esmataset

esmataset is a term used in psychology and data science to denote a dataset collected through the Experience Sampling Method (ESM), also known as ecological momentary assessment (EMA). In ESM studies, participants provide reports on their thoughts, feelings, behaviors, and surrounding context at multiple random or scheduled moments over days or weeks. The goal is to capture real-time experiences in natural settings, reducing recall bias and increasing ecological validity.

A typical esmataset is stored in long format, with one row per observation. Common fields include a unique subject identifier, a timestamp or datetime, a prompt or occasion identifier, and several measurement variables such as mood, stress, fatigue, activity, social context, location, and environmental factors. Datasets may also contain static covariates (age, sex, diagnosis) and derived variables (time of day, day in study).

Analyses of esm datasets commonly use multilevel or mixed-effects models to account for nesting of observations within individuals. Researchers may examine within-person associations, time-lagged effects, and dynamic processes using techniques such as autoregressive models, growth curves, or dynamic structural equation modeling. Visualization and data imputation strategies are also important.

Typical applications include monitoring mental health, studying mood variability, daily routines, and behavior change interventions. Limitations include participant burden, missing data due to non-response, irregular sampling intervals, and privacy concerns.

See also: Experience Sampling Method, Ecological Momentary Assessment, multilevel modeling.