This document discusses social network analysis (SNA), a tool used to analyze social relationships and networks. SNA elicits, analyzes, and visualizes how actors interact and resources move through a network. It represents actors as nodes and their relationships as ties. The document provides examples of SNA's application in education, health, and agriculture. It outlines the process of conducting SNA through workshops or surveys to collect node attribute and tie/link data, which is then analyzed using software to visualize the network. The document suggests opportunities to further develop SNA, such as presenting networks back to communities and measuring social capital.

1. Making research valuable to
communities: From social network
analysis to engaged research
Mieke Bourne and Parmutia Makui

2. What is Social Network Analysis (SNA)?
• A tool to elicit, analyse and visualise social relations and networks
• Origins in sociology and mathematics (graph theory)
• Focus on structures and relationships rather than individual actor
attributes
• Show how actors interact, resources and information move and
structure of roles and responsibilities
• Actors (social entities) presented as nodes and the connections
between them as ties representing the relationship

3. Application of SNA
• Used in education, health, security etc
• Applied to agricultural systems in smallholder
farming communities recently:
– Vulnerability to climate change
– Technologies and resources
– Agriculture innovation processes

4. Process
• In workshop or FG setting discuss relationships between
relevant stakeholders/actors
• Participation at workshop/FG advised by key informants
and literature
• Opportunity for discussion and group understanding

5. Process
• Individuals (from stakeholder groups) fill in
questionnaire for quantitative results
• Questions on contacts, type, frequency,
quality, value and use
• Can also complete as survey using ODK

6. Data collection and preparation
• Data is either automatically collected using ODK
or entered in excel after collection
• Data for SNA is prepared in two different files:
– Nodes/Attributes data – had data people interviewed
e.g education, gender, social status etc
– Ties/Links data – has data on the relationships
observed e.g frequency of meeting etc
• This data can be prepared in excel spreadsheets.

7. Data analysis and visualization
• The analysis and visual maps to create would
depend on the objective to be investigated
• Analysis can be done using different software:
– UCINET and NetDraw
– R Statistical software and R Studio
– NodeXL
– Gephi
– Cytoscape
Some of the software are suited to handling data from
specific fields.

8. Resources
• Detailed descriptions of process can be found at:
Bourne M, Makui P, Muller A, Gassner A .2014. Social network analysis for determining gender-
differentiated sources of information and tree seedlings. In: ICRAF, 2014. Catacutan D, McGaw E &
Llanza MA (Eds). In Equal Measure: A User Guide to Gender Analysis in Agroforestry. Philippines.
Accessible at: http://www.worldagroforestry.org/publication/equal-measure-user-guide-gender-
analysis-agroforestry-0
• Description of some key network characteristics and measures:
Bourne, M., Gassner, A., Makui, P., Muller, A. & Muriuki, J. 2017. A network perspective filling a gap in
assessment of agricultural advisory system performance. Journal of Rural Studies, 50, 30-44.
• Guide to stakeholder mapping in the SAIRLA project:
Bourne M, Neely C, Winowiecki L, Hughes K. 2016. Guide to Stakeholder Mapping for the project:
Brining evidence to bear on negotiated ecosystem service and livelihood trade-offs in sustainable
agricultural intensification. World Agroforestry Centre, Kenya. Accessible at:
http://www.worldagroforestry.org/sites/default/files/outputs/Guide%20to%20stakeholder%20mapping
%202016_final%20uplodad%20for%202017.pdf

9. Seedling access by men and women in
Machakos, Kenya

11. Mwingi

13. Opportunities to extend the tool
• Present networks back to community as basis
for discussion – power of visualisation
• Have not done this for gender yet –move
beyond disaggregated data
• Capture social capital

14. Group discussion
• How can we extend this tool and in what
circumstances would it add value?
• How could this tool measure social capital?
5 minute discussion with your neighbour and
plenary sharing of thoughts

15. Thank you