Details

Autor(en) / Beteiligte

• Parnell, James Michael
• Robinson, Jennifer C.

Titel

Social network analysis: Presenting an underused method for nursing research

Ist Teil von

• Journal of advanced nursing, 2018-06, Vol.74 (6), p.1310-1318

Ort / Verlag

England: Wiley Subscription Services, Inc

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Aim This paper introduces social network analysis as a versatile method with many applications in nursing research. Background Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Design Discussion paper. Data Sources English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 ‐ 2017. Discussion Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. Implications for Nursing The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Conclusion Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual‐level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models.