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A case study on the use of multiple imputation

V A Freedman1, D A Wolf

  • 1Agency for Health Care Policy and Research, Rockville, MD 20852, USA.

Demography
|August 1, 1995
PubMed
Summary
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This study applies multiple imputation, a method for handling missing survey data by replacing missing values with multiple plausible values. This technique allows for more robust statistical inferences from incomplete datasets.

Area of Science:

  • Social Sciences
  • Statistics
  • Demography

Background:

  • Missing data is a common challenge in survey research.
  • Traditional methods like deletion or single imputation can bias results.
  • Multiple imputation offers a statistically sound alternative for handling missing values.

Purpose of the Study:

  • To demonstrate the application of multiple imputation in survey data analysis.
  • To analyze the marital status of elderly mothers/mothers-in-law using the National Survey of Families and Households.
  • To present descriptive statistics for adult children with unmarried elderly mothers/mothers-in-law.

Main Methods:

  • Utilized multiple imputation to address missing item responses.
  • Applied the technique to binary indicators from the 1987-1988 National Survey of Families and Households.

Related Experiment Videos

  • Generated descriptive statistics on a specific subsample.

    • Main Results:

    • Successfully imputed missing marital status indicators for elderly mothers/mothers-in-law.
    • Provided descriptive statistics for adult children concerning their unmarried elderly mothers/mothers-in-law.
    • Demonstrated the feasibility of using multiple imputation for complex survey data.

    Conclusions:

    • Multiple imputation is an effective method for handling missing data in survey research.
    • The study successfully applied multiple imputation to a real-world dataset.
    • Findings provide insights into the characteristics of adult children with unmarried elderly mothers/mothers-in-law.