We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.

But in all but the simplest designs, it’s not that straightforward.

In this webinar, you’ll learn the difference between crossed and nested factors.

We’ll walk through a number of examples of different designs from real studies to pull apart which factors are crossed, which are nested, and which are somewhere in between. We’ll also talk about a few classic designs, like split plots, Latin squares, and hierarchical data.

Particular focus will be on how you can figure all this out in your own design and how it affects how you can and cannot analyze the data.

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Linear regression with a continuous predictor is set up to measure the constant relationship between that predictor and a continuous outcome. This relationship is measured in the expected change in the outcome for each one-unit change in the predictor. One big assumption in this kind of model, though, is that this rate of change is the same for every value of the predictor. It’s an assumption we need to question, though, because it’s not a good approach for a lot of relationships. Segmented regression allows you to generate different slopes and/or intercepts for different segments of values of the continuous predictor. This can provide you with a wealth of information that a non-segmented regression cannot.

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