Florian Keusch, Frederick G. Conrad
Using smartphones to capture and combine self-reports and passively measured behavior in social research

Journal of Survey Statistics and Methodology, 2022: 10, issue 4, pp. 863–885
ISSN: 2325-0984 (print), 2325-0992 (online)

With the ubiquity of smartphones, it is possible to collect self-reports as well as to passively measure behaviors and states (e.g., locations, movement, activity, and sleep) with native sensors and the smartphone’s operating system, both on a single device that usually accompanies participants throughout the day. This research synthesis brings structure to a rapidly expanding body of literature on the combined collection of self-reports and passive measurement using smartphones, pointing out how and why researchers have combined these two types of data and where more work is needed. We distinguish between five reasons why researchers might want to integrate the two data sources and how this has been helpful: (1) verification, for example, confirming start and end of passively detected trips, (2) contextualization, for example, asking about the purpose of a passively detected trip, (3) quantifying relationships, for example, quantifying the association between self-reported stress and passively measured sleep duration, (4) building composite measures, for example, measuring components of stress that participants are aware of through self-reports and those they are not through passively measured speech attributes, and (5) triggering measurement, for example, asking survey questions contingent on certain passively measured events or participant locations. We discuss challenges of collecting self-reports and passively tracking participants’ behavior with smartphones from the perspective of representation (e.g., who owns a smartphone and who is willing to share their data), measurement (e.g., different levels of temporal granularity in self-reports and passively collected data), and privacy considerations (e.g., the greater intrusiveness of passive measurement than self-reports). While we see real potential in this approach it is not yet clear if its impact will be incremental or will revolutionize the field.