Partialing is a statistical approach researchers use with the goal of removing extraneous variance from a variable before examining its association with other variables. Controlling for confounds through analysis of covariance or multiple regression analysis and residualizing variables for use in subsequent analyses are common approaches to partialing in clinical research. Despite its intuitive appeal, partialing is fraught with undesirable consequences when predictors are correlated. After describing effects of partialing on variables, we review analytic approaches commonly used in clinical research to make inferences about the nature and effects of partialed variables. We then use two simulations to show how partialing can distort variables and their relations with other variables. Having concluded that, with rare exception, partialing is ill-advised, we offer recommendations for reducing or eliminating problematic uses of partialing. We conclude that the best alternative to partialing is to define and measure constructs so that it is not needed.

中文翻译：

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对心理结构测量的 partialing 以提炼分数和推断的可疑做法


部分是研究人员使用的一种统计方法，其目的是在检查变量与其他变量的关联之前从变量中去除无关的方差。通过协方差分析或多元回归分析来控制混杂因素，并对残差变量进行残差化以用于后续分析，是临床研究中常见的部分化方法。尽管 partialization 具有直观的吸引力，但当预测变量相关时，它充满了不良后果。在描述了部分变量对变量的影响之后，我们回顾了临床研究中常用的分析方法，以推断部分变量的性质和影响。然后，我们使用两个模拟来说明部分如何扭曲变量及其与其他变量的关系。在得出结论，除了极少数例外，部分使用是不明智的，我们提供了减少或消除有问题的 partial 使用的建议。我们得出结论，部分化的最佳替代方案是定义和测量结构，这样就不需要它了。