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 Experiment Videos

Some sources of error and their effect on Census statistics.

B A Bailar

    Demography
    |May 1, 1976
    PubMed
    Summary

    Reliability of survey data often ignores nonsampling errors. A new model from the Bureau of the Census accounts for both sampling and nonsampling errors to improve data accuracy and reliability.

    Related Concept Videos

    You might also read

    Related Articles

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

    Sort by
    Same author

    [Correcting for an undercount. The case of the U.S. census].

    Cahiers quebecois de demographie·1989
    Same author

    The evaluation of the 1980 decennial census.

    The Statistician : journal of the Institute of Statisticians·1980
    Same author

    A composite estimator for small area statistics: discussion.

    NIDA research monograph·1979
    See all related articles

    Area of Science:

    • Statistics
    • Survey Methodology
    • Data Analysis

    Background:

    • Survey data reliability is frequently assessed solely based on sampling variances.
    • Exclusion of nonsampling errors can lead to inaccurate inferences if they constitute a significant portion of the total error.
    • Mean-square error is a critical metric for evaluating overall data quality.

    Purpose of the Study:

    • To highlight the limitations of relying only on sampling variance for survey data reliability.
    • To introduce a model developed by the Bureau of the Census that integrates sampling and nonsampling errors.
    • To demonstrate how analyzing error components can enhance survey data accuracy and reliability.

    Main Methods:

    • Development of a statistical model to quantify the combined impact of sampling and nonsampling errors.
    • Analysis of the components of error within survey data.
    • Application of error component analysis to identify areas for methodological improvement.

    Main Results:

    • Sampling variance may not always be the dominant component of mean-square error.
    • Ignoring nonsampling errors can lead to significant inferential mistakes.
    • The developed model provides a framework for a more comprehensive assessment of data quality.

    Conclusions:

    • A holistic approach considering both sampling and nonsampling errors is crucial for accurate survey data reliability.
    • The Bureau of the Census's model offers a pathway to better understand and mitigate total survey error.
    • Methodological improvements derived from error component studies can significantly enhance the trustworthiness of survey statistics.

    Related Experiment Videos