Modeling Affect Dynamics: State of the Art and Future Challenges
Modeling Affect Dynamics: State of the Art and Future Challenges
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DOI：
10.1177/1754073915590619
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发表时间：
2015-10-01
期刊：
影响因子：
5.4
通讯作者：
Tuerlinckx, F.
中科院分区：
文献类型：
--
作者：
Hamaker, E. L.;Ceulemans, E.;Tuerlinckx, F.
目前的文章旨在提供一个最新的概要可用的技术来研究影响动态使用密集的纵向数据（ILD）。我们通过引入以下八个二分法来帮助阐明我们拥有什么样的数据，感兴趣的过程方面以及正在考虑的研究问题：（1）单人与多人数据;（2）单变量与多变量模型;（3）平稳与非平稳模型;（4）线性与非线性模型;（5）离散时间与连续时间模型;（6）离散时间与非线性模型。（6）离散变量与连续变量;（7）时域与频域;以及（8）对过程建模与计算连续性。此外，我们讨论了我们认为是最迫切的未来的挑战，影响动力学建模。
The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: (1) single- versus multiple-person data; (2) univariate versus multivariate models; (3) stationary versus nonstationary models; (4) linear versus nonlinear models; (5) discrete time versus continuous time models; (6) discrete versus continuous variables; (7) time versus frequency domain; and (8) modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics.