检测调节作用时是否要对变量进行去中心化？最新的结论。

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6 E1 A; j; e! f( R9 E# A" S7 a我发现不少朋友在问Kenny，调节变量是否有去中心化或标准化，Dev K. Dalal 和 Michael J. Zickar 2012年在Organizational Research Method 上专门分析了这个问题。以下是他们的文章摘要。
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6 h+ R5 V& n0 c& E( G4 ]& A* ~Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression

* }! k0 ]0 J) R9 ]7 C' VAbstract
  q4 G$ W. C1 e" ^# s' |& T/ |! V4 z& VAdditive transformations are often offered as a remedy for the common problem of collinearity in
moderated regression and polynomial regression analysis. As the authors demonstrate in this article,
mean-centering reduces nonessential collinearity but not essential collinearity. Therefore, in most
cases, mean-centering of predictors does not accomplish its intended goal. In this article, the authors
discuss and explain, through derivation of equations and empirical examples, that mean-centering
changes lower order regression coefficients but not the highest order coefficients, does not change
the fit of regression models, does not impact the power to detect moderating effects, and does not
  c! E  ?5 X' k+ Oalter the reliability of product terms. The authors outline the positive effects of mean-centering,
namely, the increased interpretability of the results and its importance for moderator analysis in
structural equations and multilevel analysis. It is recommended that researchers center their predictor
, z- i( K, _& P, R- }5 y0 Avariables when their variables do not have meaningful zero-points within the range of the variables
. h, h# h6 p( D/ @6 u+ @7 e. ?, zto assist in interpreting the results.
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