Why is it common in moderation analysis to transform the predictors using grand mean centring?
A)Because it makes the bs for lower-order effects interpretable.
B)Because it makes the b for the higher-order interaction effect interpretable.
C)To correct for heteroscedasticity in the data.
D)It makes the data normally distributed.

Question

Quizplus · Accepted Answer

Grand mean centring is often used in moderation analysis to make the bs for lower-order effects interpretable. When predictors are not centred, the coefficients represent a deviation from zero at one specific level of that predictor. However, when we centre the predictor around its grand mean, the coefficient represents a deviation from the mean level of the predictor for all observations, making it easier to interpret the effect size. Additionally, it helps to reduce multicollinearity between the predictors, which can improve the stability and accuracy of the estimation.

Why Is It Common in Moderation Analysis to Transform the Predictors