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Reporting Proficiency Levels for Examinees With Incomplete Data

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  • Sandip Sinharay

    (Educational Testing Service, Princeton, NJ, USA)

Abstract

Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP ® ) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on these tests. The reporting of proficiency levels to the examinees with incomplete data requires estimation of the performance of the examinees on the missing part and essentially involves imputation of missing data. In this article, six approaches from the literature on missing data analysis are brought to bear on the problem of reporting of proficiency levels to the examinees with incomplete data. Data from several large-scale educational tests are used to compare the performances of the six approaches to the approach that is operationally used for reporting proficiency levels for these tests. A multiple imputation approach based on chained equations is shown to lead to the most accurate reporting of proficiency levels for data that were missing at random or completely at random, while the model-based approach of Holman and Glas performed the best for data that are missing not at random. Several recommendations are made on the reporting of proficiency levels to the examinees with incomplete data.

Suggested Citation

  • Sandip Sinharay, 2022. "Reporting Proficiency Levels for Examinees With Incomplete Data," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 263-296, June.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:3:p:263-296
    DOI: 10.3102/10769986211051379
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    References listed on IDEAS

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    1. Norman Rose & Matthias Davier & Benjamin Nagengast, 2017. "Modeling Omitted and Not-Reached Items in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 795-819, September.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Irini Moustaki & Martin Knott, 2000. "Weighting for item non‐response in attitude scales by using latent variable models with covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 445-459.
    4. Isabella Sulis & Mariano Porcu, 2017. "Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 327-359, July.
    5. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
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