Renato: A Toolkit for the Collection and Analysis of Network Data

Hogan1, C., Borgatti2, S., Halgin2, D., Floyd2, T., Fagan2, J., and Hopkins1, C.

Presented at the XXXV Sunbelt Conference of the International Network for Social Network Analysis (INSNA), Brighton, United Kingdom (June 2015)

In this talk, we will discuss and demonstrate Renato (presented as a poster at Sunbelt 2014), a software package for the collection and analysis of relational data such as found in social network analysis and cultural domain analysis. The Renato system uses a central data repository and desktop interfaces to provide a variety of functions, including: 1) a data collection designer that allows the user to create collection tasks such as pilesorting, freelisting, selecting from rosters, paired comparisons, and so on, along with traditional survey methods; (2) a server that enables storing and sharing data collection designs and the results of collections; (3) Android apps that execute the data collection on handheld devices and upload the results to a Renato server; and (4) a desktop client that allows for detailed data analysis such as multi-dimensional scaling, consensus analysis, clustering, centrality measures etc. Historically, researchers have used a combination of different packages for cultural domain analysis, including Anthropac, a DOS-based cultural domain analysis program that requires a DOS emulator to run under modern Windows operating systems. Renato moves Anthropac into the modern digital age, integrating data collection design capabilities, network and cultural domain techniques, along with powerful visualization and cloud-based storage capabilities. For example, Renato facilitates collecting a set of names (e.g., a list of drug names or networking behaviors in organizational settings) and enabling a respondent to use gestures to visually indicate relationships among them. In particular, Renato allows a researcher to design a pilesort data collection task, – in which respondents sort items into piles corresponding to perceived similarity or belonging to social groups – run that data collection on an Android handheld device with any number of participants, and aggregate participant responses on a central server in both connected and disconnected environments. The aggregated data can then be downloaded to the Renato analysis application to run a suite of cultural domain analysis and social network analysis routines such as multi-dimensional scaling, consensus analysis, and property fitting and visualize the results using modern network visualizations. Renato also supports exporting to UCINET for further analysis.

Charles River Analytics
University of Kentucky

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