"This book is one of the best statistical texts that I have ever read, and I would highly recommend using it for an advanced data analysis course. The examples and the step-by-step methods using SPSS are superb and statistically accurate. The author does a tremendous job of linking concepts to statistical procedures, as well as giving great examples! The listings for how to interpret the coefficients will really help graduate students make sense of their results. This is an obstacle that many of my graduate students have to overcome, so the examples will be much appreciated."--Alison J. Bianchi, Department of Sociology, Kent State University
"The author's use of a lot of graphs is very helpful pedagogically. Sometimes students (and professors!) need to see it to believe it, and this author does a great job of using figures and graphs to further the important points he is making."--Alison J. Bianchi, Department of Sociology, Kent State University  "This would be a good reference for sticky issues, and I really like that this book addresses issues that researchers actually struggle with when they are working on a project, such as effective sample size and maximum likelihood. I also like the writing style--casual but authoritative."--Julia McQuillan, Bureau of Sociological Research and Department of Sociology, University of Nebraska-Lincoln  "I really liked the way the text links to the tables."--Julia McQuillan, Bureau of Sociological Research and Department of Sociology, University of Nebraska-Lincoln  "The writing style is excellent for students and for applied researchers who don't consider themselves experts in statistics. One of the particular strengths of the book is how the author writes about the interpretation of results that may lead to the respecification of models and their tests. The figures of the models tested, the to-do lists, and interpretation of the corresponding output allow readers to integrate cognitively the concepts and procedures pertaining to very difficult topics. It is clear that the author spent significant amounts of time considering how best to present this information. I would tell my colleagues who don’t consider themselves experts in measurement and statistics to buy themselves a present--this book."--Jonna M. Kulikowich, Department of Educational and School Psychology and Special Education, Penn State  "This is a lucid and well-written text that cuts directly to the important issues in multilevel modeling. The regression approach is highly desirable as it builds on methods commonly taught in graduate programs in the social sciences. The text is appropriate for graduate-level teaching and could easily be used as the primary text in a multilevel modeling seminar. In addition, applied researchers with a background in multiple regression will find this an excellent resource for modeling nested data in cross-sectional and longitudinal studies."--Jeffrey D. Long, Department of Educational Psychology, University of Minnesota  "With this rigorous and detailed book, Bickel provides an unparalleled introduction to multilevel methods. This is a practical text both for experienced researchers who need to catch up with these newer methods and for students who have completed a regression course and are ready for the next step. The approach taken is conceptual and data-analytic, with extended examples analyzed in detail. There is extensive use of tables and figures to display data and report the worked examples, and each chapter’s brief discussion of additional resources and readings is very useful. All examples reference the SPSS software package, and specific instructions for using this software are included as boxed text that does not interrupt the flow of ideas but is easily found when needed. While the book is designed for the data analyst rather than the methodologist, technical issues are not ignored. For anyone who wants to learn or teach multilevel modeling using a text built on examples rather than equations, who prefers demonstrations over derivations, and who wants to begin analyzing data right away, this is the book to use."--Daniel Ozer, Department of Psychology, University of California, Riverside

"This is a very accessible and terrifically useful book."--Lisa Feldman Barrett, PhD, Department of Psychology, Boston College

"A clear and straightforward introduction to a valuable set of analytical tools, and is ideal for quantitative social researchers and post-graduate students who want to properly understand multilevel analysis."--Drug and Alcohol Review

"This would be a very good graduate text for an advanced course following a standard regression course and a useful book for anyone with solid experience in data analysis."--APA PsycCRITIQUES

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.

Key Phrases - Statistically Improbable Phrases (SIPs): 
regression growth equation, math achievement growth, multilevel regression equation, random component estimates, multilevel growth models, covariances among random components, four information criteria, one random slope, unstructured option, two random slopes, multilevel regression model, random coefficient equation, residual covariance structure, multilevel equation, multilevel regression analysis, random component variances, complex error term, vocabulary achievement, school identifier, county residents ages, random coefficient regression model, regression model specification, measured reading achievement, intercept variance, achievement disadvantage.