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

Bootstrapping01:24

Bootstrapping

603
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
603
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
Sampling Plans01:23

Sampling Plans

181
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...
181
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
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...
12.0K
Random Sampling Method01:09

Random Sampling Method

11.0K
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...
11.0K

You might also read

Related Articles

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

Sort by
Same author

Cannabis Use and Tooth Loss by Race and Sexual Orientation/Gender Identity.

AJPM focus·2026
Same author

An Introduction to Classification and Regression Trees for Risk Assessment in Pediatric Research.

The Journal of pediatrics·2026
Same author

Silenced departures: psychometric evaluation of the quiet firing scale among nurses in general hospitals.

Frontiers in psychology·2026
Same author

Chronic Health Conditions and Uptake of COVID-19 Testing and Vaccination Among Native Americans in Oklahoma in the RADx-UP Consortium.

Journal of racial and ethnic health disparities·2026
Same author

Discrimination experiences are associated with same-day and next-day smoking among adults with low socio-economic status trying to quit: A secondary analysis of data from a randomized clinical trial.

Addiction (Abingdon, England)·2026
Same author

Integrative Analysis Using LC-MS, Network Pharmacology, and Molecular Docking Elucidates the Mechanism of Yurong Fang in Melasma Treatment.

Combinatorial chemistry & high throughput screening·2026
Same journal

A Practical Framework for Incorporating Complex Survey Design in Bayesian Kernel Machine Regression.

Stats·2026
Same journal

Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness.

Stats·2025
Same journal

Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes.

Stats·2025
Same journal

Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data.

Stats·2025
Same journal

Assessing Spillover Effects of Medications for Opioid Use Disorder on HIV Risk Behaviors among a Network of People Who Inject Drugs.

Stats·2025
Same journal

Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data.

Stats·2025
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

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

7.5K

A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs.

Sixia Chen1, David Haziza2, Zeinab Mashreghi3

  • 1Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.

Stats
|May 13, 2024
PubMed
Summary
This summary is machine-generated.

Estimating variance in multi-stage sampling is challenging. This study compares bootstrap algorithms for two-stage designs, evaluating their bias, stability, and coverage probability for improved survey data analysis.

Keywords:
Taylor linearizationbootstrap algorithmsmulti-stage samplingvariance estimation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.5K

Related Experiment Videos

Last Updated: Jun 26, 2025

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

7.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.5K

Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Multi-stage sampling designs are frequently employed in household surveys due to practical constraints like unavailable sampling frames or the cost-effectiveness of face-to-face interviews.
  • Accurate variance estimation in these complex designs is hindered by the need for second-order inclusion probabilities at each sampling stage.

Purpose of the Study:

  • To review and empirically compare various bootstrap algorithms designed for variance estimation in two-stage sampling designs.
  • To assess the performance of these algorithms based on key statistical metrics.

Main Methods:

  • The study examines existing bootstrap algorithms applicable to two-stage sampling.
  • Empirical comparisons are conducted to evaluate algorithm performance.

Main Results:

  • The paper presents a comparative analysis of different bootstrap methods for variance estimation in two-stage surveys.
  • Performance is evaluated using metrics such as bias, stability, and coverage probability.

Conclusions:

  • The findings provide insights into the suitability of different bootstrap algorithms for complex survey variance estimation.
  • This research aids in selecting appropriate methods for robust statistical inference in multi-stage survey designs.