There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?
Centering predictor variables is one of those simple but extremely useful practices that is easily overlooked.
It’s almost too simple.
Centering simply means subtracting a constant from every value of a variable. What it does is redefine the 0 point for that predictor to be whatever value you subtracted. It shifts the scale over, but retains the units.
The effect is that the slope between that predictor and the response variable doesn’t [Read more…] about Should You Always Center a Predictor on the Mean?
Yesterday I gave a little quiz about interpreting regression coefficients. Today I’m giving you the answers.
If you want to try it yourself before you see the answers, go here. (It’s truly little, but if you’re like me, you just cannot resist testing yourself).
True or False?
1. When you add an interaction to a regression model, you can still evaluate the main effects of the terms that make up the interaction, just like in ANOVA. [Read more…] about Answers to the Interpreting Regression Coefficients Quiz
The intercept (often labeled the constant) is the expected mean value of Y when all X=0.
Start with a regression equation with one predictor, X.
If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value.
If X never equals 0, then the intercept has no intrinsic meaning. [Read more…] about Interpreting the Intercept in a Regression Model
There are two reasons to center predictor variables in any type 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.
I was recently asked when is centering NOT a good idea? [Read more…] about When NOT to Center a Predictor Variable in Regression
It seems very many researchers are needing to learn multilevel and mixed models, and I have to say, it’s not so easy on your own.
I too went to graduate school before it was taught in classes–we did learn mixed models as in Split Plot designs, but things have progressed a bit since then. So I too have had to learn them without benefit of a class, or teacher. So I feel your pain. But I’ve struggled through and learned a [Read more…] about Concepts in Linear Regression you need to know before learning Multilevel Models