IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v80y2015i3p689-706.html
   My bibliography  Save this article

Bayesian Checks on Cheating on Tests

Author

Listed:
  • Wim Linden
  • Charles Lewis

Abstract

Posterior odds of cheating on achievement tests are presented as an alternative to $$p$$ p values reported for statistical hypothesis testing for several of the probabilistic models in the literature on the detection of cheating. It is shown how to calculate their combinatorial expressions with the help of a reformulation of the simple recursive algorithm for the calculation of number-correct score distributions used throughout the testing industry. Using the odds avoids the arbitrary choice between statistical tests of answer copying that do and do not condition on the responses the test taker is suspected to have copied and allows the testing agency to account for existing circumstantial evidence of cheating through the specification of prior odds. Copyright The Psychometric Society 2015

Suggested Citation

  • Wim Linden & Charles Lewis, 2015. "Bayesian Checks on Cheating on Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 689-706, September.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:3:p:689-706
    DOI: 10.1007/s11336-014-9409-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-014-9409-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-014-9409-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Brian A. Jacob & Steven D. Levitt, 2003. "Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 843-877.
    2. R. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    3. George Wesolowsky, 2000. "Detecting excessive similarity in answers on multiple choice exams," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(7), pages 909-921.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Yunxiao & Li, Xiaoou, 2023. "Compound sequential change-point detection in parallel data streams," LSE Research Online Documents on Economics 111010, London School of Economics and Political Science, LSE Library.
    2. Sandip Sinharay & Matthew S. Johnson, 2021. "The Use of the Posterior Probability in Score Differencing," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 403-429, August.
    3. 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.
    4. Anton Oleinik, 2024. "A Bayesian index of association: comparison with other measures and performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 277-305, February.
    5. Sandip Sinharay, 2018. "Detecting Fraudulent Erasures at an Aggregate Level," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 286-315, June.

    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. César Martinelli & Susan W. Parker & Ana Cristina Pérez-Gea & Rodimiro Rodrigo, 2018. "Cheating and Incentives: Learning from a Policy Experiment," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 298-325, February.
    2. Jere R. Behrman & Susan W. Parker & Petra E. Todd & Kenneth I. Wolpin, 2015. "Aligning Learning Incentives of Students and Teachers: Results from a Social Experiment in Mexican High Schools," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 325-364.
    3. Yang Liu & Weimeng Wang, 2022. "Semiparametric Factor Analysis for Item-Level Response Time Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 666-692, June.
    4. Ming‐Jen Lin & Steven D. Levitt, 2020. "Catching Cheating Students," Economica, London School of Economics and Political Science, vol. 87(348), pages 885-900, October.
    5. Sergio Longobardi & Patrizia Falzetti & Margherita Maria Pagliuca, 2018. "Quis custiodet ipsos custodes? How to detect and correct teacher cheating in Italian student data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 515-543, August.
    6. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja & Tristan Zajonc, 2011. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 29-54, July.
    7. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja, 2017. "Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets," American Economic Review, American Economic Association, vol. 107(6), pages 1535-1563, June.
    8. Gill, David & Prowse, Victoria & Vlassopoulos, Michael, 2013. "Cheating in the workplace: An experimental study of the impact of bonuses and productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 96(C), pages 120-134.
    9. Marie Claire Villeval, 2019. "Comportements (non) éthiques et stratégies morales," Revue économique, Presses de Sciences-Po, vol. 70(6), pages 1021-1046.
    10. Jesse Rothstein, 2015. "Teacher Quality Policy When Supply Matters," American Economic Review, American Economic Association, vol. 105(1), pages 100-130, January.
    11. Piopiunik, Marc & Schwerdt, Guido & Woessmann, Ludger, 2013. "Central school exit exams and labor-market outcomes," European Journal of Political Economy, Elsevier, vol. 31(C), pages 93-108.
    12. Seth Gershenson, 2016. "Performance Standards and Employee Effort: Evidence From Teacher Absences," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(3), pages 615-638, June.
    13. Bruno Frey, 2011. "Tullock challenges: happiness, revolutions, and democracy," Public Choice, Springer, vol. 148(3), pages 269-281, September.
    14. Christophe Lemiére & Gaute Torsvik & Ottar Mæstad & Christopher H. Herbst & Kenneth L. Leonard, 2013. "Evaluating the Impact of Results-Based Financing on Health Worker Performance: Theory, Tools and Variables to Inform an Impact Evaluation," Health, Nutrition and Population (HNP) Discussion Paper Series 98269, The World Bank.
    15. Hinnerich, Björn Tyrefors & Höglin, Erik & Johannesson, Magnus, 2011. "Ethnic Discrimination in High School Grading: Evidence from a Field Experiment," SSE/EFI Working Paper Series in Economics and Finance 733, Stockholm School of Economics, revised 27 Jun 2011.
    16. Makofske, Matthew Philip, 2020. "Disclosure policies in inspection programs: The role of specific deterrence," Economics Letters, Elsevier, vol. 196(C).
    17. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    18. Mauricio Romero & Ã lvaro Riascos & Diego Jara, 2015. "On the Optimality of Answer-Copying Indices," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 435-453, October.
    19. Hernández-Pizarro, Helena M. & Nicodemo, Catia & Casasnovas, Guillem López, 2020. "Discontinuous system of allowances: The response of prosocial health-care professionals," Journal of Public Economics, Elsevier, vol. 190(C).
    20. Bas Hemker & Klaas Sijtsma & Ivo Molenaar & Brian Junker, 1996. "Polytomous IRT models and monotone likelihood ratio of the total score," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 679-693, December.

    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:spr:psycho:v:80:y:2015:i:3:p:689-706. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.