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Cluster Sampling Method

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...

Sampling Plans

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...

Stratified Sampling Method

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...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

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...

5-Number Summary

5-Number Summary

In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...

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Related Experiment Video

Updated: Jun 7, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Rapid Nutrition Surveys: How Many Clusters are Enough?

N Binkin¹, K Sullivan, N Staehling

¹Division of Nutrition, Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, USA.

|October 21, 2010

Summary

This summary is machine-generated.

For accurate malnutrition prevalence estimates, around 30 clusters are recommended. Using fewer clusters significantly reduces precision and validity, while more offer minimal gains.

Related Experiment Videos

Last Updated: Jun 7, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Public Health Nutrition
Epidemiology

Background:

Population-based nutrition surveys are crucial for assessing malnutrition prevalence.
Theoretical considerations suggest 30 clusters of 30 children offer valid estimates with 95% confidence.
Logistical challenges in famine-stricken areas necessitate evaluating fewer than 30 clusters for rapid nutritional assessment.

Purpose of the Study:

To evaluate the impact of using fewer than 30 clusters on the validity and precision of undernutrition prevalence estimates.
To determine the optimal number of clusters for reliable rapid nutritional assessments in resource-limited settings.

Main Methods:

Analysis of data from the 1983 Swaziland National Nutrition Survey.
Utilizing data from rapid nutrition surveys conducted in Burkina Faso, Guinea, and Niger in 1984 and 1985.
Comparing prevalence estimates derived from varying numbers of clusters against established benchmarks.

Main Results:

Using fewer than 30 clusters can lead to point prevalence estimates that significantly deviate from true values.
Reduced cluster numbers generally result in less precise estimates of undernutrition.
Collecting data from more than 30 clusters yields diminishing returns in terms of precision and validity.

Conclusions:

Approximately 30 clusters provide a balance of validity and precision for estimating undernutrition prevalence.
Efforts should focus on securing logistical support to survey around 30 clusters for reliable nutritional assessments.
The study underscores the importance of adequate sample size in epidemiological surveys for public health decision-making.