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A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo
https://uec.repo.nii.ac.jp/records/9674
https://uec.repo.nii.ac.jp/records/9674d28282e5-e194-43a5-a5c3-66e02ae3e5dc
名前 / ファイル | ライセンス | アクション |
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BHMK_clean (281.9 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2020-11-18 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Item response theory | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Many-facet Rasch model | |||||
キーワード | ||||||
言語 | en | |||||
主題 | performance assessment | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Bayesian estimation | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Markov chain Monte Carlo | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Uto, Masaki
× Uto, Masaki× Ueno, Maomi |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Performance assessments, in which raters assess examinee performance for given tasks, have a persistent difficulty in that ability measurement accuracy depends on rater characteristics. To address this problem, various item response theory (IRT) models that incorporate rater characteristic parameters have been proposed. Conventional models partially consider three typical rater characteristics: severity, consistency, and range restriction. Each are important to improve model fitting and ability measurement accuracy, especially when the diversity of raters increases. However, no models capable of simultaneously representing each have been proposed. One obstacle for developing such a complex model is the difficulty of parameter estimation. Maximum likelihood estimation, which is used in most conventional models, generally leads to unstable and inaccurate parameter estimations in complex models. Bayesian estimation is expected to provide more robust estimations. Although it incurs high computational costs, recent increases in computational capabilities and the development of efficient Markov chain Monte Carlo (MCMC) algorithms make its use feasible. We thus propose a new IRT model that can represent all three typical rater characteristics. The model is formulated as a generalization of the many-facet Rasch model. We also develop a Bayesian estimation method for the proposed model using No-U-Turn Hamiltonian Monte Carlo, a state-of-the-art MCMC algorithm. We demonstrate the effectiveness of the proposed method through simulation and actual data experiments. | |||||
書誌情報 |
en : Behaviormetrika 巻 47, 号 2, p. 469-496, 発行日 2020-07 |
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出版者 | ||||||
出版者 | Springer | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 03857417 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1007/s41237-020-00115-7 | |||||
権利 | ||||||
権利情報 | (c) 2020 Springer | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1007/s41237-020-00115-7 | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa |