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Depression, characterized by persistent feelings of sadness and loss of interest or pleasure in activities, is a pervasive mental health disorder affecting millions worldwide. Sleep deprivation, often intertwined with depression, exacerbates its symptoms, and contributes to a vicious cycle of emotional distress and cognitive impairment. Additionally, self-esteem, the subjective evaluation of one's worth and capabilities, plays a crucial role in shaping individuals' mental well-being and resilience against psychological challenges. Understanding how these factors intersect and influence each other is essential for developing targeted interventions and support systems. Understanding the complex interactions between depression, sleep deprivation, and self-esteem is paramount for advancing mental health research and intervention strategies. This study employs sophisticated statistical methodologies, including log-linear homogeneous association, multi-nominal logistic regression, and generalized linear models, to explore the intricate dynamics among these psychological variables. By scrutinizing large-scale datasets and employing advanced statistical techniques, this research aims to uncover nuanced patterns and relationships, ultimately contributing to a deeper understanding of mental health phenomena. The purpose of this study is to clarify the relationship between” depression”,” trouble sleeping” or” sleeping too much”, and” feeling bad about yourself”. This research aims to check the key reasons for prioritizing the study of depression, how a deeper understanding of this mental health condition contributes to improved prevention, intervention, and overall well-being in individuals and society, how Log-Linear Models, Multinomial Logistic Regression, and Generalized Linear Models be employed to analyze the association between depression, sleep disturbances, and self-esteem, shedding light on the intricate relationships within mental health and how the performance of proposed statistical models be effectively compared using statistical measures such as likelihood ratio test, Pearson chi-square, Mean Square Error, Bayesian Criterion (BIC), and Akaike Information Criterion (AIC). |
