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On Longitudinal Item Response Theory Models: A Didactic

Author

Listed:
  • Chun Wang

    (University of Washington)

  • Steven W. Nydick

    (Korn Ferry)

Abstract

Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and limitations of different multilevel IRT models for measuring growth. This study aims to introduce the various longitudinal IRT models, including the longitudinal unidimensional IRT model, longitudinal multidimensional IRT model, and longitudinal higher order IRT model, which cover a broad range of applications in education and social science. Following a comparison of the parameterizations, identification constraints, strengths, and weaknesses of the different models, a real data example is provided to illustrate the application of different longitudinal IRT models to model students’ growth trajectories on multiple latent abilities.

Suggested Citation

  • Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:3:p:339-368
    DOI: 10.3102/1076998619882026
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    References listed on IDEAS

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    Cited by:

    1. Hiroshi Tamano & Daichi Mochihashi, 2023. "Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 487-526, June.

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