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Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing

Author

Listed:
  • Peter W. Rijn

    (ETS Global)

  • Usama S. Ali

    (Educational Testing Service
    South Valley University)

  • Hyo Jeong Shin

    (Sogang University)

  • Sean-Hwane Joo

    (University of Kansas)

Abstract

The key assumption of conditional independence of item responses given latent ability in item response theory (IRT) models is addressed for multistage adaptive testing (MST) designs. Routing decisions in MST designs can cause patterns in the data that are not accounted for by the IRT model. This phenomenon relates to quasi-independence in log-linear models for incomplete contingency tables and impacts certain types of statistical inference based on assumptions on observed and missing data. We demonstrate that generalized residuals for item pair frequencies under IRT models as discussed by Haberman and Sinharay (J Am Stat Assoc 108:1435–1444, 2013. https://doi.org/10.1080/01621459.2013.835660 ) are inappropriate for MST data without adjustments. The adjustments are dependent on the MST design, and can quickly become nontrivial as the complexity of the routing increases. However, the adjusted residuals are found to have satisfactory Type I errors in a simulation and illustrated by an application to real MST data from the Programme for International Student Assessment (PISA). Implications and suggestions for statistical inference with MST designs are discussed.

Suggested Citation

  • Peter W. Rijn & Usama S. Ali & Hyo Jeong Shin & Sean-Hwane Joo, 2024. "Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 317-346, March.
    • Handle: RePEc:spr:psycho:v:89:y:2024:i:1:d:10.1007_s11336-023-09935-4
    DOI: 10.1007/s11336-023-09935-4
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    References listed on IDEAS

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