Linear Regression Analysis - When NOT to Center a Continuous Predictor Variable

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

1. To lessen the correlation between a multiplicative term (interaction or polynomial term) and its component variables (the ones that were multiplied).
2. To make interpretation of parameter estimates easier.

But 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 threshhold 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:

1. If all continuous predictors have a meaningful value of 0.
2. If you have no interaction terms involving any continuous predictors with categorical ones.
3. And if there are no values that are particularly meaningful.

All three of these criteria should apply before you choose to not center. If any one is false, centering will help you interpret your coefficients.

The linear regression analysis - three reasons why researchers in psychology, regression, ANOVA should know

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Back when I was doing research in psychology, I knew ANOVA. I had a series of courses that is done, and you could run back and forth. I heard ANCOVA, ANOVA, but in any class that the last argument was about the curriculum, and we are always short of time.

The other thing that drove me crazy was the statistics professors always said, "ANOVA is a special case of regression." I could not for the life of me understand how or why.

Not beforeI turned on the statistics, I finally found a class of regression and found that everything was ANCOVA. And only when I started consulting, and see hundreds of different models of regression and ANOVA finally got the connection.

But if you're not driving curiosity ANOVA and regression, regression want to know why you should, as a researcher in psychology, education or agriculture, which forms in ANOVA? There are three main reasons.

There is a firstmany, many variables and continuous independent covariates should be included in the models. Without the tools to analyze as continuous, have left, forcing the shares ANOVA with any technology, as the median. At best, it is losing power. At worst, you are publishing your article because you are missing a real impact.

According After a solid understanding of the general linear model in its various forms equipped to truly understand the variablesand their relationships. It allows to model a different way - I try not fishing data, but in uncovering the true nature of the relationship. Having the ability to interact a term or a term add to the square allows you to hear your data and makes you a better researcher.

The third multiple linear regression model is the basis for many other statistical methods - logistic regression, multilevel models and mixed Poisson regression, survival analysis,and so on. Each of these represents a step (or smaller jump) on multiple regression. If you have problems with what variables or interactions at the center means to interpret the learning of a These other techniques is difficult, if not painful.

Have thousands of researchers led to their statistical analysis of the last 10 years, I am convinced that a strong intuitive understanding of the general linear model in its variety of forms, the key is not only asafe and effective statistical analyst. You are then free to learn and explore other methods if necessary.