Making Sense of Spaghetti: How Mixed Methods and Collaborative Approaches Enhanced Social Network Analysis

Abstract Information: The graphs created from social network analysis (SNA) look like spaghetti tossed on a plate. Lines squiggle and intersect, but they are difficult to follow and tease apart. In addition, the statistics that accompany the graphs do not provide clarity for most people, except those who have had specialized training in SNA. Moore (2021) studied how well people interpret the images and statistics produced by SNA, and, in short, they do not do well. While specialized training may lead to technical knowledge in SNA, however, it does not necessarily lead to understanding. We learned these lessons together during a baseline evaluation of the Advancing Conservation Through Empathy for Wildlife (ACE for Wildlife) Network, which focuses on fostering empathy for wildlife. Woodland Park Zoo (Seattle, WA) coordinates the Network and commissioned the evaluation to understand the Network’s patterns of collaboration and information-sharing. The evaluation explored 11 sub-networks of the ACE for Wildlife Network, including self-generated collaboration and information-sharing networks and those created through Network activities (e.g., in-person and virtual events). We integrated quantitative data from SNA with qualitative data from surveys and interviews. By synergizing these methods, the evaluation avoided potential misinterpretations of quantitative data, emphasizing the importance of narrative and context in SNA. For example, while the SNA displayed low connectivity in a discussion board, we learned through interviews that some Network participants like to simply observe the board to keep up with the discussion but do not want to post. We also employed collaborative approaches to evaluation. The evaluation team led sensemaking sessions with the Empathy Team to help them make sense of the spaghetti and to learn how the team interpreted the results. These sessions brought into the light context that had been hidden from view. For example, the team was able to interpret the discussion board SNA graph based on their intimate knowledge of the Network. Using mixed methods and collaborative approaches, the evaluation found a robust culture of information sharing, new collaborations borne from Network affiliations, the pivotal role of the Empathy Team in facilitating connections, and high satisfaction from Network participants among those with high connectivity and those with low connectivity. Most meaningfully, the evaluation was meant to help the Empathy Team consider different possible pathways and set intentions. In addition to facilitating sense-making, the evaluation team reported results using different modalities, incorporating plain language to enhance understanding. Zoo staff members have engaged in intentional, action-oriented reflection to utilize the results in programming. We will share key lessons learned about the nature of the Network, the critical role of collaborative evaluation approaches, the meaning brought forth by the addition of qualitative methods to SNA, and evaluation support for action-oriented reflection and utilization. [Moore, J. D. (2021). Making sense of networks: Exploring how network participants understand and use information from social network analysis [Doctoral dissertation, Virginia Polytechnic Institute and State University]. https://vtechworks.lib.vt.edu/server/api/core/bitstreams/e392d4ca-ad0e-439d-bcb0-3a13402b9d35/content]