使用状态空间模型分析生态瞬时评估（EMA）数据：考量与建议
Analyzing Ecological Momentary Assessment Data with State-Space Models: Considerations and Recommendations
AI总结：
本研究针对生态瞬时评估（EMA）数据分析中的各类问题，提出采用状态空间建模框架，给出涵盖建模取向、缺失数据、数据平稳性等方面的建议，并推荐实现用的R包，助力心理现象的瞬时细节研究。
摘要：
生态瞬时评估（EMA）数据在时间趋势与关联研究中有着广泛的应用基础。在EMA研究中，存在诸多设计层面的考量因素，会对数据分析产生影响。有一类通用建模框架尤其适配这类分析：状态空间建模。本文提出了状态空间建模框架，并针对EMA数据建模过程中需要考量的因素给出了建议。这些建议可应对EMA数据分析中出现的问题，例如个体特异性建模与共性建模的选择、缺失数据问题，以及数据平稳性与非平稳性的区分。此外，本文还推荐了可用于在实践中落地这些建议的R包。总体而言，设计完善的EMA研究为研究者处理心理现象评估中的瞬时细节问题提供了可能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
