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Characterizing affect dynamics with a damped linear oscillator model: Theoretical considerations and recommendations for individual-level applications.
Psychological Methods ( IF 7.6 ) Pub Date : 2023-10-16 , DOI: 10.1037/met0000615
Mar J F Ollero 1 , Eduardo Estrada 1 , Michael D Hunter 2 , Pablo F Cáncer 3
Affiliation  

People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics. Focusing on negative affect, we provide an accessible interpretation of the DLO model parameters in terms of emotional lability, resilience, and vulnerability. We conducted a Monte Carlo study to test the DLO model performance under different empirically relevant conditions in terms of individual characteristics and sampling scheme. We used state-space models in continuous time. The results show that, under certain conditions, the DLO model is able to accurately and efficiently recover the parameters underlying the affective dynamics of a single individual. We discuss the results and the theoretical and practical implications of using this model, illustrate how to use it for studying psychological phenomena at the individual level, and provide specific recommendations on how to collect data for this purpose. We also provide a tutorial website and computer code in R to implement this approach. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译:

使用阻尼线性振荡器模型表征影响动态:针对个人级应用的理论考虑和建议。

人们的情感随着时间的推移而波动,表现出稳定的差异。在动力系统的一般框架内,阻尼线性振荡器(DLO)模型被提出作为研究影响动力学的有用方法。DLO 模型可以应用于单个个体提供的重复测量,并且得到的参数可以捕获该人的情感动态的相关特征。着眼于负面影响,我们在情绪不稳定性、复原力和脆弱性方面提供了 DLO 模型参数的易于理解的解释。我们进行了蒙特卡罗研究,以测试 DLO 模型在不同经验相关条件下的个体特征和抽样方案性能。我们在连续时间内使用状态空间模型。结果表明,在一定条件下,DLO模型能够准确有效地恢复单个个体情感动态的参数。我们讨论了使用该模型的结果以及理论和实践意义,说明了如何使用它来研究个体层面的心理现象,并就如何为此目的收集数据提供具体建议。我们还提供了一个教程网站和 R 语言计算机代码来实现这种方法。(PsycInfo 数据库记录 (c) 2023 APA,保留所有权利)。
更新日期:2023-10-16
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