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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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发表于 2013-3-27 10:16:34 |只看该作者 |倒序浏览
本帖最后由 mnczj 于 2013-3-28 11:05 编辑
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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。* L1 W* k5 h# |; g9 K9 O

0 G& F# p. J0 g( x( q  x! q- ?Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression
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Additive transformations are often offered as a remedy for the common problem of collinearity in
9 f9 G5 P, B1 b4 a4 M4 z& P$ y; Omoderated regression and polynomial regression analysis. As the authors demonstrate in this article,) i( c! \3 T* G8 Q0 \* d& Y1 w9 x/ c
mean-centering reduces nonessential collinearity but not essential collinearity. Therefore, in most
* X" }" ~  ~: X! l, wcases, mean-centering of predictors does not accomplish its intended goal.
In this article, the authors6 v% G0 Z' |. e
discuss and explain, through derivation of equations and empirical examples, that mean-centering8 L/ N7 P3 c( v* g, ]" j
changes lower order regression coefficients but not the highest order coefficients, does not change
. g  G2 R4 ^4 q9 n2 W+ bthe fit of regression models, does not impact the power to detect moderating effects, and does not& n# J: J8 r- r4 \
alter the reliability of product terms. The authors outline the positive effects of mean-centering,' D6 r# T, m& J! |
namely, the increased interpretability of the results and its importance for moderator analysis in; }2 z* _) y2 y- B* {
structural equations and multilevel analysis. It is recommended that researchers center their predictor
- S" i& N. f& f* M8 Ovariables when their variables do not have meaningful zero-points within the range of the variables6 e( o9 s* o8 w$ `$ {
to assist in interpreting the results.
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