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

Censoring Survival Data01:09

Censoring Survival Data

289
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
289
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

114
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...
114
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.5K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.5K
Random Error01:04

Random Error

4.5K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
4.5K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

219
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
219
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

261
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
261

You might also read

Related Articles

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

Sort by
Same author

Social influence on women's contraceptive use: Population-based, sociocentric network study in rural Uganda.

Social science & medicine (1982)·2026
Same author

Sociodemographic characteristics predict land use patterns by farmers near a protected area in Madagascar.

Scientific reports·2026
Same author

Hidden Structure of Care Coordination in Heart Failure Care Transitions: A Mixed-Method Network Analysis of Clinical Notes.

Journal of cardiac failure·2026
Same author

Spreading potential in disease relevant networks: Predicting centralities in rural Northeast Madagascar.

PLOS global public health·2026
Same author

Identifying Social-Epidemiological Roles Associated with Viral Exposure Using Regular Equivalence Blockmodeling.

medRxiv : the preprint server for health sciences·2025
Same author

Gaps in Effective HIV Pre-exposure Prophylaxis Screening and Uptake Among Fishermen in Kenya.

AIDS and behavior·2025
Same journal

Neighborhood Shocks and Network Dynamics: An Instrumental Variable Approach to Measuring Triadic Closure in Daily Mobility Networks.

Social networks·2026
Same journal

A Hybrid Mixed Methods Design for Understanding Decision Making, Social Structure, and Network Dynamics of Life Course Transitions.

Social networks·2026
Same journal

Network threats to causal inference: Variations in network position by participation in randomized controlled trials.

Social networks·2026
Same journal

Understanding the Personal Networks of People Experiencing Homelessness in King County, WA with Aggregate Relational Data.

Social networks·2026
Same journal

Beyond weak ties in prison: An investigation of core support networks of incarcerated persons.

Social networks·2026
Same journal

Friends Forever? Correlates of High School Friendship (In)stability from Adolescence to Young Adulthood.

Social networks·2025
See all related articles

Related Experiment Video

Updated: Oct 26, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K

Network sampling coverage III: Imputation of missing network data under different network and missing data

Jeffrey A Smith1, Jonathan H Morgan2, James Moody3

  • 1University of Nebraska-Lincoln, United States.

Social Networks
|July 26, 2021
PubMed
Summary
This summary is machine-generated.

Choosing the best method for handling missing network data is complex. Listwise deletion is generally the worst approach, with optimal imputation strategies depending on data type, network structure, and research measures.

Keywords:
ImputationMissing dataNetwork biasNetwork sampling

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.3K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Oct 26, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.3K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K

Area of Science:

  • Social network analysis
  • Statistical modeling
  • Data science

Background:

  • Missing data is a pervasive challenge in network studies, lacking clear methodological guidelines.
  • This paper is the third in a series addressing missing data in network analysis.

Purpose of the Study:

  • To compare the performance of various imputation methods for missing network data.
  • To provide guidance on selecting appropriate imputation strategies based on study characteristics.

Main Methods:

  • Evaluation of imputation methods ranging from simple to model-based approaches.
  • Assessment across diverse network types, measures, and missing data patterns.

Main Results:

  • Listwise deletion consistently performs poorly across most scenarios.
  • Optimal imputation method selection is contingent upon the specific characteristics of the missing data, network, and outcome measure.

Conclusions:

  • Researchers need to carefully consider data specifics when choosing imputation techniques.
  • Practical guidance and outputs are provided to aid researchers in selecting the best imputation method for their network studies.