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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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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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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 Methods: Overview01:06

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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Stratified Sampling Method01:16

Stratified Sampling Method

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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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall.

Jasper B Yang1, Bryan E Shepherd2, Thomas Lumley3

  • 1University of Washington.

Journal of Statistical Software
|December 5, 2025
PubMed
Summary
This summary is machine-generated.

The optimall R package simplifies survey sampling design. It helps define strata, allocate samples using Neyman or Wright methods, and select units for efficient survey implementation.

Keywords:
Neyman allocationRmulti-wave samplingoptimal design

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Area of Science:

  • Statistics
  • Survey Methodology
  • Computational Statistics

Background:

  • Designing complex surveys requires efficient and adaptable tools.
  • Traditional survey design methods can be time-consuming and lack flexibility.
  • Implementing stratified sampling designs presents unique challenges.

Purpose of the Study:

  • To introduce the R package optimall for streamlining survey sampling design.
  • To provide functions for interactive strata definition and sample allocation.
  • To demonstrate the utility of optimall in real-world epidemiological surveys.

Main Methods:

  • Utilizing R programming for statistical analysis and survey design.
  • Implementing functions for interactive strata cut point adjustment.
  • Applying Neyman and Wright allocation methods for optimal sample size determination.
  • Demonstrating stratified sampling unit selection within the R environment.

Main Results:

  • The optimall package offers efficient functions for survey design.
  • Interactive adjustment of strata cut points based on auxiliary covariates is supported.
  • Adaptive calculation of optimal sample allocation per stratum is enabled.
  • The package facilitates the design and implementation of complex survey sampling.

Conclusions:

  • The optimall R package provides a comprehensive solution for survey sampling design.
  • It enhances efficiency and flexibility in defining strata and allocating samples.
  • Optimall is valuable for various survey types, including multi-wave and multi-phase designs.