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, Robert Bickel 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.

Practical and user friendly, the book:

• Demonstrates the steps for specifying, estimating, testing, and reformulating multilevel models
  
• Clarifies the meaning of nesting and shows how to identify and analyze nested variables; for example, students in different schools located in different districts
  
• Explains how to make important specification decisions in multilevel modeling, such as which coefficients should be fixed and which should be random
  
• Describes how to select a suitable sample size in a multilevel model
  
• Includes separate chapters on three-level models and growth models in a multilevel framework

Note: Complete datasets, and other useful information, are available at www.itsjustregression.net.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.