論文

査読有り 最終著者 責任著者
2009年9月

A Bayesian Semiparametric Item Response Model with Dirichlet Process Priors

PSYCHOMETRIKA
  • Kei Miyazaki
  • ,
  • Takahiro Hoshino

74
3
開始ページ
375
終了ページ
393
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/s11336-008-9108-6
出版者・発行元
SPRINGER

In Item Response Theory (IRT), item characteristic curves (ICCs) are illustrated through logistic models or normal ogive models, and the probability that examinees give the correct answer is usually a monotonically increasing function of their ability parameters. However, since only limited patterns of shapes can be obtained from logistic models or normal ogive models, there is a possibility that the model applied does not fit the data. As a result, the existing method can be rejected because it cannot deal with various item response patterns.
To overcome these problems, we propose a new semiparametric IRT model using a Dirichlet process mixture logistic distribution. Our method does not rely on assumptions but only requires that the ICCs be a monotonically nondecreasing function; that is, our method can deal with more types of item response patterns than the existing methods, such as the one-parameter normal ogive models or the two- or three-parameter logistic models.
We conducted two simulation studies whose results indicate that the proposed method can express more patterns of shapes for ICCs and can estimate the ability parameters more accurately than the existing parametric and nonparametric methods. The proposed method has also been applied to Facial Expression Recognition data with noteworthy results.

Web of Science ® 被引用回数 : 15

リンク情報
DOI
https://doi.org/10.1007/s11336-008-9108-6
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000270189300001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1007/s11336-008-9108-6
  • ISSN : 0033-3123
  • Web of Science ID : WOS:000270189300001

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