Introducing Multilevel Modeling (Introducing Statistical Methods series) [Paperback]

Ita G G Kreft (Author), Jan de Leeuw (Author)

Book Description

ISBN-10: 0761951415 | ISBN-13: 978-0761951414 | Publication Date: June 1998 | Edition: 1

This is the first accessible and practical guide to using multilevel models in social research.

Multilevel approaches are becoming increasingly important in social, behavioural, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models.

While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling.

The authors' approach is user-oriented and the formal mathematics and statistics are kept to a minimum. Other key fe

Editorial Reviews

Review

`This book offers an introduction to multilevel modelling that is both clear and accessible. For this reason I strongly recommend it to anyone new to the subject.... I particularly liked the non-directive feel to the book, which leaves the reader clear that there are some issues about which they will have to make up their own mind based on the evidence presented.' - British Journal of Mathematical and Statistical Psychology

`If you teach statistics to students with little patience for Greek letters and formulas and who do not have a matrix algebra and mathematical statistics background, I recommend that you use Introducing Multilevel Modeling. - Journal of the American Statistical Association

Product Details

• Paperback: 160 pages
• Publisher: Sage Publications Ltd; 1 edition (June 1998)
• Language: English
• ISBN-10: 0761951415
• ISBN-13: 978-0761951414
• Product Dimensions: 9.2 x 6.3 x 0.5 inches
• Shipping Weight: 6.4 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #404,633 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

29 of 31 people found the following review helpful:

4.0 out of 5 stars reasonable overview of a burgeoning technique, April 6, 2000

By 

Dean McKenzie - See all my reviews

This review is from: Introducing Multilevel Modeling (Introducing Statistical Methods series) (Paperback)

When analysing data, the relationships between people that belong in the same classroom, live in the same street or suburb, are part of the same family or therapy group,etc., are often ignored. Multilevel or hierarchical linear modelling is a statistical technique for taking into account such dependencies, arranged in hierarchies (e.g., correlations between students within classrooms, correlations between classrooms within schools, correlations between schools within school districts). In other words, multilevel modeling techniques attempt to model the hierarchical relationships that are found in the real world. In the last 10 years or so there has been a growing number of books and software packages concerned with multilevel analyses. Introducing Multilevel Modeling is shorter and slightly less 'mathematical' than most and gives quite a good introduction to the subject. The book makes reference to the British MLn (MLWiN) computer program in its examples, whereas an introductory text arguably should have used the HLM program, for which a cutdown student version is available free. Taking group dependencies into account is extremely important, but unfortunately many researchers will be discouraged by the dry and heavy-going feel of these texts, which is so often the case with anything involving statistical theory. A highly approachable and readable book remains to be written, but Introducing Multilevel Modeling is probably the best of the current crop.

13 of 13 people found the following review helpful:

4.0 out of 5 stars User friendly!, May 10, 2001

By A Customer

This review is from: Introducing Multilevel Modeling (Introducing Statistical Methods series) (Paperback)

This book is the best intro to the subject that I've seen. The authors minimize the use of notation, mathematics and the like, and invoke the reader's intuition by developing some good, concrete examples. They present those examples as datasets (accessible on the web), as "run" regressions (i.e. with parameter estimates and standard errors), and graphically.

They also demo how to "run" each of the examples on a PC, using the program MLn. If you don't use MLn (and I had never heard of it); this part of the book is less helpful. It would be great if, in an accompanying website perhaps, they were to demo the same analyses using other packages (e.g. SYSTAT, SAS and the like). But this book is really quite good, and a terrific addition to any applied social scientist's library.

3 of 3 people found the following review helpful:

4.0 out of 5 stars An excellent theoretical introduction to Hierchical Linear Models, September 9, 2005

By 

Auriel A. Willette (Madison, WI) - See all my reviews
(REAL NAME)   

This review is from: Introducing Multilevel Modeling (Introducing Statistical Methods series) (Paperback)

As a foreword, I am a 2nd year psychology graduate student with ANOVA and multiple regression experience.

That said, I've found this work to be clear, precise, and straight-forward in introducing the logic and concepts behind why one would wish to use a hiearchical linear model, as well as the foundation of said statistical design. The lack of emphasis on heavy math-based calculations will undoubtedly expedite training and use for those in the behavioral sciences. On that same note, formulae are interjected only when necessary and more often clarify than confuse a given point or concept.

The only drawback of this text might be imparting exactly how one performs HLM. There are other books on the market that, though more technical and/or less clear, provide the heavy emphasis on analysis that this book does not quite have.

Even so, I am firmly convinced that there is no better book for HLM in terms of laying out the theoretical foundation. An added bonus for me was the fact that ANOVA and regression are richly elaborated upon--a feature that many graduate students might appreciate as a refresher.

In short, I highly recommend this book.