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Conditional Subscore Reporting Using Iterated Discrete Convolutions

Author

Listed:
  • Richard A. Feinberg
  • Matthias von Davier

    (44207National Board of Medical Examiners)

Abstract

The literature showing that subscores fail to add value is vast; yet despite their typical redundancy and the frequent presence of substantial statistical errors, many stakeholders remain convinced of their necessity. This article describes a method for identifying and reporting unexpectedly high or low subscores by comparing each examinee’s observed subscore with a discrete probability distribution of subscores conditional on the examinee’s overall ability. The proposed approach turns out to be somewhat conservative due to the nature of subscores as finite sums of item scores associated with a subdomain. Thus, the method may be a compromise that satisfies score users by reporting subscore information as well as psychometricians by limiting misinterpretation, at most, to the rates of Type I and Type II error.

Suggested Citation

  • Richard A. Feinberg & Matthias von Davier, 2020. "Conditional Subscore Reporting Using Iterated Discrete Convolutions," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 515-533, October.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:5:p:515-533
    DOI: 10.3102/1076998620911933
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    References listed on IDEAS

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    1. Biscarri, William & Zhao, Sihai Dave & Brunner, Robert J., 2018. "A simple and fast method for computing the Poisson binomial distribution function," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 92-100.
    2. Frank Rijmen & Minjeong Jeon & Matthias von Davier & Sophia Rabe-Hesketh, 2014. "A Third-Order Item Response Theory Model for Modeling the Effects of Domains and Subdomains in Large-Scale Educational Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 39(4), pages 235-256, August.
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    Cited by:

    1. Sijia Huang & Li Cai, 2021. "Lord–Wingersky Algorithm Version 2.5 with Applications," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 973-993, December.

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