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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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.
To choose a stratified sample, divide the population into groups called strata and then take a...
Sampling Plans01:23

Sampling Plans

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...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Systematic Sampling Method01:17

Systematic Sampling Method

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.
Systematic sampling is one of the simplest methods...
Random Sampling Method01:09

Random Sampling Method

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...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...

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Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

A simple method to generate equal-sized homogenous strata or clusters for population-based sampling.

Michael R Elliott1

  • 1Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor 48109, USA. mrelliot@umich.edu

Annals of Epidemiology
|March 8, 2011
PubMed
Summary
This summary is machine-generated.

A new algorithm efficiently creates statistically efficient population samples using clustering and stratification. This method helps construct contiguous, equal-sized strata and clusters for complex survey designs.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Computational Statistics

Background:

  • Stratification and clustering enhance statistical and cost efficiency in population sampling.
  • Existing methods for constructing contiguous, equal-sized strata/clusters with homogeneity constraints are limited.
  • The National Children's Study requires specific sample design procedures for strata and clusters.

Purpose of the Study:

  • To develop a novel algorithm for constructing equal-size strata and clusters.
  • To address limitations in existing methods for area probability sample design.
  • To meet the National Children's Study's requirements for contiguous and homogeneous sampling units.

Main Methods:

  • A search algorithm generates equal-size cluster sets.
  • The algorithm explores the space of all possible equal-size clusters.
  • Optimal cluster sets are selected using analysis of variance and convexity criteria.

Main Results:

  • The algorithm successfully constructed 10 strata in Kent County, MI, using census tract boundaries.
  • Demographic and air pollution data were used for stratification.
  • A simulation study demonstrated the algorithm's effectiveness.

Conclusions:

  • The proposed algorithm effectively identifies underlying clusters in noisy data.
  • It provides a viable solution for multi-stage sampling requiring equal-size strata or clusters.
  • The method enhances the construction of complex sample designs.