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

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

56
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
56
Stratified Sampling Method01:16

Stratified Sampling Method

12.3K
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...
12.3K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

273
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
273
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

228
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
228
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

140
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
140
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.2K

You might also read

Related Articles

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

Sort by
Same author

Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012.

Scientific data·2025
Same author

Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Estimating Average Treatment Effects With Support Vector Machines.

Statistics in medicine·2025
Same author

A summer bridge program for first-generation low-income students stretches academic ambitions with no adverse impacts on first-year GPA.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Evaluating bias and noise induced by the U.S. Census Bureau's privacy protection methods.

Science advances·2024
Same author

Census officials must constructively engage with independent evaluations.

Proceedings of the National Academy of Sciences of the United States of America·2024

Related Experiment Video

Updated: Aug 18, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name

Kosuke Imai1, Santiago Olivella2, Evan T R Rosenman3

  • 1Department of Government and Department of Statistics, Institute for Quantitative of Social Science, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA.

Science Advances
|December 9, 2022
PubMed
Summary

We improved race and ethnicity prediction using a fully Bayesian approach (fBISG) and name data, enhancing accuracy for racial minorities in disparity studies.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K
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

2.6K

Related Experiment Videos

Last Updated: Aug 18, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K
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

2.6K

Area of Science:

  • Sociology
  • Demography
  • Public Health

Background:

  • Accurate prediction of race and ethnicity is crucial for studying racial disparities.
  • Bayesian Improved Surname Geocoding (BISG) is a common method but has data limitations.
  • Census data often has zero counts for minority groups and missing surname information.

Purpose of the Study:

  • To introduce a fully Bayesian BISG (fBISG) methodology to improve race and ethnicity prediction.
  • To address data limitations in existing BISG methods.
  • To enhance the accuracy of race imputation, particularly for minority populations.

Main Methods:

  • Developed a fully Bayesian BISG (fBISG) methodology to account for census measurement error.
  • Extended naïve Bayesian inference used in standard BISG.
  • Incorporated additional data from voter files, including last, first, and middle names.

Main Results:

  • The fBISG methodology significantly improves race imputation accuracy.
  • Supplementing with name data further enhances prediction accuracy.
  • Accuracy improvements are most substantial for racial minorities.

Conclusions:

  • The fBISG methodology offers a more robust approach to race and ethnicity prediction.
  • The integration of name data is vital for accurate imputation, especially for underrepresented groups.
  • This improved methodology can lead to more reliable studies of racial disparity.