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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.
Multivariate Behavioral Research
|May 8, 2026
Summary
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.
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.
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