Project
Using measurement bursts in ESM to capture affect regulation

Momentary affective experiences in daily life are relevant for the prediction, diagnosis, and prevention of mental health problems. The current state of the art of measuring affect in daily life is the Experience Sampling Methodology (ESM): Using a smartphone app, participants self-report on their momentary affective state at random moments throughout the day. The net of measurements cast by this standard time-contingent sampling approach is often too coarse. Therefore, we run the risk of missing more intense affect episodes and lack information on how actual affect episodes unfold and are regulated. Therefore, the aim of this PhD project is to study the added value of episode-contingent sampling schemes, where the detection of an affect episode triggers a measurement burst of one or more follow-up measurements that are scheduled closely in time. Challenges to be addressed are how to detect the presence of affect episodes with minimal additional participant burden and how to optimally design and analyze the measurement bursts. Regarding the former, episodes can be detected by checking whether the standard ESM measurements exceed a specific upper or lower bound that correspond to specific percentiles of the underlying unknown distribution of affect scores of a person. But how should one determine these person-specific percentiles? Regarding the latter, it is unclear how the measurement bursts should be designed and analyzed, since the estimation improvement due to using bursts will likely depend on the number of negative and positive episodes as well as on the number and spacing of follow-up measurements.

Supervisors
Prof. Dr. E. Ceulemans
Prof. Dr. M. Schreuder

Period
2023-2027