{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101147"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101147","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Comparison between DINA model and confirmatory noncompensatory MIRT model","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","abstract_has_math":false,"creators":["Hu, Mingqi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Zhang, Jinming","Anderson, Carolyn","Köhn, Hans-Friedrich"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:34:00Z","date_published":"2018-09-04T20:34:00Z","updated_at":"2026-07-22T22:24:38Z","subjects":["multidimensional item response theory","cognitive diagnostic models"],"languages":["en"],"rights":["Copyright 2018 Mingqi Hu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101147","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Jinming","Anderson, Carolyn","Köhn, Hans-Friedrich"]},{"key":"dc:creator","label":"Author","values":["Hu, Mingqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:34:00Z","2020-09-05T09:15:29Z","2018-04-23","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["multidimensional item response theory","cognitive diagnostic 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 2018 Mingqi Hu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101147"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Mingqi Hu, accepted the attached license on 2018-04-19 at 18:16.","The student, Mingqi Hu, submitted this Thesis for approval on 2018-04-21 at 18:20.","This Thesis was approved for publication on 2018-04-23 at 10:47.","Cognitive diagnostic models (CDM) are widely used to diagnose whether or not students master specific fine-grained skills. Multidimensional item response theory (MIRT) models adopt a continuous scale to locate students’ ability position based on their performance on test items. The current study aims to evaluate the cognitive diagnostic performance of the confirmatory noncompensatory MIRT (C-NMIRT) model by comparing it with the deterministic input, noisy ‘‘and’’ gate (DINA) model. A cutoff point is needed to transform continuous latent traits in the C-NMIRT model into categorical ones: mastery and no-mastery. A pilot study was conducted and 0 was found to be the proper cutoff point. Then, two simulation studies were conducted, where datasets were generated by the C-NMIRT model in the first study and generated by the DINA model in the second study. Both the DINA model and the C-NMIRT model were used for cognitive diagnosis and their results from both simulation studies were compared. The sample size N was 3000. Nine conditions were studied: three attribute numbers (K = 2, 3, 4) and three test lengths (short = 10, medium = 30, long = 50). Pattern correct classification rates (PCCRs) and the attribute correct classification rates (ACCRs) were calculated for estimation accuracy. Overall, estimation accuracy rates for both models increased as the attribute number decreased or test length enlarged. In addition, the first study found that estimation accuracy rates of two models were close in all of the nine conditions (discrepant rates were less than 3%). In addition, the second study indicated that the C-NMIRT model achieved similar estimation accuracy rates as the DINA model in the conditions with medium and long test lengths (discrepant rates were less than 3%). The C-NMIRT model also provided precisely estimated latent traits, which was beneficial for detailed skill mastery status analysis in educational settings.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12179 on 2018-08-31 at 17:18:22","Made available in DSpace on 2018-09-04T20:34:00Z (GMT). No. of bitstreams: 2 HU-THESIS-2018.pdf: 492402 bytes, checksum: d79227f9ccab89b0745973eae33fb60a (MD5) LICENSE.txt: 4206 bytes, checksum: 0de92f394e981fa8af143ad4fbd9e385 (MD5) Previous issue date: 2018-04-23","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:34:13Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107230 on 2020-09-05T09:15:29Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Comparison between DINA model and confirmatory noncompensatory MIRT model"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Jinming","Anderson, Carolyn","Köhn, Hans-Friedrich"],"dc:creator":["Hu, Mingqi"],"dc:date":["2018-09-04T20:34:00Z","2020-09-05T09:15:29Z","2018-04-23","2018-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Mingqi Hu, accepted the attached license on 2018-04-19 at 18:16.","The student, Mingqi Hu, submitted this Thesis for approval on 2018-04-21 at 18:20.","This Thesis was approved for publication on 2018-04-23 at 10:47.","Cognitive diagnostic models (CDM) are widely used to diagnose whether or not students master specific fine-grained skills. Multidimensional item response theory (MIRT) models adopt a continuous scale to locate students’ ability position based on their performance on test items. The current study aims to evaluate the cognitive diagnostic performance of the confirmatory noncompensatory MIRT (C-NMIRT) model by comparing it with the deterministic input, noisy ‘‘and’’ gate (DINA) model. A cutoff point is needed to transform continuous latent traits in the C-NMIRT model into categorical ones: mastery and no-mastery. A pilot study was conducted and 0 was found to be the proper cutoff point. Then, two simulation studies were conducted, where datasets were generated by the C-NMIRT model in the first study and generated by the DINA model in the second study. Both the DINA model and the C-NMIRT model were used for cognitive diagnosis and their results from both simulation studies were compared. The sample size N was 3000. Nine conditions were studied: three attribute numbers (K = 2, 3, 4) and three test lengths (short = 10, medium = 30, long = 50). Pattern correct classification rates (PCCRs) and the attribute correct classification rates (ACCRs) were calculated for estimation accuracy. Overall, estimation accuracy rates for both models increased as the attribute number decreased or test length enlarged. In addition, the first study found that estimation accuracy rates of two models were close in all of the nine conditions (discrepant rates were less than 3%). In addition, the second study indicated that the C-NMIRT model achieved similar estimation accuracy rates as the DINA model in the conditions with medium and long test lengths (discrepant rates were less than 3%). The C-NMIRT model also provided precisely estimated latent traits, which was beneficial for detailed skill mastery status analysis in educational settings.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12179 on 2018-08-31 at 17:18:22","Made available in DSpace on 2018-09-04T20:34:00Z (GMT). No. of bitstreams: 2 HU-THESIS-2018.pdf: 492402 bytes, checksum: d79227f9ccab89b0745973eae33fb60a (MD5) LICENSE.txt: 4206 bytes, checksum: 0de92f394e981fa8af143ad4fbd9e385 (MD5) Previous issue date: 2018-04-23","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:34:13Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107230 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107230 on 2020-09-05T09:15:29Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/101147"],"dc:language":["en"],"dc:rights":["Copyright 2018 Mingqi Hu"],"dc:subject":["multidimensional item response theory","cognitive diagnostic models"],"dc:title":["Comparison between DINA model and confirmatory noncompensatory MIRT model"],"dc:type":["text"],"thesis:degree_discipline":["Educational Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:38Z"}