{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90701"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90701","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Item parameter drift and online calibration","abstract":"An important assumption of item response theory based computerized adaptive assessment is item parameter invariance. Sometimes, however, item parameters are not invariant across different test administrations due to factors other than sampling error; and this phenomenon is termed item parameter drift. Several methods have been developed to detect drifted items, and most of the them were designed to detect drifts in the unidimensional item response model under the paper and pencil testing framework, which may not be adequate for computerized adaptive testing. This paper introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testings in both unidimensional and multidimensional environment. Specifically, for online calibra- tion optimal design in unidimensional computerized adaptive testing model, a modified two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the pro- posed modified two-stage item selection criterion with proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and un- der multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods.","abstract_html":"An important assumption of item response theory based computerized adaptive assessment is item parameter invariance. Sometimes, however, item parameters are not invariant across different test administrations due to factors other than sampling error; and this phenomenon is termed item parameter drift. Several methods have been developed to detect drifted items, and most of the them were designed to detect drifts in the unidimensional item response model under the paper and pencil testing framework, which may not be adequate for computerized adaptive testing. This paper introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testings in both unidimensional and multidimensional environment. Specifically, for online calibra- tion optimal design in unidimensional computerized adaptive testing model, a modified two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the pro- posed modified two-stage item selection criterion with proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and un- der multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods.","abstract_has_math":false,"creators":["Guo, Rui"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Chang, Hua-Hua","Culpepper, Steven Andrew","Douglas, Jeffrey A.","Hubert, Lawrence J.","Koehn, Hans-Friedrich"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:26:30Z","date_published":"2016-07-07T20:26:30Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Item parameter drift","Online calibration","Multidimensional item response theory","Computerized adaptive testing"],"languages":["en"],"rights":["Copyright 2016 Rui Guo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90701","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Hua-Hua","Culpepper, Steven Andrew","Douglas, Jeffrey A.","Hubert, Lawrence J.","Koehn, Hans-Friedrich"]},{"key":"dc:creator","label":"Author","values":["Guo, Rui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:26:30Z","2018-07-08T09:15:23Z","2016-04-22","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Item parameter drift","Online calibration","Multidimensional item response theory","Computerized adaptive testing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Rui Guo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90701"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["An important assumption of item response theory based computerized adaptive assessment is item parameter invariance. Sometimes, however, item parameters are not invariant across different test administrations due to factors other than sampling error; and this phenomenon is termed item parameter drift. Several methods have been developed to detect drifted items, and most of the them were designed to detect drifts in the unidimensional item response model under the paper and pencil testing framework, which may not be adequate for computerized adaptive testing. This paper introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testings in both unidimensional and multidimensional environment. Specifically, for online calibra- tion optimal design in unidimensional computerized adaptive testing model, a modified two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the pro- posed modified two-stage item selection criterion with proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and un- der multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Rui Guo, accepted the attached license on 2016-04-20 at 11:55.","The student, Rui Guo, submitted this Dissertation for approval on 2016-04-20 at 11:56.","This Dissertation was approved for publication on 2016-04-22 at 12:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #6200 on 2016-07-07 at 13:48:00","Made available in DSpace on 2016-07-07T20:26:30Z (GMT). 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Sometimes, however, item parameters are not invariant across different test administrations due to factors other than sampling error; and this phenomenon is termed item parameter drift. Several methods have been developed to detect drifted items, and most of the them were designed to detect drifts in the unidimensional item response model under the paper and pencil testing framework, which may not be adequate for computerized adaptive testing. This paper introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testings in both unidimensional and multidimensional environment. Specifically, for online calibra- tion optimal design in unidimensional computerized adaptive testing model, a modified two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the pro- posed modified two-stage item selection criterion with proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and un- der multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Rui Guo, accepted the attached license on 2016-04-20 at 11:55.","The student, Rui Guo, submitted this Dissertation for approval on 2016-04-20 at 11:56.","This Dissertation was approved for publication on 2016-04-22 at 12:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #6200 on 2016-07-07 at 13:48:00","Made available in DSpace on 2016-07-07T20:26:30Z (GMT). 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