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Using information from demographic analysis in post-enumeration survey estimation.

W R Bell

    Journal of the American Statistical Association
    |September 1, 1993
    PubMed
    Summary
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    This study improves 1990 census population estimates by relaxing independence assumptions in dual system estimates (DSEs). New methods using demographic analysis (DA) provide more accurate male undercount assessments.

    Area of Science:

    • Demography
    • Statistical Methods
    • Census Analysis

    Background:

    • The 1990 Post-Enumeration Survey (PES) used dual system estimates (DSEs) to measure census undercount.
    • DSEs traditionally assume independence within demographic strata (age, race, sex, geography).
    • This assumption may not hold, particularly for males.

    Purpose of the Study:

    • To develop and apply alternative DSE methods for the 1990 U.S. census.
    • To relax the independence assumption for males within strata using demographic analysis (DA).
    • To improve the accuracy of population estimates by addressing potential sex-specific undercount biases.

    Main Methods:

    • Developed novel DSE methodologies to avoid the independence assumption for males.
    • Utilized national sex ratios from demographic analysis (DA) to inform male undercount estimates.
    Keywords:
    AmericasCensusCensus MethodsData AdjustmentData AnalysisData CollectionData QualityDeveloped CountriesDual Data CollectionError SourcesEstimation TechnicsMeasurementNorth AmericaNorthern AmericaPopulation StatisticsResearch MethodologySampling StudiesStudiesSurvey MethodologySurveysUndercountUnited States

    Related Experiment Videos

  • Applied alternative DSEs to 1990 U.S. census and PES data.

    • Main Results:

    • The study presents alternative DSEs that relax independence assumptions.
    • Demographic analysis (DA) results were incorporated to refine male undercount estimates.
    • The application of these methods to 1990 census data offers improved population estimates.

    Conclusions:

    • The developed methods provide a more robust approach to estimating census undercount.
    • Relaxing independence assumptions, especially for males, enhances the accuracy of population estimates.
    • This research contributes to better understanding and correcting census undercount through advanced statistical modeling.