Now available both in print and online: books.google.com/books/about/...

Why care about these technicalities?

Interactions are one of our major statistical tools for examining similarity or difference in statistical relationships across groups, identities, or contexts.

Interactions in logistic regression can be particularly challenging to work with: agrogan1.github.io/newstuff/cat...

Both main effects and interactions need to be interpreted together:
agrogan1.github.io/posts/intera...
agrogan1.github.io/posts/intera...

Interactions are often thought of as encompassing a main effect (x), and a moderator (m), but because of the commutative property of multiplication, interactions are essentially symmetric: agrogan1.github.io/posts/intera...

For many models, adding a main effect and interaction for group can be seen as giving each group its own regression line with its own intercept and its own slope:

agrogan1.github.io/posts/intera...
agrogan1.github.io/posts/intera...

I argue that multilevel modeling is a principled way to explore human variation and commonality.

I try to provide a tutorial for applied researchers, and to bring "down to earth" some of the more advanced ideas about multilevel modeling that I see in the methodological literature, but rarely see in applied work.

DOI for the Open Access article: doi.org/10.1016/j.ch...

Just similarity and difference across groups or countries, which I think, multilevel models let us start to see. Simply the size of the random slopes. Now that I’ve written the book, I need to figure out even better ways to quantify this.

Thank you for the kind words!

Same information as a treemap.