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 91
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 increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (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
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (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
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
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
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014