Mixed models are hard.

They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.

But they’re also extremely important to understand because many data sets require their use.

Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.

One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect.

I have found one issue particularly pervasive in making this even more confusing than it has to be. People in the know use the terms “random effects” and “random factors” interchangeably.

But they’re different.

This difference is probably not something you’ve thought about. But it’s impossible to really understand random effects if you can’t separate out these two concepts.

Here’s the basic idea:

· A factor is a variable.

· An effect is the variable’s coefficient.

Let’s unpack that so it’s meaningful.

When we’re talking about fixed factors and their effects, this doesn’t usually come up. We’re able to see easily the difference between the variables themselves and those variables’ effects.

Here’s an example of a Linear Mixed Model that is predicting an outcome Y (Number of Jobs, in Thousands) over Time (5 decades, coded 0 to 4) for a set of counties. Each county is either Rural or Non-rural and is measured across the 5 decades.

To make it easy to see, the fixed part of the model is in blue and the random part of the model is in orange.

It’s very clear that Time and Rural are both fixed predictor variables in this model and that β_{1} and β_{2} are their coefficients.

Just like in any regression model, those coefficients are called slopes and that is how we measure the effect of each predictor. We have one additional fixed effect in the model, the intercept β_{0}. The intercept simply reports the mean of Y when all predictors are 0.

So just to be clear, in the fixed part of the model, we have:

· three fixed effects: β_{0}, β_{1}, β_{2}

· two fixed variables: Time Rural

One of these variables, Rural, is a factor because it’s categorical. The other, Time, is a covariate because it’s numerical. (Some people use the term covariate to mean a control variable, not a numerical predictor. That’s not how I’m using it here).

This part is also simple because of the way we specify it in the software. Regardless of which software we use, all we have to do is specify which predictors we want in the fixed part of the model and the software will automatically estimate their coefficients.

If we wanted also to add in, say an interaction term between Rural and Time, we also just add that to the model and the software estimates a coefficient for that too.

But what about the random part of the model, in orange?

This part is a little harder, partly because of the notation, partly because of the way we specify it in the software, and partly because of the wording we use.

In the random part of the model, there is one random factor, two random effects, and the residual.

I suspect you’re familiar with residuals from linear models. Let’s focus instead on the two random terms.

Just like each fixed term in the model, each random term is made up of a random factor and a random effect. The random effects aren’t hard to see: Those are μ_{0} the random intercept, and μ_{1} the random slope over time.

There is also a random factor here: County. It doesn’t look like it’s here, but it is.

We use the term “random factor” and not “random variable” because random variables in a mixed model MUST be categorical. They are never covariates.

County is denoted in the model by the subscript i. You’ll notice that all the random terms in the model have an i subscript but none of the fixed terms do.

That’s because the fixed terms average over all the counties, but the random terms are per county.

We could rewrite the random terms like this: μ_{0}County and μ_{1}Time*County.

That random intercept term, μ_{0i} has both an intercept coefficient and a factor: County.

In statistical software, you have to specify both, but it doesn’t look like it. You’ll specify that you want a random intercept, but County is specified as the “subject.”

Likewise, μ_{1i}Time is a slope coefficient across Time for county i. Time itself is NOT a random factor. County is.

So again, when you specify it in the software, County is specified as the subject and Time is the only “variable” you’re putting in as a random effect.

It makes it look like Time is a random factor, but it’s not. You’re fitting a slope across Time for each county. This is equivalent to fitting a Time*County interaction and u_{1} is the interaction effect.

So again, to summarize, in the random part of the model, we have:

· Two random effects: μ_{0} and μ_{1} for Time

· And one random variable: County

Calling County or Time a random effect is not just technically incorrect, but it makes it much harder to conceptualize what each of the real random effects is actually measuring.