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Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics.

Yajuan Si1, Steve Heeringa2, David Johnson3

  • 1Research Assistant Professor, Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, USA.

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|January 30, 2023
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Summary

Multiple imputation (MI) improves handling missing wealth data in the Panel Study of Income Dynamics (PSID) by preserving correlations and increasing efficiency. This method offers a practical solution for complex survey data analysis.

Keywords:
DiagnosticsEfficiencyMassive DataMissing DataValidity

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Area of Science:

  • Statistics
  • Econometrics
  • Survey Methodology

Background:

  • Multiple imputation (MI) is a robust statistical technique for addressing missing data.
  • Its application to large, complex datasets like the Panel Study of Income Dynamics (PSID) presents practical challenges.
  • Current hot deck methods in PSID, while simple, introduce significant random fluctuations in wealth data.

Purpose of the Study:

  • To evaluate the practicality and effectiveness of multiple imputation (MI) for handling missing wealth data in the 2013 PSID.
  • To compare MI's performance against the existing univariate hot deck approach.
  • To address and overcome operational challenges associated with MI in complex survey data.

Main Methods:

  • Utilized a sequential regression/chained-equation approach with IVEware software for multiple imputation.
  • Applied MI to cross-sectional wealth data from the 2013 PSID.
  • Compared analyses from MI-imputed data with those from the hot deck method.

Main Results:

  • Multiple imputation (MI) demonstrated superior performance by preserving correlation structures, including associations between wealth components and relationships with sociodemographic factors.
  • MI facilitated complete data analyses and increased analytical efficiency.
  • The study successfully navigated practical difficulties such as non-normal variables, skip patterns, and multicollinearity.

Conclusions:

  • Multiple imputation (MI) offers significant improvements over traditional hot deck methods for analyzing complex survey data, particularly for wealth variables.
  • The sequential regression/chained-equation approach is a practical and effective strategy for implementing MI in large-scale surveys.
  • MI is recommended for its ability to enhance data quality, preserve relationships, and increase efficiency in statistical analyses, especially when missing data is substantial.