Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

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

Sign In to gain access to subscriptions and/or personal tools.
Educational and 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 HighWire
Right arrow Citing Articles via Web of Science (1)
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?

Empirical Histograms in Item Response Theory With Ordinal Data

Carol M. Woods

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

The purpose of this research is to describe, test, and illustrate a new implementation of the empirical histogram (EH) method for ordinal items. The EH method involves the estimation of item response model parameters simultaneously with the approximation of the distribution of the random latent variable ({tau}) as a histogram. Software for the EH method with ordinal items (having more than two response options) has not been readily available in the past but was created for the present research. Simulation results suggest that with larger (but not smaller) numbers of quadrature points, graded-model item parameter estimates from the EH method are highly accurate when the {tau} distribution is either normal or skewed. Results for expected a posteriori scores depend on the magnitude of {tau}.

Key Words: item response theory • IRT • nonnormality • empirical histogram • expectation Maximation • EM • marginal maximum likelihood • MML

Educational and Psychological Measurement, Vol. 67, No. 1, 73-87 (2007)
DOI: 10.1177/0013164406288163
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?


This article has been cited by other articles:


Home page
 Educational and Psychological MeasurementHome page
C. M. Woods
IRT-LR-DIF With Estimation of the Focal-Group Density as an Empirical Histogram
Educational and Psychological Measurement, August 1, 2008; 68(4): 571 - 586.
[Abstract] [PDF]