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Reciprocal relations in categorical variables.
Psychological Methods ( IF 10.929 ) Pub Date : 2020-02-27 , DOI: 10.1037/met0000257
Wolfgang Wiedermann 1 , Alexander von Eye 2
Affiliation  

The analysis of reciprocal relations in categorical variables poses methodological challenges. Effects that go in opposite causal directions must be integrated into the same model, and parameters must be interpretable. In this article, we propose taking an event-based perspective and present a new approach to the analysis of reciprocal relations in manifest categorical variables. Instead of asking questions about associations of categorical variables, the event-based perspective asks whether the occurrence of one event (the cause) leads to the occurrence of another event (the effect), and vice versa. Event-based reciprocal log-linear models are described. The presented approach enables one to estimate separate unidirectional causal effects in the same log-linear model. The Schuster transformation is applied to obtain interpretable parameter estimates when design matrices are nonorthogonal. A simulation study illustrates the viability and power of the proposed approach. Data examples illustrate the applicability of the proposed method, and that analysis of reciprocal relation hypotheses without Schuster transformation can lead to incorrect conclusions. Extensions of the proposed models are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译:

分类变量中的倒数关系。

对分类变量中的相互关系的分析提出了方法论上的挑战。因果方向相反的效应必须整合到同一个模型中,并且参数必须是可解释的。在本文中,我们建议采用基于事件的视角,并提出一种新方法来分析明显分类变量中的相互关系。基于事件的观点不是询问有关分类变量关联的问题,而是询问一个事件(原因)的发生是否会导致另一个事件(结果)的发生,反之亦然。描述了基于事件的倒数对数线性模型。所提出的方法使人们能够在同一对数线性模型中估计单独的单向因果效应。当设计矩阵为非正交时,舒斯特变换用于获得可解释的参数估计。模拟研究说明了所提出方法的可行性和功效。数据示例说明了所提出方法的适用性,并且在没有舒斯特变换的情况下对相互关系假设进行分析可能会导致错误的结论。讨论了建议模型的扩展。(PsycINFO 数据库记录 (c) 2020 APA,保留所有权利)。
更新日期:2020-02-27
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