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Rating: 
Summary: Good intro Multilevel modeling - uses "Englishlike" language
Review: I liked the "comman-man-language" used by the authors to explain Multilevel Modeling. The use of MLn software, which is nearly 50% of the book, was a damper. With the commercial world dominated by SAS and SPSS, I would have liked the authors to give the examples for use with SAS
Rating:
Summary: User friendly!
Review: 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.
Rating:
Summary: User friendly!
Review: 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.
Rating:
Summary: reasonable overview of a burgeoning technique
Review: 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.
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