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An IRtree Model for Aberrant Response and Missing Data.

Fangbin Chen1, Daxun Wang1, Yan Cai1

  • 1School of Psychology, Jiangxi Normal University, Nanchang, China.

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Summary
This summary is machine-generated.

This study introduces the IRTree model to accurately analyze various test-taking behaviors like guessing and cheating. The model improves the precision of ability and item parameter estimates in standardized testing.

Keywords:
IRtree modelItem response theoryaberrant responsescheatingmissing datarapid guessing

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Area of Science:

  • Psychometrics
  • Educational Measurement

Background:

  • Standardized tests can yield inaccurate results due to non-standard examinee behaviors.
  • Behaviors like rapid guessing, cheating, and nonresponse compromise test validity and fairness.

Purpose of the Study:

  • To propose an innovative IRTree model for simultaneously analyzing multiple aberrant examinee behaviors.
  • To enhance the accuracy of ability and item parameter estimation in standardized assessments.

Main Methods:

  • Developed the IRTree model to concurrently model rapid guessing, cheating, and nonresponse behaviors.
  • Applied the model to two real datasets and conducted simulation studies for validation.

Main Results:

  • The IRTree model provides superior accuracy in estimating person and item parameters compared to existing models.
  • The model offers enhanced classification of examinee behaviors at both item and examinee levels.
  • Successfully separated and simultaneously modeled guessing and cheating behaviors for the first time.

Conclusions:

  • The IRTree model effectively addresses limitations in current testing methodologies by accounting for diverse examinee behaviors.
  • The model demonstrates improved precision and validity in psychometric assessments.
  • Further research should explore the boundary conditions for the model's application.