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- All Subjects: Anxiety
- All Subjects: Adolescence
- Creators: Davis, Mary
- Creators: Luthar, Suniya
- Member of: Theses and Dissertations
- Resource Type: Text
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.
Background: Unintentional injury has been the leading cause of death for children and teenagers in the United States for the past 2 decades. Its health outcomes are often studied, but it may also relate to psychological concepts such as emotion dysregulation, which may also result in severe outcomes for individuals, families, and societies. There is no consensus on a conceptual definition of emotion dysregulation, and little prior literature on the specific relation between dysregulation and injury in the transition to adolescence. Methods: The current study aims to identify latent factors of emotion dysregulation using exploratory factor analyses. Subsequently, multilevel regressions illuminate relations between dysregulation and injury at 2 late childhood and early adolescence time points in a large ethnically, socioeconomically, and regionally representative sample of Arizona twins recruited from birth records and ongoing efforts. Results: 6 total factors representing emotion dysregulation at 2 ages were created. Factors were valid when tested against temperament and psychopathology constructs. No significant longitudinal or cross-sectional associations between emotion dysregulation factors and unintentional injury were found. Sex and rurality differences were found in factor scores and dysregulation outcomes. Discussion: The current study highlights new avenues of research and funding. Future research on this topic should reflect a concentrated and nuanced focus on injury. Concordant age 9 and age 11 factors loaded differently, which urges the field to strive toward developing a standardized definition for emotion dysregulation. Covariate differences highlight target populations for interventions in unintentional injury and emotion dysregulation, which remain independent areas of concern.