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Symptoms associated with Major Depressive Disorder (MDD) have become a source of distress and disability throughout the world, and MDD is among the most common disorders diagnosed annually in the United States. Despite this growing epidemic, scientists and practitioners continue to conceptualize MDD using a common-cause model, which suggests MDD is a result of a latent depression entity that gives rise to symptoms. This is particularly problematic in African American populations where some symptoms associated with depression (i.e., irritability and pain or numbness in the body) are different than those listed in the DSM. The current study examined depressive symptoms in African Americans using network theory, a novel theoretical and analytic approach that conceptualizes psychopathology as an inter-related system rather than one powered by an underlying entity that gives rise to manifest symptoms. Data collected via the Collaborative Psychiatric Epidemiology Surveys (CPES) was used to examine current symptoms (within the past week) of depression via the Center for Epidemiological Studies Depression Scale (CES-D) as well as symptoms within the past month, the latter analysis including otherwise excluded somatic symptoms often reported by African Americans. The goal of this study was to examine how these symptoms relate to each other in the African American population, which has yet to be investigated via network analysis. Results demonstrated that depressive networks of symptoms in African Americans are not as different from typical networks as previously indicated. Rather, psychological symptoms appear to be reported differently and focus on hopelessness and interpersonal relationships rather than sadness. Furthermore, physiological symptoms and those associated with anger did not prove to be more central within these networks.

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In recent decades, political parties espousing extreme nationalist, xenophobic, and even outright racist platforms have enjoyed variable success in national elections across Europe. While a vibrant research literature has sought to better understand the sources of support for such parties, remarkably little attention has been paid to the interplay between parties and the broader social networks of extremism in which they are embedded. To remedy this deficiency, the present study examines the relations between far-right parliamentary parties and their extra-parliamentary networks. One level of analysis tests whether there is a relationship between a party’s position within a network and its sustainability. Social network analysis is employed to assess the nature and structure of ties between Belgian organizations online. In addition, systematic textual analysis of website content is used to determine how a party’s ideological position within the network impacts its sustainability. The second level of analysis is a qualitative study based on in-depth interviews with members of Flemish nationalist organization in order to better understand how actors experience social networks. Evidence suggests that the most sustainable parties are those that have dense connections with other nationalist organizations. Mapping relations between far-right parties that compete openly within the rules of institutionalized democracy and their wider social networks can provide important policy-relevant insight into contemporary challenges posed by illiberal forces.

This study sought to understand how the application of a network analysis of rugby gameplay could inform coaches of their teams’ patterns of play in an effort to aid their teams’ performance. A qualitative case study utilizing open-ended interviews and a process of evaluation and constant comparison served as a guiding framework for this the data collection and data analysis methods incorporated during this study. Results of the study identified four key findings. First, incorporating elements of community based action research into the design of a case study provided the researcher with an opportunity to build effective working relationships with both participants. Second, providing coaches with effective feedback that informed them of their player’s performance was critical to the performance analysis (PA) process. Third, modifying the network analysis process to meet the participant’s needs was key in providing applicable analysis during the cases study. Fourth, performance analysts and coaches, like those in this case study, require video feedback, linked to the network analysis, if the network analysis process is to be considered informative. Finally, creating a PA process that is able to adapt to the coaches changing needs as well as the work cycles the organization proceeds through is a benefit of the NA process that we developed. / Graduate

There is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research. / text

Thesis advisor: Patrick McQuillan / The challenges facing our communities are complex, interconnected, and urgent (Kania & Kramer, 2011). Recognizing these challenges, policy makers, funders, and practitioners are turning to social partnerships as a promising strategy for community and educational change (Bess, 2015; Henig et al., 2015). Social partnerships involve the joining together of organizations from across sectors of society to tackle social problems (Crane & Seitanidi, 2014). The underlying premise of the Promise Neighborhoods program, one such social partnership, is that providing access to resources, services, and supports in a comprehensive manner will have the greatest effect on educational and community outcomes (U.S. Department of Education, 2018). This study seeks to shed light on the process of initiating and implementing a social partnership. In this study the author employed a two-phased, mixed methods design using social network analysis and interviews with organizational representatives to examine the network structures of communication and collaboration within one Promise Neighborhoods initiative: the Boston Promise Initiative. The sample for the social network analysis consisted of 33 individuals from 27 partner organizations. Further, follow-up interviews with 11 individuals were held to understand how network structures and processes might impact educational and community change. Findings from the social network analysis and qualitative interviews reveal networks of communication and collaboration rooted in a deep history of place-based change efforts, facilitating access to network resources and social capital among partner organizations. The findings highlight the importance of recognizing both challenges and opportunities of partnering with schools. Further, the findings highlight the importance of a lead organization’s ability to attend to both technical processes, such as facilitating communication among partners, and cultural processes, such as negotiating organizational identity. Taken together, the findings from this study point to the complex nature of cross-sector collaboration and identify structural factors and network processes that may impact the success of the efforts. By better understanding the structure and processes inherent in social partnerships, organizations can be better supported as they develop and implement cross-sector initiatives aimed at making meaningful change in their communities. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Lynch School of Education. / Discipline: Teacher Education, Special Education, Curriculum and Instruction.