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.

Building a Multilevel Model

Look, I made a hat … Where there never was a hat.

—Stephen Sondheim, Sunday in the Park With George, 1984

Introduction to Tobacco Voting Data Set

To illustrate how to develop, test, and interpret a typical multilevel model, I will use an example data set taken from a tobacco control policy study (all data used here are available for download at https://www.douglasluke.com). The main goal of this study was to identify the important influences on voting on tobacco-related legislation by members of Congress from 1997 to 2000 (Luke & Krauss, 2004). The dependent variable is Voting %, the percentage of time that a senator or representative voted in a “protobacco” direction during those 4 years. As an example, consider the 1998 Senate ...

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