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Calibration with confidence: a principled method for panel assessment.

R S MacKay1, R Kenna2, R J Low2

  • 1Mathematics Institute and Centre for Complexity Science , University of Warwick , Coventry CV4 7AL, UK.

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|April 8, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to calibrate assessor scores, accounting for varying standards and confidence levels. This method infers true object values by analyzing assessor performance and confidence, improving evaluation accuracy.

Keywords:
assessmentcalibrationconfidenceevaluationmodel comparisonuncertainty

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Area of Science:

  • Multidisciplinary evaluation methodologies
  • Statistical modeling for assessment calibration
  • Data analysis in expert judgment

Background:

  • Evaluating objects by panels often involves incomplete assessment data.
  • Panel members may exhibit differing standards and confidence levels in their scoring.
  • Existing methods like simple averaging may not adequately address these variations.

Purpose of the Study:

  • To develop a mathematically based algorithm for calibrating assessor scores.
  • To address varying standards and confidence levels among panel members.
  • To infer 'true' values of objects and estimate the reliability of these values.

Main Methods:

  • An algorithm based on the connectivity of assessor-object graphs.
  • Incorporation of declared confidences as edge weights in the graph.
  • Inference of relative standards by comparing common assessments, weighted by confidence.
  • Removal of biases to determine true object values and reliability estimates.

Main Results:

  • The algorithm successfully calibrates assessor scores by accounting for individual standards and confidence.
  • Inferred 'true' values for objects are obtained after bias removal.
  • Reliability estimates for the inferred values are generated.
  • The algorithm demonstrated effectiveness in both simulated and real-world evaluation data.

Conclusions:

  • The developed algorithm provides a robust method for calibrating scores in multi-assessor evaluations.
  • It offers a significant improvement over simple averaging and other analytical approaches.
  • The algorithm has broad applicability in various assessment scenarios, including research quality, grant proposals, and performance appraisal.