Experience sampling approaches provide researchers with a rich and nuanced snapshot of individuals’ experiences in daily life and complement in-lab studies to provide a more comprehensive understanding of various psychological phenomena. However, the high costs associated with this approach deter many researchers from incorporating experience sampling into their research programs. In collaboration with Elizabeth Page-Gould, I developed ExperienceSampler, an open-source scaffold for building smartphone apps for experience sampling studies, to minimize, and even eliminate, many of the barriers that prevent researchers from adopting this approach. ExperienceSampler minimizes the high implementation costs often associated with this approach by using participants’ own iOS and Android devices (including tablets and iPod touches) and uses technologies that are either open-source or available for free (e.g., Google Services, Dropbox, and Cordova). Furthermore, ExperienceSampler’s signaling and data collection functions do not rely on Internet connectivity or a cellular signal: Participants will receive signals and can complete questionnaires anywhere, even if they have no cell signal or WiFi access. ExperienceSampler is also flexible and designed to be easily adapted: For researchers with little experience programming, I provide instructions to adapt ExperienceSampler for their own research on its website (www.experiencessampler.com), and in a manuscript that is published at Psychological Methods (Thai & Page-Gould, 2018). Researchers with more advanced programming skills can use the ExperienceSampler template as a starting point and incorporate other smartphone features (e.g., GPS data, camera, accelerometer) to learn even more about these phenomena.
I have also developed additional tools to make the ExperienceSampler data collection process more efficient: a compliance script that counts the number of questionnaires participants have completed each day, a compliance reminder emailing system that automatically emails participants to inform them that they have fallen below the acceptable compliance rate on the previous day and that they should be more attentive to the signals, and a long-form data converter that cleans (i.e., removes duplicate entries) and converts ExperienceSampler data into long form so that researchers simply need to download and import the data into their preferred statistical program.