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.
Displaying 1 - 10 of 264
Filtering by
- All Subjects: Statistics
- All Subjects: Mental Health
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
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
Bully victimization has been associated with blunted cardiovascular responses to stress as well as elevated responses to stress. The difference between these altered physiological responses to stress is largely unknown. This study explored several possible moderators to the relationship between chronic stress and future cardiac output (an indicator of increased stress) in response to future stressors. These moderators include the difference between social and physical stressors and individual levels of loneliness. Participants were administered measures of loneliness and victimization history, and led to anticipate either a "social" (recorded speech) or "non-social" (pain tolerance test ) stressor, neither of which occurred. EKG and impedance cardiography were measured throughout the session. When anticipating both stressors, loneliness and victimization were associated with increased CO. A regression revealed a three-way interaction, with change in cardiac output depending on victimization history, loneliness, and condition in the physical stressor condition. Loneliness magnified the CO output levels of non-bullied individuals when facing a physical stressor. These results suggest that non- bullied participants high in loneliness are more stressed out when facing stressors, particularly stressors that are physically threatening in nature.
ContributorsHaneline, Magen (Author) / Newman, Matt (Thesis advisor) / Salerno, Jessica (Committee member) / Miller, Paul (Committee member) / Arizona State University (Publisher)
Created2013
Description
School bullying is a serious problem for children and adolescents, associated with a multitude of psychological and behavioral problems. Interventions at the individual level have primarily been social skills training for victims of bullying. However, investigators have had mixed results; finding little change in victimization rates. It has been suggested victims of school bullying have the social skills necessary to be effective in a bullying situation; however they experience intense emotional arousal and negative thoughts leading to an inability to use social skills. One intervention that has been getting increasing acknowledgement for its utility in the intervention literature in psychology is mindfulness. However, there has been no research conducted examining the effects of mindfulness meditation on victims of bullying. Therefore, the purpose of this study was to develop an online intervention for victims of bullying that utilizes the cutting-edge technique of mindfulness and to determine the efficacy of this intervention in the context of bullying victimization. Participants were 32 adolescents ages 11 to 14 identified by their school facilitators as victims of bullying. Repeated measures ANOVAs were used to assess the efficacy of the NMT program versus a treatment as usual (TAU) social skills program. Results revealed significant decreases in victimization and increases in mindfulness among both treatment groups from pre-test to follow-up and post-test to follow-up assessments. There were no differences found between the two treatment groups for mean victimization or mindfulness scores. Overall, the NMT program appears to be a promising online intervention for bullied teens. Directions for future research and limitations of this study were also discussed.
ContributorsYabko, Brandon (Author) / Tracey, Terence J. G. (Thesis advisor) / Homer, Judith (Committee member) / Sebren, Ann (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
There is a lack of music therapy services for college students who have problems with depression and/or anxiety. Even among universities and colleges that offer music therapy degrees, there are no known programs offering music therapy to the institution's students. Female college students are particularly vulnerable to depression and anxiety symptoms compared to their male counterparts. Many students who experience mental health problems do not receive treatment, because of lack of knowledge, lack of services, or refusal of treatment. Music therapy is proposed as a reliable and valid complement or even an alternative to traditional counseling and pharmacotherapy because of the appeal of music to young women and the potential for a music therapy group to help isolated students form supportive networks. The present study recruited 14 female university students to participate in a randomized controlled trial of short-term group music therapy to address symptoms of depression and anxiety. The students were randomly divided into either the treatment group or the control group. Over 4 weeks, each group completed surveys related to depression and anxiety. Results indicate that the treatment group's depression and anxiety scores gradually decreased over the span of the treatment protocol. The control group showed either maintenance or slight worsening of depression and anxiety scores. Although none of the results were statistically significant, the general trend indicates that group music therapy was beneficial for the students. A qualitative analysis was also conducted for the treatment group. Common themes were financial concerns, relationship problems, loneliness, and time management/academic stress. All participants indicated that they benefited from the sessions. The group progressed in its cohesion and the participants bonded to the extent that they formed a supportive network which lasted beyond the end of the protocol. The results of this study are by no means conclusive, but do indicate that colleges with music therapy degree programs should consider adding music therapy services for their general student bodies.
ContributorsAshton, Barbara (Author) / Crowe, Barbara J. (Thesis advisor) / Rio, Robin (Committee member) / Davis, Mary (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation project is a legal and policy analysis of California's involuntary psychiatric commitment laws and policy as applied to American Indians (AI). Mental health-based civil commitment and conservatorships constitute some of the most severe intrusions into personal liberties and freedom outside of the criminal justice system. In the context of AI peoples and tribal Nations, however, these intrusions implicate not only individual freedoms and well-being but also larger notions of tribal sovereignty, self-determination, culture, and the dialectic relationship between individual identity and community knowledge related to definitions of health, illness and the social meaning of difference. Yet, in the context of involuntary psychiatric commitments, the law reflects a failure to understand this relationship, alternating between strategic use of the sovereignty doctrine to deny access to services or, alternatively, wholly absenting issues of sovereignty and Indigenous worldviews from legal discourse. This project explores the nuanced ways in which these issues are weaved into the fabric of mental health law and policy and how they function to codify, enact and maintain colonization for AI peoples and Nations.
ContributorsGough, Heather Robyn (Author) / Brayboy, Bryan Mck. J. (Thesis advisor) / Romero, Mary (Committee member) / Molidor, Christian (Committee member) / Arizona State University (Publisher)
Created2013