{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124626"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124626","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A structural after measurement approach to bifactor predictive models","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Choi, Jinsoo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Zhang, Bo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Structural After Measurement","Bifactor Model","Augmentation"],"languages":["en","eng"],"rights":["Copyright 2024 Jinsoo Choi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124626","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Bo"]},{"key":"dc:creator","label":"Author","values":["Choi, Jinsoo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-02-19"]},{"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.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":["Structural After Measurement","Bifactor Model","Augmentation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jinsoo Choi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124626"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jinsoo Choi, accepted the attached license on 2024-02-15 at 14:41.","The student, Jinsoo Choi, submitted this Thesis for approval on 2024-02-15 at 14:53.","This Thesis was approved for publication on 2024-02-19 at 14:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20220 on 2024-09-16 at 00:48:50","The bifactor model has regained popularity due to its conceptual appeal. However, the bifactor predictive model, which extends a bifactor model to a criterion variable, often encounters empirical under-identification due to approximate linear dependency. This limitation leads to various statistical issues (e.g., non-convergence, estimation bias, inaccurate standard errors), hindering the use of the bifactor model for predictive purposes. To address this limitation, we introduced the recently developed Structural After Measurement (SAM; Rosseel & Loh, 2022) approach to the bifactor predictive model and examined its robustness with a series of Monte Carlo simulations. Our simulation results indicated that the SAM approach effectively enhances the statistical performance of bifactor predictive models compared to the SEM approach in terms of model convergence, stability of point estimates, accuracy of standard error estimates, coverage rates, and Type I error rates, at the cost of slight bias. Our empirical illustration also supported the simulation findings, further illustrating the effectiveness of the SAM approach."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A structural after measurement approach to bifactor predictive models"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Bo"],"dc:creator":["Choi, Jinsoo"],"dc:date":["2024-05","2024-02-19"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jinsoo Choi, accepted the attached license on 2024-02-15 at 14:41.","The student, Jinsoo Choi, submitted this Thesis for approval on 2024-02-15 at 14:53.","This Thesis was approved for publication on 2024-02-19 at 14:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20220 on 2024-09-16 at 00:48:50","The bifactor model has regained popularity due to its conceptual appeal. However, the bifactor predictive model, which extends a bifactor model to a criterion variable, often encounters empirical under-identification due to approximate linear dependency. This limitation leads to various statistical issues (e.g., non-convergence, estimation bias, inaccurate standard errors), hindering the use of the bifactor model for predictive purposes. To address this limitation, we introduced the recently developed Structural After Measurement (SAM; Rosseel & Loh, 2022) approach to the bifactor predictive model and examined its robustness with a series of Monte Carlo simulations. Our simulation results indicated that the SAM approach effectively enhances the statistical performance of bifactor predictive models compared to the SEM approach in terms of model convergence, stability of point estimates, accuracy of standard error estimates, coverage rates, and Type I error rates, at the cost of slight bias. Our empirical illustration also supported the simulation findings, further illustrating the effectiveness of the SAM approach."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124626"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jinsoo Choi"],"dc:subject":["Structural After Measurement","Bifactor Model","Augmentation"],"dc:title":["A structural after measurement approach to bifactor predictive models"],"dc:type":["text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}