A screen-time-based mixture model for identifying and monitoring careless and insufficient effort responding in ecological momentary assessment data

Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a) gauging the degree of C/IER contamination of their EMA data and (b) studying the trajectory of C/IER across the study. For separating attentive from C/IER behavior, the approach leverages collateral information from screen times, which are routinely recorded in electronically administered EMAs, and translates theoretical considerations on respondents' behavior into component models for attentive and careless screen times as well as for the functional form of C/IER trajectories. We show how a sensible choice of component models (a) allows disentangling short screen times due to C/IER from familiarity effects due to repeated exposure to the same measures, (b) aids in gaining a fine-grained understanding of C/IER trajectories by distinguishing within-day from between-day effects, and (c) allows investigating interindividual differences in attentiveness. The approach shows good parameter recovery when attentive and C/IER screen time distributions exhibit sufficient separation and yields valid conclusions even in scenarios of uncontaminated data. The approach is illustrated on EMA data from the German Socio-Economic Panel innovation sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).