An Introduction to Categorical Data Analysis (second edition) Alan Agresti (2007) ISBN: 9780471226185; 372 pages; £50.30, $94.9, €66.40 Wiley; http://www.wiley.com/

Abstract

This second edition is pitched at a level suitable for those taking an introductory course in categorical data analysis. The presentation has a low technical level and does not require familiarity with advanced mathematics. However, the author comments that as well as students it is also written for applied statisticians and practising scientists involved in data analysis. The high quality of the book can be judged from the fact that all of these are likely to find the book irreplaceable as a standard text on the subject. The author expresses a preference for logistic regression over log linear models because most applications have a single binomial or multinomial response variable. Therefore, this new edition places greater emphasis on logistic regression and less on log linear models compared with the first edition. From a pharmaceutical perspective this looks to be a good change in emphasis. The other main change from the first edition is the addition of two chapters. The new Chapter 9 covers the analysis of clustered correlated categorical data introducing marginal models and generalized estimating equations. Chapter 10 focuses on random effects and generalized linear mixed models. The content is arranged in 11 chapters as follows: Introduction; Contingency Tables; Generalized Linear Models; Logistic Regression; Building and Applying Logistic Regression Models; Multicategory Logit Models; Loglinear Models for Contingency Tables; Models for Matched Pairs; Modelling Correlated, Clustered Responses; Random Effects: Generalized Linear Mixed Models; A Historical Tour of Categorical Data Analysis. Each chapter ends with a problem section that will enable the reader to work on further examples and idiosyncratically some of the odd-numbered examples have solutions at the back of the book. An Appendix giving the SAS code for some of the examples is also included as well as a link to other software packages if SAS is not your software of choice. There are so many excellent features of this book that it is hard to know where to start. First of all the range of techniques covered is impressive. The practising statistician or scientist is likely to find all he or she needs to know day to day about categorical data analysis in this one book. Next the clarity of the exposition is superb; particularly so because the book avoids heavy mathematical development in favour of well-written English. The slow transition from basic concepts in the early chapters to the later more complex methods is handled very well and it encourages the reader to explore less familiar analysis techniques. The author also takes care to point out when methods may not be optimum due to small sample sizes or sparse cells. It is good to see the consistent focus on estimation, interpretation and understanding alongside statistical inference. Some useful guidance is given on the use of statistical software to implement the methods. The range of examples is very broad, covering data sets such as collisions involving British trains, width of female crab shells, AZT and Aids, teratology in rats and many, many more. An index of examples is provided, which covers more than three pages – showing how strongly the text is based on real data sets. All are well chosen and interesting and do an excellent job illustrating the range of analysis techniques discussed. This book has to be an essential reference for any practising pharmaceutical statistician. Is it needed alongside Alan Agresti's other superb book Categorical Data Analysis (Second Edition 2002)? I think so. The latter is more complete in terms of technical development and covers a broader range of topics. However, for me An Introduction to Categorical Data Analysis is the first volume to refer to when guidance is needed. It will also be a useful source of ideas to explain the essence of categorical data analyses to non-statisticians. Non-statisticians may well find the whole book too challenging but they will learn much from the early chapters, which are easy to follow and cover many of the important ideas in categorical data analysis.