Introducing Multilevel Modeling (Introducing Statistical Methods series)

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Kreft and DeLeeuw's Introducing Multilevel Modeling was written to mercifully place only limited mathematical demands on readers. It includes an interesting and readable account of intercepts and slopes as outcomes. However, in common with most other texts, it fails to accessibly develop parallels between multilevel modeling and more widely understood procedures. Instead, the authors' intent seems to be to highlight differences, perhaps to make the distinctiveness, virtues, and limitations of multilevel modeling clear to the reader. Whatever the value of their approach, I find it much easier to understand multilevel analysis as an extension of multiple regression, a connection which Kreft and DeLeeuw do not develop.

Simple examples are presented throughout the book using MLn software. Models with more than two levels are not discussed. Growth models are mentioned in passing. The authors emphasize that the constraints imposed by multilevel analysis make it best suited to relatively simple models, but I think they over-emphasize this judgment. Models much more complex than those used in this text are usefully employed and widely reported in a variety of books and journal articles.

The authors also treat close calls with regard to statistical significance as of dubious value. The reason for this is not given, and I much prefer setting an alpha level and then sticking with it, rather than second-guessing results.

This is a brief text that addresses some issues of importance in the Frequently Asked Questions section at the end of the book. It is not a comprehensive reference, but that was not its intended purpose. It reads especially well the second time through, and gives a good deal of needed attention to centering, an important topic that is glossed over in most other multilevel texts.

Introducing Multilevel Modeling (Introducing Statistical Methods series) Overview

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

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