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Generalized Linear Models, by John P. Hoffmann
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This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.
- Sales Rank: #492129 in Books
- Published on: 2003-08-25
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x .70" w x 6.90" l, .93 pounds
- Binding: Paperback
- 216 pages
From the Back Cover
Without requiring mathematical training beyond algebra and introductory statistics, Generalized Linear Models shows readers how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing and business.
By including numerous exercises and worked-out examples, as well as applications from many academic disciplines, Hoffman has written a book that is less theoretical and more applied than competing texts.
Most helpful customer reviews
11 of 11 people found the following review helpful.
Difficult Material Made Accessible
By not a natural
John Hoffman's Generalized Linear Models: An Applied Approach is remarkably well written. Other texts that cover some of the same topics and are advertised as minimizing mathematical development in favor of verbal exposition, such as Hosmer and Lemeshow's Applied Logistic Regression, are much more difficult. Texts not written for those with limited mathematical backgrounds but that deal with roughly the same material, including Long's Regression Models for Categorical and Limited Dependent Variables, are inaccessible to those without training comparable to that of a mathematical statistician.
I'm sure that the more densely mathematical texts have their place, and are of real value to those with technical skills that match the demands they place on the reader. Nevertheless, were it not for talented writers such as Hoffman, who are also accomplished statisticians with a pedagogical bent, many social and behavioral scientists who do first-rate quantitative research would be dealt out.
In evaluating Hoffman's book it is interesting to acknowledge that the conceptual foundation for generalized linear models was not put in place until 1972 in a now-classic article by Nelder and Wedderburn published in The Journal of the Royal Statistical Society. This path-breaking work unified a varied set of statistical procedures, including ordinary least squares regression, logistic regression, probit regression, and poisson regression.
Hoffman's book, which covers these topics and more, was first pubished in 2004. As these things go, thirty-two years is a relatively short time to move from laying the mathematical foundations to writing a comprehensive text that is accesisble to readers who have had no more than a basic course in statistics and a thorough introduction to multiple regresison. Hutcheson and Soforoniou's book The Multivariate Social Scientist, published in 1999, also deals with procedures built on the generalized linear model. However, while their book is useful, its breadth and depth do not match Hoffman's.
It is easy to underestimate the creativity, substantive knowledge, and effort that goes into transforming densely mathematical presentations into accessible accounts that enable the reader to understand and apply sophisticated statistical procedures. This is what Hoffman has done.
Nevertheless, reading Hoffman's book from cover to cover is not a walk in the park. His explanations are clear and his examples do their job, but topics such as ordered logistic regression, multinomial logistic regression, negative binomial regression, and event history analysis require a good deal of thought, and sometimes re-reading the same paragraph a couple of times. Still, if you keep in mind the link function that enables the relationships to be presented in linear form, as well as the distribution of the dependent variable and residuals, you'll find this material surprisingly readable.
Since the author is truly skilled at making complex and counter-intuitive procedures understandable, I wish he had included a chapter on loglinear analysis. One book can cover only so many topics, but I've found existing treatments of loglinear analysis confusing and would have read an account by Hoffman with real interest.
The computer output from Stata, SPSS, and SAS was very instructive. However, I could have done without the instructions for using these software packages. The instructions seemed extraneous to the presentation of statistical material, and sometimes got in the way. Also, the references to resources that provide further development of many topics were too numerous and might have been deleted or put into footnotes or back-of-the-book references.
The author sometimes covers material that seems non-essential, as with variations on survival and event history analysis that produce results known to be dubious. Hoffman's last chapter introduces a variety of pertinent procedures such as generalized estimating equations and multilevel regression. However, none of these topics is presented with sufficient detail to make them useful. Perhaps the last chapter should be deleted.
Very rarely, Hoffman betrays the fact that parts of his presentation are based on textbook accounts rather than his own research. A conspicuous example is his use of variance inflation factors of 9 or 10 to indicate troublesome multicollinearity, a recommendation found in some econometrics texts, such as early editions of Gujarati's Basic Econometrics. However, anyone who has worked with data that yields VIF's this large knows that standard errors of coefficients are certain to be inflated and coefficient estimates will be very imprecise. I've found a VIF of 4 to be a more useful cutoff.
Nevertheless, in writing Generalized Linear Models in an accessible and informative way, Hoffman has done a real favor for those of us who have to teach ourselves new and difficult material. A fine textbook and a useful reference that merits a prominent place on the bookshelf of anyone who does applied statistics.
8 of 9 people found the following review helpful.
Excellent introduction
By A Customer
Generalized linear models are a class of statistical models that allow various types of variables to be used as dependent variables. This book provides an excellent elementary introduction to this topic. It will be useful as a textbook or as an overview for statistical modelers. Even though I have many years experience with statistics, I learned much from this book. In particular, its well-written presentation and myriad examples provide wonderful access to what had heretofore been difficult material. It also shows how to run models using STATA, a widely used statistical package. I highly recommend this book!
5 of 7 people found the following review helpful.
Generalized Linear Models
By Stacey MacArthur
This book has been extremely helpful in completing the statistics for my thesis and other graduate classes. It's very easy to read and gives good examples to aid understanding.
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