IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v88y2023i3d10.1007_s11336-023-09915-8.html
   My bibliography  Save this article

Commentary: Explore Conditional Dependencies in Item Response Tree Data

Author

Listed:
  • Minjeong Jeon

    (University of California, Los Angeles)

Abstract

Item response tree (IRTree) models are widely used in various applications for their ability to differentiate sets of sub-responses from polytomous item response data based on a pre-specified tree structure. Lyu et al. (Psychometrika) article highlighted that item slopes are often lower for later nodes than earlier nodes in IRTree applications. Lyu et al. argued that this phenomenon might signal the presence of item-specific factors across nodes. In this commentary, I present a different perspective that conditional dependencies in IRTree data could explain the phenomenon more generally. I illustrate my point with an empirical example, utilizing the latent space item response model that visualizes conditional dependencies in IRTree data. I conclude the commentary with a discussion on the potential of exploring conditional dependencies in IRTree data that goes beyond identifying the sources of conditional dependencies.

Suggested Citation

  • Minjeong Jeon, 2023. "Commentary: Explore Conditional Dependencies in Item Response Tree Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 803-808, September.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:3:d:10.1007_s11336-023-09915-8
    DOI: 10.1007/s11336-023-09915-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-023-09915-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-023-09915-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Minjeong Jeon & Ick Hoon Jin & Michael Schweinberger & Samuel Baugh, 2021. "Mapping Unobserved Item–Respondent Interactions: A Latent Space Item Response Model with Interaction Map," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 378-403, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ick Hoon Jin & Minjeong Jeon & Michael Schweinberger & Jonghyun Yun & Lizhen Lin, 2022. "Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1225-1244, November.
    2. Kang, Inhan & De Boeck, Paul & Partchev, Ivailo, 2022. "A randomness perspective on intelligence processes," Intelligence, Elsevier, vol. 91(C).
    3. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    4. Inhan Kang & Minjeong Jeon & Ivailo Partchev, 2023. "A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 830-864, September.
    5. Meredith Langi & Minjeong Jeon, 2023. "Identifying and Supporting Academically Low-Performing Schools in a Developing Country: An Application of a Specialized Multilevel IRT Model to PISA-D Assessment Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 332-356, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:88:y:2023:i:3:d:10.1007_s11336-023-09915-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.