IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v28y2003i3p195-230.html
   My bibliography  Save this article

Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models

Author

Listed:
  • Matthew S. Johnson
  • Brian W. Junker

Abstract

Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993) and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT model using a latent response model (LRM; Maris, 1995 ). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.

Suggested Citation

  • Matthew S. Johnson & Brian W. Junker, 2003. "Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models," Journal of Educational and Behavioral Statistics, , vol. 28(3), pages 195-230, September.
    • Handle: RePEc:sae:jedbes:v:28:y:2003:i:3:p:195-230
    DOI: 10.3102/10769986028003195
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986028003195
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986028003195?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
    ---><---

    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:28:y:2003:i:3:p:195-230. 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.