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Beyond step-down analysis: A new test for decomposing the importance of dependent variables in MANOVA

机译：逐步分析之外：分解MANOVA中因变量重要性的新测试

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Multivariate analysis of variance (MANOVA) is often categorized as a tool for experimental psychologists. However, it also continues to be a popular statistical procedure used by organizational scientists. Unfortunately, when the dependent variables (DV) are correlated with one another, interpreting the significant omnibus test in MANOVA becomes difficult. The present article proposes a novel way of interpreting a significant MANOVA that draws from work dedicated to understanding the relative importance of correlated predictors in multiple regression. Relative importance analyses are specifically designed to overcome the limitations caused by correlated variables and permit researchers to appropriately partition shared variance. We derive and extend relative weight analysis to MANOVA designs and demonstrate how these weights may be used to draw inferences concerning the relative contribution of each DV to the overall multivariate effect. Through our example, we illustrate how researchers must consider the correlations among the DVs when interpreting a significant multivariate effect, and our procedure provides an effective mechanism for doing just that.
机译：多元方差分析（MANOVA）通常被归类为实验心理学家的工具。但是，它仍然继续是组织科学家使用的流行统计程序。不幸的是，当因变量（DV）相互关联时，在MANOVA中解释重要的综合测试变得困难。本文提出了一种解释重要MANOVA的新颖方法，该方法来自致力于理解多元回归中相关预测变量相对重要性的工作。相对重要性分析是专门为克服相关变量引起的局限性而设计的，并允许研究人员适当地划分共享方差。我们得出相对权重分析并将其扩展到MANOVA设计，并演示如何使用这些权重得出有关每个DV对整体多变量效应的相对贡献的推论。通过我们的示例，我们说明了研究人员在解释显着的多变量效应时如何必须考虑DV之间的相关性，并且我们的程序为做到这一点提供了有效的机制。

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