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Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection

Author

Listed:
  • Yunxiao Chen

    (London School of Economics and Political Science, United Kingdom)

  • Yi-Hsuan Lee

    (Educational Testing Service, Princeton, NJ, USA)

  • Xiaoou Li

    (University of Minnesota Twin Cities, Minneapolis, MN, USA)

Abstract

In standardized educational testing, test items are reused in multiple test administrations. To ensure the validity of test scores, the psychometric properties of items should remain unchanged over time. In this article, we consider the sequential monitoring of test items, in particular, the detection of abrupt changes to their psychometric properties, where a change can be caused by, for example, leakage of the item or change of the corresponding curriculum. We propose a statistical framework for the detection of abrupt changes in individual items. This framework consists of (1) a multistream Bayesian change point model describing sequential changes in items, (2) a compound risk function quantifying the risk in sequential decisions, and (3) sequential decision rules that control the compound risk. Throughout the sequential decision process, the proposed decision rule balances the trade-off between two sources of errors, the false detection of prechange items, and the nondetection of postchange items. An item-specific monitoring statistic is proposed based on an item response theory model that eliminates the confounding from the examinee population which changes over time. Sequential decision rules and their theoretical properties are developed under two settings: the oracle setting where the Bayesian change point model is completely known and a more realistic setting where some parameters of the model are unknown. Simulation studies are conducted under settings that mimic real operational tests.

Suggested Citation

  • Yunxiao Chen & Yi-Hsuan Lee & Xiaoou Li, 2022. "Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 322-352, June.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:3:p:322-352
    DOI: 10.3102/10769986211059085
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    References listed on IDEAS

    as
    1. Yi-Hsuan Lee & Charles Lewis, 2021. "Monitoring Item Performance With CUSUM Statistics in Continuous Testing," Journal of Educational and Behavioral Statistics, , vol. 46(5), pages 611-648, October.
    2. 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.
    3. Yi-Hsuan Lee & Alina Davier, 2013. "Monitoring Scale Scores over Time via Quality Control Charts, Model-Based Approaches, and Time Series Techniques," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 557-575, July.
    4. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    5. Y. Mei, 2010. "Efficient scalable schemes for monitoring a large number of data streams," Biometrika, Biometrika Trust, vol. 97(2), pages 419-433.
    6. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
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