Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment
Lizbeth Benson et al.
What the paper says
Ecological momentary assessments (EMAs) and wearable devices afford opportunities to collect real-time data on events experienced in daily life. Examples of event-based data in the psychological and behavioral sciences include smoking a cigarette, experiencing a stressor, having a disruption to sleep, experiencing a depressive or manic episode, drinking an alcoholic beverage, or engaging in a bout of exercise. The increasing availability of dense sampling approaches allows for the measurement of such events at relatively fast timescales (e.g., occurring across minutes, hours, days, or weeks), expanding the possibilities for how time can be conceptualized and modeled. Survival analysis is a modeling approach that allows researchers to address scientific questions regarding whether and when events occur in time. Although not often applied to EMA data, there are myriad research questions relevant to psychosocial and behavioral scientists that can be addressed using survival analysis. In this article, we provide an overview of survival analysis, describe several time-based considerations for modeling event-based EMA data using survival analysis, and provide several illustrative examples of the different time-based considerations. Altogether, the goals of this article are to enhance knowledge of the types of research questions that can be examined using survival analysis, illustrate nuances of applying the method to EMA data, and spark ideas for future empirical and methodological research.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.