Detecting Suspicious Persons in Multiple-Choice Tests: Comparison of Performance of [omega] and GBT Indexes
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| Title: | Detecting Suspicious Persons in Multiple-Choice Tests: Comparison of Performance of [omega] and GBT Indexes |
|---|---|
| Language: | English |
| Authors: | Arzu Uçar (ORCID |
| Source: | International Journal of Assessment Tools in Education. 2026 13(1):95-107. |
| Availability: | International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Multiple Choice Tests, Cheating, Statistical Analysis, Accuracy, Test Items, Difficulty Level, Ability |
| ISSN: | 2148-7456 |
| Abstract: | The COVID-19 pandemic has led to a widespread shift from traditional, supervised exams to unsupervised online testing environments, which has increased opportunities and motivation for cheating. Such dishonest behaviors threaten the validity and fairness of test results, underscoring the critical importance of robust test security measures. This study focuses on detecting individuals suspected of cheating by using two widely recognized statistical indexes: the [omega] and Generalized Binomial Test (GBT) indexes. Both indexes were applied within two analytical frameworks no-stage and two-stage methods. In the two-stage approach, the [omega] and GBT indexes were employed following the detection of potential cheaters using the Kullback-Leibler (KL) divergence index and person fit statistics (l[subscript z] and l[subscript z]*). To simulate realistic conditions, we manipulated key variables including test difficulty, ability levels of suspected copiers, and the proportion of copied items. Our findings demonstrate that the GBT index combined with the KL index consistently outperformed other methods across varying scenarios in terms of detection accuracy and control of false positives. These results suggest that integrating person-fit and distribution-based methods enhances the reliability of cheating detection in unsupervised testing environments. The study provides valuable insights for test administrators seeking effective statistical tools to safeguard test integrity, especially in the context of increasing online assessments. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1496093 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1496093 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Header | DbId: eric DbLabel: ERIC An: EJ1496093 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detecting Suspicious Persons in Multiple-Choice Tests: Comparison of Performance of [omega] and GBT Indexes – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arzu+Uçar%22">Arzu Uçar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0099-1348">0000-0002-0099-1348</externalLink>)<br /><searchLink fieldCode="AR" term="%22Celal+Deha+Dogan%22">Celal Deha Dogan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0683-1334">0000-0003-0683-1334</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Assessment+Tools+in+Education%22"><i>International Journal of Assessment Tools in Education</i></searchLink>. 2026 13(1):95-107. – Name: Avail Label: Availability Group: Avail Data: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 13 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+Choice+Tests%22">Multiple Choice Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Cheating%22">Cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Ability%22">Ability</searchLink> – Name: ISSN Label: ISSN Group: ISSN Data: 2148-7456 – Name: Abstract Label: Abstract Group: Ab Data: The COVID-19 pandemic has led to a widespread shift from traditional, supervised exams to unsupervised online testing environments, which has increased opportunities and motivation for cheating. Such dishonest behaviors threaten the validity and fairness of test results, underscoring the critical importance of robust test security measures. This study focuses on detecting individuals suspected of cheating by using two widely recognized statistical indexes: the [omega] and Generalized Binomial Test (GBT) indexes. Both indexes were applied within two analytical frameworks no-stage and two-stage methods. In the two-stage approach, the [omega] and GBT indexes were employed following the detection of potential cheaters using the Kullback-Leibler (KL) divergence index and person fit statistics (l[subscript z] and l[subscript z]*). To simulate realistic conditions, we manipulated key variables including test difficulty, ability levels of suspected copiers, and the proportion of copied items. Our findings demonstrate that the GBT index combined with the KL index consistently outperformed other methods across varying scenarios in terms of detection accuracy and control of false positives. These results suggest that integrating person-fit and distribution-based methods enhances the reliability of cheating detection in unsupervised testing environments. The study provides valuable insights for test administrators seeking effective statistical tools to safeguard test integrity, especially in the context of increasing online assessments. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1496093 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1496093 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 95 Subjects: – SubjectFull: Multiple Choice Tests Type: general – SubjectFull: Cheating Type: general – SubjectFull: Statistical Analysis Type: general – SubjectFull: Accuracy Type: general – SubjectFull: Test Items Type: general – SubjectFull: Difficulty Level Type: general – SubjectFull: Ability Type: general Titles: – TitleFull: Detecting Suspicious Persons in Multiple-Choice Tests: Comparison of Performance of [omega] and GBT Indexes Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arzu Uçar – PersonEntity: Name: NameFull: Celal Deha Dogan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2148-7456 Numbering: – Type: volume Value: 13 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Assessment Tools in Education Type: main |
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