Volume 34, Issues 1 & 2, 2014
Jenine K. Harris, Ellen Barnidge, Elizabeth A. Baker, Freda Motton, Catherine Radvanyi, Frank Rose
Higher rates of unemployment are found among African-American men in rural communities in the US. As part of a community-based participatory research project, we sought to identify characteristics of job-seeking networks of African-American and white employed and unemployed men and women in a rural community in Missouri. We collected cross-sectional quantitative and qualitative information about job-seeking networks through in-depth interviews with 39 local residents. Descriptive network measures were used to compare the gender, race, and employment status of the people comprising participant job-seeking networks. A novel network approach was used to simulate a whole network from individual networks depicting likely patterns of job-seeking relationships across the community. Unemployed participants had larger networks, with the exception of white women. Men had more racially homogenous networks than women; many networks had no racial diversity. Men had longer relationships than women, while women had stronger relationships. Employed participants had more linkages to alters with connections to community organizations than unemployed participants. Unemployed participants had many connections, but lacked connections to the right people and organizations to aid in their job search. Increasing employment opportunities in this community, and similar communities, will require effort from job-seekers and others to develop new relationships, programs, and policies.
Luke M. Gerdes
This paper introduces dependency centrality, a node-level measure of structural leadership in bipartite networks. The measure builds on Zhou et al.’s (2007) flow-based method to transform bipartite data and captures additional information from the second mode that existing measures of centrality typically exclude. Three previously published bipartite networks serve as test cases to demonstrate the extent of correlation among node-level centrality rankings derived from dependency centrality and those derived from canonical centrality measures: degree, closeness, betweenness, and eigenvector. Ultimately, dependency centrality appears to offer a novel means to measure importance in bipartite networks depicting social interactions.
Patrick J. Tighe, Laurie Davies, Stephalie Stephanie Patel, Stephen D. Lucas, Nikolaus Gravenstein, H. Russell Bernard
Introduction: An operating room’s (OR) organizational behavior, including its susceptibility to certain types of failure, may partially reflect its structural features. We report the results of a structural analysis of a composite OR suite in a tertiary-care teaching hospital. Methods: We conducted a simulation study of the OR interaction network in a 900-bed teaching hospital. A composite OR network was built from a single-day operating room schedule encompassing 32 anesthetizing sites. There were two aims: (1) to compare the composite, or prototypical, OR network to three network types: random, scale-free, and small-world; (2) to calculate the total degree centrality, eigenvector centrality, and betweenness centrality for each node within the prototypical OR network, and to compare these metrics by level of physician training and by OR role. Results: The complete prototypical OR network included 146 nodes linked by 329 edges. Results indicate that the OR is a scale-free network with small-world characteristics. The chief anesthesiologist, OR charge nurse, and recovery room charge nurse had the highest total degree centralities. There were significant differences in total degree centrality scores between nurses and anesthesiologists, nurses and surgeons, and anesthesiologists and surgeons; attending physicians had greater perioperative total degree centrality than did resident physicians. Conclusion: Given the homogeneity of certain scale-free network characteristics throughout nature, such a designation has potentially critical implications for coordinating anesthesiologists and nurses, whose roles will be impacted by the continued growth of operating rooms. These implications will be tested at the next stage of the project.
W. Scott Comulada
Mobile phone-based data collection encompasses the richness of social network research. Both individual-level and network-level measures can be recorded. For example, health-related behaviors can be reported via mobile assessment. Social interactions can be assessed by phone-log data. Yet the potential of mobile phone data collection has largely been untapped. This is especially true of egocentric studies in public health settings where mobile phones can enhance both data collection and intervention delivery, e.g. mobile users can video chat with counselors. This is due in part to privacy issues and other barriers that are more difficult to address outside of academic settings where most mobile research to date has taken place. In this article, we aim to inform a broader discussion on mobile research. In particular, benefits and challenges to mobile phone-based data collection are highlighted through our mobile phone-based pilot study that was conducted on egocentric networks of 12 gay men (n = 44 total participants). HIV-transmission and general health behaviors were reported through a mobile phone-based daily assessment that was administered through study participants’ own mobile phones. Phone log information was collected from gay men with Android phones. Benefits and challenges to mobile implementation are discussed, along with the application of multi-level models to the type of longitudinal egocentric data that we collected.
Olivier Walther, Dimitris Christopoulos
Javier S. Bundio
Jürgen Pfeffer, Betina Hollstein, John Skvoretz
Tracy Van Holt
John Scott, Social Network Analysis, Third Edition. Los Angeles, London and Thousand Oaks, CA: SAGE Publications, 2013, pp. x+201.
John Scott, What Is Social Network Analysis? London and New York: Bloomsbury Publishing, 2012, pp. x+126.
McCulloh, I., Armstrong, H., & Johnson, A. Social Network Analysis with Applications, John Wiley & Sons, 2013.
INSNA is the professional association for researchers interested in social network analysis. The association is a non-profit organization incorporated in the state of Delaware and founded in 1977.
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