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).

中文翻译：

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一种基于屏幕时间的混合模型，用于识别和监测生态瞬时评估数据中粗心和不足的努力响应。


生态瞬时评估（EMA）涉及对受访者当前行为和经历进行重复实时采样。密集的重复评估增加了受访者的负担，使 EMA 容易受到受访者不合规和/或粗心和不够努力响应 (C/IER) 的影响。我们开发了一种混合建模方法，为研究人员提供了一种工具，用于 (a) 衡量 EMA 数据的 C/IER 污染程度，以及 (b) 研究整个研究中 C/IER 的轨迹。为了将注意力从 C/IER 行为中分离出来，该方法利用了屏幕时间中的附带信息（这些信息通常记录在电子管理的 EMA 中），并将对受访者行为的理论考虑转化为注意力和粗心屏幕时间以及功能性组件模型。 C/IER 轨迹的形式。我们展示了如何明智地选择组件模型 (a) 可以将 C/IER 导致的屏幕时间缩短与重复接触相同措施导致的熟悉度效应分开，(b) 有助于获得对 C/IER 轨迹的细粒度理解通过区分日内和日间效应，（c）允许调查个体间注意力差异。当注意力和 C/IER 屏幕时间分布表现出足够的分离时，该方法显示出良好的参数恢复，即使在数据未受污染的情况下也能得出有效的结论。该方法通过德国社会经济小组创新样本的 EMA 数据进行了说明。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。