ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
This dissertation develops a framework for the analysis of fiscal sustainability among U.S. local governments. Fiscal sustainability is defined as a type of fiscal condition that allows a government to continue service provision now and in the future without introducing disruptive revenue or expenditure patterns. An assessment of local fiscal sustainability is based on three types of indicators: pension liability funding, debt burden, and budgetary balance. Three main factors affect a government's long-term financial condition: government structure, financial structure and performance, and local economic base. This dissertation uses a combination of the U.S. Census Bureau Annual Survey of Government Finances and Employment, the U.S. Census Bureau Decennial Census, the Bureau of Labor Statistics data, and the Government Finance Officers Association financial indicators database to study the effects of the three factors on local fiscal sustainability. It is a pioneer effort to use government-wide accounting information from Comprehensive Annual Financial Reports to predict local fiscal sustainability status. The results of econometric models suggest that pension liability funding is most affected by the size of government, debt burden is most strongly associated with the size of local economic base; and budgetary balance is influenced by the degree of local own-source revenue diversification.
ContributorsGorina, Evgenia (Author) / Chapman, Jeffrey I. (Thesis advisor) / Herbst, Chris M. (Committee member) / Miller, Gerald J (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation is an exploratory study that examined the differences in perceptions about supply chain management strategy, topics, tools, and techniques between procurement professionals in public and private sector organizations. This was accomplished through a survey of procurement professionals in a Fortune 500 company and a municipality in Arizona. The data were analyzed to understand how perceptions of supply chain management differed within this sample and whether the differences in perceptions were associated with formal education levels. Key findings indicate that for this or similar samples, public procurement respondents viewed their organizations' approach to supply chain management as a narrow function within purchasing while private sector respondents viewed their organization's approach to supply chain management as a strategic purchasing perspective that requires the coordination of cross functional areas. Second, public procurement respondents reported consistent and statistically significant lower levels of formal education than private sector respondents. Third, the supply chain management topics, tools, and techniques seem to be more important to private sector respondents than the public sector respondents. Finally, Respondents in both sectors recognize the importance of ethics and ethical behavior as an essential part of supply chain management.
ContributorsHeller, Jacob (Author) / Cayer, Joseph (Thesis advisor) / Lan, Gerald (Committee member) / Eden, Catherine (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that regard, this study proposes alternative ways to rank teacher effects that are not dependent on a given model by introducing two variable importance measures (VIMs), the node-proportion and the covariate-proportion. These VIMs are novel because they take into account the final configuration of the terminal nodes in the constitutive trees in a random forest. In a simulation study, under a variety of conditions, true rankings of teacher effects are compared with estimated rankings obtained using three sources: the newly proposed VIMs, existing VIMs, and EBLUPs from the assumed linear model specification. The newly proposed VIMs outperform all others in various scenarios where the model was misspecified. The second study develops two novel interaction measures. These measures could be used within but are not restricted to the VAM framework. The distribution-based measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. In turn, the mean-based measure is built to estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a separate simulation study, under a variety of conditions, the proposed measures are found to identify and estimate second-order interactions.
ContributorsValdivia, Arturo (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
A void exists in public administration, criminology, and criminal justice research as it relates to the study of power in American policing agencies. This has significant ramifications for academia and practitioners in terms of how they view, address, study, and interpret behaviors/actions in American policing agencies and organizations in general. In brief, mainstream research on power in organizations does not take into account relationships of power that do not act directly, and immediately, on others. By placing its emphasis on an agency centric perspective of power, the mainstream approach to the study of power fails to recognize indirect power relationships that influence discourse, pedagogy, mechanisms of communication, knowledge, and individual behavior/actions. In support of a more holistic inquiry, this study incorporates a Foucauldian perspective of power along with an ethnographical methodology and methods to build a greater understanding of power in policing organizations. This ethnography of an American policing organization illuminates the relationship between the exercise of power and the objectification of the subject through the interplay of relationships of communication, goal oriented activities, and relationships of power. Specifically, the findings demonstrate that sworn officers and civilian employees are objectified distinctly and dissimilarly. In summary, this study argues that the exercise of power in this American policing organization objectifies the civilian employee as a second class citizen.
ContributorsBentley, Paul C (Author) / Catlaw, Thomas (Thesis advisor) / Musheno, Michael (Committee member) / Lucio, Joanna (Committee member) / Arizona State University (Publisher)
Created2013
Description
In many respects, the current public child welfare system closely resembles that of over 100 years ago. Then, as well as now, nonprofit child welfare agencies are the critical providers of service delivery to vulnerable children and their families. Contemporary nonprofits, however, are confronted with social and fiscal pressures to conform to normative practices and behaviors of governmental and for-profit organizations. Simultaneously, these agencies may also feel compelled to behave in accordance with a nonprofit normative ethic. Yet, scholars and practitioners are often unaware of how these different forces may be shaping the practices of child welfare agencies and, the nonprofit sector in general. This multi-paper dissertation examines how managerial and organizational practices of child welfare nonprofits are influenced business, government, and other nonprofit organizations and the extent to which processes process of institutional isomorphism in child welfare nonprofits are happening. Data was collected from a national ample of 184 child welfare administrators to explore marketization practices, collaboration behaviors, and managerial priorities of these agencies. Multinomial logistic, ordered logistic, and ordinary least squares regression, and historical analysis help shed light on the contemporary practices of these agencies. The results reveal that these agency's behaviors are shaped by government control, influences from the business community, identification with a nonprofit mindset (i.e., nonprofitness), funding streams, and various other factors. One key finding is that identification with a nonprofit mindset encourages certain behaviors like collaboration with other nonprofits and placing greater importance on key managerial priorities, but it does not reduce the likelihood of adopting business management strategies. Another important finding is that government control and funding does not have as strong as an influence on child welfare nonprofits as expected; however, influence from the business community does strongly affect many of their practices. The implications of these findings are discussed for child welfare agencies and the nonprofit sector in general. The consequences of nonprofits operating similarly to business and government are considered.
ContributorsRobichau, Robbie Waters (Author) / Catlaw, Thomas (Thesis advisor) / Nahavandi, Afsaneh (Committee member) / Gustavsson, Nora (Committee member) / Wang, Lili (Committee member) / Arizona State University (Publisher)
Created2013
Description
In the United States, under the provisions set forth by a policy known as community benefit, nonprofit hospitals receive special tax exemptions from government in exchange for providing a wide range of health care services to the communities in which they are located. In recent years, nonprofit hospitals have claimed billions of dollars as community benefit justifying their tax-exempt status. However, growing criticism by numerous stakeholders has questioned the extent to which the level of community benefit claimed by nonprofit hospitals reflects the exemptions they receive. In addition, a dearth of research exists to understand the relationship between community benefit claims and the impact they have on improving the health of communities. In an effort to better understand the relationship between community benefit claims, tax status, and community health outcomes this study examines the community benefit policies of a nonprofit healthcare system representing hospitals in California, Nevada, and Arizona. It does so by reviewing materials produced by the system, her hospitals, vested stakeholders, and government that have shaped the development, implementation, and assessment of community benefit policy processes. Findings of the study suggest that the majority of nonprofit hospital community benefit claims are consumed by shortfalls reported between costs associated with providing care to Medicare and Medicaid patients and the compensation nonprofit hospitals receive from government. Results of the study also demonstrate that community benefit policies do positively impact the health of communities. However, future community benefit policies need to be refined to include measures that capture the magnitude of community health improvement if the relationship between policy and health outcomes is to be fully realized.
ContributorsMartz, Mark Patrick (Author) / Cayer, Joseph (Thesis advisor) / Glaser, Mark (Committee member) / Corley, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation provides a critical analysis of public administration's understanding of the relationship between rational thought and action in its discourse on ethics. It argues that rationalist ethics assume a particular relationship between thought and action: that good knowledge leads to good, proper action. While there have been many critiques of rationalist administrative ethics, scholars have not examined the way in which rationalism persists in the way in which the teaching of ethics is conducted. The use of the case study figures prominently in this. Thus, the dissertation explores the historical and theoretical intersection of rationalism, ethics, and teaching through the lens of the case study. It begins with a history of the pedagogical use of the case study and the institutional transformations of the university. While conventional accounts of the field locate its founding in the United States in the municipal reform movement, here the founding of the field of public administration is recast through connections to reforms in the university including changes in epistemic assumptions, pedagogical methods, and curricular changes in ethics in which the case study is central and remains so as the field develops. The dissertation then considers scholarship in public administration that raises questions about rationalist ethics. Three critical approaches are explored: recognition of the uncertainty and complexity of administrative practice, critique as unmasking of power relationships, and the shift of ethics from an epistemological to an ontological inquiry. The dissertation builds on the work in this third approach and shows how it attempts to articulate a non-rationalist, or immanent, ethics. This ethics is concerned with exploring the conditions that make possible mutually beneficial relationships and meaningful lives from which categorical norms of the good life could emerge. Drawing on the philosophy of Gilles Deleuze and Felix Guattari, it is argued that the distinction Deleuze and Guattari make between "arborescent" and "rhizomatic" knowledge gets to the root of the tension between thought and action and offers an innovative and useful way to advance an immanent, non-rational ethics. The challenge digital technologies and the information society present to the field is considered to illustrate the need to rethink administrative ethics and also the particular usefulness of Deleuze and Guattari in doing so. The dissertation concludes with a discussion of pedagogical practices and classroom examples that encourage a rhizomatic understanding of the theory and practice of public administration.
ContributorsCallen, Jeffrey Craig (Author) / Catlaw, Thomas J (Thesis advisor) / Corley, Elizabeth (Committee member) / Kim, Yushim (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation examines the role that business counselors in a public entrepreneurial development program play in improving the Entrepreneurial Specific Human Capital (ESHC) of nascent and active entrepreneurs. Through multiple research methodologies, this study identifies the types of ESHC provided by business counselors then compares them to the types of ESHC commonly accepted as necessary for entrepreneurial success. The comparison reveals a number of insights for policy and research, most notably a minimum portfolio of skills necessary for entrepreneurial success. This study also examines the methods counselors use to help entrepreneurs acquire higher levels of ESHC. These methods allow counselors to assist entrepreneurs in recognizing and overcoming common barriers to business growth, and a model of entrepreneurial business growth barriers has been produced which depicts these barriers as conceptual-operational transition points for the entrepreneur. Additionally, this dissertation develops important information about the use of the business plan in entrepreneurial development, and uncovers a number of moderators in the relationship between the use of the business plan and entrepreneurial success. Finally, the study produces detailed information about ESHC which has potential for scale development, and highlights a number of insights for policy and research that have not been identified previously.
ContributorsDahlstrom, Timothy R (Author) / Chapman, Jeffrey I. (Thesis advisor) / Phillips, Rhonda (Committee member) / Knopf, Richard (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013