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Compromised item detection: A Bayesian change-point perspective.

Yang Du1, Susu Zhang2, Hua-Hua Chang3

  • 1Department of Educational Psychology, University of Illinois, Urbana-Champaign, Urbana-Champaign, Illinois, USA.

The British Journal of Mathematical and Statistical Psychology
|September 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian change-point framework for detecting compromised test items. The new psychometric method offers superior accuracy and efficiency in identifying item compromise over existing procedures.

Keywords:
Bayesian change-point detectionShiryaev procedurecomputerized testsitem compromisetest security

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Accurate detection of item compromise is crucial in psychometric testing.
  • Existing methods for detecting item compromise lack efficiency.
  • Bayesian methods have not been previously applied to item compromise detection.

Purpose of the Study:

  • To propose a novel two-phase Bayesian change-point framework for detecting item compromise.
  • To enable both stationary and real-time detection of changes in item compromise status.
  • To evaluate the performance of the proposed framework.

Main Methods:

  • A two-phase Bayesian change-point framework was developed.
  • Phase I involved fitting a stationary Bayesian change-point model.
  • Phase II employed the Shiryaev procedure for sequential testing in real-time.

Main Results:

  • The proposed Bayesian framework demonstrated accurate parameter recovery.
  • The method showed superior detection accuracy and efficiency compared to the cumulative sum procedure.
  • The framework was validated through simulations and a real data example.

Conclusions:

  • The proposed two-phase Bayesian change-point framework effectively detects item compromise.
  • This novel approach offers enhanced accuracy and efficiency for psychometric item analysis.
  • The method provides a robust tool for both stationary and real-time monitoring of test item integrity.