IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v49y2024i4p499-532.html
   My bibliography  Save this article

Latent Trait Item Response Models for Continuous Responses

Author

Listed:
  • Gerhard Tutz

    (Ludwig-Maximilians-Universität München)

  • Pascal Jordan

    (University of Hamburg)

Abstract

A general framework of latent trait item response models for continuous responses is given. In contrast to classical test theory (CTT) models, which traditionally distinguish between true scores and error scores, the responses are clearly linked to latent traits. It is shown that CTT models can be derived as special cases, but the model class is much wider. It provides, in particular, appropriate modeling of responses that are restricted in some way, for example, if responses are positive or are restricted to an interval. Restrictions of this sort are easily incorporated in the modeling framework. Restriction to an interval is typically ignored in common models yielding inappropriate models, for example, when modeling Likert-type data. The model also extends common response time models, which can be treated as special cases. The properties of the model class are derived and the role of the total score is investigated, which leads to a modified total score. Several applications illustrate the use of the model including an example, in which covariates that may modify the response are taken into account.

Suggested Citation

  • Gerhard Tutz & Pascal Jordan, 2024. "Latent Trait Item Response Models for Continuous Responses," Journal of Educational and Behavioral Statistics, , vol. 49(4), pages 499-532, August.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:4:p:499-532
    DOI: 10.3102/10769986231184147
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986231184147
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986231184147?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
    ---><---

    References listed on IDEAS

    as
    1. Gerhard Tutz, 2022. "Item Response Thresholds Models: A General Class of Models for Varying Types of Items," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1238-1269, December.
    2. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    3. Bas Hemker & L. Andries van der Ark & Klaas Sijtsma, 2001. "On measurement properties of continuation ratio models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 487-506, December.
    4. Paul Holland & Machteld Hoskens, 2003. "Classical Test Theory as a first-order Item Response Theory: Application to true-score prediction from a possibly nonparallel test," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 123-149, March.
    5. Paul Jackson & Christian Agunwamba, 1977. "Lower bounds for the reliability of the total score on a test composed of non-homogeneous items: I: Algebraic lower bounds," Psychometrika, Springer;The Psychometric Society, vol. 42(4), pages 567-578, December.
    6. Bas Hemker & Klaas Sijtsma & Ivo Molenaar & Brian Junker, 1997. "Stochastic ordering using the latent trait and the sum score in polytomous IRT models," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 331-347, 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. Klaas Sijtsma & Jules L. Ellis & Denny Borsboom, 2024. "Recognize the Value of the Sum Score, Psychometrics’ Greatest Accomplishment," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 84-117, March.
    2. Jesper Tijmstra & Maria Bolsinova, 2019. "Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 846-869, September.
    3. Rudy Ligtvoet & L. Ark & Wicher Bergsma & Klaas Sijtsma, 2011. "Polytomous Latent Scales for the Investigation of the Ordering of Items," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 200-216, April.
    4. L. Ark & Wicher Bergsma, 2010. "A Note on Stochastic Ordering of the Latent Trait Using the Sum of Polytomous Item Scores," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 272-279, June.
    5. van der Ark, L. Andries, 2012. "New Developments in Mokken Scale Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i05).
    6. Ligtvoet, R., 2015. "A test for using the sum score to obtain a stochastic ordering of subjects," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 136-139.
    7. Bas Hemker & L. Andries van der Ark & Klaas Sijtsma, 2001. "On measurement properties of continuation ratio models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 487-506, December.
    8. van der Ark, L.A., 1999. "A reference card for the relationships between IRT models for ordered polytomous items and some relevant properties," WORC Paper 99.10.02, Tilburg University, Work and Organization Research Centre.
    9. 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.
    10. L. Ark, 2005. "Stochastic Ordering Of the Latent Trait by the Sum Score Under Various Polytomous IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 70(2), pages 283-304, June.
    11. Jules L. Ellis & Klaas Sijtsma, 2024. "Proof of Reliability Convergence to 1 at Rate of Spearman–Brown Formula for Random Test Forms and Irrespective of Item Pool Dimensionality," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 774-795, September.
    12. Daphna Harel & Russell J. Steele, 2018. "An Information Matrix Test for the Collapsing of Categories Under the Partial Credit Model," Journal of Educational and Behavioral Statistics, , vol. 43(6), pages 721-750, December.
    13. Wenchao Ma & Jimmy de la Torre, 2019. "Category-Level Model Selection for the Sequential G-DINA Model," Journal of Educational and Behavioral Statistics, , vol. 44(1), pages 45-77, February.
    14. Stefano Noventa & Andrea Spoto & Jürgen Heller & Augustin Kelava, 2019. "On a Generalization of Local Independence in Item Response Theory Based on Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 395-421, June.
    15. Theo Eggen & Norman Verhelst, 2006. "Loss of Information in Estimating Item Parameters in Incomplete Designs," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 303-322, June.
    16. Rudy Ligtvoet, 2012. "An Isotonic Partial Credit Model for Ordering Subjects on the Basis of Their Sum Scores," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 479-494, July.
    17. César Merino-Soto & Gina Chávez-Ventura & Verónica López-Fernández & Guillermo M. Chans & Filiberto Toledano-Toledano, 2022. "Learning Self-Regulation Questionnaire (SRQ-L): Psychometric and Measurement Invariance Evidence in Peruvian Undergraduate Students," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    18. Roberto Burro & Riccardo Sartori & Giulio Vidotto, 2011. "The method of constant stimuli with three rating categories and the use of Rasch models," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(1), pages 43-58, January.
    19. Chang, Hsin-Li & Yang, Cheng-Hua, 2008. "Explore airlines’ brand niches through measuring passengers’ repurchase motivation—an application of Rasch measurement," Journal of Air Transport Management, Elsevier, vol. 14(3), pages 105-112.
    20. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.

    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:sae:jedbes:v:49:y:2024:i:4:p:499-532. 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: SAGE Publications (email available below). General contact details of provider: .

    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.