Interpreting Lower Order Coefficients When the Model Contains an Interaction

A Linear Regression Model with an interaction between two predictors (X1 and X2) has the form: 

Y = B0 + B1X1 + B2X2 + B3X1*X2.

It doesn’t really matter if X1 and X2 are categorical or continuous, but let’s assume they are continuous for simplicity.

One important concept is that B1 and B2 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 X1 across all values of X2. That overall effect is the difference in the mean of Y for each one unit change in X1.

If there were no interaction term in the model, then B1 is a main effect, and that is how regression coefficients are generally interpreted.

But B1 is not that when there is an interaction in the model. It is the effect of X1 conditional on X2 = 0.

For all values of X2 other than zero, the effect of X1 is B1 + B3X2.

The biggest practical implication is that when you add an interaction term to a model, B1 and B2 change drastically by definition (even if B3 is not significant) because B1 and B2 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 B1 suddenly isn’t significant.  It’s measuring something else altogether.

So B1, in the presence of an interaction, is the effect of X1 only when X2 = 0.

If X2 never equals 0 in the data set, then B1 has no meaning.  None.

Centering to Improve Interpretation

This is a good reason to center X2.  If X2 is centered at its mean, then B1 is the effect of X1 when X2 is at its mean.  Much more interpretable.

Even better is to center X2 at some meaningful value even if it’s not its mean.  For example, if X2 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 X2 is categorical, the same approach applies, but with a different implication.  If X2 is dummy coded 0/1, B1 is the effect of X1 only for the reference group.

The effect of X1 for the comparison group is B1 + B3.  To see why, plug in 0 for X2 for the reference group and write out the regression equation.  Then plug in 1 for X2 for the comparison group.  Do the algebra.


Interpreting Linear Regression Coefficients: A Walk Through Output
Learn the approach for understanding coefficients in that regression as we walk through output of a model that includes numerical and categorical predictors and an interaction.

Reader Interactions


  1. Steph says

    Thank you for this helpful article. I want to clarify, without the presence of an interaction, is B1 the main effect of X1 across *all values* of X2, or is B1 the effect after holding all other predictors (X2, X3, X4, etc etc) constant? So for example with the presence of an interaction, if B1 for X1 is 10, and my X2 values range from 0-10, is B1 the effect when my X2 is any value it contains (0-10)? Or, is B1 the effect only when my X2 is 0?

    • Karen Grace-Martin says

      Hi Steph,

      B1 is the effect of X1 after holding all other predictors constant, but it doesn’t have to be held at 0. It can be at any value of X2 if there are no interactions.

  2. George says

    Hi Karen,

    I understand the point you want to make but I do believe that further clarifications are required. Mutliple regresssion models provide the unique effect of each predictor, controlling for the others (setting them to zero). So, anyway, the individual effect, say, B1 is a partial slope – i.e., conditional to setting the other predictors to zero.

    I think there is a lot of misunderstanding in interpreting B1 effects between:

    (1) y = B0+B1*X1
    (2) y = B0+B1*X1+B2*X2

    Terminology is a *pain* but it does make a difference here.


    • Karen Grace-Martin says

      Hi George,

      Oh, absolutely, each coefficients is the unique effect after holding others constant.

      It’s actually not necessary to hold others constant at 0 to control for them. Any constant will do. There are certainly advantages of choosing 0 for the other variables, but it doesn’t affect the interpretation of a variable’s coefficient unless those others are involved in an interaction with the variable of interest. That was a lot of my point.

  3. Tongming Kang says

    Hi Karan,

    Thanks for this article! It really helped me to gain a better understanding in interpreting interaction in a model.

    And I want to ask you some questions. Let’s say I have a multivariable linear model containing two independent variables. Is it OK to center both two variables before introducing the interaction term? And when it comes to intrepretation, is it always recommended to center predictors? (Or in which condition would it be nice to center predictors?)

    Thanks for your help!

  4. Bernd says

    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!

  5. Alexander Seidel says

    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.

  6. Thet says

    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,

    • Karen Grace-Martin says

      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.

  7. Elaine says

    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.

  8. Charles Lao says

    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.

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