Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
Convenience Sampling Method00:55

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...
Random Sampling Method01:09

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Stratified Sampling Method01:16

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...

You might also read

Related Articles

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

Sort by
Same author

Association of physical function and sleep with oxidative stress in adults with chronic pain: A pragmatic clinical trial secondary analysis.

The Journal of international medical research·2026
Same author

CYP gene variants show preliminary evidence of association with patient-reported outcomes in active-duty service members with chronic pain.

Molecular pain·2026
Same author

Latent profile analysis for the IPQ-R: Practical analysis recommendations informed by simulation.

Journal of health psychology·2026
Same author

A combined progressive muscle relaxation and walking intervention for adults with end-stage kidney disease receiving hemodialysis (<i>Fight Fatigue</i>): Study development and protocol.

Contemporary clinical trials communications·2026
Same author

Circulating saturated fatty acids and incident chronic kidney disease: a meta-analysis of de-novo prospective cohort investigations.

The American journal of clinical nutrition·2026
Same author

Recruitment and Retention of Patients With Ischemic Heart Disease Reporting Hopelessness in a Randomized Controlled Trial.

Nursing research·2026

Related Experiment Videos

The cost-effectiveness of reclassification sampling for prevalence estimation.

Airat Bekmetjev1, Dirk VanBruggen, Brian McLellan

  • 1Department of Mathematics, Hope College, Holland, Michigan, United States of America.

Plos One
|February 21, 2012
PubMed
Summary

Reclassification sampling, which uses repeated imperfect classifications, offers a more cost-effective method for estimating prevalence compared to traditional two-phase sampling. This approach provides robust estimates even with imperfect diagnostic tests.

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Sampling

Background:

  • Two-phase sampling is standard for error-prone classifications with a gold standard.
  • It involves imperfect classification of all samples and gold-standard classification of a subset.

Purpose of the Study:

  • To introduce and evaluate reclassification sampling as an alternative strategy.
  • To provide estimates for sensitivity, specificity, and prevalence using this new method.

Main Methods:

  • Reclassification sampling involves multiple classifications of individuals using an imperfect classifier.
  • The study considers scenarios with one or two binary classifications per individual.
  • Robustness to model assumptions and optimal design strategies were analyzed.

Main Results:

  • Estimates for sensitivity, specificity, and prevalence were derived for reclassification sampling.
  • Software was developed to compute estimates and guide sampling strategy selection.
  • The approach was evaluated for its robustness and design implications.

Conclusions:

  • Reclassification sampling is a cost-effective strategy for prevalence estimation.
  • It demonstrates lower standard errors for the same cost compared to two-phase sampling in many scenarios.
  • This method offers practical advantages for epidemiological studies with imperfect diagnostic tools.