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A method of correction for heaping error in the variables using validation data.

Amar S Ahmad1, Munther Al-Hassan2, Hamid Y Hussain3

  • 1New York University, Abu Dhabi, UAE.

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|February 27, 2023
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Summary
This summary is machine-generated.

Self-reported data often contain bias due to answer heaping. This study introduces a new method using validation data to correct bias in statistical estimates from self-reported data, improving accuracy for research and health care planning.

Keywords:
BiasHeaping errorMeasurement errorSelf-reported data

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Self-reported data are widely used in statistical analyses.
  • Heaping, where respondents round answers to specific values, introduces bias in estimates.
  • This bias affects mean, variance, and regression parameters.

Purpose of the Study:

  • To examine the bias-inducing effect of heaping error in self-reported data.
  • To study the impact of heaping error on statistical estimates.
  • To develop a method for correcting bias in self-reported data.

Main Methods:

  • Analysis of bias in statistical estimates (mean, variance, regression parameters).
  • Development of a novel correction method utilizing validation data.
  • Application and validation using publicly available datasets and simulation studies.

Main Results:

  • Heaping error significantly biases estimates of mean, variance, and regression parameters.
  • The newly developed method effectively corrects for this bias.
  • The correction method is shown to be practical and easy to apply.

Conclusions:

  • Accurate statistical conclusions can be drawn from self-reported data after bias correction.
  • The method supports improved decision-making in areas like health care planning.
  • Researchers can enhance the reliability of findings from self-reported datasets.