Matching Items (134)
Description
Arizona and Florida are unique venues are they are the only two locations in the world to host the preseason leagues known as Spring Training for all thirty Major League Baseball teams. With fan bases willing to travel and spend disposable income to follow their favorite teams and/or escape the cold spells of their home state, the sports and tourism industries in Arizona and Florida have been able to captivate a status as top spring destinations. This study takes a focus on the economic impact that Spring Training in March has on the state of Arizona; specifically the Phoenix Metropolitan area. Consumer research is presented and a SWOT analysis is generated to further assess the condition of the Cactus League and Arizona as a host state. An economic impact study driven by the Strengths, Weaknesses, Opportunities & Threats (SWOT) analysis method is the primary focuses of research due to the sum and quality of usable data that can be organized using the SWOT structure. The scope of this research aims to support the argument that Spring Training impacts the host city in which it resides in. In conjunction with the SWOT analysis, third parties will be able to get a sense of the overall effectiveness and impact of Cactus League Spring Training in the Valley of the Sun. Integration of findings from a Tampa Bay sight visit will also be assessed to determine the health of the competition. This study will take an interdisciplinary approach as it views the topics at hand from the lenses of the consumer, baseball professional, and investor.
ContributorsOlden, Kyle (Co-author) / Farmer, James (Co-author) / Eaton, John (Thesis director) / Mokwa, Michael (Committee member) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / Department of Information Systems (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This study investigates how the patient-provider relationship between lesbian, gay, and bisexual women and their healthcare providers influences their access to, utilization of, and experiences within healthcare environments. Nineteen participants, ages 18 to 34, were recruited using convenience and snowball sampling. Interviews were conducted inquiring about their health history and their experiences within the healthcare system in the context of their sexual orientation. The data collected from these interviews was used to create an analysis of the healthcare experiences of those who identify as queer. Although the original intention of the project was to chronicle the experiences of LGB women specifically, there were four non-binary gender respondents who contributed interviews. In an effort to not privilege any orientation over another, the respondents were collectively referred to as queer, given the inclusive and an encompassing nature of the term. The general conclusion of this study is that respondents most often experienced heterosexism rather than outright homophobia when accessing healthcare. If heterosexism was present within the healthcare setting, it made respondents feel uncomfortable with their providers and less likely to inform them of their sexuality even if it was medically relevant to their health outcomes. Gender, race, and,socioeconomic differences also had an effect on the patient-provider relationship. Non-binary respondents acknowledged the need for inclusion of more gender options outside of male or female on the reporting forms often seen in medical offices. By doing so, medical professionals are acknowledging their awareness and knowledge of people outside of the binary gender system, thus improving the experience of these patients. While race and socioeconomic status were less relevant to the context of this study, it was found that these factors have an affect on the patient-provider relationship. There are many suggestions for providers to improve the experiences of queer patients within the healthcare setting. This includes nonverbal indications of acknowledgement and acceptance, such as signs in the office that indicate it to be a queer friendly space. This will help in eliminating the fear and miscommunication that can often happen when a queer patient sees a practitioner for the first time. In addition, better education on medically relevant topics to queer patients, is necessary in order to eliminate disparities in health outcomes. This is particularly evident in trans health, where specialized education is necessary in order to decrease poor health outcomes in trans patients. Future directions of this study necessitate a closer look on how race and socioeconomic status have an effect on a queer patient's relationship with their provider.
ContributorsRajan, Sindhu (Co-author) / Bradley, Arianna (Co-author) / Larson, Michelle (Co-author) / Alvarado, Aimee (Co-author) / Ingram-Waters, Mary (Thesis director) / Arnold, Michelle (Committee member) / School of Molecular Sciences (Contributor) / School of Social Transformation (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Netflix has positioned itself at the forefront of the future of television with its original programming, which has been rolled out in greater and more frequent amounts just in the last couple of years. The streaming service has already experimented with creativity in ways most other shows and creators haven't, playing with the pacing of overall seasons as well as the length of episodes. So, too, Netflix has been at the forefront of increasing visibility for minority characters on television. Many of its shows incorporate racially diverse casts and depict lots of LGBTQ characters, a refreshingly realistic view of the world that many of its viewers have always lived in but haven't yet witnessed on television. Visibility and representation are critical concepts for analyzing minority characters on television. It is important for diverse characters to be seen, first and foremost, but also to be seen in positive or at least realistic lights. Care must be taken to avoid fulfilling stereotypes or tropes, and attention must be paid to what has happened to other characters who have come before. However, many of Netflix's portrayals of these characters, particularly bisexual characters, leave much to be desired. With the original dramas House of Cards, Hemlock Grove, Orange is the New Black, and Sense8, all of which include characters who identify as or behave bisexually, Netflix has been reluctant to use the specific word bisexual to describe characters, and many don't even identify their sexuality with a synonym for the term. Many of the bisexual characters that I identified died or were killed on the shows, and nearly all of them fulfilled stereotypes or tropes in some way. There were multiple scenes of threesomes or other distinctly kinky sexual encounters, which served to exoticize bisexuality and distance it from the more normatively viewed identities of heterosexuality and homosexuality. Ultimately, while Netflix's original programming has offered increased visibility to bisexual characters, it has yet to reflect the real community it seeks to portray. In particular, Netflix's refusal to label characters as bisexual is frustrating and limiting. It can be argued that this is a progressive move toward more ideas of sexual fluidity and a post-modern lack of sexual labels, but there are not enough depictions of identified bisexual characters on television yet for this to make sense. Until bisexual characters and their identities are not invisibilized or stigmatized, more work has to be done to ensure that bisexual people are represented fairly and accurately on television and in all media.
ContributorsKoerth, Kimberly Marie (Author) / Himberg, Julia (Thesis director) / Sturges, Robert (Committee member) / Department of English (Contributor) / College of Public Service and Community Solutions (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Psychologists report effect sizes in randomized controlled trials to facilitate interpretation and inform clinical or policy guidance. Since commonly used effect size measures (e.g., standardized mean difference) are not sensitive to heterogeneous treatment effects, methodologists have suggested the use of an alternative effect size δ, a between-subjects causal parameter describing the probability that the outcome of a random participant in the treatment group is better than the outcome of another random participant in the control group. Although this effect size is useful, researchers could mistakenly use δ to describe its within-subject analogue, ψ, the probability that an individual will do better under the treatment than the control. Hand’s paradox describes the situation where ψ and δ are on opposing sides of 0.5: δ may imply most are helped whereas the (unknown) underlying ψ indicates that most are harmed by the treatment. The current study used Monte Carlo simulations to investigate plausible situations under which Hand’s paradox does and does not occur, tracked the magnitude of the discrepancy between ψ and δ, and explored whether the size of the discrepancy could be reduced with a relevant covariate. The findings suggested that although the paradox should not occur under bivariate normal data conditions in the population, there could be sample cases with the paradox. The magnitude of the discrepancy between ψ and δ depended on both the size of the average treatment effect and the underlying correlation between the potential outcomes, ρ. Smaller effects led to larger discrepancies when ρ < 0 and ρ = 1, whereas larger effects led to larger discrepancies when 0 < ρ < 1. It was useful to consider a relevant covariate when calculating ψ and δ. Although ψ and δ were still discrepant within covariate levels, results indicated that conditioning upon relevant covariates is still useful in describing heterogeneous treatment effects.
ContributorsLiu, Xinran (Author) / Anderson, Samantha F (Thesis advisor) / McNeish, Daniel (Committee member) / MacKinnon, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
Mediation analysis is integral to psychology, investigating human behavior’s causal mechanisms. The diversity of explanations for human behavior has implications for the estimation and interpretation of statistical mediation models. Individuals can have similar observed outcomes while undergoing different causal processes or different observed outcomes while receiving the same treatment. Researchers can employ diverse strategies when studying individual differences in multiple mediation pathways, including individual fit measures and analysis of residuals. This dissertation investigates the use of individual residuals and fit measures to identify individual differences in multiple mediation pathways. More specifically, this study focuses on mediation model residuals in a heterogeneous population in which some people experience indirect effects through one mediator and others experience indirect effects through a different mediator. A simulation study investigates 162 conditions defined by effect size and sample size for three proposed methods: residual differences, delta z, and generalized Cook’s distance. Results indicate that analogs of Type 1 error rates are generally acceptable for the method of residual differences, but statistical power is limited. Likewise, neither delta z nor gCd could reliably distinguish between contrasts that had true effects and those that did not. The outcomes of this study reveal the potential for statistical measures of individual mediation. However, limitations related to unequal subpopulation variances, multiple dependent variables, the inherent relationship between direct effects and unestimated indirect effects, and minimal contrast effects require more research to develop a simple method that researchers can use on single data sets.
ContributorsSmyth, Heather Lynn (Author) / MacKinnon, David (Thesis advisor) / Tein, Jenn-Yun (Committee member) / McNeish, Daniel (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2022
Description
The fast pace of global urbanization makes cities the hotspots of population density and anthropogenic activities, leading to intensive emissions of heat and carbon dioxide (CO2), a primary greenhouse gas. Urban climate scientists have been actively seeking effective mitigation strategies over the past decades, aiming to improve the environmental quality for urban dwellers. Prior studies have identified the role of urban green spaces in the relief of urban heat stress. Yet little effort was devoted to quantify their contribution to local and regional CO2 budget. In fact, urban biogenic CO2 fluxes from photosynthesis and respiration are influenced by the microclimate in the built environment and are sensitive to anthropogenic disturbance. The high complexity of the urban ecosystem leads to an outstanding challenge for numerical urban models to disentangling and quantifying the interplay between heat and carbon dynamics.This dissertation aims to advance the simulation of thermal and carbon dynamics in urban land surface models, and to investigate the role of urban greening practices and urban system design in mitigating heat and CO2 emissions. The biogenic CO2 exchange in cities is parameterized by incorporating plant physiological functions into an advanced single-layer urban canopy model in the built environment. The simulation result replicates the microclimate and CO2 flux patterns measured from an eddy covariance system over a residential neighborhood in Phoenix, Arizona with satisfactory accuracy. Moreover, the model decomposes the total CO2 flux from observation and identifies the significant CO2 efflux from soil respiration. The model is then applied to quantify the impact of urban greening practices on heat and biogenic CO2 exchange over designed scenarios. The result shows the use of urban greenery is effective in mitigating both urban heat and carbon emissions, providing environmental co-benefit in cities. Furthermore, to seek the optimal urban system design in terms of thermal comfort and CO2 reduction, a multi-objective optimization algorithm is applied to the machine learning surrogates of the physical urban land surface model. There are manifest trade-offs among ameliorating diverse urban environmental indicators despite the co-benefit from urban greening. The findings of this dissertation, along with its implications on urban planning and landscaping management, would promote sustainable urban development strategies for achieving optimal environmental quality for policy makers, urban residents, and practitioners.
ContributorsLi, Peiyuan (Author) / Wang, Zhihua (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Myint, Soe (Committee member) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Concerns, such as global warming, greenhouse gas emissions, and changes in hydrological regimes, have been raised in response to the global ecosystem changes caused by humans. Understanding the ecosystem functions is crucial for assisting stakeholders in formulating viable plans to address the issues for a healthier planet. However, a systematic evaluation of recent environmental changes and current ecosystem status, focusing on terrestrial ecosystem carbon-water trade-off, in the Lower Mekong Basin (LMB) is lacking. This dissertation involves: (1) examining the long-term spatiotemporal patterns of ecosystem conditions in response to gains and losses of the forest; (2) evaluating the current consumptive water use variation across all biome and land use types with remotely sensed evapotranspiration (ET) products; (3) analyzing the trade-off between terrestrial carbon and water stress condition during the photosynthesis process in response to different climatic/ecosystem conditions, and (4) developing a spatial optimization model to effectively determine possible reforestation/afforestation options considering the balance between water conservation and carbon fluxes. These studies were conducted with many recently developed algorithms and satellite imagery. This dissertation makes significant contributions and expands the knowledge of the variation in water consumption and carbon assimilation within the ecosystem when different conditions are present. In addition, the spatial optimization model was applied to the entire region to formulate possible reforestation plans under different water-carbon tradeoff scenarios for the first time. The findings and results of this research can be used to provide constructive suggestions to policymakers, managers, planners, government officials, and any other stakeholders in LMB to formulate policies and guidelines for the environmentally responsible and sustainable development of LMB.
ContributorsLi, Yubin (Author) / Myint, Soe (Thesis advisor) / Tong, Daoqin (Thesis advisor) / Muenich, Rebecca (Committee member) / Schaffer-Smith, Danica (Committee member) / Arizona State University (Publisher)
Created2023
Description
Food insecurity is an economic and social condition involving limited or uncertain access to food. The problem of food insecurity in communities is influenced by economic conditions, food deserts, and barriers to accessing healthy food. Individuals experiencing food insecurity often endure concurrent problems of financial instability, hunger, and poor mental and physical health. Public and non-profit services in the U.S., such as the federally supported Supplemental Nutrition Assistance Program (SNAP) and community food banks, provide food-related assistance to individuals who are at a high risk of experiencing food insecurity. Unfortunately, many individuals who qualify for these services still experience food insecurity due to barriers preventing them from accessing food, which may include inadequate finances, transportation, skills, and information. Effective approaches for removing barriers that prevent individuals from accessing food are needed to mitigate the increased risk of hunger, nutritional deficiencies, and chronic disease among vulnerable populations. This dissertation tested a novel food insecurity intervention using informational nudges to promote food security through the elimination of information barriers to accessing food. The intervention used in this mixed-methods feasibility study consisted of informational nudges in the form of weekly text messages that were sent to food pantry clients experiencing food insecurity. The study aims were to test the efficacy and acceptability of the intervention by examining whether the informational nudges could enhance food pantry utilization, increase SNAP registration, and promote food security. Quantitative study results showed a lower prevalence of food insecurity in the intervention group than the control group. Qualitative findings revealed how the intervention group found the text messages to be helpful and informative. These study findings can enhance future food insecurity interventions aiming to eliminate barriers that prevent individuals who are food insecure from accessing healthy food.
ContributorsRoyer, Michael F. (Author) / Wharton, Christopher (Thesis advisor) / Buman, Matthew (Committee member) / Der Ananian, Cheryl (Committee member) / MacKinnon, David (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Arizona State University (Publisher)
Created2023
Description
Safe, readily available, and reliable sources of water are an essential component of any municipality’s infrastructure. Phoenix, Arizona, a southwestern city, has among the highest per capita water use in the United States, making it essential to carefully manage its reservoirs. Generally, municipal water bodies are monitored through field sampling. However, this approach is limited spatially and temporally in addition to being costly. In this study, the application of remotely sensed reflectance data from Landsat 7’s Enhanced Thematic Mapper Plus (ETM+) and Landsat 8’s Operational Land Imager (OLI) along with data generated through field-sampling is used to gain a better understanding of the seasonal development of algal communities and levels of suspended particulates in the three main terminal reservoirs supplying water to the Phoenix metro area: Bartlett Lake, Lake Pleasant, and Saguaro Lake. Algal abundances, particularly the abundance of filamentous cyanobacteria, increased with warmer temperatures in all three reservoirs and reached the highest comparative abundance in Bartlett Lake. Prymnesiophytes (the class of algae to which the toxin-producing golden algae belong) tended to peak between June and August, with one notable peak occurring in Saguaro Lake in August 2017 during which time a fish-kill was observed. In the cooler months algal abundance was comparatively lower in all three lakes, with a more even distribution of abundance across algae classes. In-situ data from March 2017 to March 2018 were compared with algal communities sampled approximately ten years ago in each reservoir to understand any possible long-term changes. The findings show that the algal communities in the reservoirs are relatively stable, particularly those of the filamentous cyanobacteria, chlorophytes, and prymnesiophytes with some notable exceptions, such as the abundance of diatoms, which increased in Bartlett Lake and Lake Pleasant. When in-situ data were compared with Landsat-derived reflectance data, two-band combinations were found to be the best-estimators of chlorophyll-a concentration (as a proxy for algal biomass) and total suspended sediment concentration. The ratio of the reflectance value of the red band and the blue band produced reasonable estimates for the in-situ parameters in Bartlett Lake. The ratio of the reflectance value of the green band and the blue band produced reasonable estimates for the in-situ parameters in Saguaro Lake. However, even the best performing two-band algorithm did not produce any significant correlation between reflectance and in-situ data in Lake Pleasant. Overall, remotely-sensed observations can significantly improve our understanding of the water quality as measured by algae abundance and particulate loading in Arizona Reservoirs, especially when applied over long timescales.
ContributorsRussell, Jazmine Barkley (Author) / Neuer, Susanne (Thesis advisor) / Fox, Peter (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2018
Description
Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019