Document Detail


An improved CML estimation procedure for the Rasch model with item response data.
MedLine Citation:
PMID:  11813227     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Ordinal response data are commonly observed in health and medical investigations that include several items. The primary goal in the modelling of item response data is to find a unique measurement of the person's abilities and of the item difficulties that satisfies the properties of the fundamental measurement. One such analytic method in item response theory is the Rasch measurement, which is a way to convert ordinal observations into linear measures. Current estimation strategies assume the independence of the Rasch model parameters. In this paper, based on the conditional maximum likelihood, we implemented a simultaneous estimation method that can compare the Rasch parameters more efficiently. We also obtained the asymptotic properties of these estimators and developed the conditional likelihood ratio test for the goodness-of-fit of the model. Simulation studies were used to demonstrate the improved performance of our estimators as compared to that of currently used conditional method known as the CON procedure. We conclude that our estimation method outperforms CON in both model fit and the precision of the Rasch estimators.
Authors:
Xiaoming Sheng; K C Carrière
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Statistics in medicine     Volume:  21     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2002 Feb 
Date Detail:
Created Date:  2002-01-28     Completed Date:  2002-03-20     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  407-16     Citation Subset:  IM    
Copyright Information:
Copyright 2002 John Wiley & Sons, Ltd.
Affiliation:
Department of Mathematical Sciences, University of Alberta, Edmonton, Canada.
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MeSH Terms
Descriptor/Qualifier:
Computer Simulation
Data Interpretation, Statistical
Humans
Likelihood Functions*
Models, Statistical*
Questionnaires

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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