Technical Policy Briefing Notes - 8
Social Network Analysis
Strengths and Weaknesses
Policy Briefs
Social Network Analysis
Strengths and Weaknesses
The
MEDIATION project identified the strengths and weaknesses of different
approaches to SNA. A summary is outlined below.
The main strength of Social Network Analysis is the information it provides on the existing institutional actors and relationships, the existing decision framing, and thus the influence and exchange of information for progressing adaptation.
Quantitative SNA provides additional information and can explore correlations between network variables and attribute variables or other social indicators. However, it requires a large sample size, or ego-centric partial networks. It tends to focus on methodology and technical issues rather than on hypotheses and theories, and can be subject to the over-interpretation of results. Further, data are often difficult and resource intensive to obtain, and empirical studies are often quite small, which can make it hard to use for exploration of alternative measurement strategies.
Qualitative SNA is quick and relatively easy to do and encourages participation across diverse viewpoints and actors. It also avoids some of the more complex classifications or jargon involved in more formal quantitative applications. The engagement also reveals insights that would be difficult to get any other way. The disadvantages are that results are highly dependent on which actors are involved in the exercise, and their participation which can bias results (high subjectivity). It can also be difficult to integrate different perspectives to produce cohesive maps of whole networks, especially where multiple scales are involved or to bring together actors that have very different perspectives.
A key issue (and potential weakness) in SNA is how the question is framed, because this influences the responses. This structured subjectivity contrasts with other potential methods.
The main strength of Social Network Analysis is the information it provides on the existing institutional actors and relationships, the existing decision framing, and thus the influence and exchange of information for progressing adaptation.
Quantitative SNA provides additional information and can explore correlations between network variables and attribute variables or other social indicators. However, it requires a large sample size, or ego-centric partial networks. It tends to focus on methodology and technical issues rather than on hypotheses and theories, and can be subject to the over-interpretation of results. Further, data are often difficult and resource intensive to obtain, and empirical studies are often quite small, which can make it hard to use for exploration of alternative measurement strategies.
Qualitative SNA is quick and relatively easy to do and encourages participation across diverse viewpoints and actors. It also avoids some of the more complex classifications or jargon involved in more formal quantitative applications. The engagement also reveals insights that would be difficult to get any other way. The disadvantages are that results are highly dependent on which actors are involved in the exercise, and their participation which can bias results (high subjectivity). It can also be difficult to integrate different perspectives to produce cohesive maps of whole networks, especially where multiple scales are involved or to bring together actors that have very different perspectives.
A key issue (and potential weakness) in SNA is how the question is framed, because this influences the responses. This structured subjectivity contrasts with other potential methods.
| Key strengths Can generate an understanding of socioinstitutional structures, actors and linkages, and ways to improve information and knowledge transfer Can provide information on decision framing and key actors. Can provide quantitative information and correlations to understand network variables (quantitative SNA). Qualitative SNA is quick and relatively easy to do and encourages participation across diverse viewpoints and actors. | Potential weaknesses Subjective bias and can be difficult to generalise. Time-consuming, intensive process (quantitative SNA). Does not have a temporal or spatial dimension. Networks have artificial boundaries (often necessarily). Design of process is critical to get as many differing viewpoints as possible. |
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