Contents
What is the purpose of mean Centring in moderation analysis?
For example, mean centering may provide a more parsimonious interpretation of data analysis, namely that the effects of one variable would be interpreted as a function of being above or below the mean on another variable (vs. above or below some arbitrary intercept term).
Why should variables be Centred in moderation?
It is not necessary to center the predictor variables in a moderated regression, because this will not solve multicollinearity problems. On the other hand, centered variables are more straight forward to interpret, because after centering 0 is a meaningful value, i.e. the mean value.
Do you have to mean centering interaction terms?
You don’t have to mean-center variables that are included in interaction terms. Back in the dark ages when people did statistical calculations by hand on mechanical (not electronic) calculators having limited precision, there might have been some practical advantages to centering first.
What happens when you mean center a variable?
After doing so, a variable will have a mean of exactly zero but is not affected otherwise: its standard deviation, skewness, distributional shape and everything else all stays the same. After mean centering our predictors, we just multiply them for adding interaction predictors to our data. Mean centering before doing this has 2 benefits:
When do you use mean centering in moderation?
Mean centering (and standardizing) are typically used in moderation tests where you’re looking at an interaction of an IV and a Moderator on a DV. You would normally only center (or standardize) the IV and Moderator in your equation.
How to mean center predictors for multiple regression?
For testing moderation effects in multiple regression, we start off with mean centering our predictors: mean centering a variable is subtracting its mean from each individual score.