Book review

Latent Variable Models: An Introduction to Factor, Path, and Structural AnalysisJohn C. Loehlin, Lawrence Erlbaum Associates, London and New Jersey, 1998, 3rd edition,Price £23.95, Paperback: 0-8058-2831-1. xi+309pp.

This is an excellent introductory book for those wishing to understand structural equationmodelling (SEM), its context and background, and its application within social, economic, andpsychology domains of investigation. The ®rst and second editions of this book establishedthemselves as required reading for many graduate students and professionals. The third editiontakes into account the rapid evolution in the ®eld, addressing issues of power, goodness of ®ttests (especially RMSEA), and mean structure analysis, noting the demise of some of theearlier software packages, and introducing those that are now standard in the area. The authoris a master of these techniques and thinking behind them; it shows in the manner in whichdi�cult concepts are introduced and explored in a virtually seamless and uncluttered manner.There are seven chapters and nine appendices in the book. Chapter 1 is an introduction to

path modelling concepts and models, with chapter 2 an exposition on both how tomathematically explore such models, and how to assess ®t of a path model to data. It is thischapter which fully explores the various indices for ®t, focussing on RMSEA as the preferredmethod of testing model ®t. Chapters 3 and 4 delve into the kinds of path models that can be®t, highlighting the distinction between measurement and structural models. These chapterscover such topics as con®rmatory factor analysis, reciprocal in¯uence analysis, the status oferrors in such models, and multiple group analysis. Chapters 5 and 6 are very nice expositionsof exploratory factor analysis, ranging through issues such as component vs common factoranalysis, tests of factor extraction quantity, and rotation of factor vectors to simple structure.In chapter 7, the author explores what to do next when a model is found not to ®t a user'sdata. Mis®t is examined from the perspective of both measurement and structural models.Next, models involving means analysis are brie¯y examined, along with a mention oflongitudinal analysis. Discussions also follow on non-linear e�ect modelling, non-linear factoranalysis, and higher-order factor analysis. I personally found the discussion of the Schmid±Leiman methodology within a SEM modelling approach most interesting. The authorconcludes chapter 7 with an overview of the arguments from two major critics of path andSEM models, Freedman and Cli�. Finally, the appendices cover some basic issues in SEM,including matrix operations, LISREL terminology, some example setups of analysis jobs inSAS and SPSS, and tables for conventional and non-central chi-square distributions.The problem for me in reviewing such a book is that I agree wholeheartedly with

Freedman's criticism (although from a di�erent perspective) . . . ``Despite their popularity [path

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models], I do not believe they have in fact created much new understanding of the phenomenathey are intended to illuminate'' (Freedman, 1987). When users propose latent variables thathave no known units of measurement, using manifest variables with no known units, and yetcontinue to assume an additive concatenation property for such unknown units of magnitudesin their SEM analyses, I wonder what exactly is being `measured'. If you fail to make explicitthe foundational basis of the unit with which you propose to make measurement, how can yousay for example that a latent variable is causal, except at a super®cial level of discourse (e.g.latent variable X `causes' change, yet latent variable X has no unit except that which isarbitrarily de®ned as a property of additive-unit algebra)?However, I disgress. For many working in individual di�erences, SEM methods are seen as

an essential tool for the examination of phenomena that are considered multivariate in cause.This book is one of the most practically useful and clearest expositions of the methodologythat I have seen. I am sure it will become one of the key textbooks for many graduate coursesin SEM methodology; it is a valuable contribution to this rapidly growing area of quantitativeanalysis.

References

Freedman, D. A. (1987). As others see us: a case study in path analysis. Journal of Educational Statistics, 12(2),101±128.

Paul Barrett*Department of Psychology, The State Hospital, Carstairs,

Lanark ML11 8RP, UKE-mail address: p.barrett@liv.ac.uk

* Tel.: +44-1555-841-343; fax: +44-1555-840-024.

Book review / Personality and Individual Di�erences 29 (2000) 999±10001000