Center for Research and Development on Transition from Secondary to Higher Education.
Department of Psychology, University of Reading.
Psychol Methods. 2019 Oct;24(5):637-657. doi: 10.1037/met0000210. Epub 2019 Apr 18.
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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
推断变量之间的相互影响或因果关系是行为和心理研究的核心目标。为了解决相互影响的问题，不同背景和学科提出了各种包括交叉滞后关系的纵向模型。然而，文献中并没有系统地讨论这些交叉滞后模型之间的关系。这种缺乏洞察力使得研究人员在分析纵向数据时难以选择合适的模型，一些研究人员甚至没有考虑过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相似之处和不同之处。统一的框架表明，现有的纵向模型可以根据模型是否假设独特的因素和/或动态残差以及使用哪些类型的共同因素来建模变化来有效地进行分类。后者对于理解如何解释交叉滞后参数至关重要。我们还使用实证数据提供了一个示例，表明根据使用的交叉滞后模型，得出不同的结论存在很大的风险。
