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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Methods for Handling Missing Secondary Respondent Data.

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  • 1Department of Biostatistics, University of Washington, Box 354922, Seattle, WA 98115.

Journal of Marriage and the Family
|June 27, 2015
PubMed
Summary
This summary is machine-generated.

Researchers can overcome challenges with missing data in secondary respondent studies. Maximum likelihood estimation and multiple imputation are recommended for unbiased and efficient estimates, improving data utilization.

Keywords:
maximum likelihoodmissing datamultiple imputationnonresponsesecondary respondents

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Area of Science:

  • Social Sciences
  • Statistics
  • Demography

Background:

  • Secondary respondent data are often underutilized due to significant missing data.
  • Researchers face challenges in effectively analyzing datasets with incomplete information.

Purpose of the Study:

  • To review, evaluate, and test strategies for handling missing partner data in secondary respondent datasets.
  • To identify optimal methods for maximizing data utilization and improving estimation accuracy.

Main Methods:

  • Reviewed five strategies: complete case analysis, inverse probability weighting, Heckman selection model, maximum likelihood estimation, and multiple imputation.
  • Evaluated methods using data from the National Survey of Fertility Barriers (N=1,666).
  • Conducted simulations to compare method performance against known true values.

Main Results:

  • Maximum likelihood estimation and multiple imputation were found to be advantageous.
  • These methods enable the use of all available information, leading to less biased and more efficient estimates.
  • Complete case analysis and other methods showed limitations in handling missing data.

Conclusions:

  • Maximum likelihood estimation and multiple imputation are superior methods for addressing missing data in secondary respondent research.
  • Adopting these advanced techniques can enhance the reliability and scope of findings from underutilized datasets.
  • Improved handling of missing data can unlock the full potential of valuable social science datasets.