Analysis of Ordinal Categorical Data. by A. AgrestiReview by: R. L. PlackettBiometrics, Vol. 41, No. 3 (Sep., 1985), p. 811Published by: International Biometric SocietyStable URL: http://www.jstor.org/stable/2531302 .

Accessed: 25/06/2014 00:29

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org.

.

International Biometric Society is collaborating with JSTOR to digitize, preserve and extend access toBiometrics.

http://www.jstor.org

This content downloaded from 185.44.77.146 on Wed, 25 Jun 2014 00:29:16 AMAll use subject to JSTOR Terms and Conditions

BOOK REVIEWS

EDITORS:

C. D. KEMP and A. W. KEMP

Analysis of Ordinal Categorical Data (A. Agresti) R. L. Plackett

An Introduction to Latent Variable Models (B. S. Everitt) H. Goldstein

Multivariate Morphometrics (2nd edition) (R. A. Reyment, R. E. Blackith, and N. A. Campbell) M. J. R. Healy

Cancer Clinical Trials: Methods and Practice (M. E. Buyse, M. J. Staquet, and R. J. Sylvester (eds)) L. S. Freedman

Atomic Bomb Survivor Data: Utilization and Analysis (R. L. Prentice and D. J. Thompson (eds))

M. J. Campbell

Information Analysis of Vegetation Data (E. Feoli, M. Lagonegro, and L. Orloci) M. H. Williamson

Mathematical Ecology: Proceedings, Trieste, 1982 (S. A. Levin and T. G. Hallam (eds)) E. Renshaw

Population Biology: Proceedings, Edmonton, 1982 (H. I. Freedman and C. Strobeck (eds))

C. Cannings Brief Reports by the Editors An Introduction to Multivariate Statistical Analysis,

2nd edition (T. W. Anderson) Statistical Theory and Data Analysis (K. Matusita

(ed.))

AGRESTI, A. Analysis of Ordinal Categorical Data. Wiley, New York, 1984, 287 pp. $35.95/f34.65. ISBN 0-471-89055-3.

Ordinal categorical data arise in many fields (for ex- ample, medicine and public health), and special meth- ods for their analysis are more efficient than the standard nominal procedures. Since 1969, about thirty books have been published on the analysis of cross- classified tables, and many of them give some attention to the methods that are most appropriate when at least one of the categorical variables is ordinal. This book is one of very few which emphasises such methods throughout, and includes those which have been de- veloped in recent years by L. A. Goodman and P. McCullagh.

The only prerequisite is a basic knowledge of statis- tical methods, especially in regression and analysis of variance. Familiarity with any form of categorical data analysis is not required, because the necessary ideas and results are presented in the first quarter of the book. This section introduces various kinds of odds ratio, measures of association and partial association, and the structure and analysis of hierarchical loglinear models for nominal data. All the rest of the book is concerned exclusively with ordinal data, concentrating first on suitable models and second on suitable measures of association. The section on models begins by develop- ing loglinear models which incorporate known scores assigned to each ordered variable. Logit regression is described for binary data, extended forms of logit are defined for multinomial data, and models for cumula- tive logits are discussed. Finally, other models for ordi- nal variables are reviewed, based respectively on un- known scores assigned to each variable, global odds ratios, mean response, and proportional hazards. The section on measures of association considers them first as descriptive statistics, proceeds to list guidelines re- garding category choice, and ends with methods of inference. Special attention is then given to the models

and measures that can be applied to square tables with ordinal categories. In the last chapter of the book, the principal methods are summarised and compared, with special reference to the advantages and disadvantages of model-building and measures of association. There are appendices concerned with weighted least squares, maximum likelihood estimation, delta method, com- puter packages, and a table of chi-squared. Each chapter ends with a set of Notes, in which further useful results are given, and a set of Complements, consisting of theoretical and practical exercises.

This is a well-organised and clearly written book, which emphasises the interpretation and application of the methods, and relegates most of the technical details to Appendices, Notes, and Complements. A question that is often asked concerns how far measures of asso- ciation are useful, and it is answered both directly in Chapter 12 and indirectly by repeated analyses of some of the sets of data. The view is taken that most readers will probably use the methods by applying existing computer software, and Appendix D provides descrip- tions of several packages and specialised programs. These are just two examples of the helpful way in which the material is presented throughout the book.

R. L. PLACKETT 57 Highbury

Newcastle-upon-Tyne, England

EVERITT, B. S. An Introduction to Latent Variable Models. Chapman and Hall, London, 1984, 107 pp. ?9.50. ISBN 0-412-25310-0.

Students, applied statisticians, and numerate research- ers who require an algebraic exposition of latent variable models with indications of appropriate estimation methods will find this book useful. The author describes the classical factor analysis model, then goes on to more general structural equation models, concentrating on

811

This content downloaded from 185.44.77.146 on Wed, 25 Jun 2014 00:29:16 AMAll use subject to JSTOR Terms and Conditions