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Drawing statistical inferences from historical census data, 1850-1950.

Michael Davern1, Steven Ruggles, Tami Swenson

  • 1Minnesota Population Center, University of Minnesota, USA. Daver004@umn.edu

Demography
|September 24, 2009
PubMed
Summary
This summary is machine-generated.

Social scientists using historical U.S. census microdata should use Taylor series estimation for accurate standard errors. This method accounts for complex sample designs, preventing erroneous research conclusions from simple random sample analysis.

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Area of Science:

  • Social Sciences
  • Demography
  • Quantitative Methods

Background:

  • Quantitative microdata for social scientists often uses complex sampling designs (clustering, stratification, weighting).
  • Standard error estimates from these complex samples can differ significantly from simple random samples.
  • Historical U.S. census microdata is widely used in social science and policy research.

Observation:

  • Researchers often incorrectly apply simple random sample methods to historical U.S. census microdata.
  • This can lead to inaccurate p-values and confidence intervals, potentially causing erroneous conclusions.
  • The Integrated Public Use Microdata Series (IPUMS) project provides historical census microdata from 1850-1950.

Findings:

  • This study evaluates the impact of sample design on standard error estimates for historical census microdata.
  • It assesses the applicability of modern standard error estimation software to these historical datasets.
  • Taylor series estimation is validated using 1880 census data and applied to 1850-1950 decennial censuses.

Implications:

  • Taylor series estimation is effective for historical decennial census microdata.
  • Applying this method is crucial for research with potential clustering effects.
  • Accurate standard error estimation ensures reliable conclusions in social science and policy research.