Social Network Analysis as a Method for Nursing Research
Ersig, Anne L.
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Purpose: Nursing research often relies on data obtained from individuals in an effort to understand human experiences of health and illness. These individual experiences do not occur in isolation; instead, they take place within larger contexts, such as families, social groups, and schools or communities. These organizations function as complex adaptive systems (CAS), which resist reduction into simple cause-and-effect relationships. Investigating such systems, including collection of data from multiple individuals, requires flexible and adaptable research methods. Social network analysis (SNA) facilitates data collection from multiple individuals in the same context (e.g., a family, an organization), examination of interactions among individuals, and analysis of the context within which they occur. This presentation will provide an overview of SNA and describe its utility for nursing research. Methods: The authors have used social network analysis to study families at risk of inherited conditions, families in which an adolescent has a chronic condition, and social contexts of sexual relationships. Key aspects of SNA will be illustrated, including study preparation, sampling, and data collection, analysis, and presentation. Results: Social networks are comprised of nodes and the relationships among them. The flexibility of SNA derives from the ability to define nodes and relationships, depending on the research question of interest. Networks may be defined by respondents or researchers. Statistically, SNA accounts for interdependence of linked data, and facilitates examination of change in groups over time. Analyses can describe network structure and relationships within the network, and examine the association of network data with outcomes of interest. Conclusion: Social network analysis is applicable to a wide array of nursing research questions. Many research questions can be adapted to use network approaches, providing unique and integrated data from multiple individuals.