Multilevel Modeling

by Douglas A. Luke

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.

Genres: Textbooks

128 pages, Kindle Edition

First published July 8, 2004

Reviews

[Bari Mir · Rating 5 out of 5]

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.

[Esteban Correa · Rating 5 out of 5]

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).

I love short formats from SAGE

[Molly Wy · Rating 5 out of 5]

Great, brief explanation of multilevel modeling!! Thanks Douglas!

[May Ling · Rating 5 out of 5]

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.

[Miroslav Nemčok · Rating 4 out of 5]

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.

[Jayna]

great book for beginners trying to learn HLM