# On the difference between the main effect in a one-factor and a two-factor regression

Consider a linear regression (based on least squares) on two predictors including an interaction term: $$Y=(b_0+b_1X_1)+(b_2+b_3X_1)X_2$$

$$b_2$$ here corresponds to the conditional effect of $$X_2$$ when $$X_1=0$$. A common mistake is to understand $$b_2$$ as being the main effect of $$X_2$$, i.e. the average effect of $$X_2$$ over all possible values of $$X_1$$.

Now let's assume that $$X_1$$ was centered, that is $$\overline{X_1}=0$$. It becomes now true that $$b_2$$ is the average effect of $$X_2$$ over all possible values of $$X_1$$, in the sense that $$\overline{b_2+b_3X_1}=b_2$$. In such conditions, the meaning given to $$b_2$$ is nearly indistinguishable from the meaning that we would give to the effect of $$X_2$$ in a simple regression (where $$X_2$$ would be the only variable, let's call this effect $$B_2$$).

In practice, it seems that $$b_2$$ and $$B_2$$ are reasonably close to each other.

Question:

Are there any "common knowledge" examples of situations where $$B_2$$ and $$b_2$$ are remarkably far from each other?

Are there any known upper bounds to $$|b_2-B_2|$$?

Edit (came after @Robert Long's answer):

For the record, a very rough calculation of what the difference $$|b_2-B_2|$$ might look like.

$$B_2$$ can be computed via the usual covariance formula, giving $$B_2=b_2+b_3\dfrac{Cov(X_1X_2,X_2)}{Var(X_2)}$$ The last fraction is roughly distributed like the ratio of two normal variables, $$\mathcal N(\mu,\frac{3+2\mu^2}{\sqrt N})$$ and $$\mathcal N(0,\frac{2}{\sqrt N})$$ (not independent, unfortunately), assuming that $$X_1\sim \mathcal N(0,1)$$ and $$X_2\sim \mathcal N(\mu,1)$$. I've asked a separate question to try to circumvent my limited calculation skills.

• (+1) Interesting question Intuitively I think your statement that b_2 and B2 are close isn't correct but I am looking into it :) Jul 21, 2020 at 5:50
• @RobertLong Oh I've just noticed your edit. I'd always thought that "factor" was a synonym for "explanatory variable" - just like "predictor". Jul 21, 2020 at 13:33
• I guess it can be used that way but by far the most common use of "factor" is as another word for a "categorical variable." Jul 21, 2020 at 14:09

$$b_2$$ here corresponds to the conditional effect of $$X_2$$ when $$X_1=0$$. A common mistake is to understand $$b_2$$ as being the main effect of $$X_2$$, i.e. the average effect of $$X_2$$ over all possible values of $$X_1$$.

Indeed. I typically answer at least one question per week where this mistake is made. It it also worth pointing out for completeness that $$b_1$$ here corresponds to the conditional effect of $$X_1$$ when $$X_2= 0$$ and not the main effect of $$X_1$$ which is easily seen by rearranging the formula

$$Y=(b_0+b_2X_2)+(b_1+b_3X_2)X_1$$

In practice, it seems that $$b_2$$ and $$B_2$$ are reasonably close to each other.

I think this is false in general for this model and will will only be true when the interaction term $$b_3$$ is very small.

Are there any "common knowledge" examples of situations where $$B_2$$ and $$b_2$$ are remarkably far from each other?

Yes, when the $$b_3$$ is meaningfully large then $$B_2$$ and $$b_2$$ will be meaningfully apart. I am thinking of how to show this algebraiclly and graphically but I don't have much time now, so I will resort to a simple simulation for now. First with no interaction:

> set.seed(25)
> N <- 100
>
> dt <- data.frame(X1 = rnorm(N, 0, 1), X2 = rnorm(N, 5, 1))
>
> X <- model.matrix(~ X1 + X2 + X1:X2, dt)
>
> betas <- c(10, -2, 2, 0)
>
> dt$Y <- X %*% betas + rnorm(N, 0, 1) > > (m1 <- lm(Y ~ X1*X2, data = dt))$coefficients
X2
2.06
> (m2 <- lm(Y ~ X2, data = dt))$coefficients X2 1.96  as expected. And now with an interaction: > set.seed(25) > N <- 100 > > dt <- data.frame(X1 = rnorm(N, 0, 1), X2 = rnorm(N, 5, 1)) > > X <- model.matrix(~ X1 + X2 + X1:X2, dt) > > betas <- c(10, -2, 2, 10) > > dt$Y <- X %*% betas + rnorm(N, 0, 1)
>
> (m1 <- lm(Y ~ X1*X2, data = dt))$coefficients X2 2.06 > (m2 <- lm(Y ~ X2, data = dt))$coefficients
X2
3.29


Are there any known upper bounds to $$|b_2-B_2|$$

I don't think so. As you increase $$|b_3|$$ then $$|b_2-B_2|$$ should increase

• I see: when $b_2$ is small with respect to the interaction effect, meaning the average slope is close to $0$ compared to the extremal slopes, then $B_2$ is likely to be (statistically insignificant and) very sensitive to error. It makes perfect sense, thank you very much. As for the upper bound I would still be happy with something that depends on $b_3$. But in light of your answer, this second question matters less. I think it would also be interesting to have some kind of estimates of how sensitive $B_2$ becomes in such situations. Jul 21, 2020 at 8:23
• I agree. I need to expand this answer. The standard error for $B_2$ will become large as the interaction gets larger. In the continuous x continous case here the confidence interval gets very wide (actually containing $b_2$ in the simulations I looked at). I will look at this some more later today as it's very interesting, and also look at categorical $X1$ and $X2$ Jul 21, 2020 at 8:30
• Regarding the possible values for $|𝑏_2−𝐵_2|$, in fact using the equation we can get $$𝐵_2=𝑏_2+𝑏_3\dfrac{𝐶𝑜𝑣(𝑋_1𝑋_2,𝑋_2)}{𝑉𝑎𝑟(𝑋_2)}$$ which does support the fact that the absolute difference grows with $|𝑏_3|$. I believe that the law of the sample covariance of $𝑋_1𝑋_2$ and $𝑋_2$ can be derived from the CLT, which settles this part of the question. Jul 21, 2020 at 11:53
• Ahh that's very useful. I will incorporate it into my simulations later. Jul 21, 2020 at 11:59
• In an orthogonal factorial design (i.e., a balanced design with equal sample sizes in each combination of X1 and X2), B2 will equal b2. In this case, the covariances are 0. Thus, using @ArnaudMortier's equation, b3 drops out. In an unbalanced design, centering the variables reduces but does not eliminate, the covariances, thus resulting in a B2 that is generally closer to b2. Jul 21, 2020 at 12:08

Adding to @RobertLong's answer, there is a slight conceptual mistake in the way $$b_2$$ is described in the question in the case where $$X_1$$ was centered. It is indeed true that $$b_2$$ becomes the average effect of $$X_2$$ over all possible values of $$X_1$$, in the sense that $$\overline{b_2+b_3X_1}=b_2$$, but it should be emphasized that this is an average of simple effects. It may have nothing to do with the main effect of $$X_2$$ on the DV, which means that $$b_2$$ may be really far from $$B_2$$ even without interaction.

Here is an example where there is no interaction, and $$b_2$$ and $$B_2$$ have nothing in common: the vertical axis is the DV $$Y$$, the horizontal axis is for the covariate $$X_2$$, and the colors stand for levels of the covariate $$X_1$$. For any value of $$X_1$$, the simple effect $$b_2+b_3X_1$$ is around $$-1$$, while the main effect $$B_2$$ is clearly positive. • (+1) Interesting !! I will try t take a look at this in more detail after you add the simulation code. May 6, 2021 at 14:17