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 137
Description
This study examined the role of substance use in the relationship between the working alliance and outcome symptomatology. In this study, two groups of participants were formed: the at risk for substance abuse (ARSA) group consisted of participants who indicated 'almost always,' 'frequently,' 'sometimes,' or 'rarely' on either of two items on the Outcome Questionnaire-45.2 (OQ-45.2) (i.e., the eye-opener item: "After heavy drinking, I need a drink the next morning to get going" and the annoyed item: "I feel annoyed by people who criticize my drinking (or drug use)"). The non-ARSA group consisted of participants who indicated 'never' on both of the eye-opener and annoyed screening items on the OQ-45.2. Data available from a counselor-training center for a client participant sample (n = 68) was used. As part of the usual counselor training center procedures, clients completed questionnaires after their weekly counseling session. The measures included the Working Alliance Inventory and the OQ-45.2. Results revealed no significant differences between the ARSA and non-ARSA groups in working alliance, total outcome symptomology, or in any of the three subscales of symptomatology. Working alliance was not found to be significant in predicting outcome symptomatology in this sample and no moderation effect of substance use on the relationship between working alliance and outcome symptomatology was found. This study was a start into the exploration of the role of substance use in the relationship between working alliance and outcome symptomatology in individual psychotherapy. Further research should be conducted to better understand substance use populations in individual psychotherapy.
ContributorsHachiya, Laura Y (Author) / Bernstein, Bianca (Thesis advisor) / Tran, Giac-Thao (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
Previous research indicates that difficulties in emotion regulation and greater dissociation from one's emotions are often observed among trauma survivors. Further, trauma survivors often show greater negative emotions such as anger, and diminished positive emotions such as happiness. Relatively less is known about the relationship between posttraumatic stress symptoms, dissociation, emotion regulation difficulties, and non-trauma related emotional experiences in daily life. This study examined whether greater reports of posttraumatic stress symptoms, difficulties in emotion regulation, and dissociative tendencies were associated with greater intensity of anger and lower intensity of happiness during a relived emotions task (i.e., recalling and describing autobiographical memories evoking specific emotions). Participants were 50 individuals who had experienced a traumatic event and reported a range of posttraumatic stress symptoms. Participants rated how they felt while recalling specific emotional memories, as well as how they remembered feeling at the time of the event. Results showed that dissociative tendencies was the best predictor of greater intensity of anger and, contrary to the hypothesis, dissociative tendencies was predictive of greater happiness intensity as well. These findings are consistent with previous research indicating a paradoxical effect of heightened anger reactivity among individuals with dissociative tendencies. In addition, researchers have argued that individuals with a history of traumatization do not report lower positive emotional experiences. The present findings may suggest the use of dissociation as a mechanism to avoid certain trauma related emotions (e.g, fear and anxiety), in turn creating heightened experiences of other emotions such as anger and happiness.
ContributorsTorres, Dhannia L (Author) / Robinson Kurpius, Sharon (Thesis advisor) / Roberts, Nicole A. (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study explored several training variables that may contribute to counseling trainees' multicultural counseling self-efficacy and multicultural case conceptualization ability. Specifically, this study aimed to examine the cognitive processes that contribute to multicultural counseling competence (MCC) outcome variables. Clinical experience, multicultural knowledge, and multicultural awareness are assumed to provide the foundation for the development of these outcome variables. The role of how a counselor trainee utilizes this knowledge and awareness in working with diverse populations has not been explored. Diversity cognitive complexity (DCC) quantifies the process by which a counselor thinks about different elements of diversity in a multidimensional manner. The current study examined the role of DCC on the relationship between training variables of direct clinical experience with diverse populations, multicultural knowledge, and multicultural awareness and the two training outcomes (multicultural counseling self-efficacy and multicultural case conceptualization ability). A total of one hundred and sixty-one graduate trainees participated in the study. A series of hypotheses were tested to examine the impact of DCC on the relationship between MCC predictors (multicultural knowledge, multicultural awareness, and direct contact hours with diverse clinical populations) and two MCC outcomes: multicultural counseling self-efficacy and multicultural case conceptualization ability. Hierarchical regression analyses were utilized to test whether DCC mediated or moderated the relationship between the predictors and the outcome variables. Multicultural knowledge and clinical hours with diverse populations were significant predictors of multicultural counseling self-efficacy. Multicultural awareness was a significant predictor of multicultural case conceptualization ability. Diversity cognitive complexity was not a significantly related to any predictor or outcome variable, thus all hypotheses tested were rejected. The results of the current study support graduate programs emphasizing counselor trainees gaining multicultural knowledge and awareness as well as direct clinical experience with diverse clinical populations in an effort to foster MCC. Although diversity cognitive complexity was not significantly related to the predictor or outcome variables in this study, further research is warranted to determine the validity of the measure used to assess DCC. The findings in this study support the need for further research exploring training variables that contribute to multicultural counseling outcomes.
ContributorsRigali-Oiler, Marybeth (Author) / Robinson Kurpius, Sharon E (Thesis advisor) / Arciniega, Guillermo M (Committee member) / Nakagawa, Kathryn (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study examined the relationship that gender in interaction with interpersonal problem type has with outcome in psychotherapy. A sample of 200 individuals, who sought psychotherapy at a counselor training facility, completed the Outcome Questionnaire-45(OQ-45) and the reduced version of the Inventory of Interpersonal Problems (IIP-32). This study was aimed at examining whether gender (male and female), was related to treatment outcome, and whether this relationship was moderated by two interpersonal distress dimensions: dominance and affiliation. A hierarchical regression analyses was performed and indicated that gender did not predict psychotherapy treatment outcome, and neither dominance nor affiliation were moderators of the relationship between gender and outcome in psychotherapy.
ContributorsHoffmann, Nicole (Author) / Tracey, Terence (Thesis advisor) / Kinnier, Richard (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (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
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Believe It! is an animated interactive computer program that delivers cognitive restructuring to adolescent females' irrational career beliefs. It challenges the irrational belief and offers more reasonable alternatives. The current study investigated the potentially differential effects of Asian versus Caucasian animated agents in delivering the treatment to young Chinese American women. The results suggested that the Asian animated agent was not significantly superior to the Caucasian animated agent. Nor was there a significant interaction between level of acculturation and the effects of the animated agents. Ways to modify the Believe It! program for Chinese American users were recommended.
ContributorsZhang, Xue (Author) / Horan, John J (Thesis advisor) / Homer, Judith (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Generativity was first described by Erikson (1963) as an adult's concern for and commitment to promoting the welfare and development of future generations. Generativity is juxtaposed by stagnation in Erikson's stage of midlife (35-65 years old). The developmental hurdle faced at this point in the developmental cycle is whether a person will produce something of real value, both in the present and impacting future generations. Generative adults seek to give something back to society, generally behaving in a way to make the world a better place for others with no personal gain attached. The goal of the current study was to assess differences in levels of generativity at the final stage of adult life, and the potential functions that generativity can serve individuals. Results suggest that lowly generative individuals in older adult life tend to experience doubts about the impact they have had on the world and the lack of legacy they are leaving behind. Themes of highly generative participants included having felt they lived a purposeful and meaningful life, along with feeling fortunate and lucky in their lives. Also highly generative participants seemed to feel confident in the legacy they will leave behind after death. Results are discussed in light of the theories and findings of Erikson and McAdams.
ContributorsHauser, David (Author) / Kinnier, Richard T. (Thesis advisor) / Homer, Judith (Committee member) / Arciniega, Guillermo M. (Committee member) / Arizona State University (Publisher)
Created2013