The Uniform Prior for Bayesian Estimation of Ability in Item Response Theory Models
Abstract
Item
Response Theory (IRT) models traditionally assume a normal distribution for
ability. Although normality is often a reasonable assumption for ability, it is
rarely met for observed scores in educational and psychological measurement.
Assumptions regarding ability distribution were previously shown to have an
effect on IRT parameter estimation. In this study, the normal and uniform
distribution prior assumptions for ability were compared for IRT parameter
estimation when the actual distribution was either normal or uniform. A
simulation study that included a short test with a small sample size and a long
test with a large sample size was conducted for this purpose. The results
suggested using a uniform distribution prior for ability to achieve more accurate
estimates of the ability parameter in the 2PL and 3PL models when the true
distribution of ability is not known. For the Rasch model, an explicit pattern
that could be used to obtain more accurate item parameter estimates was not
found.
Keywords
References
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Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Authors
Tuğba Karadavut
Türkiye
Publication Date
January 5, 2020
Submission Date
June 22, 2019
Acceptance Date
October 17, 2019
Published in Issue
Year 2019 Volume: 6 Number: 4
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