{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24416"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24416","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Item Selection Methods in Polytomous Computerized Adaptive Testing","abstract":"Given the rapid advancement of computer technology, the importance of administering adaptive tests with polytomous items is in great need. With regard to the applicability of adaptive testing using polytomous IRT models, adaptive testing can use polytomous items of either rating scales, or in some testing situations of multiple choice. Additionally, the availability of computerized polytomous scoring of open-ended items enhances such applicability. This need promotes the research in polytomous adaptive testing (PAT). This dissertation is an e ort to focus on item selection methods, as a major component, in polytomous computerized adaptive testing. So, it consists of ve chapters that cover the following: Chapter 1 focuses on a thorough introduction to the item response theory (IRT) models and adaptive testing related to polytomous items. Such an important overview and introduction to basic concepts in test theory and mathematical models for polytomous items is needed for the ow of consequent chapters. Chapter 2 is devoted to the development of a central location index (LI) to uniquely represent the polytomous item with a scale value parameter using most commonly used polytomous models. The motivation and rationale to search for a central or an overall location parameter is twofold: a) the confusion of multiple and di erent parameterizations for a polytomous item even for the same model, and b) the unavailability of such single location parameter block the usage of certain item selection methods in adaptive testing. Two approaches are used to derive the proposed LIs, one is based on the item category response functions (ICRFs) and the other is based on the polytomous item response function (IRF). As a result, four LIs are proposed. Chapter 3 is particularly assigned to development of an item selection method based on the developed location index and primarily assess its performance in the PAT context relative to existing methods. This method belongs to the non-information based item selection methods and we referred it as Matching-LI method. The results support that this proposed method is promising and is capable to produce accurate ability estimates and successfully manage the item pool usage. Chapter 4 introduces new item selection methods taking in consideration the previous chapter's results. The new methods are the hybrid, stage-based information, polytomous a-strati cation methods. The first two methods try to merge more than one criterion for selecting items of each PAT (e.g., the hybrid method merges both the Matching-LI and maximum information (MI) methods). The last method uses Matching-LI method within each stratum. Chapter 5 provides discussion, conclusions, and limitations and future research directions with respect to important components of an adaptive testing program (i.e., item selection methods, item response models, item banks, and trait versus attribute estimation).","abstract_html":"Given the rapid advancement of computer technology, the importance of administering adaptive tests with polytomous items is in great need. With regard to the applicability of adaptive testing using polytomous IRT models, adaptive testing can use polytomous items of either rating scales, or in some testing situations of multiple choice. Additionally, the availability of computerized polytomous scoring of open-ended items enhances such applicability. This need promotes the research in polytomous adaptive testing (PAT). This dissertation is an e ort to focus on item selection methods, as a major component, in polytomous computerized adaptive testing. So, it consists of ve chapters that cover the following: Chapter 1 focuses on a thorough introduction to the item response theory (IRT) models and adaptive testing related to polytomous items. Such an important overview and introduction to basic concepts in test theory and mathematical models for polytomous items is needed for the ow of consequent chapters. Chapter 2 is devoted to the development of a central location index (LI) to uniquely represent the polytomous item with a scale value parameter using most commonly used polytomous models. The motivation and rationale to search for a central or an overall location parameter is twofold: a) the confusion of multiple and di erent parameterizations for a polytomous item even for the same model, and b) the unavailability of such single location parameter block the usage of certain item selection methods in adaptive testing. Two approaches are used to derive the proposed LIs, one is based on the item category response functions (ICRFs) and the other is based on the polytomous item response function (IRF). As a result, four LIs are proposed. Chapter 3 is particularly assigned to development of an item selection method based on the developed location index and primarily assess its performance in the PAT context relative to existing methods. This method belongs to the non-information based item selection methods and we referred it as Matching-LI method. The results support that this proposed method is promising and is capable to produce accurate ability estimates and successfully manage the item pool usage. Chapter 4 introduces new item selection methods taking in consideration the previous chapter's results. The new methods are the hybrid, stage-based information, polytomous a-strati cation methods. The first two methods try to merge more than one criterion for selecting items of each PAT (e.g., the hybrid method merges both the Matching-LI and maximum information (MI) methods). The last method uses Matching-LI method within each stratum. Chapter 5 provides discussion, conclusions, and limitations and future research directions with respect to important components of an adaptive testing program (i.e., item selection methods, item response models, item banks, and trait versus attribute estimation).","abstract_has_math":false,"creators":["Ali, Usama S."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Chang, Hua-Hua","Anderson, Carolyn J.","Douglas, Jeffrey A.","Zhang, Jinming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:36:16Z","date_published":"2011-05-25T14:36:16Z","updated_at":"2026-07-22T22:25:23Z","subjects":["Polytomus items","computerize adaptive testing","item selection","item response theory","polytomous IRT models."],"languages":["en"],"rights":["Copyright 2011 Usama Ali"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24416","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Hua-Hua","Anderson, Carolyn J.","Douglas, Jeffrey A.","Zhang, Jinming"]},{"key":"dc:creator","label":"Author","values":["Ali, Usama S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T14:36:16Z","2013-05-26T10:00:25Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational 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":["Polytomus items","computerize adaptive testing","item selection","item response theory","polytomous IRT models."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Usama Ali"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24416"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Given the rapid advancement of computer technology, the importance of administering adaptive tests with polytomous items is in great need. With regard to the applicability of adaptive testing using polytomous IRT models, adaptive testing can use polytomous items of either rating scales, or in some testing situations of multiple choice. Additionally, the availability of computerized polytomous scoring of open-ended items enhances such applicability. This need promotes the research in polytomous adaptive testing (PAT). This dissertation is an e ort to focus on item selection methods, as a major component, in polytomous computerized adaptive testing. So, it consists of ve chapters that cover the following: Chapter 1 focuses on a thorough introduction to the item response theory (IRT) models and adaptive testing related to polytomous items. Such an important overview and introduction to basic concepts in test theory and mathematical models for polytomous items is needed for the ow of consequent chapters. Chapter 2 is devoted to the development of a central location index (LI) to uniquely represent the polytomous item with a scale value parameter using most commonly used polytomous models. The motivation and rationale to search for a central or an overall location parameter is twofold: a) the confusion of multiple and di erent parameterizations for a polytomous item even for the same model, and b) the unavailability of such single location parameter block the usage of certain item selection methods in adaptive testing. Two approaches are used to derive the proposed LIs, one is based on the item category response functions (ICRFs) and the other is based on the polytomous item response function (IRF). As a result, four LIs are proposed. Chapter 3 is particularly assigned to development of an item selection method based on the developed location index and primarily assess its performance in the PAT context relative to existing methods. This method belongs to the non-information based item selection methods and we referred it as Matching-LI method. The results support that this proposed method is promising and is capable to produce accurate ability estimates and successfully manage the item pool usage. Chapter 4 introduces new item selection methods taking in consideration the previous chapter's results. The new methods are the hybrid, stage-based information, polytomous a-strati cation methods. The first two methods try to merge more than one criterion for selecting items of each PAT (e.g., the hybrid method merges both the Matching-LI and maximum information (MI) methods). The last method uses Matching-LI method within each stratum. Chapter 5 provides discussion, conclusions, and limitations and future research directions with respect to important components of an adaptive testing program (i.e., item selection methods, item response models, item banks, and trait versus attribute estimation).","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-16T16:49:22Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) Ali_Usama.pdf: 2354500 bytes, checksum: 2762759cf969017069aa9e7fbbf09c8d (MD5)","Made available in DSpace on 2011-05-25T14:36:16Z (GMT). No. of bitstreams: 3 Ali_Usama.pdf: 2339715 bytes, checksum: 2db09897be14e4f6e286e5f138056703 (MD5) Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) license.txt: 4059 bytes, checksum: 07e43adc81917cc61789e25d456992a6 (MD5)","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by William Ingram (wingram2@illinois.edu) on 2011-05-25T14:42:07Z Item is restricted until 2013-05-25T14:41:28Z","Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2013-05-26T10:00:25Z Item was in collections: University of Illinois Dissertations and Theses (ID: 204) College of Education Dissertations and Theses (ID: 434) No. of bitstreams: 3 Ali_Usama.pdf: 2339715 bytes, checksum: 2db09897be14e4f6e286e5f138056703 (MD5) Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) license.txt: 4059 bytes, checksum: 07e43adc81917cc61789e25d456992a6 (MD5)","Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2013-05-26T10:00:25Z"]},{"key":"dc:title","label":"Title","values":["Item Selection Methods in Polytomous Computerized Adaptive Testing"]}]}],"canonical_facts":{"dc:contributor":["Chang, Hua-Hua","Anderson, Carolyn J.","Douglas, Jeffrey A.","Zhang, Jinming"],"dc:creator":["Ali, Usama S."],"dc:date":["2011-05-25T14:36:16Z","2013-05-26T10:00:25Z","2011-05"],"dc:description":["Given the rapid advancement of computer technology, the importance of administering adaptive tests with polytomous items is in great need. With regard to the applicability of adaptive testing using polytomous IRT models, adaptive testing can use polytomous items of either rating scales, or in some testing situations of multiple choice. Additionally, the availability of computerized polytomous scoring of open-ended items enhances such applicability. This need promotes the research in polytomous adaptive testing (PAT). This dissertation is an e ort to focus on item selection methods, as a major component, in polytomous computerized adaptive testing. So, it consists of ve chapters that cover the following: Chapter 1 focuses on a thorough introduction to the item response theory (IRT) models and adaptive testing related to polytomous items. Such an important overview and introduction to basic concepts in test theory and mathematical models for polytomous items is needed for the ow of consequent chapters. Chapter 2 is devoted to the development of a central location index (LI) to uniquely represent the polytomous item with a scale value parameter using most commonly used polytomous models. The motivation and rationale to search for a central or an overall location parameter is twofold: a) the confusion of multiple and di erent parameterizations for a polytomous item even for the same model, and b) the unavailability of such single location parameter block the usage of certain item selection methods in adaptive testing. Two approaches are used to derive the proposed LIs, one is based on the item category response functions (ICRFs) and the other is based on the polytomous item response function (IRF). As a result, four LIs are proposed. Chapter 3 is particularly assigned to development of an item selection method based on the developed location index and primarily assess its performance in the PAT context relative to existing methods. This method belongs to the non-information based item selection methods and we referred it as Matching-LI method. The results support that this proposed method is promising and is capable to produce accurate ability estimates and successfully manage the item pool usage. Chapter 4 introduces new item selection methods taking in consideration the previous chapter's results. The new methods are the hybrid, stage-based information, polytomous a-strati cation methods. The first two methods try to merge more than one criterion for selecting items of each PAT (e.g., the hybrid method merges both the Matching-LI and maximum information (MI) methods). The last method uses Matching-LI method within each stratum. Chapter 5 provides discussion, conclusions, and limitations and future research directions with respect to important components of an adaptive testing program (i.e., item selection methods, item response models, item banks, and trait versus attribute estimation).","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-16T16:49:22Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) Ali_Usama.pdf: 2354500 bytes, checksum: 2762759cf969017069aa9e7fbbf09c8d (MD5)","Made available in DSpace on 2011-05-25T14:36:16Z (GMT). No. of bitstreams: 3 Ali_Usama.pdf: 2339715 bytes, checksum: 2db09897be14e4f6e286e5f138056703 (MD5) Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) license.txt: 4059 bytes, checksum: 07e43adc81917cc61789e25d456992a6 (MD5)","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by William Ingram (wingram2@illinois.edu) on 2011-05-25T14:42:07Z Item is restricted until 2013-05-25T14:41:28Z","Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2013-05-26T10:00:25Z Item was in collections: University of Illinois Dissertations and Theses (ID: 204) College of Education Dissertations and Theses (ID: 434) No. of bitstreams: 3 Ali_Usama.pdf: 2339715 bytes, checksum: 2db09897be14e4f6e286e5f138056703 (MD5) Ali_Usama_2011.tex: 173838 bytes, checksum: 06d0870ace67964ce4c7f851c7f5252a (MD5) license.txt: 4059 bytes, checksum: 07e43adc81917cc61789e25d456992a6 (MD5)","Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2013-05-26T10:00:25Z"],"dc:identifier":["http://hdl.handle.net/2142/24416"],"dc:language":["en"],"dc:rights":["Copyright 2011 Usama Ali"],"dc:subject":["Polytomus items","computerize adaptive testing","item selection","item response theory","polytomous IRT models."],"dc:title":["Item Selection Methods in Polytomous Computerized Adaptive Testing"],"thesis:degree_discipline":["Educational Psychology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:23Z"}