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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

249
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
249
Multiple Regression01:25

Multiple Regression

3.8K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.8K
Longitudinal Studies01:26

Longitudinal Studies

479
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
479
Variability: Analysis01:11

Variability: Analysis

448
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
448
Variation01:19

Variation

7.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.7K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

480
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...
480

You might also read

Related Articles

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

Sort by
Same author

Efficacy of high-frequency low-dose rituximab and acalabrutinib in chronic lymphocytic leukemia.

HemaSphere·2026
Same author

Probiotic-driven gut-liver redox crosstalk modulates hepatic Nrf2 signaling pathway and attenuates metabolic dysfunction-associated steatohepatitis.

Free radical biology & medicine·2026
Same author

Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor.

Biometrics·2026
Same author

IL1β/IL1R1/IRAK4 Drives Inflammatory Ovarian Cancer Seeding at the inflamed sites and Is Reversed by an IRAK4 inhibitor UR241-2.

bioRxiv : the preprint server for biology·2026
Same author

Complement-mediated ADCP as a distinct and finite cytotoxic mechanism of monoclonal antibodies.

Frontiers in immunology·2026
Same author

The Impact of Layering Tobacco 21 Laws and Smoke-Free Laws on U.S. Adolescent Smoking Behaviors.

American journal of preventive medicine·2026

Related Experiment Video

Updated: Jan 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K

A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling.

Yibo Wang1, Sunghee Lee2, Michael R Elliott1,2

  • 1Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA.

Journal of the American Statistical Association
|October 1, 2025
PubMed
Summary

This study introduces a new method to improve data accuracy from hidden populations using respondent-driven sampling (RDS). The novel approach enhances population estimates by correcting for errors in reported network sizes.

Keywords:
heapingmeasurement errornetwork sizesocial network

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

Related Experiment Videos

Last Updated: Jan 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

Area of Science:

  • Social Sciences
  • Biomedical Sciences
  • Statistics

Background:

  • Respondent-driven sampling (RDS) is a key method for studying hidden populations.
  • RDS relies on network size (degree) for accurate analysis, but reported degrees are often inaccurate.
  • Existing methods struggle with generalizing findings due to sampling biases and measurement errors.

Purpose of the Study:

  • To develop a novel degree estimator for respondent-driven sampling (RDS).
  • To address measurement errors in self-reported network sizes within RDS.
  • To improve the accuracy of population parameter estimation from hidden populations.

Main Methods:

  • Developed a latent variable model for true degree, accounting for reporting errors.
  • Incorporated respondent recruitment patterns and external demographic data.
  • Validated the method using a case study and simulation studies.

Main Results:

  • The novel degree estimator provides accurate and reliable network size estimates.
  • The proposed method significantly improves population parameter estimation in RDS.
  • Successfully addressed issues of reporting errors and sampling biases.

Conclusions:

  • The new latent variable model offers a robust solution for improving RDS data quality.
  • This method enhances the generalizability of findings from hidden populations.
  • Accurate degree estimation is crucial for reliable social and biomedical research using RDS.