Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes
Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes
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DOI：
10.1111/j.2044-8317.2012.02043.x
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发表时间：
2013-02-01
影响因子：
2.6
通讯作者：
Oud, Johan H. L.
中科院分区：
文献类型：
--
作者：
Voelkle, Manuel C.;Oud, Johan H. L.
在设计纵向研究时，研究人员通常以相等的间隔为目标。然而，在实践中，这个目标几乎从来没有实现过，评估浪潮之间的不同时间间隔和个人之间的不同时间间隔更多的是规则而不是例外。通过结构方程建模引入连续时间模型的原因之一是为了处理间隔不规则的评估波（例如，Oud & Delsing, 2010）。在本文中，我们将该方法扩展到振荡和非振荡过程的单独变化时间间隔。此外，我们不仅证明了相等的间隔是不必要的，而且还证明了使用不等的采样间隔是有利的，特别是当采样率很低的时候。提供了两个例子来支持我们的论点。在第一个例子中，我们将具有不同时间间隔的二元耦合过程的连续时间模型与标准离散时间模型进行比较，以说明考虑精确时间间隔的重要性。在第二个例子中，通过蒙特卡罗模拟研究了不同采样间隔对估计阻尼线性振荡器的影响。我们的结论是，重要的是要考虑到个体不同的时间间隔，并鼓励研究人员将个体内部和个体之间不同时间间隔的纵向研究视为一个机会，而不是一个问题。
When designing longitudinal studies, researchers often aim at equal intervals. In practice, however, this goal is hardly ever met, with different time intervals between assessment waves and different time intervals between individuals being more the rule than the exception. One of the reasons for the introduction of continuous time models by means of structural equation modelling has been to deal with irregularly spaced assessment waves (e.g., Oud & Delsing, 2010). In the present paper we extend the approach to individually varying time intervals for oscillating and non-oscillating processes. In addition, we show not only that equal intervals are unnecessary but also that it can be advantageous to use unequal sampling intervals, in particular when the sampling rate is low. Two examples are provided to support our arguments. In the first example we compare a continuous time model of a bivariate coupled process with varying time intervals to a standard discrete time model to illustrate the importance of accounting for the exact time intervals. In the second example the effect of different sampling intervals on estimating a damped linear oscillator is investigated by means of a Monte Carlo simulation. We conclude that it is important to account for individually varying time intervals, and encourage researchers to conceive of longitudinal studies with different time intervals within and between individuals as an opportunity rather than a problem.