EVALUATION OF A COMPREHENSIVE MULTIDIMENSIONAL MODEL OF BIPOLAR SPECTRUM PSYCHOPATHOLOGY THROUGH STATISTICAL AND MACHINE LEARNING METHODS - Dissertation

Aug 4, 2026

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Researchers conceptualize bipolar disorders as a continuum from subclinical to clinical severity and impairment, and evidence supports viewing bipolar spectrum psychopathology as multidimensional. However, prior work has not established a consistent multidimensional structure. Based on an extensive literature review, the study tested a nine-dimensional model spanning elation-euphoria, irritability, mood lability, energy activation, impulsivity-disrupted reward sensitivity, unstable self-esteem, unstable sociability, decreased need for sleep, and lack of insight.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages163

Reading Time6 h 51 min

AccessFree to download

Chapter 1: Introduction

Chapter 2:

EVALUATION OF A COMPREHENSIVE MULTIDIMENSIONAL MODEL OF BIPOLAR SPECTRUM PSYCHOPATHOLOGY THROUGH STATISTICAL AND MACHINE LEARNING METHODS BY TALIA REBECCA BERSON CHIA DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology in the Graduate College of the University of Illinois Urbana-Champaign, 2024 Urbana, Illinois Doctoral Committee: Professor Thomas R. Kwapil, Chair Professor Benjamin L. Hankin Associate Professor D. Ava Briley Assistant Professor Yan Tang Assistant Professor Yan Xia ABSTRACT 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 elationeuphoria, 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 ofthe 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. ACKNOWLEDGMENTS To my advisor, Dr. Tom Kwapil, thank you for taking a chance on a student who listens to The Eagles. You have challenged me to become a better researcher, a better writer, and abetter clinician throughout my time with you. Thank you for always being ready with a joke, putting up with my sarcasm, and making me feel more at home through your hospitality. You help me recognize the many different identities I hold. Here’s to many more tunes from Marlene Sandersfield. To my committee members: Dr. Ava Briley, Dr. Ben Hankin, Dr. Yan Tang, and Dr. Yan Xia, you have put up with so many emails and meetings and anxious messages from me that I cannot imagine making it through this project without you. I do not believe any committee has ever scheduled milestones so quickly. You have each been a joy to learn from and work with. Iam incredibly grateful. Dedication This dissertation, and the work that paved its way, took a village. To my friends back home: You read my graduate school application essays, patiently listened tomy rants, and kindly excused my absences over the past six years. I am so excited for the adventures to come, and I am immensely proud of all the amazing things each of you have achieved throughout this time. To my friends in Illinois: Between mac and cheese nights, sports movies, dance classes, and farmers markets, you brought so much joy and comfort into this time. I will miss Tres Nopalesand between class phone calls. Here’s to the next chapter on the othe

FAQs

What is the main goal of this dissertation?

To evaluate a proposed nine-dimensional model of bipolar spectrum psychopathology using both statistical and machine learning methods.

How was the proposed model assessed and what sample was used?

The model was evaluated in a multisite, non-clinically ascertained sample of young adults (n=1264) using self-report measures for each domain and a broad bipolar spectrum questionnaire.

What did the analyses show about model fit and variance explained?

Confirmatory factor analysis indicated the model did not achieve overall acceptable fit, but both factor and regression analyses suggested many dimensions were relevant; the models accounted for about 60% of variance in the HPS with comparable results across statistical and machine learning approaches.

Aug 4, 2026

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