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解读调节的多元回归分析:对 Van Iddekinge、Aguinis、Mackey 和 DeOrtentiis(2018)的评论。

Interpreting moderated multiple regression: A comment on Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018).

机构信息

Ohio University.

出版信息

J Appl Psychol. 2021 Mar;106(3):467-475. doi: 10.1037/apl0000522.

DOI:10.1037/apl0000522
PMID:33871271
Abstract

When data contradict theory, data usually win. Yet, the conclusion of Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018) that performance is an additive rather than multiplicative function of ability and motivation may not be valid, despite applying a meta-analytic lens to the issue. We argue that the conclusion was likely reached because of a common error in the interpretation of moderated multiple-regression results. A Monte Carlo study is presented to illustrate the issue, which is that moderated multiple regression is useful for detecting the presence of moderation but typically cannot be used to determine whether or to what degree the constructs have independent or nonjoint (i.e., additive) effects beyond the joint (i.e., multiplicative) effect. Moreover, we argue that the practice of interpreting the incremental contribution of the interaction term when added to the first-order terms as an effect size is inappropriate, unless the interaction is perfectly symmetrical (i.e., X-shaped), because of the partialing procedure that moderated multiple regression uses. We discuss the importance of correctly specifying models of performance as well as methods that might facilitate drawing valid conclusions about theories with hypothesized multiplicative functions. We conclude with a recommendation to fit the entire moderated multiple-regression model in a single rather than separate steps to avoid the interpretation error highlighted in this article. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

摘要

当数据与理论相矛盾时,数据通常会胜出。然而,尽管 Van Iddekinge、Aguinis、Mackey 和 DeOrtentiis(2018)运用元分析的方法来研究这个问题,但他们得出的绩效是能力和动机的可加函数而不是可乘函数的结论可能并不成立。我们认为,之所以会得出这样的结论,是因为在解释调节多元回归结果时出现了一个常见的错误。本文提出了一个蒙特卡罗研究来阐明这个问题,即调节多元回归有助于检测调节的存在,但通常不能用于确定构念是否具有独立(即可加)的效应,或者具有多大程度的独立效应,而不仅仅是联合(即可乘)效应。此外,我们认为,除非交互作用是完全对称的(即 X 形),否则将交互项添加到一阶项中后解释为效应大小的做法是不合适的,因为调节多元回归使用了偏分程序。我们讨论了正确指定绩效模型的重要性,以及可能有助于对具有假设的可乘函数的理论得出有效结论的方法。最后,我们建议在单个步骤中拟合整个调节多元回归模型,以避免本文中强调的解释错误。(美国心理协会,2021)。

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