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 study reports on research that explores local manifestations of Arizona's English-only language education policy by investigating the experiences of selected English language learners (ELLs) with reclassification into mainstream classrooms and four of their classroom teachers. In this study, I employed ethnographic methods (participant observation, document collection, interviewing, and focus groups) to investigate what practices emerge after ELLs are reclassified as "Fluent English Proficient" (FEP) students and moved from "the four-hour English Language Development (ELD) block" into mainstream classrooms. With a focus on the perspectives and experiences of twelve 5th and 6th grade elementary school students and four of their teachers, I examined how students and teachers viewed and responded to restrictive language policies and the practices that accompany them. One finding from this study is that students and teachers believed that the four-hour ELD block helped prepare students to learn English, but "proficiency" in English as determined by the Arizona English Language Learner Assessment (AZELLA) did not always indicate a solid understanding of the language used in the mainstream classrooms. A second finding from this study is that ideologies of language that position English over multilingualism are robust and further strengthened by language policies that prohibit the use of languages other than English in ELD and mainstream classrooms. A third finding from this study is that, in part because of the language restrictive policies in place, particular groups of students continued to engage in practices that enact ideologies of language that devalue multilingualism (e.g., "language policing"). At the same time, however, a close examination of student-to-student interaction indicates that these same students use their multiple linguistic and communicative resources in a variety of creative and purposeful ways (e.g., through language crossing and language sharing). The close examination of policy as practice in a restrictive educational language policy context conducted here has implications for debates about English-only as a method and medium of instruction, about how the ideologies of language operate in situated interactional contexts, and about how youth might use existing resources to challenge restrictive ideologies and policies.
ContributorsFredricks, Daisy Ellen (Author) / Warriner, Doris S. (Thesis advisor) / Arias, M. Bea (Committee member) / Warhol, Larisa (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
There is a documented gap between research-based recommendations produced by university-based scholars in the field of education in the United States and the evidence that U.S. politicians' use when deciding which educational policies to implement or amend. This is a problem because university-based education scholars produce vast quantities of research each year, some of which could, and more importantly should, be useful to politicians in their decision-making processes and yet, politicians continue to make policy decisions about education without the benefit of much of the knowledge that has been gained through scholarly research. I refer to the small fraction of university-based education scholars who are demonstrably successful at getting scholarly research into the hands of politicians to be used for decision-making purposes as "university-based bipartisan scholarship brokers". They are distinct from other university-based education scholars in that they engage with politicians from both political parties around research and, as such, are able to use scholarly research to influence the education policymaking process. The problem that this dissertation addresses is the lack of use, by U.S. politicians, of scholarly research produced by United States university-based education scholars as input in education policy decisions. The way in which this problem is explored is through studying university-based bipartisan scholarship brokers. I focused on three areas for exploration: the methods university-based bipartisan scholarship brokers use to successfully get U.S. politicians to consider scholarly research as an input in their decision-making processes around education policy, how these scholars are different than the majority of university-based education policy scholars, and how they conceive of the education policy-setting agenda. What I uncovered in this dissertation is that university-based bipartisan scholarship brokers are a complete sub-group of university-based education scholars. They work above the rigorous promotion and tenure requirements of their home universities in order to use scholarly research to help serve the research needs of politicians. Their engagement is distinct among university-based education scholars and through this dissertation their perspective is presented in participants' own authentic language.
ContributorsAckman, Emily Rydel (Author) / Garcia, David R. (Thesis advisor) / Powers, Jeanne (Committee member) / Fischman, Gustavo E (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
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
This study sought to analyze the messages being conveyed through the discourse utilized in presenting the public face of The Arizona Early Childhood Development and Health Board, popularly known as First Things First (FTF) and to reveal how the different discourses and ideologies within FTF have been in the past and currently are "contending and struggling for dominance (Wodak, 2007)." FTF is located within the policy realm of Early Childhood Education and Care (ECEC). The people and the system have been very influential in guiding the course and policies set forth in Arizona since the citizen initiative, Proposition 203, passed in 2006, which allowed for the creation of the Early Childhood Development and Health Board. Lakoff's techniques for analyzing frames of discourse were utilized in conjunction with critical discourse analysis in order to tease out frames of reference, shifts in both discourse and frames, specific modes of messaging, and consistencies and inconsistencies within the public face presented by FTF.
ContributorsMiller, Lisa (Author) / Swadener, Elizabeth B (Thesis advisor) / Nakagawa, Kathy (Committee member) / Romero, Mary (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
Description
This study examined the experiences of first-generation college students who were enrolled in online degree programs at a traditional brick-and-mortar university located in the western United States. These students were viewed as "double first-generation" because they were not only the first in their family to pursue a bachelor's degree, but were also among the first generation in the history of American higher education to pursue public, postsecondary education in an entirely online format. The research was designed as a multiple methods case study that emphasized qualitative methods. Being exploratory in nature, the study focused on participant characteristics and the ways that they responded to and persisted in online degree programs. Data was collected through research that was conducted entirely online; it included an e-survey, two asynchronous focus groups, and individual interviews that were conducted via Skype. Grounded theory served as the primary method for data analysis, while quantitative descriptive statistics contextualized the case. The results of this study provide a window into the micro- and macro-level tensions at play in public, online postsecondary education. The findings indicate that these pioneering and traditionally underserved students drew from their diverse backgrounds to persist toward degree completion, overcoming challenges associated with time and finances, in hopes that their efforts would bring career and social mobility. As one of the first studies to critically examine the case of double first-generation college students, this study extends the literature in meaningful ways to provide valuable insights for policymakers, administrators, faculty, and staff who are involved with this population.
ContributorsShea, Jennifer Dawn (Author) / Fischman, Gustavo E. (Thesis advisor) / De Los Santos Jr, Alfredo G. (Committee member) / Ewing, Kris (Committee member) / Archambault, Leanna (Committee member) / Arizona State University (Publisher)
Created2013