Detecting excessive similarity in answers on multiple choice exams

Wesolowsky, George O.; McMaster University, Michael G. DeGroote School of Business

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/5570

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DC Field	Value	Language
dc.contributor.author	Wesolowsky, George O.	en_US
dc.contributor.author	McMaster University, Michael G. DeGroote School of Business	en_US
dc.date.accessioned	2014-06-17T20:36:59Z	-
dc.date.available	2014-06-17T20:36:59Z	-
dc.date.created	2013-12-23	en_US
dc.date.issued	1999-10	en_US
dc.identifier.other	dsb/30	en_US
dc.identifier.other	1029	en_US
dc.identifier.other	4944050	en_US
dc.identifier.uri	http://hdl.handle.net/11375/5570	-
dc.description	<p>23 leaves : ; Includes bibliographical references (leaves 21-22). ; "October, 1999".</p>	en_US
dc.description.abstract	<p>This paper provides a simple and robust method for detecting cheating. Unlike some methods, non-cheating behaviour and not cheating behaviour is modelled because this requires the fewest assumptions. The main concern is the prevention of false accusations. The model is suitable for screening large classes and the results are simple to interpret. Simulation and the Bonferroni inequaltty are used to prevent false accusation due to 'data dredging'. The model has received considerable application in practice and has been verified through the adjacent seating method.</p>	en_US
dc.relation.ispartofseries	Research and working paper series (Michael G. DeGroote School of Business)	en_US
dc.relation.ispartofseries	no. 442	en_US
dc.subject	Business	en_US
dc.subject	Business	en_US
dc.subject.lcc	Cheating (Education) Multiple-choice examinations	en_US
dc.title	Detecting excessive similarity in answers on multiple choice exams	en_US
dc.type	article	en_US
Appears in Collections:	DeGroote School of Business Working Paper Series

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