Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
8-2024
Committee Chair or Co-Chairs
Dr. Mostafa Zahed
Committee Members
Dr. Robert Price, Dr. Michele Joyner
Abstract
Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, and generalized linear models, shed light on their intricate dynamics. Findings offer insights into risk and protective factors associated with these variables, guiding tailored interventions for individuals in psychological distress. Additionally, policymakers can utilize these insights to develop comprehensive strategies promoting mental health and well-being at a societal level.
Document Type
Thesis - unrestricted
Recommended Citation
Gaffari, Muslihat, "An Application of an In-Depth Advanced Statistical Analysis in Exploring the Dynamics of Depression, Sleep Deprivation, and Self-Esteem" (2024). Electronic Theses and Dissertations. Paper 4428. https://dc.etsu.edu/etd/4428
Copyright
Copyright by the authors.
Included in
Biostatistics Commons, Categorical Data Analysis Commons, Vital and Health Statistics Commons