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Evaluating census data quality using intensive reinterviews: a comparison of U.S. Census Bureau methods and Rasch

R A Johnson, H F Woltman

    Sociological Methodology
    |January 1, 1987
    PubMed
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    The Rasch measurement methodology offers an alternative to U.S. Census Bureau methods for data quality evaluation. It models respondents and instruments on latent traits, providing invariant measures for disability data.

    Area of Science:

    • Social Statistics
    • Psychometrics
    • Survey Methodology

    Background:

    • Conventional U.S. Census Bureau methodology assumes fixed respondent states.
    • This approach may not accurately reflect nuanced data from diverse populations.
    • Evaluating data quality requires robust measurement techniques.

    Purpose of the Study:

    • To present the Rasch measurement methodology as a superior alternative to conventional U.S. Census Bureau methods.
    • To evaluate the quality of data from different measurements of the same characteristic.
    • To illustrate differing conclusions from Census and Rasch approaches using disability data.

    Main Methods:

    • The Rasch model treats respondents and instruments as varying on continuous latent traits.
    • It requires measurement invariance across respondent subgroups.
    Keywords:
    AmericasCensusCensus MethodsComparative StudiesDemographic FactorsDeveloped CountriesDeveloping CountriesEconomic FactorsEvaluationEvaluation MethodologyHandicappedHuman ResourcesLabor ForceMeasurementMethodological StudiesNorth AmericaNorthern AmericaPopulationPopulation CharacteristicsPopulation StatisticsQualitative EvaluationReliabilityResearch MethodologyUnited States

    Related Experiment Videos

  • Parameter estimates serve as sufficient statistics for discrete sampling models.

    • Main Results:

    • The Rasch approach assumes continuous latent traits, unlike the Census approach's fixed states.
    • Measurement invariance across respondent subclasses is a key requirement of the Rasch model.
    • This methodology yields different conclusions in data quality evaluations.

    Conclusions:

    • Rasch methodology provides a more nuanced and invariant approach to data quality assessment.
    • It is particularly useful for evaluating questionnaire items measuring complex characteristics like disability.
    • This framework enhances the reliability and validity of survey data analysis.