Item Response Theory with Estimation of the Latent Population Distribution Using Spline-Based Densities

Carol M. Woods () and David Thissen
Additional contact information
Carol M. Woods: Washington University in St. Louis
David Thissen: University of North Carolina at Chapel Hill

Psychometrika, 2006, vol. 71, issue 2, No 4, 301 pages

Abstract: Abstract The purpose of this paper is to introduce a new method for fitting item response theory models with the latent population distribution estimated from the data using splines. A spline-based density estimation system provides a flexible alternative to existing procedures that use a normal distribution, or a different functional form, for the population distribution. A simulation study shows that the new procedure is feasible in practice, and that when the latent distribution is not well approximated as normal, two-parameter logistic (2PL) item parameter estimates and expected a posteriori scores (EAPs) can be improved over what they would be with the normal model. An example with real data compares the new method and the extant empirical histogram approach.

Keywords: item response theory; marginal maximum likelihood; latent variable; population distribution; density estimation; splines (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://link.springer.com/10.1007/s11336-004-1175-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:psycho:v:71:y:2006:i:2:d:10.1007_s11336-004-1175-8

Ordering information: This journal article can be ordered from
http://www.springer. ... gy/journal/11336/PS2

DOI: 10.1007/s11336-004-1175-8

Access Statistics for this article

Psychometrika is currently edited by Irini Moustaki

More articles in Psychometrika from Springer, The Psychometric Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().