Mobile Log Data

I had the opportunity today to attend a talk by Jeffrey Boase of the University of Toronto. Boase has done extensive work around mobile log data – having research participants install apps that gather their (anonymized) call data and engaging participants in short, mobile-based surveys.

The motivation for this work can be seen in part from his earlier research –  while 40% of mobile phone use studies base their findings on self-reported data, this data correlate only moderately with the server log data. In other words, self-reported data has notable validity issues while log data provides a much more accurate picture.

Of course, phone records of call time and duration lacks the context needed to make useful inferences. So Boase works to supplement log data with more traditional data collection techniques.

A research participant, for example, may complete a daily survey asking them to self-report data on how they know a certain person in their address book. Researchers can also probe further, not only getting at familial and social relationships but also asking whether a participant enjoys discussing politics with someone.

By using this survey data in concert with log data, Boase can build real-time social networks and track how they change.

His current work, the E-Rhythms Project, seeks to provide a rich understanding of mobile phone based peer bonding during adolescence and its consequences for social capital using an innovative data collection technique that triangulates smartphone log data, on-screen survey questions, and in-depth interviews.


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