IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v86y2021i3d10.1007_s11336-021-09790-1.html
   My bibliography  Save this article

Commentary: Matching IRT Models to PRO Constructs— Modeling Alternatives, and Some Thoughts on What Makes a Model Different

Author

Listed:
  • Matthias von Davier

    (Boston College)

Abstract

This commentary is an attempt to present some additional alternatives to the suggestions made by Reise et al. (2021). IRT models as they are used for patient-reported outcome (PRO) scales may not be fully satisfactory when used with commonly made assumptions. The suggested change to an alternative parameterization is critically reflected with the intent to initiate discussion around more comprehensive alternatives that allow for more complex latent structures having the potential to be more appropriate for PRO scales as they are applied to diverse populations.

Suggested Citation

  • Matthias von Davier, 2021. "Commentary: Matching IRT Models to PRO Constructs— Modeling Alternatives, and Some Thoughts on What Makes a Model Different," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 825-832, September.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:3:d:10.1007_s11336-021-09790-1
    DOI: 10.1007/s11336-021-09790-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-021-09790-1
    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/s11336-021-09790-1?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. Lisa M Lix & Xiuyun Wu & Wilma Hopman & Nancy Mayo & Tolulope T Sajobi & Juxin Liu & Jerilynn C Prior & Alexandra Papaioannou & Robert G Josse & Tanveer E Towheed & K Shawn Davison & Richard Sawatzky, 2016. "Differential Item Functioning in the SF-36 Physical Functioning and Mental Health Sub-Scales: A Population-Based Investigation in the Canadian Multicentre Osteoporosis Study," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-13, March.
    2. Gerhard Fischer, 1981. "On the existence and uniqueness of maximum-likelihood estimates in the Rasch model," Psychometrika, Springer;The Psychometric Society, vol. 46(1), pages 59-77, March.
    3. Dean Follmann, 1988. "Consistent estimation in the rasch model based on nonparametric margins," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 553-562, December.
    4. Irini Moustaki & Martin Knott, 2000. "Generalized latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 391-411, September.
    5. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    6. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
    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. Matthew S. Johnson & Sandip Sinharay, 2020. "The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 5-31, February.
    2. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
    3. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
    4. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference for the DINA Model," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 569-597, October.
    5. Qingrong Tan & Yan Cai & Fen Luo & Dongbo Tu, 2023. "Development of a High-Accuracy and Effective Online Calibration Method in CD-CAT Based on Gini Index," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 103-141, February.
    6. Xin Xu & Guanhua Fang & Jinxin Guo & Zhiliang Ying & Susu Zhang, 2024. "Diagnostic Classification Models for Testlets: Methods and Theory," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 851-876, September.
    7. Laine Bradshaw & Jonathan Templin, 2014. "Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 403-425, July.
    8. Peida Zhan & Wen-Chung Wang & Xiaomin Li, 2020. "A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 328-351, July.
    9. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    10. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 973-995, December.
    11. Chia-Yi Chiu & Yuan-Pei Chang, 2021. "Advances in CD-CAT: The General Nonparametric Item Selection Method," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 1039-1057, December.
    12. Chen-Wei Liu & Björn Andersson & Anders Skrondal, 2020. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 322-357, June.
    13. Chun Wang & Jing Lu, 2021. "Learning Attribute Hierarchies From Data: Two Exploratory Approaches," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 58-84, February.
    14. Hans Friedrich Köhn & Chia-Yi Chiu, 2021. "A Unified Theory of the Completeness of Q-Matrices for the DINA Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 500-518, October.
    15. Xuliang Gao & Wenchao Ma & Daxun Wang & Yan Cai & Dongbo Tu, 2021. "A Class of Cognitive Diagnosis Models for Polytomous Data," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 297-322, June.
    16. Muhamad Firdaus Mohd Noh & Mohd Effendi Ewan bin Mohd Matore, 2024. "Conundrum and Considerations in Cognitive Diagnostic Assessment for Language Proficiency Evaluation," Information Management and Business Review, AMH International, vol. 16(2), pages 63-72.
    17. Kevin Carl P. Santos & Jimmy Torre & Matthias Davier, 2020. "Adjusting Person Fit Index for Skewness in Cognitive Diagnosis Modeling," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 399-420, July.
    18. Jimmy de la Torre & Xue-Lan Qiu & Kevin Carl Santos, 2022. "An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 693-724, June.
    19. Cheng-Hsuan Li & Yi-Jin Ju & Pei-Jyun Hsieh, 2022. "A Nonparametric Weighted Cognitive Diagnosis Model and Its Application on Remedial Instruction in a Small-Class Situation," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    20. Wenyi Wang & Lihong Song & Teng Wang & Peng Gao & Jian Xiong, 2020. "A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing," SAGE Open, , vol. 10(1), pages 21582440198, January.

    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:86:y:2021:i:3:d:10.1007_s11336-021-09790-1. 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.