Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.

Extending the Basic Model

It’s a dangerous business, Frodo, going out your door. You step onto the road, and if you don’t keep your feet, there’s no knowing where you might be swept off to.

—J. R. R. Tolkien, 1954

The Flexibility of the Mixed-Effects Model

The previous chapter focused on building, estimating, checking, and interpreting the basic multilevel model using mixed-effects techniques. By basic, we mean that the model was limited to two levels, and the dependent variable was continuous. However, mixed-effects modeling is quite a bit more flexible than implied by the basic models explored in Chapter 3. Most important, we can extend the model to handle a wide variety of noncontinuous dependent variables. In addition, multilevel models are not limited to just two levels, ...

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