{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110540"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110540","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Interest item diagnostics through IRT: an application of GPCM and tree-based IRT models to contemporary measures of interests","abstract":"Item Response Theory (IRT) is a contemporary test evaluation technique that is frequently used in the fields of achievement and aptitude testing. However, IRT research within the field of vocational interest test evaluation is nascent at best. This dissertation advances the use of IRT in vocational interest through two studies of contemporary interest measures: The O*NET Interest Profiler (Rounds, Hoff & Lewis, 2021) and the Comprehensive Assessment of Basic Interests (CABIN; Su, Tay, Liao, Zhang & Rounds, 2018). For the O*NET Interest Profiler (IP), I use IRT to demonstrate the viability of a new tree-based response model for answering inventory items and compare the results of test shortening using the tree-based IRT model versus a more standard 2 Parameter Logistic Model. I found that the tree based model was more apt for describing the ‘Like/Dislike/Unsure’ response data of the O*NET IP and discovered that respondents tend to form similar thought processes when approaching these interest items—They make a two-step decision of whether or not they like the item first, before deciding if they actually dislike the item second. For the CABIN, I use a Generalized Partial Credit Model (GPCM) to obtain item diagnostics for the 8 interest subscales and propose a shortened version of each scale using item information curves and marginal reliability coefficients. I proposed two short forms of the CABIN based on these diagnostics—One that preserves the 41 basic interest scales of the original, and an even shorter version that simply measures the 8 overarching interest constructs. Researchers should select a short form that matches their desired level of specificity of the measured construct, and I illustrate how IRT can help in this endeavor. Both studies reveal the untapped potential IRT has for refining measures in vocational interest research and understanding the item response process of interest items.","abstract_html":"Item Response Theory (IRT) is a contemporary test evaluation technique that is frequently used in the fields of achievement and aptitude testing. However, IRT research within the field of vocational interest test evaluation is nascent at best. This dissertation advances the use of IRT in vocational interest through two studies of contemporary interest measures: The O*NET Interest Profiler (Rounds, Hoff & Lewis, 2021) and the Comprehensive Assessment of Basic Interests (CABIN; Su, Tay, Liao, Zhang & Rounds, 2018). For the O*NET Interest Profiler (IP), I use IRT to demonstrate the viability of a new tree-based response model for answering inventory items and compare the results of test shortening using the tree-based IRT model versus a more standard 2 Parameter Logistic Model. I found that the tree based model was more apt for describing the ‘Like/Dislike/Unsure’ response data of the O*NET IP and discovered that respondents tend to form similar thought processes when approaching these interest items—They make a two-step decision of whether or not they like the item first, before deciding if they actually dislike the item second. For the CABIN, I use a Generalized Partial Credit Model (GPCM) to obtain item diagnostics for the 8 interest subscales and propose a shortened version of each scale using item information curves and marginal reliability coefficients. I proposed two short forms of the CABIN based on these diagnostics—One that preserves the 41 basic interest scales of the original, and an even shorter version that simply measures the 8 overarching interest constructs. Researchers should select a short form that matches their desired level of specificity of the measured construct, and I illustrate how IRT can help in this endeavor. Both studies reveal the untapped potential IRT has for refining measures in vocational interest research and understanding the item response process of interest items.","abstract_has_math":false,"creators":["Wee, Jian Ming Colin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Rounds, James B","Newman, Daniel A","Kern, Justin L","Napolitano, Christopher M","Hoff, Kevin A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:11:12Z","date_published":"2021-09-17T01:11:12Z","updated_at":"2026-07-22T22:24:52Z","subjects":["vocational interests","item response theory","psychometrics"],"languages":["en"],"rights":["Copyright 2021 Colin Wee Jian Ming"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110540","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rounds, James B","Newman, Daniel A","Kern, Justin L","Napolitano, Christopher M","Hoff, Kevin A"]},{"key":"dc:creator","label":"Author","values":["Wee, Jian Ming Colin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:11:12Z","2021-04-23","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["vocational interests","item response theory","psychometrics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Colin Wee Jian Ming"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110540"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Item Response Theory (IRT) is a contemporary test evaluation technique that is frequently used in the fields of achievement and aptitude testing. However, IRT research within the field of vocational interest test evaluation is nascent at best. This dissertation advances the use of IRT in vocational interest through two studies of contemporary interest measures: The O*NET Interest Profiler (Rounds, Hoff & Lewis, 2021) and the Comprehensive Assessment of Basic Interests (CABIN; Su, Tay, Liao, Zhang & Rounds, 2018). For the O*NET Interest Profiler (IP), I use IRT to demonstrate the viability of a new tree-based response model for answering inventory items and compare the results of test shortening using the tree-based IRT model versus a more standard 2 Parameter Logistic Model. I found that the tree based model was more apt for describing the ‘Like/Dislike/Unsure’ response data of the O*NET IP and discovered that respondents tend to form similar thought processes when approaching these interest items—They make a two-step decision of whether or not they like the item first, before deciding if they actually dislike the item second. For the CABIN, I use a Generalized Partial Credit Model (GPCM) to obtain item diagnostics for the 8 interest subscales and propose a shortened version of each scale using item information curves and marginal reliability coefficients. I proposed two short forms of the CABIN based on these diagnostics—One that preserves the 41 basic interest scales of the original, and an even shorter version that simply measures the 8 overarching interest constructs. Researchers should select a short form that matches their desired level of specificity of the measured construct, and I illustrate how IRT can help in this endeavor. Both studies reveal the untapped potential IRT has for refining measures in vocational interest research and understanding the item response process of interest items.","Submission original under an indefinite embargo labeled 'Open Access'. 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For the CABIN, I use a Generalized Partial Credit Model (GPCM) to obtain item diagnostics for the 8 interest subscales and propose a shortened version of each scale using item information curves and marginal reliability coefficients. I proposed two short forms of the CABIN based on these diagnostics—One that preserves the 41 basic interest scales of the original, and an even shorter version that simply measures the 8 overarching interest constructs. Researchers should select a short form that matches their desired level of specificity of the measured construct, and I illustrate how IRT can help in this endeavor. Both studies reveal the untapped potential IRT has for refining measures in vocational interest research and understanding the item response process of interest items.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Jian Ming Colin Wee, accepted the attached license on 2021-04-21 at 19:04.","The student, Jian Ming Colin Wee, submitted this Dissertation for approval on 2021-04-21 at 19:23.","This Dissertation was approved for publication on 2021-04-23 at 11:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16486 on 2021-09-16 at 16:46:52","Made available in DSpace on 2021-09-17T01:11:12Z (GMT). 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