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

Multiple Regression01:25

Multiple Regression

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...
Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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:
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

You might also read

Related Articles

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

Sort by
Same author

Effects of a Personalized Stress Management Intervention on Maternal Mental Health: A Randomized Clinical Trial.

Archives of women's mental health·2025
Same author

Sexual and Gender Minority Adolescents' Preferences for HIV Pre-Exposure Prophylaxis Social Marketing Campaigns: Qualitative Preimplementation Study.

JMIR formative research·2025
Same author

A hybrid type II effectiveness-implementation trial of a positive emotion regulation intervention among people living with HIV engaged in Ryan White Medical Case Management: protocol and design for the ORCHID study.

Trials·2024
Same author

REPRODUCTIVE HEALTH IN TRANS AND GENDER-DIVERSE PATIENTS: Psychosocial counseling of transgender and gender-diverse individuals in fertility and reproductive medicine: a narrative review.

Reproduction (Cambridge, England)·2024
Same author

Feasibility, acceptability, and efficacy of a positive emotion regulation intervention to promote resilience for healthcare workers during the COVID-19 pandemic: A randomized controlled trial.

PloS one·2024
Same author

Grandmothers residing with grandchildren: Social determinants of health, health behaviors, and health outcomes.

Journal of women & aging·2023
Same journal

Parent Mental Health Interventions in the NICU: A Systematic Review and Meta-Analysis.

JAMA pediatrics·2026
Same journal

Data-Entry Error in Meta-Analysis of Suicide Prevention in Children and Adolescents.

JAMA pediatrics·2026
Same journal

Data-Entry Error in Meta-Analysis of Safety Planning Interventions for Suicide Prevention in Children and Adolescents.

JAMA pediatrics·2026
Same journal

Error in Effect Size.

JAMA pediatrics·2026
Same journal

Error in Effect Size in a Study Included in Meta-Analysis.

JAMA pediatrics·2026
Same journal

Longitudinal Predictive Power of Youth Diabetes Screening Guidelines.

JAMA pediatrics·2026
See all related articles

Related Experiment Video

Updated: May 11, 2026

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

Missing data and multiple imputation.

Peter Cummings1

  • 1Department of Epidemiology and Harborview Injury Prevention and Research Center, University of Washington, Seattle, USA. peterc@uw.edu

JAMA Pediatrics
|May 24, 2013
PubMed
Summary
This summary is machine-generated.

Missing data can bias study results and reduce precision. Multiple imputation is a valuable technique for analysts to address missing data, often improving estimates compared to complete-case analysis.

Related Experiment Videos

Last Updated: May 11, 2026

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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Data Science

Background:

  • Missing data can introduce bias and reduce the precision of statistical estimates.
  • Understanding missing data patterns and their causes is crucial for accurate analysis.

Purpose of the Study:

  • To highlight the impact of missing data on statistical analyses.
  • To advocate for the adoption of multiple imputation as a standard analytical tool.
  • To provide guidance on reporting missing data and minimizing its occurrence.

Main Methods:

  • Discussion of the challenges posed by missing data in statistical modeling.
  • Introduction to multiple imputation as a method for handling missing data.
  • Comparison of multiple imputation with complete-case analysis.

Main Results:

  • Multiple imputation can often reduce bias and increase precision compared to complete-case analysis.
  • Exceptions exist where multiple imputation may not improve results.
  • Proper reporting of missing data is essential for study transparency.

Conclusions:

  • Multiple imputation should be a standard part of any data analyst's toolkit.
  • Investigators should proactively plan to minimize missing data.
  • Transparency in reporting missing data is vital for scientific integrity.