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Generalized measurement error: Intrinsic and incidental measurement error.
1Measurement, Evaluation, and Research Methodology, University of British Columbia, Vancouver, British Columbia, Canada.
Plos One
|June 29, 2023
Summary
This study introduces two new types of measurement error (intrinsic and incidental) for random-valued data. It generalizes classical error models and statistical theories to this broader measurement domain.
Area of Science:
- Statistics
- Measurement Theory
- Data Science
Background:
- Traditional measurement error models apply to deterministic data.
- Handling random-variable-valued data requires new approaches to measurement error.
Purpose of the Study:
- To generalize measurement error concepts for random-variable-valued data.
- To introduce intrinsic and incidental measurement error.
- To extend classical statistical methods to this new data domain.
Main Methods:
- Formulation of intrinsic and incidental measurement error.
- Definition of calibrating conditions for generalized error models.
- Exploration of generalized point estimation, inference, and likelihood theory.
Main Results:
- Distinction between intrinsic and incidental measurement error.
- Generalization of classical measurement error models, including Berkson error.
- Adaptation of statistical inference for random-variable-valued measurements.
Conclusions:
- The proposed framework accommodates a broader range of measurement data.
- Generalized statistical theories provide tools for analyzing complex measurement processes.
- This work advances the understanding and modeling of measurement error in diverse applications.
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