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

Autor/inn/en	Wang, Chun; Xu, Gongjun; Shang, Zhuoran; Kuncel, Nathan
Titel	Detecting Aberrant Behavior and Item Preknowledge: A Comparison of Mixture Modeling Method and Residual Method
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 4, S.469-501 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998618767123
Schlagwörter	Computer Assisted Testing; Cheating; Guessing (Tests); Item Response Theory; Reaction Time; Accuracy; Bayesian Statistics + Suchen Sie Ihr Suchwort? Prellen; Erraten; Item-Response-Theorie; Reaktionsvermögen
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01