A Unified Framework of Longitudinal Models to Examine Reciprocal Relations
A Unified Framework of Longitudinal Models to Examine Reciprocal Relations
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DOI：
10.1037/met0000210
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发表时间：
2019-10-01
影响因子：
7
通讯作者：
Hamaker, Ellen L.
中科院分区：
文献类型：
--
作者：
Usami, Satoshi;Murayama, Kou;Hamaker, Ellen L.
推断变量之间的相互作用或因果关系是行为学和心理学研究的中心目标。为了解决互惠效应，在不同的背景和学科中提出了包括交叉滞后关系的各种纵向模型。然而，这些交叉滞后模型之间的关系在文献中还没有得到系统的讨论。这种洞察力的缺乏使得研究人员在分析纵向数据时很难选择合适的模型，一些研究人员甚至没有考虑替代的交叉滞后模型。本研究提供了一个统一的框架，阐明了这些模型在概念和数学上的异同。统一的框架表明，现有的纵向模型可以根据模型是否具有独特的因素和/或动态残差，以及使用什么类型的公共因素来进行模型变化来有效地分类。后者对于理解如何解释交叉滞后参数是至关重要的。我们还给出了一个使用经验数据的例子来证明，根据所使用的交叉滞后模型，得出不同结论的风险很大。
Inferring reciprocal effects or causality between variables is a central aim of behavioral and psychological research. To address reciprocal effects, a variety of longitudinal models that include cross-lagged relations have been proposed in different contexts and disciplines. However, the relations between these cross-lagged models have not been systematically discussed in the literature. This lack of insight makes it difficult for researchers to select an appropriate model when analyzing longitudinal data, and some researchers do not even think about alternative cross-lagged models. The present research provides a unified framework that clarifies the conceptual and mathematical similarities and differences between these models. The unified framework shows that existing longitudinal models can be effectively classified based on whether the model posits unique factors and/or dynamic residuals and what types of common factors are used to model changes. The latter is essential to understand how cross-lagged parameters are interpreted. We also present an example using empirical data to demonstrate that there is great risk of drawing different conclusions depending on the cross-lagged models used.