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Bayesian Checks on Cheating on Tests

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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