Table 1: Nine constraints for designing a study using logger technology.

LabelConsideration

VariablesWhat variables are needed? Logging is selective and intentional regarding what is collected and what is ignored. For instance, researchers can collect a large number of contextual and demographic variables to a small number (e.g., just time and type of searches). Common dependent variables include physical data alone (e.g., call frequency & duration) to physical and content data (e.g., SMS message).
PrivacyAre data required potentially sensitive to participants? Many actions performed on smartphones are considered private.
ObtrusivenessHow do I collect the data? This can range from fully automated (low interruptions) to requiring participants to report (e.g., experience sampling with logger).
InterfaceWhat interface(s) will participants use? New interfaces can be introduced or logging can be embedded and run as a background process on current interfaces.
TasksWhat tasks will participants perform? These tasks can be completely naturalistic (i.e., participant constructed) or experimenters can construct artificial tasks.
TechnologyWhat technology is used? Logs can be pulled from public files (e.g., search databases) which would allow participants to use familiar technology. On the other end of the spectrum, researchers can provide new instrumented technologies to participants.
ParticipantsWho are the participants? Subjects may consist of a random population of people that are totally unaware they are being studied to individuals within an academic department or domain of interest (e.g., pilots) that are highly aware of the measurement.
SettingWhere will the study take place? One benefit of smartphone logging is that communication data can be collected in real environments (instead of a laboratory).
Study durationHow long to measure usage? This could range from one task of interest to longitudinal measurements over a period of months or years.