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A sequential Bayesian changepoint detection procedure for aberrant behaviours in computerized testing.

Jing Lu1, Chun Wang2, Jiwei Zhang3

  • 1Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China.

The British Journal of Mathematical and Statistical Psychology
|May 11, 2023
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Summary
This summary is machine-generated.

This study introduces a sequential Bayesian changepoint detection algorithm to identify aberrant behaviors like rapid guessing and cheating in educational assessments. The method improves ability estimates by accurately detecting and removing problematic response data.

Keywords:
Bayesian analysisaberrant behaviourschangepoint detectionresponses and response times

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Area of Science:

  • Statistical Inference
  • Psychometrics
  • Educational Measurement

Background:

  • Ensuring test reliability and validity requires distinguishing aberrant examinee behaviors from genuine responses.
  • Abrupt variations in data sequences, known as changepoints, are crucial in statistical inference.

Purpose of the Study:

  • To propose a sequential Bayesian changepoint detection algorithm for real-time monitoring of response times.
  • To identify aberrant behaviors (rapid guessing, cheating) and improve ability estimates in educational and psychological assessments.

Main Methods:

  • Developed a sequential Bayesian changepoint detection algorithm.
  • Conducted simulation studies to assess accuracy in identifying single and multiple changepoints.
  • Investigated the impact of rapid guessing and cheating behaviors on detection.

Main Results:

  • The proposed algorithm effectively identified changepoints and aberrant behaviors.
  • Removing aberrant responses identified by the algorithm led to improved ability estimates.
  • Demonstrated the practical application through two empirical examples.

Conclusions:

  • The sequential Bayesian changepoint detection procedure is efficient and accurate for identifying aberrant behaviors in assessments.
  • This method enhances the reliability and validity of psychometric and educational measurements.
  • The approach offers a valuable tool for real-time monitoring and data cleaning in testing environments.