IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v59y2005i1p95-106.html
   My bibliography  Save this article

Bayesian modification indices for IRT models

Author

Listed:
  • Jean‐Paul Fox
  • Cees A. W. Glas

Abstract

Bayesian modification indices are presented that provide information for the process of model evaluation and model modification. These indices can be used to investigate the improvement in a model if fixed parameters are re‐specified as free parameters. The indices can be seen as a Bayesian analogue to the modification indices commonly used in a frequentist framework. The aim is to provide diagnostic information for multi‐parameter models where the number of possible model violations and the related number of alternative models is too large to render estimation of each alternative practical. As an example, the method is applied to an item response theory (IRT) model, that is, to the two‐parameter model. The method is used to investigate differential item functioning and violations of the assumption of local independence.

Suggested Citation

  • Jean‐Paul Fox & Cees A. W. Glas, 2005. "Bayesian modification indices for IRT models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 95-106, February.
    • Handle: RePEc:bla:stanee:v:59:y:2005:i:1:p:95-106
    DOI: 10.1111/j.1467-9574.2005.00282.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2005.00282.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9574.2005.00282.x?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. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    2. Guastadisegni, Lucia & Cagnone, Silvia & Moustaki, Irini & Vasdekis, Vassilis, 2022. "Use of the Lagrange multiplier test for assessing measurement invariance under model misspecification," LSE Research Online Documents on Economics 110358, London School of Economics and Political Science, LSE Library.
    3. Azevedo, Caio L.N. & Andrade, Dalton F. & Fox, Jean-Paul, 2012. "A Bayesian generalized multiple group IRT model with model-fit assessment tools," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4399-4412.

    More about this item

    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:bla:stanee:v:59:y:2005:i:1:p:95-106. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.