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A Review of Test Equating Methods with a Special Focus on IRT-Based Approaches

Author

Listed:
  • Valentina Sansivieri

    (Alma Mater Studiorum - Università di Bologna - Italy)

  • Marie Wiberg

    (Umeå University - Sweden)

  • Mariagiulia Matteucci

    (Alma Mater Studiorum - Università di Bologna - Italy)

Abstract

The overall aim of this work is to review test equating methods with a particularly detailed description of item response theory (IRT) equating. Test score equating is used to compare different test scores from different test forms. Several methods have been developed to conduct equating: traditional methods, kernel method, and IRT equating. We synthetically explain the traditional equating methods which include mean equating, linear equating and equipercentile equating and which have been developed under all the possible data collection designs. We also briefly describe the idea of the kernel method: this is a unified approach to test equating for which recent interesting developments have been proposed. Then we focus on IRT equating, by describing old and new methods: in particular, we define IRT observed-score kernel equating and IRT observed-score equating using covariates, as well as other recent proposals in this field. We conclude the review by describing strengths and weaknesses of the different discussed approaches and by identifying future research topics.

Suggested Citation

  • Valentina Sansivieri & Marie Wiberg & Mariagiulia Matteucci, 2017. "A Review of Test Equating Methods with a Special Focus on IRT-Based Approaches," Statistica, Department of Statistics, University of Bologna, vol. 77(4), pages 329-352.
    • Handle: RePEc:bot:rivsta:v:77:y:2017:i:4:p:329-352
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    Cited by:

    1. Alexander Robitzsch, 2023. "Comparing Robust Linking and Regularized Estimation for Linking Two Groups in the 1PL and 2PL Models in the Presence of Sparse Uniform Differential Item Functioning," Stats, MDPI, vol. 6(1), pages 1-17, January.
    2. Alexander Robitzsch, 2020. "L p Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups," Stats, MDPI, vol. 3(3), pages 1-38, August.
    3. Alexander Robitzsch, 2024. "Bias-Reduced Haebara and Stocking–Lord Linking," J, MDPI, vol. 7(3), pages 1-12, September.

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