Taking a practical, hands-on approach to multilevel modeling, this book provides readers with an accessible and concise introduction to HLM and how to use the technique to build models for hierarchical and longitudinal data. Each section of the book answers a basic question about multilevel modeling, such as, "How do you determine how well the model fits the data?" After reading this book, readers will understand research design issues associated with multilevel models, be able to accurately interpret the results of multilevel analyses, and build simple cross-sectional and longitudinal multilevel models.
A must have for any statistics student or beginner statistician. I read this book many many times while working on my dissertation. It also helped me to better communicate my vision, methodology, and results to my less data literate committee members. The book overall enhanced my understanding and skill set with multilevel modeling.
Simple monography about multilevel modeling in Statistics. Highly recommended to complement any Stats 101 course This is the kind of book that gets better every time you read it (Already finished my second read).
Within the series, this is one of the better articulated books. Luke explains multi-level modeling in very basic terms. He does not skip over the rational behind why the technique is used, what its benefits and weaknesses are and any qualitative rigor that should be applied in advance of employing the technique.
He recognizes that many statistical packages are the norm and provides some commentary on the benefits of each. All examples are well described with jargon defined in a straight forward manner. He employs examples that are easily accessible to any person in any discipline reading this book.
I also greatly appreciate the additional ML Modeling reference websites in the back pages.
Well written introductory piece into the multilevel modeling that will immediately force you to doubt most of your conclusions based on a single level analysis. Though, that is partially caused due to a very little discussion about the limits accompanying the method.