Sunbelt XXXI International Network for Social Network ... - INSNA

Increasing The Reliability, Sustainability And Scalability Of Social Network Data CollectionWang, Yiran; Steffen‐Fluhr, NancyCollecting Network DataAcademic Networks, Data Collection, Quality Improvement, Sustainability, Multiple‐network Studies, Burden ReductionTHURS.PM1The power of social network analysis is limited by a tension between the need to collect complete, high quality data and the corresponding burden. Also it’soften not feasible to collect self‐reported data multiple times to track changes. In this paper, we discuss how we have addressed the need to reduce bothrespondents’ and researchers’ burden, while simultaneously increasing data quality. The NSF‐funded ADVANCE program at NJIT studies collaborative patternsin faculty research networks to advance the careers of female STEM faculty. We have constructed a co‐authorship network and have established correlationsbetween collaboration, retention, and career advancement. Initial attempts to collect faculty’s free recall networks through an online survey were limited by alow response rate and incomplete data, however. In our new study, we use multiple name generators to collect different organizational ties. To addressfaculty’s burden with multi‐generators and their reluctance to participate, we provide subjects with real‐time visual feedback in an integrated and interactivemodality in order to increase playfulness and recall rate. To reduce researcher’s burden and meet the challenge of automating data collection, we comparenetworks generated from self‐reported data with bibliometric data to test our hypothesis that the latter is a valid proxy for the former. We thus help totransform social network data collection into a more accessible, reliable, and sustainable process.Individual, Tie, And Network Level Predictors Of Access To Social Capital: Applying Multi‐level Analysis To The Study Of Ego‐network CapitalYoung, Lindsay E.; Contractor, NoshirSocial CapitalSocial Capital, Multilevel Analysis, Ego‐centered NetworksSAT.PM2By virtue of being connected to others, networks provide access to resources that can be mobilized toward productive outcomes. These embedded resourcesare called social capital. The relational nature of social capital means that the extent to which a person has access to social resources should be thought of as afunction of individual, dyadic and network characteristics. Drawing from a sample of 218 ego‐networks, this study estimates a multi‐level model for thelikelihood of a ‘resourceful’ tie, operationalized as the presence of a tie with someone with at least a college degree. The proposed model posits that a tie islikely to be resourceful based on individual characteristics such as their education and organizational affiliation, dyadic characteristics such as tie strength andeducational heterogeneity, and network characteristics such as mean educational heterogeneity and network constraint. Identifying factors that affect thelikelihood of having a ‘resourceful’ tie requires a method that accounts for the nestedness of units (i.e., individuals and ties nested within networks) and thepotential interactions across levels. Traditional methods assume independence between units of analysis, making them inappropriate tools for nestedobservations. Instead, a multi‐level approach (van Duijn, van Busschbach & Snijders, 1999; Wellman & Frank, 2001) accounts for the hierarchical structure ofego‐network data and allows us to simultaneously examine effects at all levels.