A Method for Longitudinal Behavioral Data Collection in Second Life

Persistent online virtual environments, whether game worlds like World of Warcraft or social worlds like Second Life, provide social scientists with the opportunity to collect longitudinal behavioral profiles from users. These environments allow behavioral measures of interesting variables at the individual and group level to be collected and analyzed. For example, studies have examined mutual gaze and personal space in Second Life (Friedman, Steed, & Slater, 2007; Yee, Bailenson, Urbanek, Chang, & Merget, 2007). Behavioral data also allows researchers to avoid self-report questionnaires, which have been shown to produce unreliable measures (Slater, 2004). On the other hand, even though these virtual environments are treasure troves of data for social scientists, typical social science curriculums do not provide researchers with the necessary background skills (e.g., programming, databases, integration across platforms) to collect data from these emerging environments. Our goal here is not to provide, by any means, a cutting-edge solution from a technical perspective, but rather, to provide a foundational framework that others can easily modify for a wide variety of purposes. The solution we describe allows researchers to capture avatar-related data from Second Life (SL) at a resolution of one minute or less over a period of weeks.