Developing Dynamic Tools for Analyzing Irregularly Spaced Longitudinal Affect Data

开发用于分析不规则间隔纵向影响数据的动态工具

• 批准号: 1357666
• 负责人: Sy-Miin Chow
• 金额: $ 35万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357666&HistoricalAwards=false
• 关键词: Developing; Dynamic; Tools; Analyzing; Irregularly

The regulation of emotions is an integral part of everyday life. Despite the widespread interest in emotions, very few statistical models exist for formally evaluating the dynamics of emotion regulation. The collaborative work stemming from this project will enhance theoretical and methodological developments in the field of dynamic systems modeling by introducing novel methodologies suited for testing existing theories of emotions. It also will create opportunities for methodologists to refine existing techniques and develop new ones for studying dynamic systems that will benefit other scientific disciplines. The tools developed in this project can be used to examine irregularly spaced survey data frequently observed in the studies of other dynamic processes, such as family dynamics, social networks, and the propagation of diseases. Physicians and other clinical practitioners may benefit from findings concerning how emotions vary over time. To broaden the educational impact of this project, graduate students will be involved in all phases of the project. Statistical tools developed in this study also will be disseminated to a broader research audience through online tutorial, forums, and conference workshops.Empirical studies of human dynamic processes, such as studies of circadian rhythms, emotions, propagation of diseases, and dyadic and family-level interactions, frequently involve irregularly spaced longitudinal survey data. In the study of human emotions, researchers often adopt ecological momentary assessment (EMA) procedures to obtain responses at random or event-contingent time intervals. Such designs facilitate the collection of data that reflect an individual's ongoing emotional states "in the moment." Common approaches based on ordinary and stochastic differential equations can be used to accommodate the irregular time intervals observed in such data, but they are not directly suited for handling the noisy, high-dimensional nature and diverse time scales characterizing EMA data. The diverse range of time intervals also leads to computational challenges in determining the appropriate interpolation intervals in fitting differential equation models to empirical data. The study will yield an integrated set of software tools for: (1) fitting and evaluating continuous-time regime-switching models for extracting key phases of emotion processes with homogeneous dynamical structures; (2) conducting Bayesian local influence analysis to assess the sensitivity of the proposed modeling extensions to perturbations to the hypothesized prior, sampling distribution, and data; and (3) determining the most robust interpolation intervals for fitting continuous-time models with regime-switching features under conditions that mirror real-life EMA studies. Simulation studies as well as two existing EMA data sets will be used to test and validate the techniques developed in this study.

情绪的调节是日常生活中不可或缺的一部分。 尽管人们对情绪有着广泛的兴趣，但很少有统计模型可以正式评估情绪调节的动态。 来自这个项目的协作工作将通过引入适合于测试现有情绪理论的新方法来加强动态系统建模领域的理论和方法发展。 它还将为方法学家创造机会，改进现有技术，并开发新的技术来研究动态系统，这将有利于其他科学学科。 本项目开发的工具可用于检查在其他动态过程研究中经常观察到的不规则间隔的调查数据，如家庭动态、社交网络和疾病传播。 医生和其他临床从业者可能会受益于有关情绪如何随时间变化的研究结果。 为了扩大该项目的教育影响，研究生将参与该项目的所有阶段。 本研究开发的统计工具也将通过在线教程、论坛和会议研讨会传播给更广泛的研究受众。人类动态过程的实证研究，如昼夜节律、情绪、疾病传播以及二元和家庭层面的相互作用的研究，经常涉及不规则间隔的纵向调查数据。 在人类情绪的研究中，研究者经常采用生态瞬时评估（EMA）程序来获得随机或事件相关时间间隔的反应。 这样的设计有助于收集反映个人当前情绪状态的数据。“基于普通和随机微分方程的常见方法可用于适应在此类数据中观察到的不规则时间间隔，但它们不直接适用于处理EMA数据的噪声，高维性质和不同的时间尺度。 不同的时间间隔范围也导致在将微分方程模型拟合到经验数据中确定适当的插值间隔的计算挑战。 该研究将产生一套完整的软件工具，用于：（1）拟合和评估连续时间状态转换模型，用于提取具有均匀动力学结构的情绪过程的关键阶段;（2）进行贝叶斯局部影响分析，以评估所提出的建模扩展对假设先验、采样分布和数据扰动的敏感性;以及（3）在反映真实EMA研究的条件下，确定用于拟合具有状态转换特征的连续时间模型的最稳健的插值区间。 模拟研究以及两个现有的EMA数据集将用于测试和验证本研究中开发的技术。