828 Book Reviews

navigate since the key points and caveats are high-lighted by using various methods. For example, thealgorithms are separately boxed off, making themstand out from the text. In addition, key paragraphsare highlighted with a grey background, and‘lightning-bolt’ icons indicate warnings or impor-tant points to note.

There are nine algorithms in total (the accept–reject method, simulated annealing, the EM algo-rithm and several versions of Metropolis–HastingsandGibbssampling)whicharegivenasbothpseudo-code and R code, and accompanied by examples.Exercises are provided throughout, as well as at theend of the chapters, making it a useful text for bothself-studyorasatextbook.Thecodeanddatasetsarealso available in the mcsm R package and a solutionmanual for odd-numbered exercises is available.

My only criticism is that the graphics are lowquality, which detracts from the aesthetics, butnot the understanding, of the material. Anotherpoint is that the contents on the back cover aresomewhat misleading (perhaps due more to themarketing department than to the authors). Theystate that a knowledge of R and Monte Carlomethods and an advanced mathematical back-ground are not required. Having none of these,however, would make the book difficult to com-prehend. Although the first chapter covers Rbasics, knowledge of at least one programminglanguage would be useful before jumping intoMonte Carlo methods. In addition, the book isset within a Bayesian framework and thereforeknowledge of Bayesian methods is needed to putthe material into context. Nevertheless, it wouldbe accessible to individuals with an undergradu-ate degree in statistics, mathematics, engineering,computer science or the physical sciences.

S. E. LazicF. Hoffmann-La Roche

Basel

A Beginner’s Guide to Structural EquationModeling, 3rd ednR. E. Schumacker and R. G. Lomax, 2010New York, Routledgexx + 510 pp., £39.95ISBN 978-1-841-69891-5

Structural equation modelling (SEM) techniquesare widely used in many disciplines such as econom-ics, psychology, marketing and health sciences. Thisis one of the first books published solely on this topicandprovidesabasic introductiontothese techniquesfrom scratch by using plenty of examples. From thevery first edition this book has been the leading book

on this topic, providing an authoritative and system-atic treatment of SEM for both researchers andpractitioners. The thoroughly revised and updatedthird edition provides an up-to-date exposition andcomprehensive treatment of SEM by using theLISREL 8.8 student version, which is freely avail-able software.Severalnewexampleshavebeenaddedfrom various disciplines and several chapters havebeen expanded. This book will be helpful to gradu-ate students and applied statisticians working in thearea of statistical and econometric modelling as wellas researchers in the areas of psychology, engineer-ing,medicineandbiologywhereSEMiswidelyused.

The book is divided into 17 chapters. The firstthree chapters are quite introductory, which maybe helpful to researchers in other disciplines whodo not have a statistics background. The first chap-ter introduces types of models, the history of SEMand software programs that are available to con-duct SEM. In the second chapter the importance ofexamining data for issues such as normality, linear-ity, outliers and missing data is discussed. The thirdchapter discusses the importance of correlation inSEM and includes a discussion on various factorsthat affect the correlation coefficient. Chapter Fourintroduces the basic steps in SEM, which includemodel specification, identification, estimation, test-ing and modification. Chapter Five includes issuesthat are related to model fitting, model and param-eter significance, power, sample size etc. ChaptersSix, Seven and Eight discuss the basic issues ofmultiple-regression models, path models and con-firmatory factor models respectively. ChaptersNine–Eleven deal with the development of struc-tural equation models. This is demonstrated byusing the examples and various steps discussed inChapter Four. Chapter Twelve outlines different ap-proaches to model validation. Chapter Thirteen–Sixteen provide examples from various disciplinesand demonstrate how SEM can be developed byusing the LISREL-SIMPLIS program. Finally,Chapter Seventeen outlines the matrix approach toSEM.

A Web site has been provided which includes rawdata for various examples and exercises that aregiven in the book. All the chapters in this book coverbasic concepts and principles followed by examplesusing LISREL to demonstrate the application ofthese concepts. Exercises and references are alsogiven at the end of each chapter.

Overall the book is well organized and clearlywritten. This book can be recommended as a text-book to teach a full course in SEM. This book is agood mixture of theory and practical applications.It may not be a suitable book for beginners. How-ever, graduate and research students will definitelyenjoy reading this book. Also, practitioners may

Book Reviews 829

find the book quite useful. I also recommend it forlibrary purchase.

Kuldeep KumarBond University

Gold CoastE-mail: kkumar@staff.bond.edu.au

Handbook of Parametric and NonparametricStatistical Procedures, 5th ednD. J. Sheskin, 2011Boca Raton, Chapman and Hall–CRCxxxx + 1886 pp., £108.00ISBN 978-1-439-85801-1

First published in 1997 and now in its fifth edition,Sheskin’s handbook is an applications-oriented vol-ume covering a huge number (nearly 200) of statis-tical procedures. The book has over 1900 pages andweighs 2.43 kg; it really must be read at a desk!

There is an introduction, which covers basicconcepts of probability and statistics in 130 pages.This is followed by two useful reference sections:one giving an outline of statistical tests that is re-lated to the chapters in the book in which they ap-pear; the other providing guidelines and decisiontables to choose an adequate statistical proceduregiven the number of samples and the hypothesis ofinterest. Once these basics have been dealt with wehave 43 chapters (or ‘tests’ as they are called here)each with eight fixed sections: hypothesis evalu-ated with test and relevant background informa-tion, example, null versus alternative hypotheses,test computations, interpretation of tests results,additional analytical procedures, additional dis-cussion and additional examples. A few chaptersalso have addenda covering related issues: for in-stance, the chapter on the single-sample t-test in-cludes a section on statistical quality control, thechapter on the Kruskal–Wallis test is on the Jonc-kheere–Terpsta test for ordered alternatives, andthe chapter on factorial analysis of variance dealswith computational methods for further proce-dures for factorial designs. All chapters have theirown references and endnotes sections. The latterare rather entertaining and cover miscellaneoushistorical or technical details. The current editionhas substantial amendments and additions; nota-bly it includes chapters on path analysis, struc-tural equations models and meta-analysis. Thereare 28 statistical tables and a very detailed index.

Despite such scope and format, this is neither arecipe book nor an encyclopaedia: all proceduresare presented in detail. It is not a conventional text-book either: it is a reference book par excellence. Itis very well edited and produced. I cannot think of

a single-volume text which is close to the range anddepth of this handbook. Professor Sheskin writesclearly and accessibly; the result is a rather old-fash-ioned book, but I mean that as a compliment.

There is substantial material that any applied stat-istician, regardless of their interests or training,should know about and this handbook provides thatand more in one remarkable volume. I am sure thatthis new edition of Sheskin’s handbook will be anextremely useful resource for researchers—what theauthor calls ‘consumers of statistics’—and also aninvaluable reference for teachers and students whoare engaged in applied statistics courses. Despite itsrather steep price, I very much recommend it.

Mario Cortina-BorjaUniversity College London

E-mail: m.cortina@ucl.ac.uk

Statistical Methods in Environmental EpidemiologyD. C. Thomas, 2009Oxford, Oxford University Pressxvi + 432 pp., £55.00ISBN 978-0-199-23289-5

As a comprehensive summary of statistical meth-ods that are used in analysing the health effects ofenvironmental exposures, this book fills a notice-able gap in the epidemiology literature. Althoughit is described as accessible to epidemiologists andstatisticians, it is likely to be of most use to read-ers in the latter category, as much of the materialrequires considerable mathematical expertise.

The book begins with four chapters thatsummarize basic epidemiological and statisticalconcepts, but it quickly moves on to much more spe-cific topics to address methodological problems thatarise frequently in environmental epidemiology.Issues relating to exposure measurement, for in-stance, are particularly well represented, and rangefrom analysis of exposure time–response relation-ships to discussion of the effects of exposure mea-surement error. Ecological inference, mechanisticmodels and meta-analysis for risk assessment areamong many other topics that are covered in the17 chapters. The chapters are largely self-contained,and range from 20 to 40 pages in length, togethercovering most topics in the field. The writing styleis fluent and readable, even if the number of simpletypographical errors is greater than we might expectfrom a book by this publisher.

Researchers requiring detailed information aboutparticular methodologies may have to look else-where, but this book is likely to save considerabletime in doing so: the reference list is over 50 pageslong and, importantly for a subject that has changed