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
The connection between Hollywood costume design and the films of the 007/James Bond franchise, especially in regards to the changing perspective of the “Bond Girl”, is an intricate relationship that has previously been little researched. In the most recent Bond films, in particular, the female characters have become more powerful than the early characters and their roles within the narratives have changed with their characters taking on stronger and more integral roles. This thesis seeks to examine the films of the 007/James Bond franchise and how the rhetoric of the franchise’s costume design affects the representation of femininity and power in regards to the Bond Girls. After an overview of Bond history and costume theory, two films are analyzed as case studies: Dr. No (1962) which marks the beginning of the film franchise and Casino Royale (2006), which marks the more recent turn the films have taken. This thesis examines how the representations of Bond Girls and the use of costume design for their characters have changed over the course of the franchise from the days of Sean Connery to the recent reboot of the franchise with Daniel Craig as 007 James Bond. In addition to an examination of Bond Girl costume design, this thesis considers the role and influence of the costume designers. A designer’s vision of a character is derived from both the writing and the physical features of the actresses before them. Here this thesis considers how the rhetorical choices made by designers have contributed to an understanding of the relationship between femininity and power. Finally it shows how the costumes effect the power of the female characters and how the Bond Girls of today (Casino Royale) compare and/or contrast to Bond Girls of the past (Dr. No). This thesis combines the areas of feminist film theory and costume theory to provide an original rhetorical analysis of the Bond series in relation to costume design and examines the rhetorical statements made by the costume designers in their designs for the characters and how those statements influence the representations of the characters.
ContributorsSeverson, Andrea (Author) / Goggin, Maureen (Thesis advisor) / Ore, Ersula (Committee member) / Lamp, Kathleen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Simultaneously culture heroes and stumbling buffoons, Tricksters bring cultural tools to the people and make the world more habitable. There are common themes in these figures that remain fruitful for the advancement of culture, theory, and critical praxis. This dissertation develops a method for opening a dialogue with Trickster figures. It draws from established literature to present a newly conceived and more flexible Trickster archetype. This archetype is more than a collection of traits; it builds on itself processually to form a method for analysis. The critical Trickster archetype includes the fundamental act of crossing borders; the twin ontologies of ambiguity and liminality; the particular tactics of humor, duplicity, and shape shifting; and the overarching cultural roles of culture hero and stumbling buffoon. Running parallel to each archetypal element, though, are Trickster's overarching critical spirit of Quixotic utopianism and underlying telos of manipulating human relationships. The character 'Q' from Star Trek: The Next Generation is used to demonstrate the critical Trickster archetype. To be more useful for critical cultural studies, Trickster figures must also be connected to their socio-cultural and historical contexts. Thus, this dissertation offers a second set of analytics, a dialogical method that connects Tricksters to the worlds they make more habitable. This dialogical method, developed from the work of M. M. Bakhtin and others, consists of three analytical tools: utterance, intertextuality, and chronotope. Utterance bounds the text for analysis. Intertextuality connects the utterance, the text, to its context. Chronotope suggests particular spatio-temporal relationships that help reveal the cultural significance of a dialogical performance. Performance artists Andre Stitt, Ann Liv Young, and Steven Leyba are used to demonstrate the method of Trickster dialogics. A concluding discussion of Trickster's unique chronotope reveals its contributions to conceptions of utopia and futurity. This dissertation offers theoretical advancements about the significance and tactics of subversive communication practices. It offers a new and unique method for cultural and performative analyses that can be expanded into different kinds of dialogics. Trickster dialogics can also be used generatively to direct and guide the further development of performative praxis.
ContributorsSalinas, Chema (Author) / de la Garza, Amira (Thesis advisor) / Carlson, Cheree (Committee member) / Olson, Clark (Committee member) / Ellsworth, Angela (Committee member) / Arizona State University (Publisher)
Created2013
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
This study analyzes syllabi for first-year college composition courses and interview responses to examine how the choices made by instructors affect online course design. Using the Syllabus Assessment Instrument designed by Madson, Melchert and Whipp (2004), this dissertation looks specifically at attendance and participation policies, course behavior policies, contact information, required material choices, course organization decisions and tool decisions to reveal how instructors do or do not accommodate online class pedagogies. This study finds that the choices instructors make in syllabus design provide significant information about the overall online course design itself. Using Selber's multiliteracies as a frame for understanding the choices made by instructors, this study finds that instructors focus primarily on functional literacies in their discourses and in the way they communicate their choices to students. Instructors vary in how they inform students of the mechanics of how to interact with tools, how often to interact with the online course, and how to use the tools within the online course. While these aspects of online courses are important, focusing on these aspects of the online course overshadows alternative perspectives on tool use that could encourage critical reflection by both instructors and students. To help instructors and departments design more effective syllabi and courses, this study raises questions and offers observations about how instructors communicate policies and how they understand these policies and pedagogies in online courses. In providing general guidelines for syllabus design and course design, this study will help writing instructors and composition programs better understand the significance of the choices they make in online course design.
ContributorsPfannenstiel, Amber Nicole (Author) / Goggin, Peter (Thesis advisor) / Daly Goggin, Maureen (Committee member) / Hayes, Elisabeth (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
The current research examines the influence of disciplines, advisors, committees, language, culture, and previous experiences in students' search and selection of dissertation topics, as well as whether and how students react to those influences during this process. Invention has been an area of research for rhetoricians for centuries, but most modern research focuses exclusively on the pre-writing process in first composition classrooms (Young, 1976). The current research collected survey and interview data from second- and third-year Ph.D. students in natural sciences, social sciences, and humanities at a large research university in the United States. 80 second- and third-year Ph.D. students completed an online survey; 11 students and four of their advisors participated in a semi-structured interview. The results demonstrate that the majority of students spent over three months in the selection of dissertation topics, and the humanities students tended to spend longer time in this process than social sciences or humanities students. Additionally, students have much in common in their perception of the criteria they would use in the selection of dissertation topics, and those criteria are similar to what previous researchers (Isaac, Koenigsknecht, Malaney, & Karras, 1989; Kozma, 1997; Sessions, 1971) have identified. However, when it comes to the actual selection experiences, the interviews show that students do not necessarily apply those criteria rationally. Moreover, disciplines appear to have an overarching effect on students' topic selection. Natural sciences advisors appeared to have more direct involvement in students' topic choice than advisors in social sciences or humanities. The linguistic and cultural backgrounds of the eleven doctoral participants were not found influential in their selection of dissertation topics. Finally, although Ph.D. advisors generally have a good understanding of students' academic progress, their knowledge of the students' personal and professional concerns may differ, and the latter knowledge is crucial in their advising on students' dissertation topic choice. The current study suggests invention in the scholar and researcher level is significantly different from that of first-year composition classrooms. The successful invention of dissertation topics is indispensable of the influence of disciplines, programs as well as the intellectual and practical support students can receive.
ContributorsXia, Jing (Author) / Matsuda, Paul K (Thesis advisor) / James, Mark (Committee member) / Renaud, Claire (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
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
Parallel Monte Carlo applications require the pseudorandom numbers used on each processor to be independent in a probabilistic sense. The TestU01 software package is the standard testing suite for detecting stream dependence and other properties that make certain pseudorandom generators ineffective in parallel (as well as serial) settings. TestU01 employs two basic schemes for testing parallel generated streams. The first applies serial tests to the individual streams and then tests the resulting P-values for uniformity. The second turns all the parallel generated streams into one long vector and then applies serial tests to the resulting concatenated stream. Various forms of stream dependence can be missed by each approach because neither one fully addresses the multivariate nature of the accumulated data when generators are run in parallel. This dissertation identifies these potential faults in the parallel testing methodologies of TestU01 and investigates two different methods to better detect inter-stream dependencies: correlation motivated multivariate tests and vector time series based tests. These methods have been implemented in an extension to TestU01 built in C++ and the unique aspects of this extension are discussed. A variety of different generation scenarios are then examined using the TestU01 suite in concert with the extension. This enhanced software package is found to better detect certain forms of inter-stream dependencies than the original TestU01 suites of tests.
ContributorsIsmay, Chester (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Kao, Ming-Hung (Committee member) / Lanchier, Nicolas (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2013