Customer Reviews

A Beginner's Guide to Structural Equation Modeling (The Inquiry and Pedagogy Across Diverse Contexts Series)

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No one expects statistics to be easy reading, especially when it concerns complicated models such as structural equation modeling (SEM). Nevertheless, this book manages to do just that.

Schumacker and Lomax have successfully put together a guide that explains to beginners (like myself) in simple terms how the whole thing works. As with most books that treat complex models, some basic knowledge of statistics is preferable before you begin to read it. But if your statistics is rusty and you have only vague impressions of probability sketches in your memory, fear not! Schumacker and Lomax are kind to us poor souls, and begin by introducing some basics in chapter 1 to prod your memory: terminology, variable scales, how to treat missing data, outliers and normality. And in chapter 2, they discuss correlation and covariance.

Before talking about structural equation models, Schumacker and Lomax dedicate chapter 3 to a number of statistical methods on which SEM is built. This chapter gives a basic overview of regression, path analysis and factor analysis. The review of these methods helps you to understand SEM better later on. They also provide an excellent understanding of the methods, in case you have not used them before or it's been a while ...

The rest of the guide covers SEM: how to develop and measure a model (chapters 4 and 5), how the model parameters are estimated and how you can check for reliability and validity (chapter 6), and checking for goodness of fit of your model (chapter 7).

In chapter 8, you are shown some examples of computer outputs by two software packages that can conduct SEM, EQS5 and LISREL8-SIMPLIS.

Chapter 9 goes into more detail on models and diagrams (regression, analysis of covariance, path, measurement and structural models). For those that feel by this point that they've gained enough experience, advanced topics such as cross validation, simulation, bootstrap and jacknife methods as well as multiple same and interaction models are covered in chapter 10. And for the super-keen, the technical bits are covered in chapter 11 (health warning: you better be up to speed on matrix algebra).

The great thing about this book is that you most likely will be able to run models and interpret results by chapter 7, and you don't need to go into the nitty-gritty if you don't want to. On the other hand, the details are there if you need them. In essence, the authors start at the beginning, building up slowly until you are able to handle a basic model, before going into more complex issues.

One drawback, I have found, is that this book was published in 1996. That's nearly a decade ago, and (fortunately) computer power and statistical modeling has come a long way since then. The authors, for example, are convinced that WordPerfect is the software of choice for word processing and that at some point in the future it would be possible to copy and paste diagrams into a word processing program. We've come a long way since then. Today, many SEM packages exist that are much more user friendly than some of the older stodgy packages that require you to enter data in a very specific way and interpret results by going through reams of data output. As such, the authors (or publishers) probably ought to update it with a second edition.

Nevertheless, the strength of the book lies in its guidance and explanatory power. And even if you use a different package, you can skim through the data outputs they use, and focus on your model, how to construct it, and of what pitfalls to beware.

I highly recommend this for anyone starting on SEM - your modeling days will be much happier with this guide.

Schumacker and Lomax make for a good first course in SEM. Although they are somewhat less technical than Bollen (1989), they are a little more up-to-date, and very good reading for a beginning student of SEM. I found the sections on confirmatory factor analysis and identification very useful.

The authors present remarkably the basic principles and concepts underlying SEQ, but also give numerous notions about technical aspects. An Excellent book, even for people who are not very keen on statistical writings.

This book lays out the basics of SEM in an understandable way. Doesn't address EVERY issue, but then again it's very accessible and application-oriented, with language not too highly techinical (in relation to other SEM texts).

It is a long time ago I started looking for good textbook that could introduce my students into SEM. After reading many boring titles dealing with SEM I have finally found what I was looking for: A beginner's guide to SEM.

First of all, this is didactically very well written book: authors sketch every selected problem quit simply but in the same time absolutely correctly. Then they explain it (without reduntant tiny details) and finally, they provide reader with practical ilustration how to deal with it.
Structure of the book is built logically and chapters lead reader gradually and comprehensibly through all main problems of SEM. I really like this book and my students too.

This is a very good book about SEM for the beginners and advanced. The book gives a clear and concise principles and examples about SEM. This book definitely enables the readers clearly understand the subject.

SEM can be complicated but authors do a great job in making it as simple as possible. It uses three softwares to explain SEM. Its a nice Beginner's guide, nicely structured and great to read.

This book on SEM has been extremely helpful to me. It is well written, particularly, for those who have some knowledge of SEM.

The authors bring us the world of SEM into 1 book. However, there is no such thing as only 1 big having all of the information that you will need about SEM. Yet, this book does a fine job describing many of the important aspects of analyzing data using latent modeling, such as estimators, parameter estimating, and certain assumptions.

This is quite easy to follow for beginners but you def. need a good stats background to do so!