{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124465"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124465","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Order-constrained inference: a nuanced approach to hypothesis testing","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Chen, Meichai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Regenwetter, Michel","Koehn, Hans Friedrich"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Order-constrained Inference","Nuanced Hypotheses","Model Competition"],"languages":["en","eng"],"rights":["Copyright 2024 Meichai Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124465","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Regenwetter, Michel","Koehn, Hans Friedrich"]},{"key":"dc:creator","label":"Author","values":["Chen, Meichai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-05-03"]},{"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":["Order-constrained Inference","Nuanced Hypotheses","Model Competition"]}]},{"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 Meichai Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124465"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Meichai Chen, accepted the attached license on 2024-05-02 at 15:34.","The student, Meichai Chen, submitted this Thesis for approval on 2024-05-02 at 15:49.","This Thesis was approved for publication on 2024-05-03 at 09:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20764 on 2024-09-16 at 00:37:56","Many statistical analyses performed in psychological studies add extraneous assumptions that are not part of the theory. These added assumptions could adversely influence the conclusions one derives from the analyses. Order-constrained inference allows researchers to avoid unnecessary assumptions, translate verbal predictions into direct testable hypotheses, and run model selection among competing theories. We reanalyzed data from two separate case studies to highlight how one can use order-constrained modeling to formulate more nuanced hypotheses and test these hypotheses jointly. To further leverage order-constrained inference, we compared the performance of competing theories using Bayesian model selection methods in the second case study. We observe that order-constrained inference not only provides us with a coarse view of all the hypotheses at the joint level, it also offers a fine-grained perspective of all the hypotheses at the item level that might otherwise stay hidden if we only assessed trends at the aggregate level."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Order-constrained inference: a nuanced approach to hypothesis testing"]}]}],"canonical_facts":{"dc:contributor":["Regenwetter, Michel","Koehn, Hans Friedrich"],"dc:creator":["Chen, Meichai"],"dc:date":["2024-05","2024-05-03"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Meichai Chen, accepted the attached license on 2024-05-02 at 15:34.","The student, Meichai Chen, submitted this Thesis for approval on 2024-05-02 at 15:49.","This Thesis was approved for publication on 2024-05-03 at 09:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20764 on 2024-09-16 at 00:37:56","Many statistical analyses performed in psychological studies add extraneous assumptions that are not part of the theory. These added assumptions could adversely influence the conclusions one derives from the analyses. Order-constrained inference allows researchers to avoid unnecessary assumptions, translate verbal predictions into direct testable hypotheses, and run model selection among competing theories. We reanalyzed data from two separate case studies to highlight how one can use order-constrained modeling to formulate more nuanced hypotheses and test these hypotheses jointly. To further leverage order-constrained inference, we compared the performance of competing theories using Bayesian model selection methods in the second case study. We observe that order-constrained inference not only provides us with a coarse view of all the hypotheses at the joint level, it also offers a fine-grained perspective of all the hypotheses at the item level that might otherwise stay hidden if we only assessed trends at the aggregate level."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124465"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Meichai Chen"],"dc:subject":["Order-constrained Inference","Nuanced Hypotheses","Model Competition"],"dc:title":["Order-constrained inference: a nuanced approach to hypothesis testing"],"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":"2026-07-22T22:25:00Z"}