Equal area logistic estimation for item response theory

Shih Ching Lo, Kuo Chang Wang, Hsin-Li Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

Original languageEnglish
Title of host publicationComputational Methods in Science and Engineering - Advances in Computational Science, Lectures Presented at the Int. Conference on Computational Methods in Science and Engineering 2008, ICCMSE 2008
Pages485-488
Number of pages4
DOIs
StatePublished - 1 Dec 2009
Event6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008 - Hersonissos, Crete, Greece
Duration: 25 Sep 200830 Sep 2008

Publication series

NameAIP Conference Proceedings
Volume1148 2
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008
CountryGreece
CityHersonissos, Crete
Period25/09/0830/09/08

Keywords

  • item response theory
  • logistics model
  • parameter estimation

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    Lo, S. C., Wang, K. C., & Chang, H-L. (2009). Equal area logistic estimation for item response theory. In Computational Methods in Science and Engineering - Advances in Computational Science, Lectures Presented at the Int. Conference on Computational Methods in Science and Engineering 2008, ICCMSE 2008 (pp. 485-488). (AIP Conference Proceedings; Vol. 1148 2). https://doi.org/10.1063/1.3225354