This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, the author emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps. This book will serve as a core text in graduate-level courses on multilevel analysis and advanced data analysis. The table of contents is divided into 11 sections as follows: (1) Broadening the Scope of Regression Analysis; (2) The Meaning of Nesting; (3) Contextual Variables; (4) From OLS to Random Coefficient to Multilevel Regression; (5) Developing the Multilevel Regression Model; (6) Giving OLS Regression Its Due; (7) Does Multilevel Regression Have Anything to Contribute?; (8) Multilevel Regression Models with Three Levels; (9) Familiar Measures Applied to a Three-Level Model; (10) Determining Sample Sizes for Multilevel Regression; and (11) Multilevel Regression Growth Models.