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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

3.7K
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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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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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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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

858
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
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...
11.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Sampling Soils in a Heterogeneous Research Plot
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A comparison of spatial smoothing methods for small area estimation with sampling weights.

Laina Mercer1, Jon Wakefield1, Cici Chen2

  • 1Department of Statistics, University of Washington, United States.

Spatial Statistics
|June 25, 2014
PubMed
Summary
This summary is machine-generated.

This study addresses bias in small area estimation (SAE) by evaluating spatial smoothing models that incorporate sampling weights. Findings are crucial for accurate public health and government resource allocation.

Keywords:
Complex surveysDesign-based inferenceIntrinsic CAR modelsRandom effects modelsWeighting

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Area of Science:

  • Statistics
  • Spatial Analysis
  • Public Health

Background:

  • Small area estimation (SAE) is vital for resource allocation in public health and government.
  • Complex survey data often includes sampling weights, which are frequently omitted in spatial smoothing models.
  • Ignoring sampling weights can introduce bias into SAE analyses.

Purpose of the Study:

  • To investigate the impact of sampling weights on spatial smoothing models for small area estimation.
  • To evaluate various SAE approaches through simulation, considering non-response and non-random sampling biases.
  • To apply and compare methods for SAE of smoking prevalence at the zip code level.

Main Methods:

  • Simulation study to assess bias from non-response and non-random sampling.
  • Application of small area estimation techniques to smoking prevalence data.
  • Utilized data from the 2006 Behavioral Risk Factor Surveillance System (BRFSS) for Washington State.
  • Implementation of methods in R using available packages.

Main Results:

  • Spatial models that incorporate sampling weights are less prone to bias.
  • Simulation results highlight the importance of accounting for survey design in SAE.
  • The study identified efficient computational methods for SAE.

Conclusions:

  • Incorporating sampling weights into spatial smoothing models is essential for accurate small area estimation.
  • The developed methods provide reliable estimates for public health surveillance and resource allocation.
  • All evaluated SAE approaches demonstrated short computation times, facilitating practical application.