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Related Experiment Videos

Estimating men's fertility from vital registration data with missing values.

Christian Dudel1, Sebastian Klüsener1,2

  • 1Max Planck Institute for Demographic Research.

Population Studies
|July 14, 2018
PubMed
Summary

Estimating men's fertility rates is challenging due to missing paternal age data. A conditional approach, considering maternal age, is generally superior to unconditional methods for imputing this missing fertility information.

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Area of Science:

  • Demography
  • Reproductive Health
  • Statistical Modeling

Background:

  • Assessing men's fertility is limited by incomplete paternal age data in vital registration.
  • Existing methods struggle with substantial missing paternal age information.

Purpose of the Study:

  • To compare two imputation methods for estimating men's age-specific fertility rates.
  • To evaluate imputation performance when paternal age data is missing.

Main Methods:

  • Developed and simulated two imputation approaches: unconditional and conditional on maternal age.
  • Used simulated vital registration data from Sweden, USA, Spain, and Estonia.
  • Varied the proportion and selectivity of missing paternal age values.

Main Results:

Keywords:
EstoniaSpainSwedenUnited Statesbirth register dataimputation methodsmen’s fertility

Related Experiment Videos

  • The conditional imputation approach demonstrated superior performance across most simulation scenarios.
  • The unconditional approach was less accurate in estimating men's fertility rates.

Conclusions:

  • The conditional imputation method is recommended for estimating men's age-specific fertility rates with missing data.
  • Accurate imputation is crucial for comparative demographic analyses of male fertility.