Open AccessItem difficulty parameter estimation using the idea of the graded response model and computerized adaptive testing
Author(s) -
OZAKI KOKEN,
TOYODA HIDEKI
Publication year - 2009
Publication title -
japanese psychological research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.392
H-Index - 30
eISSN - 1468-5884
pISSN - 0021-5368
DOI - 10.1111/j.1468-5884.2009.00383.x
Subject(s) - computerized adaptive testing , item response theory , perspective (graphical) , test (biology) , estimation , computer science , field (mathematics) , estimation theory , statistics , artificial intelligence , machine learning , psychology , cognitive psychology , psychometrics , mathematics , algorithm , paleontology , management , pure mathematics , economics , biology
In test operations using IRT (item response theory), items are included in a test before being used to rate subjects and the response data is used to estimate their item parameters. However, this method of test operation may lead to item content leakage and an adequate test operation can become difficult. To address this problem, Ozaki and Toyoda (2005, 2006) developed item difficulty parameter estimation methods that use paired comparison data from the perspective of the difficulty of items as judged by raters familiar with the field. In the present paper, an improved method of item difficulty parameter estimation is developed. In this new method, an item for which the difficulty parameter is to be estimated is compared with multiple items simultaneously, from the perspective of their difficulty. This is not a one‐to‐one comparison but a one‐to‐many comparison. In the comparisons, raters are informed that items selected from an item pool are ordered according to difficulty. The order will provide insight to improve the accuracy of judgment.