A momentary assessment of the future of experience sampling research

The experience sampling method (ESM) enables data collection from everyday life,improving assessment of cognitive, affective, and behavioral constructs as they vary between- and within people over time and context. However, the issues of how we best can measure constructs using ESM remains largely unknown as existing measurement theory, which was developed primarily to describe the measurement of retrospective, self-report, cross-sectional data, is misaligned with the temporally granular and contextually complex nature of ESM data. To improve research using ESM, better measures are needed, along with a measurement theory and statistical methods that address the unique features of ESM data. In this research agenda, we propose a series of i) qualitative, ii) observational, iii) experimental, and iv) meta-analytic research to advance knowledge on survey (item content, response options) and protocol design (measurement reactivity, timescales, stationarity, and missingness) for ESM research. We also discuss considerations regarding the reproducibility and generalizability of the ESM method. Results from the research proposed here can strengthen our understanding of measurement theory for ESM data, facilitating the development of best practices for ESM design and implementation, and thereby advancing future applications of ESM.