Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to submit your manuscript to SPPS

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Applied Psychological Measurement
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Woods, C. M.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Consequences of Ignoring Guessing When Estimating the Latent Density in Item Response Theory

Carol M. Woods

Washington University in St. Louis, cwoods{at}artsci.wustl.edu

In Ramsay-curve item response theory (RC-IRT), the latent variable distribution is estimated simultaneously with the item parameters. In extant Monte Carlo evaluations of RC-IRT, the item response function (IRF) used to fit the data is the same one used to generate the data. The present simulation study examines RC-IRT when the IRF is imperfectly matched to the data. In particular, guessing is ignored: The two-parameter logistic IRF is fitted to data generated from the three-parameter logistic IRF. The empirical histogram method (EHM) implemented in the BILOG-MG program is also applied for comparison. Results indicate that apparent nonnormality in a density estimate from either RC-IRT or the EHM can be entirely due to misspecification of the IRF, and it may be difficult to tell which IRF is best when the latent density is estimated. It is recommended that practitioners first identify the best IRF with the latent density fixed at normal and then subsequently examine the normality assumption about the latent density.

Key Words: Ramsay curve • RC-IRT • normality assumption • model misspecification • empirical histogram

Applied Psychological Measurement, Vol. 32, No. 5, 371-384 (2008)
DOI: 10.1177/0146621607307691
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?