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[Modeling matching error and its effect on estimates of census coverage error].

P P Biemer

    Survey Methodology
    |June 1, 1988
    PubMed
    Summary
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    This study introduces a model to assess how matching errors impact census undercount estimates, crucial for improving accuracy in the U.S. census undercount evaluation program.

    Area of Science:

    • Statistics
    • Demography
    • Survey Methodology

    Background:

    • Census data is vital for resource allocation and policy.
    • Accurate census counts are essential for representative democracy.
    • Undercounts in censuses can lead to significant demographic and socioeconomic misrepresentations.

    Purpose of the Study:

    • To propose a statistical model for evaluating the impact of matching error on census undercount estimators.
    • To analyze the components of mean square error (MSE) attributable to matching discrepancies.
    • To illustrate the application of the model using data from the 1990 U.S. census undercount evaluation program.

    Main Methods:

    • Development of a statistical model to quantify matching error effects.
    • Derivation of the mean square error (MSE) for the dual system estimator under the proposed model.
    Keywords:
    AmericasCensusCensus MethodsDeveloped CountriesDeveloping CountriesError SourcesEstimation TechnicsMeasurementMethodological StudiesModels, TheoreticalNorth AmericaNorthern AmericaPopulation StatisticsResearch MethodologyUndercountUnited States

    Related Experiment Videos

  • Analysis of MSE components specifically related to matching errors.

    • Main Results:

    • The study defines and explains the components of MSE arising from matching error.
    • It quantifies the effect of matching error on the estimator of census undercount under the assumed model.
    • A methodology for optimizing the design of matching error evaluation studies is presented.

    Conclusions:

    • Matching error significantly influences the accuracy of census undercount estimates.
    • The proposed model provides a framework for understanding and mitigating these errors.
    • The findings support the development of more robust census evaluation methodologies.