Response times in Ecological Momentary Assessment (EMA): shedding light on the response process with a drift diffusion model

Response times in Ecological Momentary Assessment (EMA): shedding light on the response process with a drift diffusion model

Stefan Schneider, Raymond Hernandez, Doerte U. Junghaenel, Bart Orriens, Pey-Jiuan Lee & Arthur A. Stone

Journal paper

Current Psychology

2023

Mental processes underlying people’s responses to Ecological Momentary Assessments (EMA) have rarely been studied. In cognitive psychology, one of the most popular and successful mental process models is the drift diffusion model. It decomposes response time (RT) data to distinguish how fast information is accessed and processed (“drift rate”), and how much information is accessed and processed (“boundary separation”). We examined whether the drift diffusion model could be successfully applied to people’s RTs for EMA questions and could shed light on between- and within-person variation in the mental process components underlying momentary reports. We analyzed EMA data (up to 6 momentary surveys/day for one week) from 954 participants in the Understanding America Study (29,067 completed measurement occasions). An item-response-theory diffusion model was applied to RTs associated with 5 momentary negative affect ratings. As hypothesized, both diffusion model parameters showed moderate stability across EMA measurement occasions. Drift rate and boundary separation together explained a majority of the variance in the observed RTs and demonstrated correspondence across different sets of EMA items, both within and between individuals. The parameters related in theoretically expected ways to within-person changes in activities (momentary work and recreation) and person-level characteristics (neuroticism and depression). Drift rate increased and boundary separation decreased over the study, suggesting that practice effects in EMA consist of multiple distinctive cognitive processes. The results support the reliability and validity of the diffusion model parameters derived from EMA and provide initial evidence that the model may enhance understanding of process underlying EMA affect ratings.