Smartphone sensors (e.g., GPS, camera, accelerometer), apps, and wearables (e.g., smartwatches, fitness bracelets) allow researchers to collect rich behavioral data, potentially with less measurement error and lower respondent burden than self-reports through surveys. Passive mobile data collection (e.g., location tracking, call logs, browsing history) and respondents performing additional tasks on smartphones (e.g., taking pictures, scanning receipts) can augment or replace self-reports in surveys. However, there are multiple challenges to collecting these data: participant selectivity, (non)willingness to provide sensor data or perform additional tasks, privacy concerns and ethical issues, quality and usefulness of the data, and practical issues of implementation. This course will address the challenges by reviewing state-of-the-art practices of smartphone sensors, apps, and wearables data collection, ranging from small-scale studies of hard-to-reach populations to large-scale studies to produce official statistics, and discuss design best-practices for this type of measurement. Recommendations provided will include:

 

 

As part of this course, participants will have the chance to work on practical issues of implementing smartphone sensors, apps, and wearables into social science research. Participants will discuss their own research study designs using new technology and have the opportunity to present the scenarios of combining survey data with data from health, accelerometery, and location sensors. Participants will be able to conduct first analyses of data collected through smartphone sensors. The exercise is designed to be conducted in R and can be followed by those without extensive R experience. Participants may use their own software of choice (e.g., Python, Stata, or SAS), but we do not provide the code/syntax for these programs. The course will not demonstrate how to program smartphone sensor apps.

Participants will find the course useful if: