Predictions in Time Series Using Regression Models
than for the rest of the book; accordingly, I suspect only the most sophisticated will  nd this chapter accessible. Overall, this book is an admirable attempt both to update the popular  rst edition to re ect the state-of-the-art in hierarchical modeling and to offer comprehensive, current coverage of this important area at a level accessible to researchers and graduate students whose primary focus is not statistical methodological development (although they may use statistical methods frequently in their work). On both counts, I do have a few reservations. First, Goldstein (1993), in a review of the  rst edition, found the restriction to examples involving education a drawback, remarking that this focus limits the book’s audience. This feature is retained in the new edition, and I agree that it does limit the potential readership, and in particular makes the book more dif cult to approach for practitioners from other  elds who would bene t most from motivation of the techniques via diverse examples that might place the material in more familiar territory. There continues to be a dearth of books at a “popular” level that cover material such as longitudinal data methods—or, more generally, hierarchical modeling—at a level accessible to, say, individuals with training in statistics at the “terminal master’s” level or researchers and graduate students in substantive  elds who have some statistical background. A good part (but not all) of this book would partly  ll that niche in a much broader way were the scope of subject matter demonstrations expanded. Nonetheless, I understand the authors’ focus on the subject matter with which they are most familiar, and I applaud their goal of communicating old and new (sometimes complex) developments to their target audience. My second, minor quibble has to do mainly with the new material. Of necessity, the discussion of some complicated topics, such as missing data and the Bayesian paradigm, including Gibbs sampling, are rather abbreviated. For example, the usual de nitions of missing completely at random, missing at random, and nonignorable nonresponse are given along with brief discussion of the consequences of each and citation of relevant references; similarly, issues such as sensitivity to choice of prior distribution are touched on brie y in Chapter 13. For some readers, the account in this book may be their  rst or perhaps only exposure to these ideas and techniques, for which guidelines for use are still not agreed upon by methodologists and for which debate on many important issues is still not resolved. On these grounds, I would have liked to have seen sterner cautions and explicit mention of lack of consensus within the statistical research community where appropriate. In summary, I would recommend Hierarchical Linear Models: Applications and Data Analysis Methods to individuals who would like a mostly nontechnical survey of the rationale and applicability of hierarchical models; however, I would add a warning that some parts of the book are advanced and require a certain level of background and sophistication. The book is quite different in focus and level from other recent, popular texts on related material that do not necessarily cover the same breadth of topics, such as the texts of Verbeke and Molenberghs (2000) and Diggle, Heagerty, Liang, and Zeger (2002). As such, it is likely to appeal to a narrower audience. Nonetheless, it  lls a void in the continuum of texts from the most technical to the most accessible.