{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T13:00:22Z","timestamp":1771851622071,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819571376","type":"print"},{"value":"9789819571383","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-7138-3_28","type":"book-chapter","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:08:01Z","timestamp":1771848481000},"page":"435-451","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Rater Severity Drift in\u00a0Single-Rater Performance Assessments Using Bayesian Hierarchical Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-2879","authenticated-orcid":false,"given":"Yuichiro","family":"Yokouchi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-1648","authenticated-orcid":false,"given":"Kuangzhe","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1993-6883","authenticated-orcid":false,"given":"Shuichi","family":"Takaki","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2773-5829","authenticated-orcid":false,"given":"Haruhiko","family":"Mitsunaga","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1201\/b16018","volume-title":"Bayesian Data Analysis","author":"A Gelman","year":"2013","unstructured":"Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis, 3rd edn., p. 131. CRC Press, Boca Raton (2013)","edition":"3"},{"issue":"2","key":"28_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1075\/intp.17.2.05han","volume":"17","author":"C Han","year":"2015","unstructured":"Han, C.: Investigating rater severity and leniency in interpreter performance assessment. Interpreting 17(2), 255\u2013283 (2015). https:\/\/doi.org\/10.1075\/intp.17.2.05han","journal-title":"Interpreting"},{"issue":"2","key":"28_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.29333\/ejmste\/12900","volume":"19","author":"E Hi\u011fde","year":"2023","unstructured":"Hi\u011fde, E., Demirci, S., Y\u0131ld\u0131r\u0131m, Z.: Evaluating laboratory activities using multi-faceted Rasch measurement analysis: a case study in science education. Eurasia J. Math. Sci. Technol. Educ. 19(2), 1\u201316 (2023). https:\/\/doi.org\/10.29333\/ejmste\/12900","journal-title":"Eurasia J. Math. Sci. Technol. Educ."},{"issue":"1","key":"28_CR4","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1111\/j.1467-985X.2004.00347_5.x","volume":"168","author":"H Goldstein","year":"2003","unstructured":"Goldstein, H.: Multilevel statistical models. J. Roy. Statist. Soc. A 168(1), 252\u2013253 (2003). https:\/\/doi.org\/10.1111\/j.1467-985X.2004.00347_5.x","journal-title":"J. Roy. Statist. Soc. A"},{"key":"28_CR5","unstructured":"Linacre, J.M.: Many-Faceted Rasch Measurement. MESA Press, Chicago (1989). https:\/\/www.winsteps.com\/a\/Linacre-MFRM-book.pdf"},{"key":"28_CR6","unstructured":"Linacre, J.M.: A User\u2019s Guide to FACETS (64-bit) Rasch-Model Computer Programs Program Manual 4.3.3 (2025). https:\/\/www.winsteps.com\/a\/Facets64-Manual.pdf"},{"issue":"8","key":"28_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0309887","volume":"18","author":"K Sakamoto","year":"2023","unstructured":"Sakamoto, K., Yamamoto, H., Aoki, S.: An item response theory model considering rater severity and item interaction effects. PLoS ONE 18(8), e0309887 (2023). https:\/\/doi.org\/10.1371\/journal.pone.0309887","journal-title":"PLoS ONE"},{"issue":"3","key":"28_CR8","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3102\/1076998606298033","volume":"32","author":"LT Mariano","year":"2016","unstructured":"Mariano, L.T., Junker, B.W.: Covariates of the rating process in hierarchical models for multiple ratings of test items. J. Educ. Behav. Stat. 32(3), 287\u2013314 (2016). https:\/\/doi.org\/10.3102\/1076998606298033","journal-title":"J. Educ. Behav. Stat."},{"issue":"2","key":"28_CR9","doi-asserted-by":"publisher","first-page":"147","DOI":"10.36349\/EASJEHL.2020.v03i05.001","volume":"11","author":"MFM Noh","year":"2020","unstructured":"Noh, M.F.M., Matore, M.E.E.M.: Rating performance among raters of different experience through multi-facet rasch measurement (MFRM) model. J. Meas. Eval. Educ. Psychol. 11(2), 147\u2013162 (2020). https:\/\/doi.org\/10.36349\/EASJEHL.2020.v03i05.001","journal-title":"J. Meas. Eval. Educ. Psychol."},{"issue":"1","key":"28_CR10","doi-asserted-by":"publisher","first-page":"55","DOI":"10.24690\/jart.12.1_55","volume":"12","author":"M Uto","year":"2016","unstructured":"Uto, M., Ueno, M.: A review of item response models for performance assessment. Jpn. J. Res. Test. 12(1), 55\u201375 (2016). https:\/\/doi.org\/10.24690\/jart.12.1_55","journal-title":"Jpn. J. Res. Test."},{"issue":"2","key":"28_CR11","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s41237-020-00115-7","volume":"47","author":"M Uto","year":"2020","unstructured":"Uto, M., Ueno, M.: A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo. Behaviormetrika 47(2), 469\u2013496 (2020). https:\/\/doi.org\/10.1007\/s41237-020-00115-7","journal-title":"Behaviormetrika"},{"issue":"7","key":"28_CR12","doi-asserted-by":"publisher","first-page":"3910","DOI":"10.3758\/s13428-022-01997-z","volume":"55","author":"M Uto","year":"2023","unstructured":"Uto, M.: A Bayesian many-facet Rasch model with Markov modeling for rater severity drift. Behav. Res. Methods 55(7), 3910\u20133928 (2023). https:\/\/doi.org\/10.3758\/s13428-022-01997-z","journal-title":"Behav. Res. Methods"},{"issue":"3","key":"28_CR13","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1037\/a0019620","volume":"15","author":"PJ Curran","year":"2010","unstructured":"Curran, P.J., Lee, T., Howard, A.L., Lane, S.T., MacCallum, R.C.: Disaggregating within-person and between-person effects in longitudinal models using multilevel modeling and structural equation modeling. Psychol. Methods 15(3), 247\u2013262 (2010). https:\/\/doi.org\/10.1037\/a0019620","journal-title":"Psychol. Methods"},{"issue":"4","key":"28_CR14","doi-asserted-by":"publisher","first-page":"341","DOI":"10.3102\/10769986027004341","volume":"27","author":"RJ Patz","year":"2002","unstructured":"Patz, R.J., Junker, B.W., Johnson, M.S., Mariano, L.T.: The hierarchical rater model for rated test items and its application to large-scale educational assessment data. J. Educ. Behav. Stat. 27(4), 341\u2013384 (2002). https:\/\/doi.org\/10.3102\/10769986027004341","journal-title":"J. Educ. Behav. Stat."},{"issue":"2","key":"28_CR15","doi-asserted-by":"publisher","first-page":"163","DOI":"10.5926\/jjep.58.163","volume":"58","author":"S Usami","year":"2010","unstructured":"Usami, S.: A polytomous item response model that simultaneously considers bias factors of raters and examinees: estimation through a Markov Chain Monte Carlo algorithm. Jpn. J. Educ. Psychol. 58(2), 163\u2013175 (2010). https:\/\/doi.org\/10.5926\/jjep.58.163","journal-title":"Jpn. J. Educ. Psychol."},{"issue":"3\u20134","key":"28_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/15305058.2021.1963260","volume":"21","author":"T Eckes","year":"2021","unstructured":"Eckes, T., Jin, K.-Y.: Examining severity and centrality effects in TestDaF writing and speaking assessments: an extended Bayesian many-facet Rasch analysis. Int. J. Test. 21(3\u20134), 131\u2013153 (2021). https:\/\/doi.org\/10.1080\/15305058.2021.1963260","journal-title":"Int. J. Test."},{"key":"28_CR17","unstructured":"Wang, X., Yang, J., Liu, R.: Development of a differential index for evaluating rater ability in educational assessments using generalized multi-facet models. arXiv preprint arXiv:2502.09099 (2025)"},{"key":"28_CR18","unstructured":"Yokouchi, Y., Takaki, S., Xu, K.: Model development to examine the rater severity drift for single rater: proposal for a new model based on many-facet Rasch model. In: 19th Pacific Rim Objective Measurement Symposium (PROMS 2024), Kuala Lumpur, Malaysia (2024). https:\/\/proms2024.promsociety.org\/"},{"key":"28_CR19","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s40468-024-00338-5","volume":"15","author":"Y Yokouchi","year":"2025","unstructured":"Yokouchi, Y.: Revisiting the effectiveness of a performance decision tree-style rubric compared to a grid-style rubric. Lang. Test. Asia 15, 8 (2025). https:\/\/doi.org\/10.1186\/s40468-024-00338-5","journal-title":"Lang. Test. Asia"}],"container-title":["Lecture Notes in Computer Science","Behavioural and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7138-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:08:05Z","timestamp":1771848485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7138-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819571376","9789819571383"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7138-3_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BESC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Behavioural and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"besc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/besc-conf.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}