{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/23452"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/23452","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Some issues in item response theory","abstract":"\"The issues studied in this thesis are: the assumption of local independence (LI) in item response theory (IRT) models in the first three chapters, and differential item functioning (DIF) in IRT in the last chapter. A new term \"\"conditional trivariance\"\" for three items is defined, which is a measurement of joint local dependence among item triples. This is extended to any J items. Relevant theorems are stated and proved, which provide necessary and sufficient conditions involving conditional co-variances, trivariances, etc. for local independence to hold. These theorems lead us to a method to test LI that goes beyond conditional covariance exploration. We realize this procedure by using a kernel smoothing technique. Asymptotic normality of the test statistic is given and proved, which in turn justifies our procedure. Simulation studies show the procedure works well for the case of large examinee sample sizes. Some Graduate Record Examination (GRE) verbal test data is analyzed.\"","abstract_html":""The issues studied in this thesis are: the assumption of local independence (LI) in item response theory (IRT) models in the first three chapters, and differential item functioning (DIF) in IRT in the last chapter. A new term ""conditional trivariance"" for three items is defined, which is a measurement of joint local dependence among item triples. This is extended to any J items. Relevant theorems are stated and proved, which provide necessary and sufficient conditions involving conditional co-variances, trivariances, etc. for local independence to hold. These theorems lead us to a method to test LI that goes beyond conditional covariance exploration. We realize this procedure by using a kernel smoothing technique. Asymptotic normality of the test statistic is given and proved, which in turn justifies our procedure. Simulation studies show the procedure works well for the case of large examinee sample sizes. Some Graduate Record Examination (GRE) verbal test data is analyzed."","abstract_has_math":false,"creators":["Wu, Hongsheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Stout, William F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T14:14:42Z","date_published":"2011-05-07T14:14:42Z","updated_at":"2026-07-22T22:25:22Z","subjects":["Statistics"],"languages":["eng"],"rights":["Copyright 1996 Wu, Hongsheng"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591254853","AAI9717348","(UMI)AAI9717348"],"render_values":[{"text":"9780591254853","href":null,"code":true},{"text":"AAI9717348","href":null,"code":true},{"text":"(UMI)AAI9717348","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/23452","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Stout, William F."]},{"key":"dc:creator","label":"Author","values":["Wu, Hongsheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T14:14:42Z","10000-01-01","1996"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"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":["Statistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1996 Wu, Hongsheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591254853","AAI9717348","(UMI)AAI9717348","http://hdl.handle.net/2142/23452"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"The issues studied in this thesis are: the assumption of local independence (LI) in item response theory (IRT) models in the first three chapters, and differential item functioning (DIF) in IRT in the last chapter. A new term \"\"conditional trivariance\"\" for three items is defined, which is a measurement of joint local dependence among item triples. This is extended to any J items. Relevant theorems are stated and proved, which provide necessary and sufficient conditions involving conditional co-variances, trivariances, etc. for local independence to hold. These theorems lead us to a method to test LI that goes beyond conditional covariance exploration. We realize this procedure by using a kernel smoothing technique. Asymptotic normality of the test statistic is given and proved, which in turn justifies our procedure. Simulation studies show the procedure works well for the case of large examinee sample sizes. Some Graduate Record Examination (GRE) verbal test data is analyzed.\"","Chapter 4 deals DIF. A two-stage kernel smoothed DIF detecting method is proposed. Two-stage method is obtained by the amalgamation both Douglas, J., Stout, W., & DiBello, L.'s kernel smoothed DIF detection method and Shealy & Stout's linear regression correction used in SIBTEST. It is compared with Douglas, et al.'s one-stage method. The factors influencing the performance of the procedures are studied. A best combination of these factors is recommended.","Made available in DSpace on 2011-05-07T14:14:42Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9717348.pdf: 2765998 bytes, checksum: ecfa806b5f255957fce41221375c1ce3 (MD5) Previous issue date: 1996","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:04:34Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:30:52-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Some issues in item response theory"]}]}],"canonical_facts":{"dc:contributor":["Stout, William F."],"dc:creator":["Wu, Hongsheng"],"dc:date":["2011-05-07T14:14:42Z","10000-01-01","1996"],"dc:description":["\"The issues studied in this thesis are: the assumption of local independence (LI) in item response theory (IRT) models in the first three chapters, and differential item functioning (DIF) in IRT in the last chapter. A new term \"\"conditional trivariance\"\" for three items is defined, which is a measurement of joint local dependence among item triples. This is extended to any J items. Relevant theorems are stated and proved, which provide necessary and sufficient conditions involving conditional co-variances, trivariances, etc. for local independence to hold. These theorems lead us to a method to test LI that goes beyond conditional covariance exploration. We realize this procedure by using a kernel smoothing technique. Asymptotic normality of the test statistic is given and proved, which in turn justifies our procedure. Simulation studies show the procedure works well for the case of large examinee sample sizes. Some Graduate Record Examination (GRE) verbal test data is analyzed.\"","Chapter 4 deals DIF. A two-stage kernel smoothed DIF detecting method is proposed. Two-stage method is obtained by the amalgamation both Douglas, J., Stout, W., & DiBello, L.'s kernel smoothed DIF detection method and Shealy & Stout's linear regression correction used in SIBTEST. It is compared with Douglas, et al.'s one-stage method. The factors influencing the performance of the procedures are studied. A best combination of these factors is recommended.","Made available in DSpace on 2011-05-07T14:14:42Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9717348.pdf: 2765998 bytes, checksum: ecfa806b5f255957fce41221375c1ce3 (MD5) Previous issue date: 1996","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:04:34Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:30:52-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["9780591254853","AAI9717348","(UMI)AAI9717348","http://hdl.handle.net/2142/23452"],"dc:language":["eng"],"dc:rights":["Copyright 1996 Wu, Hongsheng"],"dc:subject":["Statistics"],"dc:title":["Some issues in item response theory"],"dc:type":["text"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:22Z"}