Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Systematic Sampling Method

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...

Convenience Sampling Method

Convenience 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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...

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...

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...

Random Sampling Method

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 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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Tractor overturn concerns in Iowa: perspectives from the Keokuk county rural health study.

Journal of agricultural safety and health·2006

Same author

A population based study of reporting patterns and characteristics of men who abuse their female partners.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention·2005

Same author

Vegetable oil sprinkling as a dust reduction method in swine confinement.

Journal of agricultural safety and health·2004

Same author

Identification of risk factors for non-fatal child injury in a rural area: Keokuk County Rural Health Study.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention·2003

Same author

Integrative assessment of multiple pesticides as risk factors for non-Hodgkin's lymphoma among men.

Occupational and environmental medicine·2003

Same author

Risk factors for agricultural injury: a case-control analysis of Iowa farmers in the Agricultural Health Study.

Journal of agricultural safety and health·2003

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Published on: November 30, 2015

Cluster sampling in hospital surveillance.

L F Burmeister¹

¹Department of Preventive Medicine and Environmental Health, University of Iowa, Iowa City 52242.

Infection Control and Hospital Epidemiology

|December 1, 1989

Summary

Hospital surveillance often involves sampling groups of individuals. Using incorrect statistical methods can lead to misleading conclusions. This study compares three estimators for accurate proportion estimation in cluster sampling, crucial for reliable health event surveillance.

More Related Videos

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Related Experiment Videos

Last Updated: Jun 26, 2026

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting

Published on: November 30, 2015

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

Area of Science:

Epidemiology
Biostatistics
Health Services Research

Background:

Hospital event surveillance frequently involves cluster sampling of individuals.
Inaccurate statistical methods can arise from assuming simple random sampling when cluster sampling is used.
This can lead to misleading conclusions in health event surveillance.

Purpose of the Study:

To compare three appropriate estimators for proportions and their standard errors in cluster sampling scenarios.
To provide guidance on selecting the correct estimator based on cluster characteristics.
To ensure statistical consistency with employed sampling techniques in hospital surveillance.

Main Methods:

Consideration of three distinct estimators for proportions.
Comparison of the standard errors associated with each estimator.
Analysis based on the relationship between cluster size and the prevalence of the characteristic of interest.

Main Results:

The unbiased estimator P is recommended when larger clusters have fewer subjects with the characteristic.
Estimators PR or Pppz are suitable when larger clusters have more subjects with the characteristic.
These estimators provide correct and relatively precise estimates under specific cluster conditions.

Conclusions:

Selecting the appropriate estimator is critical for accurate health event surveillance in hospitals.
The choice of estimator depends on the correlation between cluster size and the prevalence of the outcome.
Consistent statistical methods ensure reliable conclusions from cluster sampling data.