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Dealing with missing data under stratified sampling designs where strata are study domains.

Carlos E Rodríguez1, Luis E Nieto-Barajas2, Carlos S Pérez-Pérez2

  • 1Department of Probability and Statistics, IIMAS-UNAM, Ciudad de Mexico, Mexico.

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|January 5, 2024
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Summary
This summary is machine-generated.

This study presents two methods for accurate election quick counts with partial data. These Bayesian and frequentist approaches improve voting trend estimations even with incomplete sampling.

Keywords:
62D0562D1062F1562F4062P25Clusteringfinite population samplingmissing datamultiple imputationpost-stratificationquick count

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Area of Science:

  • Statistics
  • Political Science
  • Survey Methodology

Background:

  • Quick counts are essential for timely election results.
  • Partial data and incomplete sampling pose challenges for accurate estimations.
  • Stratified designs and study domains complicate data analysis.

Purpose of the Study:

  • To develop and compare methods for accurate interval estimation in quick counts using partial data.
  • To address the challenges of incomplete sampling and missing information in election forecasting.
  • To provide reliable voting trend estimates on election night.

Main Methods:

  • A Bayesian approach utilizing dynamic post-stratification and historical data imputation.
  • A credibility level correction to address variance underestimation in the Bayesian model.
  • A frequentist alternative combining multiple imputation with classic sampling techniques for missing data.

Main Results:

  • Both proposed methods provide accurate interval estimates despite partial information.
  • The Bayesian and frequentist strategies were successfully illustrated using the 2021 Mexican Chamber of Deputies election data.
  • The study demonstrates effective handling of incomplete sampling in quick count scenarios.

Conclusions:

  • The developed Bayesian and frequentist methods offer robust solutions for quick count estimation with incomplete data.
  • These strategies enhance the reliability of election trend reporting on election night.
  • The research contributes to improved methodologies in survey sampling for electoral studies.