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Experiment-based calibration: Inference and decision-making
Federico Mancinelli1, Dominik R Bach2,3,4,5,6
1Centre for Artificial Intelligence and Neuroscience, Transdisciplinary Research Area Life and Health, University of Bonn, Bonn, Germany. f.mancinelli@uni-bonn.de.
Behavior Research Methods
|June 4, 2026
Summary
Experiment-based calibration enhances measurement accuracy in behavioral sciences. Bayesian meta-analysis and decision theory offer robust inference and economic gain for method validation.
Area of Science:
- Behavioral Sciences
- Psychometrics
- Statistical Modeling
Background:
- Experiment-based calibration is an emerging method for validating measurements in behavioral sciences.
- It assesses measurement accuracy by comparing how well methods reproduce known experimental manipulations.
Purpose of the Study:
- To address inference and decision-making questions in calibration.
- To review statistical challenges in calibration processes.
- To introduce a statistical framework and software for calibration inference.
Main Methods:
- Bayesian meta-analytic model comparison for inference.
- Decision-theoretic approach for economic gain calculation.
- Development of the CalibR R package for calibration analysis.
Main Results:
- Bayesian meta-analytic model comparison is suitable for calibration inference.
- A decision-theoretic approach can calculate economic gain from reduced sample sizes.
- The CalibR package facilitates analysis of calibration experiments.
Conclusions:
- Bayesian meta-analysis provides a robust framework for measurement method comparison.
- Decision theory aids in optimizing investment in new measurement systems.
- CalibR offers practical solutions for analyzing complex calibration experiments.
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