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Smoothness-constrained Model for Nonparametric Item Response Theory
https://doi.org/10.34360/00011106
https://doi.org/10.34360/00011106fcbe0aeb-3fdc-4dea-b900-7a005bc73a82
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
3093_0027_05.pdf (421.8 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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| 公開日 | 2020-08-27 | |||||
| タイトル | ||||||
| タイトル | Smoothness-constrained Model for Nonparametric Item Response Theory | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題 | item response theory, nonparametric estimation, smoothness constraint, optimization, EM algorithm, latent class | |||||
| 資源タイプ | ||||||
| 資源タイプ | departmental bulletin paper | |||||
| ID登録 | ||||||
| ID登録 | 10.34360/00011106 | |||||
| ID登録タイプ | JaLC | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| 作成者 |
Sato, Toshiki
× Sato, Toshiki× Takano, Yuichi |
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| 内容記述 | ||||||
| 内容記述 | This paper is concerned with the nonparametric item response theory (NIRT) for estimating item characteristic curves (ICCs) and latent abilities of examinees on educational and psychological tests. NIRT models can estimate various forms of ICCs under mild shape restrictions, such as the constraints of monotone homogeneity and double monotonicity. However, NIRT models frequently suffer from estimation instability because of the great flexibility of nonparametric ICCs. To improve the estimation accuracy, we propose a novel NIRT model constrained by monotone homogeneity and smoothness based on ordered latent classes. Our smoothness constraints avoid overfitting of nonparametric ICCs by keeping them close to logistic curves. We also implement a tailored expectation–maximization algorithm to calibrate our smoothness-constrained NIRT model efficiently. We conducted computational experiments to assess the effectiveness of our smoothness-constrained model in comparison with the common two-parameter logistic model and the monotone-homogeneity model. The computational results demonstrate that our model obtained more accurate estimation results than did the two-parameter logistic model when the latent abilities of examinees for some test items followed bimodal distributions. Moreover, our model outperformed the monotonehomogeneity model because of the effect of the smoothness constraints. | |||||
| 公開者 | ||||||
| 出版者 | The Institute of Information Science Senshu University | |||||
| ISSN | ||||||
| 収録物識別子 | 1349-1938 | |||||
| 書誌レコードID | ||||||
| 収録物識別子 | AB00033239 | |||||
| 書誌情報 |
en : Information Science and Applied Mathematics 巻 27, p. 1-20, 発行日 2019 |
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| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
