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…)
In a statistical model–any statistical model–there is generally one way that a predictor X and a response Y can relate:
This relationship can take on different forms, of course, like a line or a curve, but there’s really only one relationship here to measure.
Usually the point is to model the predictive or explanatory ability, the effect, of X on Y.
In other words, there is a clear response variable*, although not necessarily a causal relationship. We could have switched the direction of the arrow to indicate that Y predicts X. Or used a two-headed arrow to show a correlation, with no direction, but that’s a whole other story.
For our purposes, Y is the response variable and X the predictor.
But a third variable–another predictor–can relate to X and Y in a number of different ways. How this predictor relates to X and Y changes how we interpret the relationship between X and Y. (more…)
One of the biggest questions I get is about the difference between mediators, moderators, and how they both differ from control variables.
I recently found a fabulous free video tutorial on the difference between mediators, moderators, and suppressor variables, by Jeremy Taylor at Stats Make Me Cry. The witty example is about the different types of variables–talent, practice, etc.–that explain the relationship between having a guitar and making lots of $$.
