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Scoring Tests With Contaminated Response Vectors

Author

Listed:
  • Arnond Sakworawich

    (National Institute of Development Administration)

  • Howard Wainer

    (Pennington, New Jersey)

Abstract

Test scoring models vary in their generality, some even adjust for examinees answering multiple-choice items correctly by accident (guessing), but no models, that we are aware of, automatically adjust an examinee’s score when there is internal evidence of cheating. In this study, we use a combination of jackknife technology with an adaptive robust estimator to reduce the bias in examinee scores due to contamination through events such as having access to some of the test items in advance of the test administration. We illustrate our methodology with a data set of test items we knew to have been divulged to a subset of the examinees.

Suggested Citation

  • Arnond Sakworawich & Howard Wainer, 2020. "Scoring Tests With Contaminated Response Vectors," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 209-226, April.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:2:p:209-226
    DOI: 10.3102/1076998619882902
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    References listed on IDEAS

    as
    1. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    2. Howard Wainer & Benjamin Wright, 1980. "Robust estimation of ability in the Rasch model," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 373-391, September.
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