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A Beginner's Guide to Structural Equation Modeling: Third Edition [Paperback]

Randall E. Schumacker , Richard G. Lomax
4.7 out of 5 stars  See all reviews (3 customer reviews)
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Book Description

21 May 2010

This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding.

The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters.

Highlights of the new edition include:

  • A website with raw data sets for the book's examples and exercises so they can be used with any SEM program, all of the book's exercises, hotlinks to related websites, and answers to all of the exercises for Instructor’s only
  • New troubleshooting tips on how to address the most frequently encountered problems
  • Examples now reference the free student version of Lisrel 8.8
  • Expanded coverage of advanced models with more on multiple-group, multi-level, & mixture modeling (Chs. 13 & 15), second-order and dynamic factor models (Ch. 14), and Monte Carlo methods (Ch. 16)
  • Increased coverage of sample size and power (Ch. 5) and reporting research (Ch. 11)
  • New journal article references help readers better understand published research (Chs. 13 – 17) and 25 % new exercises with answers to half in the book for student review.

Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.

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A Beginner's Guide to Structural Equation Modeling: Third Edition + Principles and Practice of Structural Equation Modeling: Third Edition (Methodology in the Social Sciences) + Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Second Edition (Multivariate Applications Series)
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Product details

  • Paperback: 536 pages
  • Publisher: Routledge; 3 edition (21 May 2010)
  • Language: English
  • ISBN-10: 1841698911
  • ISBN-13: 978-1841698915
  • Product Dimensions: 22.6 x 15.7 x 2.8 cm
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Bestsellers Rank: 506,880 in Books (See Top 100 in Books)
  • See Complete Table of Contents

More About the Author

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"From the first edition this book has been the leading book on this topic, providing an authoritative and systematic treatment of SEM for both researchers and practitioners. [It is] well organised and clearly written [and] can be recommended as a textbook to teach a full course in SEM. [A] good mixture of theory and practical applications ... graduate and research students will definitely enjoy reading this book [and] practitioners may find the book useful. I would also recommend it for library purchase." - Kuldeep Kumar, Bond University, Gold Coast, in the Journal of the Royal Statistical Society

"The authors’ considerable experience as modelers and teachers really shines throughout this edition, as reflected in the accessibility and coverage of the writing, the extensive practical software examples, and the useful troubleshooting and reporting tips." - Gregory R. Hancock, University of Maryland, USA

"The authors guide us through SEM basics to more advanced techniques in an easily comprehensible style. As such, it is a great resource for both novice and veteran users of SEM." - Maria Regina Reyes, Yale University, USA

"Their step-by-step approach … makes the "how-to" extremely clear… The reader comes away not only knowing the logistics of how to run the models but also the conceptual of when to run them and how to interpret the findings. Their coverage of assumptions, data cleaning and screening, and common SEM errors is extremely refreshing for those who work with real, messy data. This is a much anticipated edition to the already classical text." - Debbie Hahs-Vaughn, University of Central Florida, USA

"There are a number of features that set this book apart ... it covers a variety of applications ... from simple regression models to highly complex analyses. ...[and] it takes a non-mathematical approach which makes [it] less intimidating.... students have found it to be quite readable and friendly ... I have continued to use it because it is the most comprehensive and helpful to students." - Philip Smith, Dept. of Ed Leadership, Counseling, & Special Education, Augusta State University, USA

About the Author

Randall E. Schumacker is Professor of Educational Research at The University of Alabama where he teaches courses in structural equation modeling. He received his Ph.D. in Educational Psychology from Southern Illinois University. A Past-President of the Southwest Educational Research Association and Emeritus Editor of Structural Equation Modeling, Dr. Schumacker has also served on the editorial boards of numerous journals. His research interests include modeling interaction in SEM, robust statistics, measurement model issues related to estimation, and reliability.

Richard G. Lomax is a Professor in the School of Educational Policy and Leadership at The Ohio State University where he teaches courses in structural equation modeling. He received his Ph.D. in Educational Research Methodology from the University of Pittsburgh. He has served on the editorial boards of numerous journals. His research focuses on models of literacy acquisition, multivariate statistics, and assessment.

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Customer Reviews

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Most Helpful Customer Reviews
11 of 13 people found the following review helpful
By A Customer
With Structural Equation Modeling (SEM) becoming an important statistical tool, this book serves as an excellant guide to the topic. It covers the rudimentary topics that are important in SEM (e.g., sample size, interpretation of goodness of fit indices) as well as a short chapter on some advanced topics (e.g., Bootstrap methods, interactions). Lots of examples included at the end of each chapter as well as one chapter devoted to complete examples (i.e., from formulating models, command lines for the computer package and interpretation of outputs)! Though the many examples stated in the book involve using EQS5 and LISREL8 computer packages, even if the reader does not have these programs, the book is still very useful. If you ever wanted to ask what this or that mean in a SEM analysis that you are doing or have read - this book most probably has the answers! Personally, I have found this to be a helpful introduction to SEM without too much distracting mathematical details. My advice - GET IT!
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1 of 1 people found the following review helpful
5.0 out of 5 stars Just useful 10 Mar 2013
If you are an intermediate statistician (i.e. you are well grounded in regression in all its forms and aspects) then this will help you on the journey to being advanced. SEQ is difficult but incredibly powerful and use hIt takes you through the process one step at a time and briefly reviews some of the background you need. But don't expect to get enough background just from the book.
I think it is now out of print - I got my copy second hand. But an update and re-release would be a good idea.
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0 of 1 people found the following review helpful
4.0 out of 5 stars good depth of content 4 Jun 2013
Format:Paperback|Verified Purchase
content i would rate 4/5.

not for dummies though.

not many books do a good SEM book for people/students just wanting to complete a dissertation.
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Amazon.com: 4.5 out of 5 stars  15 reviews
35 of 36 people found the following review helpful
5.0 out of 5 stars Easy to read and understand; needs a second edition 3 Oct 2004
By Phome - Published on Amazon.com
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.
11 of 12 people found the following review helpful
5.0 out of 5 stars Good for a first course. 3 April 2002
By swingreen - Published on Amazon.com
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.
5 of 6 people found the following review helpful
4.0 out of 5 stars Good Basic Guide 2 Mar 2006
By Jacob Gray - Published on Amazon.com
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).
7 of 9 people found the following review helpful
5.0 out of 5 stars Excellent for a first glance 2 July 1999
By "joojo-banana" - Published on Amazon.com
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.
2 of 2 people found the following review helpful
4.0 out of 5 stars well done! 16 Oct 2011
By Nylph - Published on Amazon.com
Format:Paperback|Verified Purchase
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.
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