One of those “rules” about statistics you often hear is that you can’t interpret a main effect in the presence of an interaction.
Stats professors seem particularly good at drilling this into students’ brains.
Unfortunately, it’s not true.
At least not always. (more…)
Not too long ago, a client asked for help with using Spotlight Analysis to interpret an interaction in a regression model.
Spotlight Analysis? I had never heard of it.
As it turns out, it’s a (snazzy) new name for an old way of interpreting an interaction between a continuous and a categorical grouping variable in a regression model. (more…)
One of the most confusing things about statistical analysis is the different vocabulary used for the same, or nearly-but-not-quite-the-same, concepts.
Sometimes this happens just because the same analysis was developed separately within different fields and named twice.
So people in different fields use different terms for the same statistical concept. Try to collaborate with a colleague in a different field and you may find yourself awed by the crazy statistics they’re insisting on.
Other times, there is a level of detail that is implied by one term that isn’t true of the wider, more generic term. This level of detail is often about how the role of variables or effects affects the interpretation of output. (more…)
Understanding moderation is one of those topics in statistics that is so much harder than it needs to be.
Here are three suggestions to make it just a little easier.
I have spoken with a number of researchers who are surprised to learn that moderation is just another term for interaction.
Perhaps it’s because moderation often appears with discussions of mediation. Or because we tend to think of interaction as being part of ANOVA, but not regression.
In any case, both an interaction and moderation mean the same thing: the effect of one predictor on a response variable is different at different values of the second predictor. (more…)
In all linear regression models, the intercept has the same definition: the mean of the response, Y, when all predictors, all X = 0.
But “when all X=0” has different implications, depending on the scale on which each X is measured and on which terms are included in the model.
So let’s specifically discuss the meaning of the intercept in some common models:
Every statistical software procedure that dummy codes predictor variables uses a default for choosing the reference category.
This default is usually the category that comes first or last alphabetically.
That may or may not be the best category to use, but fortunately you’re not stuck with the defaults.
So if you do choose, which one should you choose? (more…)