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.

Planning A Multilevel Model

It’s turtles all the way down!

—Stephen Hawking, 1988

The Basic Two-Level Multilevel Model

The goal of a multilevel model is to predict values of some dependent variable based on a function of predictor variables at more than one level. For example, we might want to examine how a child’s score on a standardized reading exam is influenced both by characteristics of the child (e.g., amount of study time) as well as characteristics of the child’s classroom (e.g., size of class). In this example, we consider the child to be measured and modeled at Level 1, and the classroom at Level 2.

This simple two-level structure can be seen in the following multilevel model, with one predictor variable each at Level 1 and Level ...

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