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Damien Clement and Monna Arvinen-Barrow

rehabilitation as a guide, an instrument was developed. 1 , 6 , 7 Consistent with the research aims outlined above, the instrument was composed of the following: (1) a demographic information section; (2) an empty multidisciplinary team diagram 1 ; and (3) an example sociogram, 1 with a space provided for the

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Marion E. Hambrick

Sport industry groups including athletes, teams, and leagues use Twitter to share information about and promote their products. The purpose of this study was to explore how sporting event organizers and influential Twitter users spread information through the online social network. The study examined two bicycle race organizers using Twitter to promote their events. Using social network analysis, the study categorized Twitter messages posted by the race organizers, identified their Twitter followers and shared relationships within Twitter, and mapped the spread of information through these relationships. The results revealed that the race organizers used their Twitter home pages and informational and promotional messages to attract followers. Popular Twitter users followed the race organizers early, typically within the first 4 days of each homepage’s creation, and they helped spread information to their respective followers. Sporting event organizers can leverage Twitter and influential users to share information about and promote their events.

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Ashley M. Duguay, Todd M. Loughead, and James M. Cook

to take advantage of visually searching each team’s data for meaningful relational patterns using network diagrams (i.e., sociograms) or analyzing the quantitative data of each team. Searching the data in this way may help researchers further quantify the shared nature of athlete leadership by

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

socialized, turned to for advice, and with whom they could talk about team-related issues. After creating the sociogram, we calculated four SNA-derived centrality measures. Eigenvector centrality assesses the extent that high-degree players are connected to other high-degree players and is measure of

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Zoë A. Poucher, Katherine A. Tamminen, and Gretchen Kerr

provided to the Canadian Sport Institute of Ontario to maintain confidentiality. Athletes were also asked to forward the study information to other athletes who met the participation requirements. Data Collection At the start of the interview, participants were asked to draw a sociogram which was used to

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Milena M. Parent, Michael L. Naraine, and Russell Hoye

yield meaningful insights beyond conventional SNA measures. Finally, a network sociogram of the multiplex was produced to visually depict the relationships between the NSOs and stakeholders. Results and Discussion The NSOs examined had all been undergoing significant changes to their governance at the

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Matthew Katz, Nefertiti A. Walker, and Lauren C. Hindman

visualized using UCINET ( Borgatti, Everett, & Freeman, 2002 ) and NetDraw ( Borgatti, 2002 ). Alongside a visual examination of the resulting sociograms, we also calculated several metrics to empirically characterize the various networks in terms of examining each network in its entirety and assigning

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Matthew Katz, Thomas A. Baker III, and Hui Du

—Network sociograms. (a) Consumption network. (b) Outside socializing network. In examining the cohesion statistics (Table  4 ), our visualized observations were supported. The consumption network was far more cohesive than the outside socializing network based on average degree, density, connectedness