الإحصاء النظريعلم البيانات

When NOT to Center a Predictor Variable in Regression

When NOT to Center a Predictor Variable in Regression


There are two reasons to center predictor variables in any time of regression analysis–linear, logistic, multilevel, etc

a- To lessen the correlation between a multiplicative term (interaction or polynomial  term) and its component variables (the ones that were multiplied)

.b- To make interpretation of parameter estimates easier

?I was recently asked when is centering NOT a good idea

Well, basically when it doesn’t help

For reason #1, it will only help if you have multiplicative terms in a model. If you don’t have any multiplicative terms–no interactions or polynomials–centering isn’t going to help

For reason #2, centering especially helps interpretation of parameter estimates (coefficients) when

a) you have an interaction in the model

b) particularly if that interaction includes a continuous and a dummy coded categorical variable and

c) if the continuous variable does not contain a meaningful value of 0

d) even if 0 is a real value, if there is another more meaningful value, such as a threshold point. For example, if you’re doing a study on the amount of time parents work, with a predictor of Age of Youngest Child, an Age of 0 is meaningful and will be in the data set, but centering at 5, when kids enter school, might be more meaningful

:So when NOT to center

.a) If all continuous predictors have a meaningful value of 0

. b) If you have no interaction terms involving that predictor

.c) And if there are no values that are particularly meaningful

 

:The original article

Here

اترك تعليقاً

لن يتم نشر عنوان بريدك الإلكتروني. الحقول الإلزامية مشار إليها بـ *