{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125517"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125517","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluation of a comprehensive multidimensional model of bipolar spectrum psychopathology through statistical and machine learning methods","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Chia, Talia Rebecca Berson"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Kwapil, Thomas R","Hankin, Benjamin L","Briley, D. 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The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Talia Chia, accepted the attached license on 2024-06-12 at 16:38.","The student, Talia Chia, submitted this Dissertation for approval on 2024-06-12 at 16:42.","This Dissertation was approved for publication on 2024-06-13 at 09:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20830 on 2025-02-04 at 21:03:27","Researchers have proposed that bipolar disorders are better understood as a continuum, ranging from subclinical through clinical severity and impairment. Furthermore, there is compelling evidence to reconceptualize bipolar spectrum psychopathology as multidimensional. However, researchers have struggled to capture a consistent multidimensional structure. Based on an extensive literature review, a nine-dimensional model was proposed consisting of elation-euphoria, irritability, mood lability, energy-activation, impulsivity-disrupted reward sensitivity, unstable self-esteem, unstable sociability, decreased need for sleep, and lack of insight. This study evaluated the proposed multidimensional model in a multisite, non-clinically ascertained sample of young adults (n = 1264). Participants completed self-report assessments evaluating each domain, as well as a questionnaire assessing bipolar spectrum psychopathology broadly. The proposed model was evaluated using both traditional statistical techniques, such as confirmatory factor analysis and multiple linear regression, as well as machine learning techniques, such as parallel coordinates, elastic net regression, support vector regression, and artificial neural networks. Confirmatory factor analysis indicated that this model did not result in overall acceptable fit. However, both confirmatory factor analysis and regression analyses suggested that many dimensions represented relevant features of bipolar spectrum psychopathology. Both statistical and machine learning models indicated that this model accounted for approximately 60% of the variance of the HPS. Furthermore, statistical and machine learning techniques yielded comparable results. Therefore, next steps were proposed for refinement and evaluation of a comprehensive and theoretically valuable multidimensional model of bipolar spectrum psychopathology."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Evaluation of a comprehensive multidimensional model of bipolar spectrum psychopathology through statistical and machine learning methods"]}]}],"canonical_facts":{"dc:contributor":["Kwapil, Thomas R","Hankin, Benjamin L","Briley, D. Ava","Tang, Yan","Xia, Yan"],"dc:creator":["Chia, Talia Rebecca Berson"],"dc:date":["2024-06-13","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Talia Chia, accepted the attached license on 2024-06-12 at 16:38.","The student, Talia Chia, submitted this Dissertation for approval on 2024-06-12 at 16:42.","This Dissertation was approved for publication on 2024-06-13 at 09:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20830 on 2025-02-04 at 21:03:27","Researchers have proposed that bipolar disorders are better understood as a continuum, ranging from subclinical through clinical severity and impairment. Furthermore, there is compelling evidence to reconceptualize bipolar spectrum psychopathology as multidimensional. However, researchers have struggled to capture a consistent multidimensional structure. Based on an extensive literature review, a nine-dimensional model was proposed consisting of elation-euphoria, irritability, mood lability, energy-activation, impulsivity-disrupted reward sensitivity, unstable self-esteem, unstable sociability, decreased need for sleep, and lack of insight. This study evaluated the proposed multidimensional model in a multisite, non-clinically ascertained sample of young adults (n = 1264). Participants completed self-report assessments evaluating each domain, as well as a questionnaire assessing bipolar spectrum psychopathology broadly. The proposed model was evaluated using both traditional statistical techniques, such as confirmatory factor analysis and multiple linear regression, as well as machine learning techniques, such as parallel coordinates, elastic net regression, support vector regression, and artificial neural networks. Confirmatory factor analysis indicated that this model did not result in overall acceptable fit. However, both confirmatory factor analysis and regression analyses suggested that many dimensions represented relevant features of bipolar spectrum psychopathology. Both statistical and machine learning models indicated that this model accounted for approximately 60% of the variance of the HPS. Furthermore, statistical and machine learning techniques yielded comparable results. Therefore, next steps were proposed for refinement and evaluation of a comprehensive and theoretically valuable multidimensional model of bipolar spectrum psychopathology."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125517"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Talia Chia"],"dc:subject":["Bipolar","Multidimensional","Statistical Modeling","Regression","Machine Learning"],"dc:title":["Evaluation of a comprehensive multidimensional model of bipolar spectrum psychopathology through statistical and machine learning methods"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}