence surveys to compare with traditional cross-sectional sampling.

Main Results:

  • The proposed sequential adaptive sampling strategies demonstrated improved efficiency and tailored data collection capabilities.
  • The developed estimators were proven unbiased for population mean (prevalence) and provided unbiased variance estimation.
  • Simulation results highlighted the strengths of the adaptive strategies over traditional methods in tuberculosis prevalence surveys.

Conclusions:

  • The proposed adaptive sampling strategies offer a flexible and efficient framework for surveying rare, spatially clustered traits.
  • These methods provide robust estimation techniques to manage selection bias and logistical challenges.
  • The findings support the adoption of improved sampling designs, exemplified by tuberculosis prevalence surveys, over current World Health Organization guidelines.