Understanding connectivity: The parallax and disruptive-productive effects of mixed methods social network analysis in occupational science

Abstract

This article introduces social network analysis (SNA), a theoretical perspective accompanied by a set of methodologies, to occupational science. The convergence of SNA and occupational science is timely for both fields. By providing methodological approaches that flesh out a structural view of social networks, SNA measurements and mathematical terminology can effectively bridge the complexity of diverse interpretive frameworks used to understand occupational engagement and other constructs for humans as socially occupied beings. By focusing attention on the relationship of occupations to connectivity between agents, occupational science can make significant contributions to the ways in which the mattering or meaning of what people do with others nurtures the development and sustainability of social networks. We provide a brief history and roots of SNA in naturalistic observation, current terminology, and four widely used SNA research designs: egocentric, sociometric, sequenced, and two-mode. Drawing examples from our decade-long journey using SNA with narrative phenomenological conceptual frameworks, we illustrate how we used SNA with experience-near ethnographies to meet different objectives. In the discussion, we reflect on the parallax view created by the synergies between the disciplines and how the disruptive-productive effects that occur with mixing narrative phenomenology and SNA methods could address (mutual) methodological gaps that have seemingly limited conceptual development in the social sciences.

Does Gender Impact Technology Adoption in Dual-Purpose Cattle in Mexico?

“…To analyse the dual-purpose technology network, we constructed a two-mode network of 383 farmers and those technologies based on the technological package of each respondent. Two-mode networks, also known as affiliation networks [ 51 , 52 , 53 ], consist of recording instances in which individuals participate in or attend the same events, or where there are archival data indicating which people belong to which organizations [ 54 , 55 ]. After considering the different ways of analysing two-mode networks developed by [ 52 , 53 , 56 , 57 ], the two-mode data were transformed into a bipartite graph.…”

Special Issue: 30 Years of Occupational Science

“…Baranek et al end with several questions and discussions of potential critiques, paving the way for others in the discipline to join the conversation and examine the relevance of meliorism and knowledge mobilization for occupational science in other contexts. Continuing efforts to chart forward paths for the discipline, Park et al (2021) describe how adopting social network analysis theories and methodologies can complement and extend understandings about occupation. Drawing from Valente's keynote lecture at the 2019 USC Chan Occupational Science Symposium as well as ongoing research collaborations between Park, Lawlor, and Solomon, the paper illustrates the coupling of 'experience-near' narrative and ethnographic methods with 'experience-distant' social network analysis.…”

“…As occupational scientists work to understand how intersecting crises can change the shape of everyday life and occupation, they can turn to the fruitful ideas put forth in this issue. Occupational science inquiries can further theorize causes of and solutions for differential opportunities for occupation (Pereira, 2021;Peters & Galvaan, 2021), and scholars may learn much about people's disparate occupational experiences through exploring various social network configurations (Park et al, 2021). Occupational scientists will undoubtedly continue important efforts to recognize and dismantle enduring forces such as racism (Johnson & Lavalley, 2020;Kronenberg, 2020) to guard against unintentional othering, exoticizing, and complicity with exclusion and oppression in this work (Kiepek, 2021).…”

Towards explainable community finding

“…alcohol misuse). Interventions are designed by using social network analysis metrics and community finding algorithms (Valente 2012;Park et al 2020;Hunter et al 2015Hunter et al , 2019Valente et al 2013Valente et al , 2015Gesell et al 2013) as well as developing understanding of how these phenomena spread through a community, known as social contagion (Valente and Yon 2020;Brown et al 2017). Therefore, as researchers in this field are well versed in social network metrics, permitting more explainable community finding results would bring benefits to studies in these areas.…”