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A New Multiprocess IRT Model With Ideal Points for Likert-Type Items

Author

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  • Kuan-Yu Jin

    (Hong Kong Examinations and Assessment Authority, Wan Chai, Hong Kong)

  • Yi-Jhen Wu

    (The Center for Research on Education and School Development, TU Dortmund, Dortmund, Germany
    Department of Medical Psychology and Medical, University Medical Center Göttingen, Germany)

  • Hui-Fang Chen

    (Department of Social and Behavioural Sciences, City University of Hong Kong, Kowloon, Hong Kong)

Abstract

For surveys of complex issues that entail multiple steps, multiple reference points, and nongradient attributes (e.g., social inequality), this study proposes a new multiprocess model that integrates ideal-point and dominance approaches into a treelike structure (IDtree). In the IDtree, an ideal-point approach describes an individual’s attitude and then a dominance approach describes their tendency for using extreme response categories. Evaluation of IDtree performance via two empirical data sets showed that the IDtree fit these data better than other models. Furthermore, simulation studies showed a satisfactory parameter recovery of the IDtree. Thus, the IDtree model sheds light on the response processes of a multistage structure.

Suggested Citation

  • Kuan-Yu Jin & Yi-Jhen Wu & Hui-Fang Chen, 2022. "A New Multiprocess IRT Model With Ideal Points for Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 297-321, June.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:3:p:297-321
    DOI: 10.3102/10769986211057160
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    References listed on IDEAS

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