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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Description
Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias in results if the missing mechanism is not taken into account. In other words, the missing mechanism is related to the unobserved responses. Data are said to be non-ignorable missing if the probabilities of missingness depend on quantities that might not be included in the model. Classical pattern-mixture models for non-ignorable missing values are widely used for longitudinal data analysis because they do not require explicit specification of the missing mechanism, with the data stratified according to a variety of missing patterns and a model specified for each stratum. However, this usually results in under-identifiability, because of the need to estimate many stratum-specific parameters even though the eventual interest is usually on the marginal parameters. Pattern mixture models have the drawback that a large sample is usually required. In this thesis, two studies are presented. The first study is motivated by an open problem from pattern mixture models. Simulation studies from this part show that information in the missing data indicators can be well summarized by a simple continuous latent structure, indicating that a large number of missing data patterns may be accounted by a simple latent factor. Simulation findings that are obtained in the first study lead to a novel model, a continuous latent factor model (CLFM). The second study develops CLFM which is utilized for modeling the joint distribution of missing values and longitudinal outcomes. The proposed CLFM model is feasible even for small sample size applications. The detailed estimation theory, including estimating techniques from both frequentist and Bayesian perspectives is presented. Model performance and evaluation are studied through designed simulations and three applications. Simulation and application settings change from correctly-specified missing data mechanism to mis-specified mechanism and include different sample sizes from longitudinal studies. Among three applications, an AIDS study includes non-ignorable missing values; the Peabody Picture Vocabulary Test data have no indication on missing data mechanism and it will be applied to a sensitivity analysis; the Growth of Language and Early Literacy Skills in Preschoolers with Developmental Speech and Language Impairment study, however, has full complete data and will be used to conduct a robust analysis. The CLFM model is shown to provide more precise estimators, specifically on intercept and slope related parameters, compared with Roy's latent class model and the classic linear mixed model. This advantage will be more obvious when a small sample size is the case, where Roy's model experiences challenges on estimation convergence. The proposed CLFM model is also robust when missing data are ignorable as demonstrated through a study on Growth of Language and Early Literacy Skills in Preschoolers.
ContributorsZhang, Jun (Author) / Reiser, Mark R. (Thesis advisor) / Barber, Jarrett (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St Louis, Robert D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sustainable development in an American context implies an ongoing shift from quantitative growth in energy, resource, and land use to the qualitative development of social-ecological systems, human capital, and dense, vibrant built environments. Sustainable urban development theory emphasizes locally and bioregionally emplaced economic development where the relationships between people, localities, products, and capital are tangible to and controllable by local stakeholders. Critical theory provides a mature understanding of the political economy of land development in capitalist economies, representing a crucial bridge between urban sustainability's infill development goals and the contemporary realities of the development industry. Since its inception, Phoenix, Arizona has exemplified the quantitative growth paradigm, and recurring instances of land speculation, non-local capital investment, and growth-based public policy have stymied local, tangible control over development from Phoenix's territorial history to modern attempts at downtown revitalization. Utilizing property ownership and sales data as well as interviews with development industry stakeholders, the political economy of infill land development in downtown Phoenix during the mid-2000s boom-and-bust cycle is analyzed. Data indicate that non-local property ownership has risen significantly over the past 20 years and rent-seeking land speculation has been a significant barrier to infill development. Many speculative strategies monopolize the publicly created value inherent in zoning entitlements, tax incentives and property assessment, indicating that political and policy reforms targeted at a variety of governance levels are crucial for achieving the sustainable development of urban land. Policy solutions include reforming the interconnected system of property sales, value assessment, and taxation to emphasize property use values; replacing existing tax incentives with tax increment financing and community development benefit agreements; regulating vacant land ownership and deed transfers; and encouraging innovative private development and tenure models like generative construction and community land trusts.
ContributorsStanley, Benjamin W (Author) / Boone, Christopher G. (Thesis advisor) / Redman, Charles (Committee member) / Bolin, Robert (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
During the months from June to November 2012, the city of Bangalore was faced with a serious solid waste management (SWM) crisis. In the wake of the upheaval, the state court declared source segregation to be mandatory. Yet, while the legislation was clear, the pathway towards a course of action for the transition was not clear and hence, Bangalore was stuck in a state of limbo. The objectives for this thesis spiraled organically from this crisis. The first objective was to examine the gaps in Bangalore's transition to a more sustainable SWM system. Six particular gaps were identified, which in essence, were opportunities to re-shape the system. The gaps identified included: conflicting political agendas, the exclusion of some key actors, and lack of adequate attention to cultural aspects, provision of appropriate incentives, protection of livelihoods and promotion of innovation. Opportunities were found in better incentivization of sustainable SWM goals, protecting livelihoods that depend on waste, enhancing innovation and endorsing local, context based SWM solutions. Building on this understanding of gaps, the second objective was to explore an innovative, local, bottom-up waste-management model called the Vellore Zero Waste Model, and assess its applicability to Bangalore. The adaptability of the model depended on several factors such as, willingness of actors to redefine their roles and change functions, ability of the municipality to assure quality and oversight, willingness of citizen to source segregate, and most importantly, the political will and collective action needed to ensure and sustain the transition. The role of communication as a vital component to facilitate productive stakeholder engagement and to promote role change was evident. Therefore, the third objective of the study was to explore how interpersonal competencies and communication strategies could be used as a tool to facilitate stakeholder engagement and encourage collective action. In addressing these objectives, India was compared with Austria because Austria is often cited as having some of the best SWM practices in the world and has high recycling rates to show for its reputation.
ContributorsRengarajan, Nivedita (Author) / Aggarwal, Rimjhim (Thesis advisor) / Chhetri, Nalini (Committee member) / Manuel-Navarrete, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
Mixed-income housing policy has been an approach to address the problem of concentrated poverty since the 1990s. The idea of income mix in housing is founded on the proposition that economic opportunities of the poor can be expanded through the increasing of their social capital. The current in-depth case study of Vineyard Estates, a mixed-income housing development in Phoenix, AZ tests a hypothesis that low-income people improve their chances of upward social mobility by building ties with more affluent residents within the development. This study combines qualitative and quantitative methods to collect and analyze information including analysis of demographic data, resident survey and in-depth semi-structured interviews with residents, as well as direct observations. It focuses on examining the role of social networks established within the housing development in generating positive economic outcomes of the poor. It also analyzes the role of factors influencing interactions across income groups and barriers to upward social mobility. Study findings do not support that living in mixed-income housing facilitates residents' upward social mobility. The study concludes that chances of upward social mobility are restrained by structural factors and indicates a need to rethink the effectiveness of mixed-income housing as an approach for alleviating poverty.
ContributorsDurova, Aleksandra (Author) / Kamel, Nabil (Committee member) / Pfeiffer, Deirdre (Committee member) / Lucio, Joanna (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sustainable urbanism offers a set of best practice planning and design prescriptions intended to reverse the negative environmental consequences of urban sprawl, which dominates new urban development in the United States. Master planned developments implementing sustainable urbanism are proliferating globally, garnering accolades within the planning community and skepticism among social scientists. Despite attention from supporters and critics alike, little is known about the actual environmental performance of sustainable urbanism. This dissertation addresses the reasons for this paucity of evidence and the capacity of sustainable urbanism to deliver the espoused environmental outcomes through alternative urban design and the conventional master planning framework for development through three manuscripts. The first manuscript considers the reasons why geography, which would appear to be a natural empirical home for research on sustainable urbanism, has yet to accumulate evidence that links design alternatives to environmental outcomes or to explain the social processes that mediate those outcomes. It argues that geography has failed to develop a coherent subfield based on nature-city interactions and suggests interdisciplinary bridging concepts to invigorate greater interaction between the urban and nature-society geographic subfields. The subsequent chapters deploy these bridging concepts to empirically examine case-studies in sustainable urbanism. The second manuscript utilizes fine scale spatial data to quantify differences in ecosystem services delivery across three urban designs in two phases of Civano, a sustainable urbanism planned development in Tucson, Arizona, and an adjacent, typical suburban development comparison community. The third manuscript considers the extent to which conventional master planning processes are fundamentally at odds with urban environmental sustainability through interviews with stakeholders involved in three planned developments: Civano (Tucson, Arizona), Mueller (Austin, Texas), and Prairie Crossing (Grayslake, Illinois). Findings from the three manuscripts reveal deep challenges in conceptualizing an empirical area of inquiry on sustainable urbanism, measuring the outcomes of urban design alternatives, and innovating planning practice within the constraints of existing institutions that facilitate conventional development. Despite these challenges, synthesizing the insights of geography and cognate fields holds promise in building an empirical body of knowledge that complements pioneering efforts of planners to innovate urban planning practice through the sustainable urbanism alternative.
ContributorsTurner, Victoria (Author) / Gober, Patricia (Thesis advisor) / Eakin, Hallie (Committee member) / Kinzig, Ann (Committee member) / Talen, Emily (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of items, may even render them unusable for some small-scale assessments. Other measures designed to assess dimensionality do not come with such limitations and, as such, may perform better under conditions that do not lend themselves to evaluation via statistics that rely on asymptotic theory. The current work aimed to evaluate the performance of one such metric, the standardized generalized dimensionality discrepancy measure (SGDDM; Levy & Svetina, 2011; Levy, Xu, Yel, & Svetina, 2012), under both large- and small-scale testing conditions. A Monte Carlo study was conducted to compare the performance of DIMTEST and the SGDDM statistic in terms of evaluating assumptions of unidimensionality in item response data under a variety of conditions, with an emphasis on the examination of these procedures in small-scale assessments. Similar to previous research, increases in either test length or sample size resulted in increased power. The DIMTEST procedure appeared to be a conservative test of the null hypothesis of unidimensionality. The SGDDM statistic exhibited rejection rates near the nominal rate of .05 under unidimensional conditions, though the reliability of these results may have been less than optimal due to high sampling variability resulting from a relatively limited number of replications. Power values were at or near 1.0 for many of the multidimensional conditions. It was only when the sample size was reduced to N = 100 that the two approaches diverged in performance. Results suggested that both procedures may be appropriate for sample sizes as low as N = 250 and tests as short as J = 12 (SGDDM) or J = 19 (DIMTEST). When used as a diagnostic tool, SGDDM may be appropriate with as few as N = 100 cases combined with J = 12 items. The study was somewhat limited in that it did not include any complex factorial designs, nor were the strength of item discrimination parameters or correlation between factors manipulated. It is recommended that further research be conducted with the inclusion of these factors, as well as an increase in the number of replications when using the SGDDM procedure.
ContributorsReichenberg, Ray E (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Canal oriented development (COD) is a placemaking concept that aims to create mixed use developments along canal banks using the image and utility of the waterfront as a natural attraction for social and economic activity. COD has the potential to for landlocked cities, which are lacking a traditional harbor, to pursue waterfront development which has become an important economic development source in the post-industrial city. This dissertation examines how COD as a placemaking technique can and has been used in creating urban development. This topic is analyzed via three separate yet interconnecting papers. The first paper explores the historical notion of canals as an urban economic development tool with particular attention paid to the Erie Canal. The second paper explores the feasibility of what it would take for canal development to occur in the Phoenix region. The third and final paper explores the importance of place in urban design and the success or nonsuccess of COD as a place maker through the examination of three different CODs.
ContributorsBuckman, Stephen Thomas (Author) / Talen, Emily (Thesis advisor) / Ellin, Nan (Committee member) / Crewe, Katherine (Committee member) / Arizona State University (Publisher)
Created2013