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.
Displaying 41 - 50 of 86
Description
Depression, anxiety, and suicidal thoughts or actions are on the rise in adolescents (National Institute of Mental Health, 2015; Bridge, Asti, & Horowitz, 2015). Parents, school administrators, and therapists are searching for resiliency factors with in at-risk groups to aid students in need. In previous work, Luthar and Zigler (1992) reported that intelligent youth are more resilient than less intelligent youth under low stress conditions but they lose their advantage under high stress conditions. This study examined whether intelligence (reflected in grade point average; GPA) and maladaptive (internalizing and externalizing symptoms) behaviors are negatively related in adolescents, and tested whether level of stress, reflected in emotion regulation and friendship quality, moderated that association. It also probed whether the relationships differ by gender. Sixth-graders (N=506) were recruited with active parental consent from three middle schools. Adolescents completed self-report questionnaires Regarding demo graphics, maladaptive behaviors, emotion regulation, and friendship quality, and GPA data were collected from the school. Regression analyses found that GPA was negatively related to externalizing symptoms. Girls with poor friendship communication report significantly higher maladaptive behaviors. This relation was more pronounced for girls with high GPAs, as predicted. Results support the theory that intelligent female adolescents are more reactive under adverse circumstances. Future efforts should follow students through middle school into high school to evaluate whether friendships remain important to adjustment, hold for boys as well as girls, and have implications for relationship interventions.
ContributorsGonzales, Ashlyn Carol (Author) / Luthar, Suniya (Thesis director) / Davis, Mary (Committee member) / Infurna, Frank (Committee member) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Emotions have been defined as coordinated and functional changes in subjective experience, motivation, physiological activation, instrumental behavior, expressive behavior, and cognition that are evoked by important threats or opportunities in the environment. The proposed study looks at cognitive changes associated with the experience of several positive emotions, with a specific focus on awe. Prior research shows that positive emotions tend to increase people's use of cognitive heuristics (i.e. mental shortcuts used to simplify information we intake from the environment) and changes how they apply rules of thumb from stored knowledge to make decisions. Stereotypes, or assumptions about the characteristics held by individual members of a group, are one such heuristic. Awe, in contrast to other positive emotions, has been found to reduce people's tendency to rely on heuristics, rather than increasing its use. Thus, awe should tend to reduce stereotyping specifically. Participants made judgments on three characteristics and two types of theoretically valuable true/false statements. However, for both our measures, awe had no significant effect on stereotyping. Participants in the enthusiasm condition were significantly more likely than those in the awe condition to correctly identify stereotype-inconsistent statements present in the biography, which is the opposite of the predicted direction. Patterns for all four emotion conditions trended similarly to our predictions for stereotype-consistent statements correctly marked as being absent in the biography. There were no significant differences in ratings of three traits. Implications for enthusiasm and awe are discussed in the context of stereotypes of social objects and schemas of nonsocial objects.
ContributorsMurwin, Paige Elizabeth (Co-author) / O'Neil, Makenzie (Co-author) / Shiota, Michelle (Thesis director) / Davis, Mary (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The frontostriatal reward circuit serves an underlying role in reward processing, cognitive planning, and motor control in the context of achieving a goal. Furthermore, research suggests a relationship between the reward circuits and behavior expressed in Attention Deficit Hyperactivity Disorder (ADHD); however, the specific structural differences of the reward circuits in those with ADHD remain ambiguous. Diffusion tensor imaging (DTI) techniques were used to analyze diffusion weighted magnetic resonance imaging (DWI) data in order to examine the structural connectivity of frontostriatal reward pathways in ADHD adolescents compared to typically developing (TD) adolescents. It was hypothesized that measures of impulsivity would be predicted by white matter tract integrity measures in frontostriatal tracts related to affective processing (ventromedial prefrontal cortex to ventral striatum, vmPFC) in adolescents with ADHD, and that there would be reduced tract integrity in tracts related to executive control (dorsolateral prefrontal and anterior cingulate cortex—dlPFC and ACC, respectively). Frontostriatal tracts as well as the hippocampus and amygdala were examined in relation to age and impulsivity using both correlation and regression models. Results indicated that impulsivity declined with age in the TD group while no significant trend was identified for the ADHD group. The hypotheses were not supported and results for both predictions on the affective and executive circuits showed opposite trends from what was expected.
ContributorsHarrison, Sydney Rae (Author) / McClure, Samuel (Thesis director) / Brewer, Gene (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Posttraumatic Stress Disorder (PTSD) affects nearly 10% of adult women in general population samples. In populations of impoverished ethnic minority women, those lifetime prevalence rates may possibly exceed national averages due to lack of mental health resources. Mothers with PTSD are more likely to exhibit negative parenting styles and experience higher levels of perceived parenting stress, both of which are associated with poor child outcomes. However, there is a lack of evidence on how maternal PTSD may affect parenting for ethnic minority mothers. This study evaluated the prevalence of lifetime PTSD and its effects on parenting stress and infant problem behaviors in a sample of 322 low-income Mexican-American mothers (mean age = 27.8; 86% born in Mexico). Lifetime PTSD diagnoses were assessed at a prenatal home visit (24-36 weeks gestation) using the WHO Composite International Diagnostic Interview (CIDI). Mothers reported parenting hassles at 24-weeks postpartum (PDLH; Crnic & Greenberg, 1990), and child problem behaviors at infant age one-year (BITSEA; Briggs-Gowan et al., 2004). I hypothesized that 1) women with PTSD would report more parenting stress than women without PTSD, 2) women with PTSD would report more infant problem behavior symptoms than women without PTSD, and 3) parenting stress mediates the relationship between PTSD and infant problem behavior. Results found that 16.5% of women met criteria for past or present PTSD. Compared to women without PTSD, women with PTSD reported more parenting stress but a similar level of infant problem behaviors. Parenting stress significantly mediated the relationship between maternal PTSD and infant problem behaviors. Study findings suggest a need for mental health screenings during prenatal care in order to promote the healthy development of high-risk children.
ContributorsPreves, Ashley Maria (Author) / Luecken, Linda (Thesis director) / Davis, Mary (Committee member) / Mauricio, Anne (Committee member) / Department of Psychology (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This paper explores the idea of xenophilia and the circumstances under which it may occur. Xenophilia is the preference for an outgroup member over an ingroup member. This preference does not have to be amicable, and in fact can be exploitative under certain circumstances. Previous research indicates that xenophobia is much more common, but a few researchers have found support for the existence of xenophilia. To experimentally test the circumstances under which xenophilia might occur, I conducted a survey-based experiment on Amazon’s Mechanical Turk. This consisted of directed visualizations that manipulated participant goal (self-protection vs. mate acquisition) and the resources offered by both a fictitious outgroup and the hometown ingroup, followed by measures of ingroup/outgroup preference. I hypothesized that when the resource offered by the group addressed the participants’ goal, they would prefer the group with the “matched” resource—even if it was the outgroup providing that resource. My hypothesis was not supported, as the univariate analysis of variance for preference for the outgroup was not significant, F (2, 423) = .723, p = .486. This may have occurred because the goal manipulations were not strong enough to counteract the strong natural preference for ingroup members.
ContributorsDrury, Margaret E. (Author) / Neuberg, Steven (Thesis director) / Davis, Mary (Committee member) / Kenrick, Douglas (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three enddiastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.
ContributorsShin, Jaeyul (Author) / Liang, Jianming (Thesis advisor) / Maciejewski, Ross (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Maciejewski, Ross (Committee member) / Craig, Scotty D. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Testing and Verification of Cyber-Physical Systems (CPS) is a challenging problem. The challenge arises as a result of the complex interactions between the components of these systems: the digital control, and the physical environment. Furthermore, the software complexity that governs the high-level control logic in these systems is increasing day by day. As a result, in recent years, both the academic community and the industry have been heavily invested in developing tools and methodologies for the development of safety-critical systems. One scalable approach in testing and verification of these systems is through guided system simulation using stochastic optimization techniques. The goal of the stochastic optimizer is to find system behavior that does not meet the intended specifications.
In this dissertation, three methods that facilitate the testing and verification process for CPS are presented:
1. A graphical formalism and tool which enables the elicitation of formal requirements. To evaluate the performance of the tool, a usability study is conducted.
2. A parameter mining method to infer, analyze, and visually represent falsifying ranges for parametrized system specifications.
3. A notion of conformance between a CPS model and implementation along with a testing framework.
The methods are evaluated over high-fidelity case studies from the industry.
In this dissertation, three methods that facilitate the testing and verification process for CPS are presented:
1. A graphical formalism and tool which enables the elicitation of formal requirements. To evaluate the performance of the tool, a usability study is conducted.
2. A parameter mining method to infer, analyze, and visually represent falsifying ranges for parametrized system specifications.
3. A notion of conformance between a CPS model and implementation along with a testing framework.
The methods are evaluated over high-fidelity case studies from the industry.
ContributorsHoxha, Bardh (Author) / Fainekos, Georgios (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Maciejewski, Ross (Committee member) / Ben Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2017
Description
A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation.
The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.
The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.
ContributorsKolak, Marynia Aniela (Author) / Anselin, Luc (Thesis advisor) / Rey, Sergio (Committee member) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2017
Description
Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
ContributorsMorstatter, Fred (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Maciejewski, Ross (Committee member) / Carley, Kathleen M. (Committee member) / Arizona State University (Publisher)
Created2017