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

Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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...
12.1K
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

262
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
262
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
134
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
Sampling Plans01:23

Sampling Plans

225
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...
225

You might also read

Related Articles

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

Sort by
Same author

A Novel Alpha-Power X Family: A Flexible Framework for Distribution Generation with Focus on the Half-Logistic Model.

Entropy (Basel, Switzerland)·2025
Same author

Neural networks to model COVID-19 dynamics and allocate healthcare resources.

Scientific reports·2025
Same author

Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks.

Scientific reports·2023
Same author

Mathematical modeling of sustainable development goals of India agenda 2030: a Neutrosophic programming approach.

Environment, development and sustainability·2021
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Aug 2, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

Multiobjective intuitionistic fuzzy programming under pessimistic and optimistic applications in multivariate

Yashpal Singh Raghav1, Ahteshamul Haq2, Irfan Ali2

  • 1Department of Mathematics, Jazan University, Jizan, Saudi Arabia.

Plos One
|April 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for optimizing multivariate stratified sampling with nonresponse, using intuitionistic fuzzy programming to determine sample allocations for improved population mean estimation in surveys.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Related Experiment Videos

Last Updated: Aug 2, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Area of Science:

  • Statistics
  • Survey Methodology
  • Operations Research

Background:

  • Multivariate stratified sampling is crucial for estimating population means efficiently.
  • Handling nonresponse in surveys presents significant challenges to data accuracy.
  • Optimizing sample allocation under budget constraints is a key issue in survey design.

Purpose of the Study:

  • To develop a compromise allocation method for multivariate stratified sampling that accounts for both complete response and nonresponse.
  • To formulate the sampling problem as a mathematical programming problem for estimating p-population means.
  • To apply intuitionistic fuzzy programming for determining optimal sample allocations.

Main Methods:

  • Mathematical programming formulation for multivariate stratified sampling with nonresponse.
  • Intuitionistic fuzzy programming (IFP) with optimistic and pessimistic strategies.
  • Simulation study using Stratify R software.
  • Optimization using Lingo-18 software.

Main Results:

  • The study successfully formulates and solves the compromise allocation problem for multivariate stratified sampling with nonresponse.
  • Intuitionistic fuzzy programming provides effective strategies for sample allocation under fixed costs.
  • Simulation results demonstrate the applicability and completeness of the proposed solution process.

Conclusions:

  • The proposed compromise allocation method enhances the estimation of population means in the presence of nonresponse.
  • This approach is valuable for optimizing survey designs in fields like wildlife, agriculture, and marketing.
  • The study contributes to better national planning policies for survey data collection and analysis.