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Simple Strategies for Improving Inference with Linked Data: A Case Study of the 1850-1930 IPUMS Linked Representative

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New strategies improve linked historical data for quantitative history and demography. Validation variables and regression weighting enhance data quality and representativeness in historical research.

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Area of Science:

  • Quantitative History
  • Historical Demography
  • Data Science

Background:

  • Large-scale linked data are transforming quantitative history and demography.
  • Historical datasets offer rich opportunities for demographic and historical analysis.

Purpose of the Study:

  • To propose and evaluate methods for improving inference from linked historical data.
  • To enhance the quality and representativeness of research samples derived from linked historical datasets.

Main Methods:

  • Utilizing validation variables to identify and improve the quality of data links.
  • Implementing a regression-based weighting procedure to adjust sample representativeness.
  • Applying these methods to the Integrated Public Use Microdata Series Linked Representative Samples (IPUMS-LRS) dataset (1850-1930).

Main Results:

  • Validation variables can further reduce error rates in linked datasets, even when initial linking quality is high.
  • Regression-based weighting effectively balances observed characteristics between linked samples and reference populations.
  • Demonstrated the practical utility of these strategies on a high-quality, publicly available linked historical dataset.

Conclusions:

  • The proposed strategies offer practical and effective ways to improve the reliability and representativeness of historical research using linked data.
  • These methods empower researchers to derive more accurate and robust findings from complex historical datasets.
  • Enhancements in linked data analysis are crucial for advancing quantitative history and demography.