IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v11y1986i3p183-196.html
   My bibliography  Save this article

Maximum Likelihood Estimation in Generalized Rasch Models

Author

Listed:
  • Jan De Leeuw
  • Norman Verhelst

Abstract

We review various models and techniques that have been proposed for item analysis according to the ideas of Rasch. A general model is proposed that unifies them, and maximum likelihood procedures are discussed for this general model. We show that unconditional maximum likelihood estimation in the functional Rasch model, as proposed by Wright and Haberman, is an important special case. Conditional maximum likelihood estimation, as proposed by Rasch and Andersen, is another important special case. Both procedures are related to marginal maximum likelihood estimation in the structural Rasch model, which has been studied by Sanathanan, Andersen, Tjur, Thissen, and others. Our theoretical results lead to suggestions for alternative computational algorithms.

Suggested Citation

  • Jan De Leeuw & Norman Verhelst, 1986. "Maximum Likelihood Estimation in Generalized Rasch Models," Journal of Educational and Behavioral Statistics, , vol. 11(3), pages 183-196, September.
    • Handle: RePEc:sae:jedbes:v:11:y:1986:i:3:p:183-196
    DOI: 10.3102/10769986011003183
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986011003183
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986011003183?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:jss:jstsof:20:i09 is not listed on IDEAS
    2. Anton K. Formann, 2003. "Latent Class Model Diagnosis from a Frequentist Point of View," Biometrics, The International Biometric Society, vol. 59(1), pages 189-196, March.
    3. Frank Rijmen & Paul De Boeck, 2005. "A relation between a between-item multidimensional IRT model and the mixture rasch model," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 481-496, September.
    4. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.

    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:11:y:1986:i:3:p:183-196. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.