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Creating Synthetic Data for Complex Surveys Using the Research and Development Survey: A Comparison Study.

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    Creating synthetic data for complex surveys is challenging. Incorporating survey design information improves data utility, with nonparametric methods offering better utility but slightly higher disclosure risk.

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    Area of Science:

    • Statistics
    • Data Science
    • Survey Methodology

    Background:

    • Synthetic data generation is increasingly used to balance data utility and confidentiality.
    • Creating synthetic data for complex surveys presents significant challenges.

    Purpose of the Study:

    • To compare methods for incorporating survey design information into synthetic data generation.
    • To evaluate the impact on data utility and disclosure risk for complex surveys.

    Main Methods:

    • Utilized the Research and Development Survey (RANDS) data.
    • Compared parametric (logistic/linear regression) and nonparametric (CART) methods.
    • Incorporated survey design elements like stratification, clustering, and sampling weights.

    Main Results:

    • Parametric methods showed improved data utility when survey design information was used as predictors.
    • Nonparametric methods (CART) achieved higher data utility but introduced a marginal increase in disclosure risk.
    • Evaluation metrics included confidence interval overlap, propensity scores, and re-identification probabilities.

    Conclusions:

    • Survey design information is crucial for enhancing synthetic data utility in complex surveys.
    • Method choice involves a trade-off between data utility and disclosure risk.
    • Findings inform best practices for generating privacy-preserving synthetic survey data.