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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部分改进心理建构测量的分数和推论的可疑做法


偏分是研究人员使用的一种统计方法，目的是在检查变量与其他变量的关联之前消除变量的无关方差。通过协方差分析或多元回归分析来控制混杂因素以及在后续分析中使用残差变量是临床研究中偏倚的常见方法。尽管它具有直观的吸引力，但当预测变量相关时，偏向会带来不良后果。在描述偏化对变量的影响之后，我们回顾了临床研究中常用的分析方法，以推断偏化变量的性质和影响。然后，我们使用两个模拟来展示偏化如何扭曲变量及其与其他变量的关系。我们得出的结论是，除了极少数例外，部分化是不明智的，因此我们提出了减少或消除部分化有问题的使用的建议。我们的结论是，部分化的最佳替代方案是定义和测量构造，以便不需要它。