Publication details
Identifying Careless Responding in Ecological Momentary Assessment : Inconsistent Signals From Different Detection Methods in the WARN-D Data
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | PSYCHOLOGICAL METHODS |
| MU Faculty or unit | |
| Citation | |
| web | https://psycnet.apa.org/fulltext/2027-97459-001.html?sr=1 |
| Doi | https://doi.org/10.1037/met0000845 |
| Keywords | ecological momentary assessment; experience sampling methodology; data quality; insufficient effort responding; measurement |
| Attached files | |
| Description | Collecting intensive longitudinal data via methods like ecological momentary assessment (EMA) is necessary to study dynamics in psychological constructs. However, detecting careless responding is more important than ever, as this methodology burdens participants more than traditional single-survey studies. If undetected, careless responding reduces data quality and can lead to invalid findings by distorting correlations between items and constructs and compromising the psychometric properties of scales. In turn, this hinders progress toward cumulative psychological science. In recent years, several data-driven approaches have been proposed that assess whether an individual was attentive or careless for each time point-specific measurement after data collection. However, the variety and complexity of available methods can be overwhelming for researchers. In this article, we summarized, applied, and compared 13 methods that flag careless responses in a rich EMA data set from the WARN-D study with 206,318 observations (from n 5 1,194 participants). Together with our detailed online supplementary tutorials, the article serves as a guide for researchers to evaluate and report careless responding in their own data. At the same time, we show that the development of methods for careless responding detection is still in its early stages, as the studied approaches produced very different results. We provide recommendations for which methods are currently most promising based on our findings and discuss important next steps in validating them empirically. |