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
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 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
Arizona has become infamous for its strong nativist and anti-immigrant climate, gaining national and international attention for legislation and policing practices that are in violation of civil and human rights. Despite the grave injustices perpetuated against migrants and communities of color, they exist in an environment of acceptance. Applying Critical Pedagogy, Critical Race Theory/ Latina(o) Critical Race Theory, and Chicana Feminist epistemologies, this study interrogates the polarized discourse that has intensified in Arizona, within the immigration movement and across its political spectrum, from 2006 to 2008. I present an auto-ethnographic account, including use of participant action research, narrative, and storytelling methods that explores ways in which resistance is manifested and the implications for creating sustainable social change. I argue that legislation, raids, and local immigration enforcement tactics reinforce the dominant group's fear of the "other," resulting in micro and macro aggressions that legitimize racial profiling and help safeguard and fortify White privilege through the fabrication of racialized identities. Simultaneously, organizing strategies and discourse of immigrant rights advocates reflect an entanglement of perceived identities and a struggle to negotiate, contest, and redefine boundaries of public space. The raids, coupled with protests and counter demonstrations, produced a public spectacle that reinforces anti-immigrant connections between race and crime. Lastly, I apply and introduce Border Crit, a new and emerging theory I propose to better address research in the borderlands.
ContributorsMaldonado, Angeles (Author) / Swadener, Elizabeth B. (Thesis advisor) / Scott, Kimberly (Committee member) / Mckinley Jones Brayboy, Bryan (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
Exiting prostitution is a process whereby women gradually leave prostitution after a number of environmental, relational, and cognitive changes have taken place. Most women attempting to leave street prostitution reenter five or more times before successfully exiting, if they are able to at all. Prostitution-exiting programs are designed to alleviate barriers to exiting, but several studies indicate only about 20-25% of participants enrolled in such programs are successful. There is little quantitative knowledge on the prostitution exiting process and current literature lacks a testable theory of exiting. This mixed-methods study defined and operationalized key cognitive processes by applying the Integrative Model of Behavioral Prediction (IMBP) to measure intentions to exit street-level prostitution. Intentions are thought to be a determinant of behavior and hypothesized as a function of attitudes, norms, and efficacy beliefs. The primary research objective was to measure and test a theory-driven hypothesis examining intentions to exit prostitution. To accomplish these aims, interviews were conducted with 16 men and women involved in prostitution to better capture the latent nuances of exiting (e.g., attitudinal changes, normative influence). These data informed the design of a quantitative instrument that was pilot-tested with a group of former prostitutes and reviewed by experts in the field. The quantitative phase focused on validating the instrument and testing the theory in a full latent variable structural equation model with a sample of 160 former and active prostitutes. Ultimately, the theory and instrument developed in this study will lay the foundation to test interventions for street prostituted women. Prior research has only been able to describe, but not explain or predict, the prostitution exiting process. This study fills a gap in literature by providing a quantitative examination of women's intentions to leave prostitution. The results contribute to our understanding of the cognitive changes that occur when a person leaves prostitution, and the validated instrument may be used as an intervention assessment or an exit prediction tool. Success in predicting an individual's passage through the exiting process could have important and far-reaching implications on recidivism policies or interventions for this vulnerable group of women.
ContributorsCimino, Andrea N (Author) / Gerdes, Karen E. (Thesis advisor) / Sun, Fei (Committee member) / Gillmore, Mary R (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
Purpose: This study examines the role of social support on adjustment to widowhood. Past research has indicated that the role of social support on adjustment to widowhood remains inconclusive, and needs further examination. This study examines the varying coping trajectories of middle-aged and retired bereaved spouses. Additionally, this study examines how bereavement stage may also influence one's adaptation to widowhood. Methods: This study used in-depth and semi-structured interviews as a means of understanding the role of social support on adjustment to widowhood. Participants were recruited through two hospice services available in a major metropolitan area in the United States. Convenient and purposive samplings are used in this study; this study will execute a grounded theory approach in order to determine the inconclusive role of social support on adjustment to widowhood. This study is contrasting between two stages- life course stages (middle aged versus retirement aged people) and bereavement stages (a year or less time following the death of a spouse versus three or more years following the death of a spouse). As a means of reducing bias and subjectivity, all data collected during the interview will be transcribed immediately. Results: Middle-aged bereaved spouses reported higher levels of motivation for adjusting positively and quickly towards widowhood due to their concern for protecting the well-being of their surviving young children compared to retired bereaved spouses. Differences between middle-aged widows and widowers have been found in this study; middle-aged widowers have a higher linkage to negative health behaviors. Retired bereaved spouses may fare better depending upon their housing location. Living in a retirement center may lower negative effects of bereavement on retired spouses' health. Conclusions: Types of social support received and expected varied between middle-aged widows and widowers. Gender norms may influence the type of social support widows and widowers receive. Middle-aged widowers are less likely to receive emotional support which may explain their higher linkage to negative health behaviors. Bereavement stage and housing location may be the key factors that influence widowhood trajectories of retired bereaved spouses. Living in a retirement center may lower the negative effects of bereavement on overall health.
ContributorsRafieei, Noshin (Author) / Kronenfeld, Jennie (Thesis advisor) / Haas, Steven (Committee member) / Damgaard, Anni (Committee member) / Arizona State University (Publisher)
Created2013
Description
Criminologists have directed significant theoretical and empirical attention toward the institution of marriage over the past two decades. Importantly, the momentum guiding this line of research has increased despite the fact that people are getting married far less often and much later in the life course than in any point in American history. The aim of this dissertation is to address this disconnect by focusing attention to nonmarital romantic relationships and their instability during emerging adulthood. To do so, it uses data from the Pathways to Desistance Study, a longitudinal study of 1,354 at-risk males and females who were adjudicated from the juvenile and adult systems in Phoenix and Philadelphia between 2000 and 2003. The project focuses attention to the following issues: (1) the effect of romantic dissolution on aggressive and income-based offenses; (2) the extent to which strain
egative emotionality and peer influence/exposure account for the effect of romantic dissolution on crime; and (3) the extent to which certain relationship and individual circumstances moderate the effect of romantic dissolution. The models reveal a few key findings. First, romantic dissolution is strongly related to an increase in both aggressive and income-based crime, but is more strongly related to income-based crime. Second, the effect of romantic dissolution is reduced when measures of strain
egative emotionality and peer influence/exposure measures are added to models, but the peer influence/exposure measures account for the strongest reduction. Finally, romantic dissolution does not serve as a positive life event among these at-risk youth, but its effect is exacerbated under a number of contexts (e.g. when an individual is unemployed). This study closes with a summary of these findings as well as its key limitations, and offers insight into potential policy implications and avenues of future research.
egative emotionality and peer influence/exposure account for the effect of romantic dissolution on crime; and (3) the extent to which certain relationship and individual circumstances moderate the effect of romantic dissolution. The models reveal a few key findings. First, romantic dissolution is strongly related to an increase in both aggressive and income-based crime, but is more strongly related to income-based crime. Second, the effect of romantic dissolution is reduced when measures of strain
egative emotionality and peer influence/exposure measures are added to models, but the peer influence/exposure measures account for the strongest reduction. Finally, romantic dissolution does not serve as a positive life event among these at-risk youth, but its effect is exacerbated under a number of contexts (e.g. when an individual is unemployed). This study closes with a summary of these findings as well as its key limitations, and offers insight into potential policy implications and avenues of future research.
ContributorsLarson, Matthew Joseph (Author) / Sweeten, Gary (Thesis advisor) / Piquero, Alex (Committee member) / Spohn, Cassia (Committee member) / Wallace, Danielle (Committee member) / Arizona State University (Publisher)
Created2013