Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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A Comparison of Hand and Computer Coding of Expressions of Emotion

Description
Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is…

Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is still new, Affectiva and its validity remain understudied, and no psychological research has been conducted to compare Affectiva computer coding and hand coding of children’s emotions. The purpose of this study was to compare hand and computer coding of children’s expressions of emotion during a videotaped parent-child interaction. The study answered the following questions: 1) Do hand and computer coding agree?; and 2) Are hand and computer coding in higher agreement for some emotions than others? The sample included 25 pairs of twins from the Arizona Twin Project. Facial expressions were coded from videotape by a trained and reliable human coder and using the software Affectiva. The results showed that hand and computer coded emotion were in agreement for positive, but not negative emotions. Changing the context of the interaction to elicit more negative emotion, and using the same indicators of each emotion in computer and hand coding are suggested to improve the comparison of computer and hand coding.
ContributorsKwok, Connie (Author) / Lemery-Chalfant, Kathryn (Thesis director) / Davis, Mary (Committee member) / Miadich, Samantha (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Ethnic Discrimination, Socioeconomic Status, and Health in India

Description

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other…

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.
ContributorsClauss, Colleen (Author) / Hruschka, Daniel (Thesis director) / Davis, Mary (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor) / Department of Psychology (Contributor)
Created2022-05