A Linear Regression Model with an interaction between two predictors (X_{1} and X_{2}) has the form:

Y = B_{0} + B_{1}X_{1} + B_{2}X_{2} + B_{3}X_{1}*X_{2}.

It doesn’t really matter if X_{1} and X_{2} are categorical or continuous, but let’s assume they are continuous for simplicity.

One important concept is that B_{1} and B_{2} are *not main effects*, the way they would be if there were no interaction term. Rather, they are *conditional effects*.

### Main Effects and Conditional Effects

A main effect is the overall effect of X_{1 }across all values of X_{2. }That overall effect is the difference in the mean of Y for each one unit change in X_{1}.

If there were no interaction term in the model, then B_{1} is a main effect, and that is how regression coefficients are generally interpreted.

But B_{1} is not that *when there is an interaction in the model*. It is the effect of X_{1} *conditional on X _{2} = 0.*

For all values of X_{2} other than zero, the effect of X_{1} is B_{1} + B_{3}X_{2}.

The biggest practical implication is that when you add an interaction term to a model, B_{1} and B_{2} change drastically by definition (even if B_{3} is not significant) *because B _{1} and B_{2} are measuring a different effect than they were in a model without the interaction term*.

But it isn’t labeled differently on the output. You have to know how to interpret those effects.

So don’t panic if B_{1} suddenly isn’t significant. It’s measuring something else altogether.

So B_{1}, in the presence of an interaction, is the effect of X_{1} only when X_{2} = 0.

If X_{2} never equals 0 in the data set, then B_{1} has no meaning. None.

### Centering to Improve Interpretation

This is a good reason to center X_{2}. If X_{2} is centered at its mean, then B_{1} is the effect of X_{1} when X_{2} is at its mean. Much more interpretable.

Even better is to center X_{2} at some meaningful value even if it’s not its mean. For example, if X_{2} is Age of children, perhaps the sample mean is 6.2 years. But 5 is the age when most children begin school, so centering Age at 5 might be more meaningful, depending on the topic being studied.

If X_{2} is categorical, the same approach applies, but with a different implication. If X_{2} is dummy coded 0/1, B_{1} is the effect of X_{1} *only for the reference group*.

The effect of X_{1} for the comparison group is B_{1} + B_{3}. To see why, plug in 0 for X_{2} for the reference group and write out the regression equation. Then plug in 1 for X_{2} for the comparison group. Do the algebra.

### Related Posts

- Your Questions Answered from the Interpreting Regression Coefficients Webinar
- Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
- Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
- Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?

{ 10 comments… read them below or add one }

Hi Karen,

It was excellent. your explanation was simple, practical and suitable.

I made this observation when I compared the outcomes of a mixed-model analysis with two fixed effects, one categorical (A) and one continuous (B), the latter entered the model as covariate. The fixed effect stats for A were quite different in two models with or without the interaction with B in the way you described it. In fact, after using a z-transform of B (in SPSS a z-transform of a variable can be requested via the Descriptives command), the meaning of the main effect of A was established.

Thanks for the explanation!

Bernd

For categorial predictors it is indeed important how to center them. Setting them to -1 and 1 (deviation coding) compares the level at 1 to the mean of the predictor, while setting them to -0.5 and 0.5 (simple coding) compares the level at 0.5 to the level -0.5. It also changes the meaning of your parameter estimate (distance from the center of the predictor to distance between levels)

For more predictor levels this becomes more complicated to code with simple coding, but google helps.

Hi Karen,

Thanks a lot for your posts. They are extremely valuable to my thesis.

I am now having a situation with the confidence intervals of the coefficients in a model with statistical interaction term.

Using the example in your post, how can I know the confidence interval of each effect?

1. When X2=0, Y = (B1+B3*0)*X1 = B1*X1

Can I simply use the confidence interval of B1 generated/calculated by the software output?

2. When X2=1, Y= (B1+B3*1)*X1

How can I calculate the confidence interval of that (B1+B3)?

Or is it nonsense to calculate confidence intervals of each coefficients in such models with interaction?

With Much Thanks,

Thet

Hi Thet,

It’s more common to report the confidence intervals for each parameter estimate, which is what the software generates. You can’t use the confidence interval of B1 for B1+B3.

Omer, it’s not a silly question. Depending on your software, you command it to show the basic info of x. For STATA, its sum X2. I forget for sas but something similar.

The sum command will show the mean value for X2 in your data. Say it’s 6.2. You must now generate a new variable for X2. In stata, you would say

gen X2_c (or whatever name you like) = X2-6.2

Your data is now centered. I believe to center at a different value you would just subtract that value from X2, but that seems too simple. I’m a beginner, too.

Hi Elaine,

No, you got it–it really is that simple. For whatever value you want to center on, just subtract that value from X2.

Nice post. However, I think the condition only apply if your design matrix is a offset from reference model. For a over-parameterized model or sigma-restricted model B1 will the your main effect.

Hi Karen,

This may be a silly question but how do you center X2?

Hi Omer,

You can center X2 by standardizing it, i.e. (X2-mean(X2))/sd(X2)

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