IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v47y2022i4p485-504.html

Two Statistical Tests for the Detection of Item Compromise

Author

Listed:
  • Wim J. van der Linden

    (University of Twente, Enschede, the Netherlands)

Abstract

Two independent statistical tests of item compromise are presented, one based on the test takers’ responses and the other on their response times (RTs) on the same items. The tests can be used to monitor an item in real time during online continuous testing but are also applicable as part of post hoc forensic analysis. The two test statistics are simple intuitive quantities as the sum of the responses and RTs observed for the test takers on the item. Common features of the tests are ease of interpretation and computational simplicity. Both tests are uniformly most powerful under the assumption of known ability and speed parameters for the test takers. Examples of power functions for items with realistic parameter values suggest maximum power for 20–30 test takers with item preknowledge for the response-based test and 10–20 test takers for the RT-based test.

Suggested Citation

  • Wim J. van der Linden, 2022. "Two Statistical Tests for the Detection of Item Compromise," Journal of Educational and Behavioral Statistics, , vol. 47(4), pages 485-504, August.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:4:p:485-504
    DOI: 10.3102/10769986221094789
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986221094789
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986221094789?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    2. Xi Wang & Yang Liu, 2020. "Detecting Compromised Items Using Information From Secure Items," Journal of Educational and Behavioral Statistics, , vol. 45(6), pages 667-689, December.
    3. Wim Linden & Fanmin Guo, 2008. "Bayesian Procedures for Identifying Aberrant Response-Time Patterns in Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 365-384, September.
    4. Ying Cheng & Ke-Hai Yuan, 2010. "The Impact of Fallible Item Parameter Estimates on Latent Trait Recovery," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 280-291, June.
    5. Sandip Sinharay, 2017. "Some Remarks on Applications of Tests for Detecting A Change Point to Psychometric Problems," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1149-1161, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hyeon-Ah Kang, 2023. "Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 672-696, June.
    2. Hongyue Zhu & Hong Jiao & Wei Gao & Xiangbin Meng, 2023. "Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 490-520, August.
    3. Yang Du & Susu Zhang, 2025. "Detecting Compromised Items With Response Times Using a Bayesian Change-Point Approach," Journal of Educational and Behavioral Statistics, , vol. 50(2), pages 296-330, April.
    4. Edison M. Choe & Jinming Zhang & Hua-Hua Chang, 2018. "Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 650-673, September.
    5. Steffi Pohl & Esther Ulitzsch & Matthias Davier, 2019. "Using Response Times to Model Not-Reached Items due to Time Limits," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 892-920, September.
    6. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
    7. Dylan Molenaar & Paul Boeck, 2018. "Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 279-297, June.
    8. Sandip Sinharay & Peter W. van Rijn, 2020. "Assessing Fit of the Lognormal Model for Response Times," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 534-568, October.
    9. Chun Wang & Gongjun Xu & Zhuoran Shang, 2018. "A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 223-254, March.
    10. Renske E. Kuijpers & Ingmar Visser & Dylan Molenaar, 2021. "Testing the Within-State Distribution in Mixture Models for Responses and Response Times," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 348-373, June.
    11. Cardenas Hurtado, Camilo & Moustaki, Irini & Chen, Yunxiao & Marra, Giampiero, 2025. "Generalized latent variable models for location, scale, and shape parameters," LSE Research Online Documents on Economics 127387, London School of Economics and Political Science, LSE Library.
    12. Ebru Balta & Celal Deha Dogan, 2024. "Investigation of Preknowledge Cheating via Joint Hierarchical Modeling Patterns of Response Accuracy and Response Time," SAGE Open, , vol. 14(4), pages 21582440241, November.
    13. Onur Demirkaya & Ummugul Bezirhan & Jinming Zhang, 2023. "Detecting Item Preknowledge Using Revisits With Speed and Accuracy," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 521-542, August.
    14. Chongqin Xi & Wenjing Guo & Qingrong Tan & Dongbo Tu & Yan Cai, 2025. "A Mixture Response Model for Identifying Item Preknowledge," Journal of Educational and Behavioral Statistics, , vol. 50(5), pages 863-895, October.
    15. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    16. Wim J. van der Linden, 2009. "A Bivariate Lognormal Response-Time Model for the Detection of Collusion Between Test Takers," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 378-394, September.
    17. Yue Liu & Hongyun Liu, 2021. "Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 717-752, December.
    18. Hyeon-Ah Kang & Yi Zheng & Hua-Hua Chang, 2020. "Online Calibration of a Joint Model of Item Responses and Response Times in Computerized Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 175-208, April.
    19. Matthias von Davier & Lale Khorramdel & Qiwei He & Hyo Jeong Shin & Haiwen Chen, 2019. "Developments in Psychometric Population Models for Technology-Based Large-Scale Assessments: An Overview of Challenges and Opportunities," Journal of Educational and Behavioral Statistics, , vol. 44(6), pages 671-705, December.
    20. Benjamin W. Domingue & Klint Kanopka & Ben Stenhaug & Michael J. Sulik & Tanesia Beverly & Matthieu Brinkhuis & Ruhan Circi & Jessica Faul & Dandan Liao & Bruce McCandliss & Jelena Obradović & Chris , 2022. "Speed–Accuracy Trade-Off? Not So Fast: Marginal Changes in Speed Have Inconsistent Relationships With Accuracy in Real-World Settings," Journal of Educational and Behavioral Statistics, , vol. 47(5), pages 576-602, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:47:y:2022:i:4:p:485-504. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.