Detecting Aberrant Behavior and Item Preknowledge: A Comparison of Mixture Modeling Method and Residual Method

Chun Wang, Gongjun Xu, Zhuoran Shang, Nathan R Kuncel

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The modern web-based technology greatly popularizes computer-administered testing, also known as online testing. When these online tests are administered continuously within a certain “testing window,” many items are likely to be exposed and compromised, posing a type of test security concern. In addition, if the testing time is limited, another recognized aberrant behavior is rapid guessing, which refers to quickly answering an item without processing its meaning. Both cheating behavior and rapid guessing result in extremely short response times. This article introduces a mixture hierarchical item response theory model, using both response accuracy and response time information, to help differentiate aberrant behavior from normal behavior. The model-based approach is compared to the Bayesian residual-based fit statistic in both simulation study and two real data examples. Results show that the mixture model approach consistently outperforms the residual method in terms of correct detection rate and false positive error rate, in particular when the proportion of aberrance is high. Moreover, the model-based approach is also able to correctly identify compromised items better than residual method.

Original languageEnglish (US)
Pages (from-to)469-501
Number of pages33
JournalJournal of Educational and Behavioral Statistics
Volume43
Issue number4
DOIs
StatePublished - Aug 1 2018

Keywords

  • aberrant behavior
  • item preknowledge
  • item response theory
  • mixture model
  • person-fit
  • response time

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