{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/30910"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/30910","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Kullback-Leibler information and its applications in multidimensional adaptive testing","abstract":"This paper first discusses the relationship between Kullback-Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods in multi-dimensional adaptive tests (MAT) which are based on these two information measures. The KL information index (KI) method is then discussed and two theorems are derived to quantify the relationship between KI and item parameters. Due to the fact that most of the existing item selection algorithms for MAT bear severe computational complexity, which substantially lowers the applicability of MAT, two versions of simplified KL index (SKI), built from the analytical results, are proposed to mimic the behavior of KI, while reducing the overall computational intensity.","abstract_html":"This paper first discusses the relationship between Kullback-Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods in multi-dimensional adaptive tests (MAT) which are based on these two information measures. The KL information index (KI) method is then discussed and two theorems are derived to quantify the relationship between KI and item parameters. Due to the fact that most of the existing item selection algorithms for MAT bear severe computational complexity, which substantially lowers the applicability of MAT, two versions of simplified KL index (SKI), built from the analytical results, are proposed to mimic the behavior of KI, while reducing the overall computational intensity.","abstract_has_math":false,"creators":["Wang, Chun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.A.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Chang, Hua-Hua","Douglas, Jeffrey A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-05-22T00:14:36Z","date_published":"2012-05-22T00:14:36Z","updated_at":"2026-07-22T22:25:29Z","subjects":["Kullback-Leibler information","Fisher information","multi-dimensional adaptive testing"],"languages":["en"],"rights":["Copyright 2012 Chun Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/30910","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Hua-Hua","Douglas, Jeffrey A."]},{"key":"dc:creator","label":"Author","values":["Wang, Chun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-05-22T00:14:36Z","2012-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.A."]},{"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":["Kullback-Leibler information","Fisher information","multi-dimensional 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 2012 Chun Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/30910"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This paper first discusses the relationship between Kullback-Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods in multi-dimensional adaptive tests (MAT) which are based on these two information measures. The KL information index (KI) method is then discussed and two theorems are derived to quantify the relationship between KI and item parameters. Due to the fact that most of the existing item selection algorithms for MAT bear severe computational complexity, which substantially lowers the applicability of MAT, two versions of simplified KL index (SKI), built from the analytical results, are proposed to mimic the behavior of KI, while reducing the overall computational intensity.","Item withdrawn by Katherine Eriksen (eriksen3@illinois.edu) on 2012-01-30T17:49:39Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Chun_Wang.pdf: 1482757 bytes, checksum: f11148d7d2eba756abc85fe23513d857 (MD5) Wang_Chun.pdf: 1482757 bytes, checksum: f11148d7d2eba756abc85fe23513d857 (MD5)","Made available in DSpace on 2012-05-22T00:14:36Z (GMT). No. of bitstreams: 2 Wang_Chun.pdf: 1482796 bytes, checksum: d05b00ead529ec3046df71caea8eb43f (MD5) license.txt: 4058 bytes, checksum: acd2b090a4dac252740ca98d9a9d3eb4 (MD5)"]},{"key":"dc:title","label":"Title","values":["Kullback-Leibler information and its applications in multidimensional adaptive testing"]}]}],"canonical_facts":{"dc:contributor":["Chang, Hua-Hua","Douglas, Jeffrey A."],"dc:creator":["Wang, Chun"],"dc:date":["2012-05-22T00:14:36Z","2012-05"],"dc:description":["This paper first discusses the relationship between Kullback-Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods in multi-dimensional adaptive tests (MAT) which are based on these two information measures. The KL information index (KI) method is then discussed and two theorems are derived to quantify the relationship between KI and item parameters. Due to the fact that most of the existing item selection algorithms for MAT bear severe computational complexity, which substantially lowers the applicability of MAT, two versions of simplified KL index (SKI), built from the analytical results, are proposed to mimic the behavior of KI, while reducing the overall computational intensity.","Item withdrawn by Katherine Eriksen (eriksen3@illinois.edu) on 2012-01-30T17:49:39Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Chun_Wang.pdf: 1482757 bytes, checksum: f11148d7d2eba756abc85fe23513d857 (MD5) Wang_Chun.pdf: 1482757 bytes, checksum: f11148d7d2eba756abc85fe23513d857 (MD5)","Made available in DSpace on 2012-05-22T00:14:36Z (GMT). No. of bitstreams: 2 Wang_Chun.pdf: 1482796 bytes, checksum: d05b00ead529ec3046df71caea8eb43f (MD5) license.txt: 4058 bytes, checksum: acd2b090a4dac252740ca98d9a9d3eb4 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/30910"],"dc:language":["en"],"dc:rights":["Copyright 2012 Chun Wang"],"dc:subject":["Kullback-Leibler information","Fisher information","multi-dimensional adaptive testing"],"dc:title":["Kullback-Leibler information and its applications in multidimensional adaptive testing"],"dc:type":["text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.A."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:29Z"}