IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v88y2023i3d10.1007_s11336-023-09916-7.html
   My bibliography  Save this article

Item-Specific Factors in IRTree Models: When They Matter and When They Don’t

Author

Listed:
  • Thorsten Meiser

    (University of Mannheim)

  • Fabiola Reiber

    (University of Mannheim)

Abstract

Lyu et al. (Psychometrika, 2023) demonstrated that item-specific factors can cause spurious effects on the structural parameters of IRTree models for multiple nested response processes per item. Here, we discuss some boundary conditions and argue that person selection effects on item parameters are not unique to item-specific factors and that the effects presented by Lyu et al. (Psychometrika, 2023) may not generalize to the family of IRTree models as a whole. We conclude with the recommendation that IRTree model specification should be guided by theoretical considerations, rather than driven by data, in order to avoid misinterpretations of parameter differences.

Suggested Citation

  • Thorsten Meiser & Fabiola Reiber, 2023. "Item-Specific Factors in IRTree Models: When They Matter and When They Don’t," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 739-744, September.
  • Handle: RePEc:spr:psycho:v:88:y:2023:i:3:d:10.1007_s11336-023-09916-7
    DOI: 10.1007/s11336-023-09916-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-023-09916-7
    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/s11336-023-09916-7?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. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    2. Weicong Lyu & Daniel M. Bolt & Samuel Westby, 2023. "Exploring the Effects of Item-Specific Factors in Sequential and IRTree Models," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 745-775, September.
    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. Andrés López-Sepulcre & Sebastiano De Bona & Janne K. Valkonen & Kate D.L. Umbers & Johanna Mappes, 2015. "Item Response Trees: a recommended method for analyzing categorical data in behavioral studies," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(5), pages 1268-1273.
    2. 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.
    3. Dora Matzke & Conor Dolan & William Batchelder & Eric-Jan Wagenmakers, 2015. "Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 205-235, March.
    4. Yingbin Zhang & Zhaoxi Yang & Yehui Wang, 2022. "The Impact of Extreme Response Style on the Mean Comparison of Two Independent Samples," SAGE Open, , vol. 12(2), pages 21582440221, June.
    5. Anne Thissen-Roe & David Thissen, 2013. "A Two-Decision Model for Responses to Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 522-547, October.
    6. Quentin F. Gronau & Eric-Jan Wagenmakers & Daniel W. Heck & Dora Matzke, 2019. "A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 261-284, March.
    7. Nana Kim & Daniel M. Bolt & James Wollack, 2022. "Noncompensatory MIRT For Passage-Based Tests," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 992-1009, September.
    8. Sun-Joo Cho & Sarah Brown-Schmidt & Paul De Boeck & Jianhong Shen, 2020. "Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 154-184, March.
    9. Gerhard Tutz & Moritz Berger, 2016. "Response Styles in Rating Scales," Journal of Educational and Behavioral Statistics, , vol. 41(3), pages 239-268, June.
    10. Niccolò Cao & Antonio Calcagnì, 2022. "Jointly Modeling Rating Responses and Times with Fuzzy Numbers: An Application to Psychometric Data," Mathematics, MDPI, vol. 10(7), pages 1-11, March.
    11. Brooke E. Magnus & David Thissen, 2017. "Item Response Modeling of Multivariate Count Data With Zero Inflation, Maximum Inflation, and Heaping," Journal of Educational and Behavioral Statistics, , vol. 42(5), pages 531-558, October.
    12. Dylan Molenaar & Paul Boeck, 2018. "Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 279-297, June.
    13. Mark L. Davison & David J. Weiss & Joseph N. DeWeese & Ozge Ersan & Gina Biancarosa & Patrick C. Kennedy, 2023. "A Diagnostic Tree Model for Adaptive Assessment of Complex Cognitive Processes Using Multidimensional Response Options," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 914-941, December.
    14. Gerhard Tutz, 2021. "Hierarchical Models for the Analysis of Likert Scales in Regression and Item Response Analysis," International Statistical Review, International Statistical Institute, vol. 89(1), pages 18-35, April.
    15. Weicong Lyu & Daniel M. Bolt, 2023. "Rejoinder to Commentaries on Lyu, Bolt and Westby’s “Exploring the Effects of Item Specific Factors in Sequential and IRTree Models”," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1026-1031, September.
    16. Minjeong Jeon & Paul De Boeck & Wim van der Linden, 2017. "Modeling Answer Change Behavior: An Application of a Generalized Item Response Tree Model," Journal of Educational and Behavioral Statistics, , vol. 42(4), pages 467-490, August.

    More about this item

    Keywords

    IRTree models; item-specific factors;

    Statistics

    Access and download statistics

    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_s11336-023-09916-7. 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.