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Putting affective dynamics into context: Towards a realistic and reliable modeling approach

将情感动态置于背景中：迈向现实可靠的建模方法

基本信息

批准号：
424706845
负责人：
Dr. Janne Adolf
金额：
--
依托单位：
Faculteit Psychologische en Pedagogische Wetenschappen (PPW)
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2019
资助国家：
德国
起止时间：
2018-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/424706845?language=en
关键词：
Putting affective dynamics into context

项目摘要

The aim of recent affect research is to understand affective phenomena as continuously changing within-person processes. By unraveling the dynamics that underlie these processes, one hopes to gain deeper insights into affective functioning and affect regulation. To this end, the field relies on intense longitudinal data, and dynamic, especially vector autoregressive (VAR), time series modeling. The new research paradigm has been embraced by many due to its supposed potential – but it also bears non-trivial challenges. Two of them, developing realistic (i.e., sufficiently complex) modeling approaches to affective functioning, and ensuring adequate reliability of the obtained modeling solutions given typically limited data, form an intriguing trade-off. In the here proposed research project, I aim to address both challenges, and tackle the resulting balancing act. This yields the following twofold research objective.First, for a realistic modeling approach, I draw upon notions of regulatory flexibility, suggesting that affective dynamics depend on and thus change with context. To empirically study this, VAR models are required to include contextual variables that moderate their parameters, and the presumably associated affective dynamics, over time. Fixed moderated time series analysis (fmTSA; Adolf et al., 2017) implements such an extended VAR model by incorporating observed contextual variables as moderators. Here, I propose to further develop this method along two important lines. One concerns the incorporation of moderators of different formats to enable the identification of different patterns or forms of context-dependent affective functioning. The other concerns the rigorous handling of measurement intervals of unequal length, which could otherwise lead to biased modeling solutions.Second, I propose to investigate the reliability of fmTSA under typical longitudinal data conditions. Reliability is thereby evaluated in terms of the precision of modeling solutions (i.e., estimated model parameters), and in terms of the precision of predictions derived from parameter estimates. I thus cover the explanatory as well as the predictive performance of the method. Besides establishing and implementing routines for retrospective reliability diagnosis, I also aim at prospective reliability assessment and improvement. That is, for expected parameter values, I will a-priori determine how reliability varies as a function of data conditions. The knowledge about these associations can then be used to optimize the design of longitudinal studies, and thus produce data that guarantee reliable fmTSA solutions.Taken together, the here proposed research project should culminate in a highly usable and credible modeling approach to putting affective dynamics into context.

最近的情感研究的目的是理解情感现象作为不断变化的人内过程。通过揭示这些过程背后的动力学，人们希望更深入地了解情感功能和影响调节。为此，该领域依赖于密集的纵向数据和动态，特别是向量自回归（VAR），时间序列建模。新的研究范式因其假定的潜力而受到许多人的欢迎，但它也面临着不小的挑战。其中两个，发展现实的（即，足够复杂的）情感功能的建模方法，并确保所获得的建模解决方案在给定通常有限的数据的情况下具有足够的可靠性，这形成了一种有趣的权衡。在这里提出的研究项目中，我的目标是解决这两个挑战，并解决由此产生的平衡行为。这产生了以下两个研究目标：第一，一个现实的建模方法，我借鉴了监管灵活性的概念，表明情感动力学依赖于，从而随着上下文的变化。为了从经验上研究这一点，VAR模型需要包括上下文变量，这些变量会随着时间的推移而调节其参数，以及可能相关的情感动态。修复了适度的时间序列分析（fmTSA; Adolf等人，2017）通过将观察到的上下文变量作为调节器来实现这种扩展的VAR模型。在这里，我建议沿着沿着两条重要路线进一步发展这种方法。一个关注的是纳入不同格式的主持人，使识别不同的模式或形式的上下文依赖的情感功能。另一个关注的严格处理的测量间隔不等的长度，否则可能会导致有偏见的建模solutions.Second，我建议调查fmTSA的可靠性在典型的纵向数据条件下。从而根据建模解决方案的精度来评估可靠性（即，估计的模型参数），以及从参数估计导出的预测精度。因此，我涵盖了解释以及预测性能的方法。除了建立和实施常规的回顾性可靠性诊断，我也旨在前瞻性的可靠性评估和改进。也就是说，对于预期的参数值，我将先验地确定可靠性如何作为数据条件的函数而变化。这些协会的知识，然后可以用来优化设计的纵向研究，从而产生的数据，保证可靠的fmTSA solutions. Together，这里提出的研究项目应最终在一个高度可用和可信的建模方法，把情感动态的背景。