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秋水长空: 罗老师好:在你书中《使用基本模型》一部分中,x自变量,y因变量,M与W分别为两个并列的调节变量,既然为并列,那么能不能分开考察二者各自的调节作用呢?也就是 ... " s8 l- C' d+ p3 ~4 I6 X( ^7 S0 j
1. 以后请发到圈子的帖子。我很少看涂鸦的。
! @( p; S i/ y7 P2. 不可以,理论上应该做 full model testing,把所有的关系一同测验。
" k# c n& I/ D+ Z4 E4 W0 p3. 我的理解是中心化已经是一个标准了。
1 { e; ]: D$ a; R! X7 P/ z你可以把Hayes的整个reference 给我,让我看看吗' J( N! e; G/ p2 s8 h* e& b8 h
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Hayes:[size=11.818181991577148px]The explanation that seems to have resulted in the most misunderstanding is that X and M are likely to be highly correlated with XM and this will produce estimation problems caused by multicollinearity and result in poor or “strange” estimates of regression coefficients, large standard errors, and reduced power of the statistical test of the interaction. This is, in large part, simply a myth. As described later, there are some reasons that centering can be a beneficial thing to do, which is why it has been recommended by some.6 o# _+ A8 d% d5 s& W0 a0 e* @
However, it is incorrect to claim that it is necessary, that a failure to do so will lead one to incorrect inferences about moderation, or that the resulting regression coefficients are somehow strange or uninterpretable.
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