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Measuring Network Impact Over Time in Ethiopia: How Evolving Technologies Improve Social Network Analysis

In Ethiopia, beginning in 2017, LINC has enjoyed the unique and exciting opportunity to apply Social Network Analysis (SNA) over time to better understand and strengthen the relationships among Water, Sanitation, and Hygiene (WASH) actors in four areas of the country. During this time, and through three successive SNAs – Baseline, Mid-Term and End-Line – applied to the same groups of actors, LINC’s application of network analysis tools and platforms has evolved to keep pace with recent technological advancements in the field, stimulated by new interest in the use of SNA. This post explores how LINC used SNA in Ethiopia and how the various technologies have evolved over time.

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Map of Ethiopia

The Sustainable WASH Systems Learning Partnership (SWS) is a global USAID cooperative agreement to demonstrate and generate learning about systems-based approaches to sustain WASH services in four countries: Kenya, Uganda, Ethiopia, and Cambodia.

In Ethiopia, SWS is working in four separate locations: two small-town sanitation and two rural water supply communities. In each location, SWS partners Tetra Tech (small-town sanitation) and IRC WASH (rural water supply) facilitated “Learning Alliances” consisting of 15-25 key WASH stakeholders representing public, private, civil society, and other sectors. The Learning Alliances served as coordination platforms to increase collaboration and knowledge sharing among stakeholders to improve the efficiency, effectiveness, and sustainability of local WASH services. SWS facilitators supported each Learning Alliance to develop and implement specific action plans to advance priority WASH goals and activities in their areas.

SNA is increasingly being recognized in international development as a valuable tool to better understand and work with networks of stakeholders like Learning Alliances, and importantly, the relationships among them. More broadly, development practitioners are seeing the importance of systems thinking in our work, because understanding the local actors and the complex systems in which they operate is fundamental to the effective design, implementation, and measurement of the success of locally-owned and led development.

As one of many systems-thinking tools, SNA provides a quantitative, objective means, using visuals, or “maps,” combined with metrics to better understand the relationships and dynamics within a system, identify opportunities to improve how actors cooperate or share information, and develop network capacity and leadership in ways that improve collective results. This knowledge can then support and promote the effectiveness of the overall development program.

The multi-year use of SNA on the Ethiopia SWS program thus presented LINC with an opportunity to not only analyze the evolution of the networks and relationships in all four Learning Alliances, but also to review the evolution of SNA tools and technologies. This allowed us to learn lessons related to network metrics and analyses, their applicability to different environments, and involving field teams to strengthen our findings and contextualization.

SNA tools have evolved with increased visibility, massive advancements in computational power, and global accessibility, accommodating an ever-greater number of users and SNA applications. LINC is regarded as one of the early practitioners applying accessible approaches to SNA in the development field, beginning in 2015 with our analysis of workforce development systems in Nicaragua.

At that time, there were several SNA analysis and visualization platforms in various stages of development and release, each with their own sets of capabilities, advantages, and limitations. Early on in our own use of SNA, in 2015, LINC conducted an in-depth comparison of four of the leading software platforms with potential in the development field before eventually settling on a combination of two platforms, UCINet and NodeXL, which in many ways complemented one another. Both were used for the Ethiopia Baseline and Mid-Term SNA analyses, UCINet primarily for metrics and NodeXL for graphics.

UCINet, a Windows-based program long preferred in academic fields, features among the most robust analytical capabilities covering virtually any field of application. It is, however, considerably less user-friendly, requiring users to upload data files to generate output reports, and limited visualization capabilities.

In some ways at the opposite end of the spectrum is NodeXL, an Excel “add-on” that provides SNA capabilities directly in Excel. While it offers less in terms of metrics and analyses – sticking more to the most common measures – it is much more user-friendly and intuitive than UCINet, especially for those familiar with Excel, and delivers solid, manageable mapping capabilities. It also performs well for analyzing both intra- and inter- “group” relationships between actors bound by common attributes.

Enter Kumu. While the SWS project was supporting WASH systems in Ethiopia, network analysis evolved, including the advent of web-based platforms and apps with powerful capabilities that allow users anywhere to analyze networks simultaneously. For LINC, this meant improved ability of our partners in the field to access and analyze networks with a minimum of training and orientation.

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Network Analysis map made in Kumu

The Kumu platform, adopted by LINC for nearly all our current SNA efforts, integrates these capabilities, allowing SNA to become more practical and mainstreamed in development fields. For the recent End-Line analysis of SWS, our IRC and Tetra Tech partners benefitted with hands-on access to data and visualizations of their respective networks, all of which remain available online to the teams. An anonymized version of one of the maps is available at this link.

We also learned that not all SNA terms and calculations are consistent across the different platforms. As an illustration, one of the most basic metrics, “Average Degree” (the average number of connections of each actor in the network) results in a value exactly twice higher in UCINet than in Kumu. LINC was able to conduct a more in-depth analysis of the metrics generated by Kumu, UCINet, and NodeXL that will help ensure consistent comparison of different SNA methodologies and tools going forward.

Additionally, LINC has been in routine communication with the Kumu Technical Support team since we began experimenting with Kumu in 2017. Inquiries generated through LINC’s work have contributed as well to Kumu, which has integrated numerous of our recommendations and issues into the software and user manual. Learning indeed flows in all directions.

The ability to conduct successive SNAs using different platforms can also be challenging in terms of personnel or consultants knowledgeable across the different platforms. Our efforts in Ethiopia have enjoyed consistent leadership and we have retained many of our former staff and experts engaged in the work. While all three analyses were undertaken by different individuals, each of them remained in contact to discuss the SWS project and the SNA findings over time.

SNA continues to be a valuable tool for international development, where the relationships among stakeholders can support success or just as easily impede progress. As systems thinking continues to be adopted by more practitioners, LINC expects that the increased accessibility of SNA, thanks to evolving technology like Kumu, will continue to lead to better programming and results. To build on the sector’s knowledge and expand the accessibility of SNA in development work, SWS is also now reviewing the use of SNA across 19 applications in four countries. This learning will result in a report and an interactive workshop – but that’s a story for another post. Stay tuned!

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