Application Of Social Network Analysis For Better Comprehens ✓ Solved
Application Of The Social Network Analysis To Better Comprehend
Objective: To explore social network analysis (SNA) as an additional approach to elucidate quantifiable insight from qualitative health-related textual data.
Methods: Key concepts gained from thematic analyses of a set of qualitative health data obtained from an implementation study was analysed using the Excel Add-on module NodeXL.
Results: Our results show that SNA provided useful visualisation and quantifiable information of the relationship between key concepts obtained from the thematic analysis.
Discussion: SNA is a useful technique for exploring and analysing qualitative data, particularly when the research interest is in complex relationships that may exist among a large number of qualitative variables.
Conclusion: The SNA approach allows researchers to explore deeper relationships that may exist among various variables and enable researchers to derive potentially a fuller and more complete appreciation and comprehension of health-related data.
Paper For Above Instructions
Social Network Analysis (SNA) has emerged as a powerful tool in various fields, particularly in qualitative health research. By visualizing connections between qualitative data points, SNA offers insights that traditional qualitative methods may overlook. This paper aims to discuss the application of SNA to improve our understanding of qualitative health data, drawing upon methodologies used in recent studies.
Understanding Social Network Analysis
Social Network Analysis is a methodological framework used to examine the relationships between individuals—referred to as "nodes"—and the connections or ties that exist among them, known as "links" (Wasserman & Faust, 1994). The primary advantage of SNA lies in its ability to quantify relationships, providing metrics such as centrality, density, and connectivity, which are pivotal for uncovering patterns within qualitative datasets (Prell, 2012).
The Importance of Qualitative Data in Health Research
Qualitative data serves as a rich source of insights in health research, especially when exploring experiences, barriers, and facilitators affecting health outcomes. Routine health data often comprises vast volumes of qualitative information generated from interviews, open-ended survey questions, or focus groups. However, traditional analysis approaches like content analysis or thematic analysis may not fully exploit the complex interrelationships inherent in qualitative datasets, as they often focus on individual themes or issues without considering their interconnectedness (Hennink, 2011).
Application of SNA in Recent Studies
Recent studies have demonstrated the effective application of SNA in health-related research. For instance, Lam et al. (2018) used SNA to explore barriers and facilitators in implementing primary healthcare practices in community pharmacies in Spain. Key themes were identified from qualitative data and coded to form a network that visually represented relationships among barriers and facilitators. This multiplicative insight highlighted the centrality of certain variables, such as “time,” which emerged as crucial in both facilitating and hindering implementation efforts.
Methodological Framework: Implementing SNA
To utilize SNA effectively, researchers must follow a systematic methodology involving:
- Data Collection: Gather qualitative data through open-ended questions, interviews, or focus groups that encapsulate the phenomena under study.
- Data Coding: Employ thematic analysis to identify and code key concepts or variables from the qualitative data. Each key concept is assigned a numeric value, facilitating quantitative measurement.
- Network Construction: Construct a network using software tools like NodeXL, which allows for visualization and calculation of network metrics.
- Analysis of Results: Analyze the network to uncover patterns of relationships and identify central themes based on network metrics such as degree centrality, betweenness, and closeness.
Visualizing Relationships: Findings from SNA
The SNA conducted by Lam et al. revealed significant findings. In the analysis of barriers to program implementation, the results indicated that key variables like "personal characteristics" and "time" had high betweenness centrality, signifying their pivotal role in influencing other variables. Moreover, clustering patterns identified distinct interrelations among barriers and facilitators, with “time” consistently recognized as a critical factor.
Implications for Health Research
The integration of SNA into qualitative health research presents numerous implications. First, it allows for a multidimensional approach to understanding complex health interventions by visualizing how different factors interact. Furthermore, it can identify critical leverage points for intervention, helping policymakers prioritize actions that enhance health outcomes. Lastly, SNA aids in refining qualitative research design by necessitating the forethought of relational dynamics among variables in the study's conceptual framework (Clemson et al., 2017).
Challenges and Limitations
Despite its advantages, SNA is not without challenges. The interpretative nature of qualitative data means researcher bias can influence the coding process. To mitigate this, employing multiple data extractors can enhance validity and reliability (Grosser et al., 2018). Moreover, the effectiveness of SNA heavily relies on the quality and depth of the qualitative data collected. Without rich descriptive data, the network's insights may remain superficial.
Conclusion
In conclusion, Social Network Analysis provides a robust framework for unpacking the complexities inherent in qualitative health data. By visualizing relationships and quantifying connections among variables, researchers can gain deeper insights into healthcare implementation and practice. As SNA continues to gain traction in qualitative research, it holds promise for enhancing data interpretation and facilitating better health outcomes.
References
- Hennink, M. M. (2011). Qualitative research methods. Sage Publication, California.
- Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. Cambridge University, New York.
- Prell, C. (2012). Social network analysis. Sage Publication, California.
- Scott, J. (2013). Social network analysis. Sage Publication, California.
- Clemson, L., Mackenzie, L., Roberts, C., Poulos, R., Tan, A., & Lovarini, M. (2017). Integrated solutions for sustainable fall prevention in primary care, the iSOLVE project: a type 2 hybrid effectiveness-implementation design. Implementation Science.
- Grosser, T., Lopez-Kidwell, V., & Labianca, G. (2018). A social network analysis of positive and negative gossip in organizational life. Group and Organization Management.
- Martinez, A., Dimitriadis, Y., Rubia, B., Gomez, E., & de la Fuente, P. (2017). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers and Education.
- Kase, R., King, Z., & Minbaeva, D. (2017). Using social network research in HRM; Scratching the surface of a fundamental basis of HRM. Human Resource Management.
- Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying twitter topic-networks using social network analysis. Social Media + Society.
- Zou, Q., Li, J., Hong, Q., Lin, Z., Wu, Y., Shi, H., & Ju, Y. (2015). Prediction of micro-RNA-disease associations based on social network analysis methods. BioMed Research International.