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August 12, 2026Journal of Educational Measurement

Optimizing Operational Cut Scores under Misclassification Costs and Retake Policies Using IRT‐Based Conditional Uncertainty

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Authors

PBPeter BaldwinBCBrian E. Clauser

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Overview

Randomized trial evaluates cut score optimization for testing, indicating potential improvements in accuracy.

Key Points

  • To develop a method for setting operational cut scores that reduce misclassification errors while aligning with criterion standards.
  • Applied a decision-theoretic approach to determine optimal cut scores incorporating misclassification costs.
  • Utilized item response theory (IRT) models, specifically a three-parameter logistic model, for score estimation.
  • Employed Monte Carlo integration to estimate false-negative and false-positive rates.
  • Optimal cut scores increase when the cost of false positives is emphasized and with more retake opportunities.
  • Found that misclassification probabilities derived from the conditional sampling distribution improved score reliability.
  • Demonstrated practical alignment of cut-score policies with misclassification costs.

Cite This Study

Baldwin et al. (2026) studied this question.

synapsesocial.com/papers/6a7c209506a85aed514b764dhttps://doi.org/10.1111/jedm.70056
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Using ROC Analysis to Refine Cut Scores Following a Standard Setting Process2024
  2. 2A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification2024 · 2 citations
  3. 3IRT Scoring and Recursion for Estimating Reliability and Other Accuracy Indices2025
  4. 4Comparison of Models for Simultaneous Estimation of Overall Score and Subscores: Estimation Accuracy, Reliability, and Classification Accuracy2025
  5. 5Reliability-Targeted Simulation of Item Response Data: Solving the Inverse Design Problem2025