{"created":"2023-07-25T10:36:01.213592+00:00","id":11120,"links":{},"metadata":{"_buckets":{"deposit":"0a02ad91-c6b3-4cc1-990b-e87400692b6c"},"_deposit":{"created_by":10,"id":"11120","owners":[10],"pid":{"revision_id":0,"type":"depid","value":"11120"},"status":"published"},"_oai":{"id":"oai:senshu-u.repo.nii.ac.jp:00011120","sets":["27:32:861:862:1242"]},"author_link":["4611","4612"],"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"20","bibliographicPageStart":"1","bibliographicVolumeNumber":"27","bibliographic_titles":[{},{"bibliographic_title":"Information Science and Applied Mathematics","bibliographic_titleLang":"en"}]}]},"item_10002_creator_2":{"attribute_name":"作成者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Sato, Toshiki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takano, Yuichi"}],"nameIdentifiers":[{}]}]},"item_10002_description_25":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_10002_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.34360/00011106","subitem_identifier_reg_type":"JaLC"}]},"item_10002_publisher_26":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"The Institute of Information Science Senshu University"}]},"item_10002_source_id_28":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1349-1938","subitem_source_identifier_type":"ISSN"}]},"item_10002_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_be7fb7dd8ff6fe43","subitem_version_type":"NA"}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2020-08-27"}],"displaytype":"detail","filename":"3093_0027_05.pdf","filesize":[{"value":"421.8 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"3093_0027_05.pdf","url":"https://senshu-u.repo.nii.ac.jp/record/11120/files/3093_0027_05.pdf"},"version_id":"043d2182-fce5-44d0-9b12-99552fafc3b2"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"item response theory","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"nonparametric estimation","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"smoothness constraint","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"optimization","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"EM algorithm","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"latent class","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Smoothness-constrained Model for Nonparametric Item Response Theory","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Smoothness-constrained Model for Nonparametric Item Response Theory","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"10","path":["1242"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-08-27"},"publish_date":"2020-08-27","publish_status":"0","recid":"11120","relation_version_is_last":true,"title":["Smoothness-constrained Model for Nonparametric Item Response Theory"],"weko_creator_id":"10","weko_shared_id":-1},"updated":"2023-07-25T12:09:40.303331+00:00"}