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Related Concept Videos

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...
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...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

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Related Experiment Video

Updated: Jun 30, 2026

Micro-Colony Forming Unit Assay for Efficacy Evaluation of Vaccines Against Tuberculosis
06:26

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Published on: July 28, 2023

The design effect and cluster samples: optimising tuberculosis prevalence surveys.

B Williams1, P G Gopi, M W Borgdorff

  • 1World Health Organization, Geneva, Switzerland. williamsbg@who.int

The International Journal of Tuberculosis and Lung Disease : the Official Journal of the International Union Against Tuberculosis and Lung Disease
|September 25, 2008
PubMed
Summary

Cluster sampling for disease surveys like tuberculosis (TB) balances reduced costs with increased statistical uncertainty. Finding the optimal design is key to accurately estimating prevalence while managing resources effectively.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cross-sectional disease prevalence surveys, particularly for tuberculosis (TB), frequently employ multi-stage sampling designs.
  • Cluster sampling is a common strategy to reduce logistical costs by randomly selecting groups of individuals.
  • This approach, however, can amplify statistical uncertainty in prevalence estimates, necessitating a careful balance between cost reduction and precision.

Purpose of the Study:

  • To describe cluster sampling methodologies for disease prevalence surveys.
  • To explore methods for determining optimal survey designs in the context of cluster sampling.
  • To assess the impact of deviations from optimal designs on prevalence estimates.

Main Methods:

  • The study outlines the principles of cluster sampling in epidemiological surveys.
  • It discusses strategies for optimizing survey design to balance cost and statistical uncertainty.
  • Analysis incorporates data from a TB prevalence survey conducted in Cambodia.

Main Results:

  • Cluster sampling significantly reduces survey costs but increases statistical uncertainty.
  • Methods for determining optimal cluster sizes and sampling fractions are presented.
  • The impact of suboptimal design choices on the precision of TB prevalence estimates was evaluated using Cambodian survey data.

Conclusions:

  • Optimizing cluster sampling design is crucial for accurate and cost-effective disease prevalence estimation.
  • Understanding the trade-offs between cost and statistical uncertainty is essential for survey planning.
  • The findings provide practical guidance for conducting future tuberculosis and other disease surveys.