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Matthew Katz, Bob Heere, and E. Nicole Melton

study, we aimed to integrate network theory ( Borgatti & Halgin, 2011 ) and egocentric network analysis ( Perry et al., 2018 ) into the study of season-ticket holders within the college football setting. Sport marketing scholars have illustrated the salient role of networks in examining how fans

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Tyler Prochnow, Haley Delgado, Megan S. Patterson, and M. Renée Umstattd Meyer

analysis is needed to determine and understand the effects of particular relationships and connections. The potential effects of these relationships can be examined by using social network analysis (SNA). 13 The SNA is a set of theories and methodologies designed to help researchers understand the social

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Heidi A. Wayment, Ann H. Huffman, Monica Lininger, and Patrick C. Doyle

Key Points ▸ This study used social network analysis (SNA) to examine relationships between social structure, identity perceptions, and concussion-reporting support in an NCAA Division I football team. ▸ Team belonging was positively correlated with having more friends and being highly connected

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Grace Yan, Ann Pegoraro, and Nicholas M. Watanabe

following the methodology laid out in Freelon, McIlwain, and Clark ( 2016 ) in their study of #BlackLivesMatter. Specifically, Freelon et al. ( 2016 ) used community detection from the field of network analysis to sort the users in the #BlackLivesMatter dataset. This method sorted hashtag users into subsets

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Sandrine Rangeon, Wade Gilbert, and Mark Bruner

The purpose of the present study was to use citation network analysis to identify key publications and influential researchers in coaching science. A citation network analysis was conducted on references of English-language peer-reviewed coaching research articles published in 2007 and 2008 (n=141 articles; 3,891 references). Publications were coded for type (e.g., conceptual, empirical) and topic (e.g., efficacy, coach development). The structure of the field was revealed through the creation of a co-authorship network. Results show that coaching science is highly influenced by a small set of key publications and researchers. The results provide a unique overview of the field and influential authors, and complement recent overviews of coaching science (Gilbert & Trudel, 2004; Lyle & Cushion, 2010; McCullick et al., 2009).

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Thaynã Alves Bezerra, Paulo Felipe Ribeiro Bandeira, Anastácio Neco de Souza Filho, Cain Craig Truman Clark, Jorge Augusto Pinto Silva Mota, Michael Joseph Duncan, and Clarice Maria de Lucena Martins

sex were tested by Student t test and effect sizes were calculated using Cohen d . For analysis of associations, a machine learning technique called network analysis was used, which aims to establish interactions between variables from a graphical representation. Before performing the network

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Opal Vanessa Buchthal, Nicole Taniguchi, Livia Iskandar, and Jay Maddock

Background:

Physical inactivity is a growing problem in the United States, one that is being addressed through the development of active living communities. However, active living promotion requires collaboration among organizations that may not have previously shared goals.

Methods:

A network analysis was conducted to assess Hawaii’s active living promotion network. Twenty-six organizations playing a significant role in promoting active living in Hawaii were identified and surveyed about their frequency of contact, level of collaboration, and funding flow with other agencies.

Results:

A communication network was identified linking all agencies. This network had many long pathways, impeding information flow. The Department of Health (DOH) and the State Nutrition and Physical Activity Coalition (NPAC) were central nodes, but DOH connected state agencies while NPAC linked county and voluntary organizations. Within the network, information sharing was common, but collaboration and formal partnership were low. Linkages between county and state agencies, between counties, and between state agencies with different core agendas were particularly low.

Conclusions:

Results suggest that in the early stages of development, active living networks may be divided by geography and core missions, requiring work to bridge these divides. Network mapping appears helpful in identifying areas for network development.

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Ross C. Brownson, Diana C. Parra, Marsela Dauti, Jenine K. Harris, Pedro C. Hallal, Christine Hoehner, Deborah Carvalho Malta, Rodrigo S. Reis, Luiz Roberto Ramos, Isabela C. Ribeiro, Jesus Soares, and Michael Pratt

Background:

Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil.

Methods:

An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization.

Results:

Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy.

Conclusion:

Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

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Megan S. Patterson and Patricia Goodson

Background:

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people.

Methods:

SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET.

Results:

QAP regression analysis revealed a statistically significant model (R 2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model.

Discussion:

In our sample, women who are connected to “important” or “powerful” people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

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Sara Santarossa, Paige Coyne, Sarah J. Woodruff, and Craig G. Greenham

. Text and Network Analysis The Netlytic program ( Gruzd, 2016 ) finds and explores emerging themes of discussion on social media sites. Using Netlytic ( Gruzd, 2016 ), an open-source software program, all tagged media with the #BodyIssue hashtag on Instagram were downloaded (i.e., when the post was