Ecological momentary assessment (EMA) data have a broad base of application in the study of time trends and relations. In EMA studies, there are a number of design considerations which influence the analysis of the data. One general modeling framework is particularly well-suited for these analyses: state-space modeling. Here, we present the state-space modeling framework with recommendations for the considerations that go into modeling EMA data. These recommendations can account for the issues that come up in EMA data analysis such as idiographic versus nomothetic modeling, missing data, and stationary versus non-stationary data. In addition, we suggest R packages in order to implement these recommendations in practice. Overall, well-designed EMA studies offer opportunities for researchers to handle the momentary minutiae in their assessment of psychological phenomena.

翻译：生态瞬时评估（EMA）数据在时间趋势与关系研究领域具有广泛的应用基础。在EMA研究中，存在若干影响数据分析的研究设计考量因素。其中，状态空间建模这一通用框架尤为适配此类分析：本文系统阐述了状态空间建模框架，并就EMA数据建模中的关键考量因素提出建议。这些建议能够有效应对EMA数据分析中的典型问题，包括个体化建模与群体化建模的选择、缺失数据处理、平稳与非平稳数据的判别等。此外，我们推荐了相应的R语言软件包以便在实践中落实这些建议。总体而言，精心设计的EMA研究为研究者提供了把握心理现象评估中瞬时细节的独特契机。