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(More customer reviews)Sage university papers on quantitative applications are presented as brief and inexpensive treatments of specialized topics in statistics and data analysis. Some are well worth the price, while some leave you wishing you used the money towards acquiring a full-length treatise or textbook. If you need to learn about multilevel modeling on your own, Douglas Luke's Multilevel Modeling is worth much more than its price, especially if you buy it from Amazon.com, because it is a model of compositional economy in addressing a complex idea, and of what a truly introductory textbook should be. Luke maintains focus, precision, and masterful clarity in a fashion that is rarely encountered among books which claim to be "An Introduction to ... " a topic as specialized, intricate, and novel as is multilevel statistical modeling. Luke defines the terms more lucidly than some of the most popular full-sized books which aim to introduce multilevel analysis (and which still leave the reader mired in ambiguity). The author does not attempt to impart any gratuitous complexity to his exposition and manages to integrate textual clarity with statistical notation and equations, figures, and tables which are equally clear for someone who, while familiar with concepts beyond one-variable statistics and simple linear regression and ANOVA, has never studied or engaged in this type of data analysis or research design before. You may need to proceed to thicker treatises to make a thorough analysis and find out how to use your favorite software, but if you begin with one or more of those and find the topic still unclear in its elements - either the big picture or the basic details - you will find Luke's 78 pages (including reference to data online) enlightening.
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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.
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