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Structural Equation Modeling: Applications Using Mplus (Wiley Series in Probability and Statistics) Hardcover – 21 Sep 2012


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Product Description

From the Back Cover

A reference guide for applications of SEM using Mplus

Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non–mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model–based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy–to–use interface and graphical displays of data and analysis results.

Key features:

  • Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi–group and mixture models using Mplus.
  • Discusses and demonstrates various SEM models using both cross–sectional and longitudinal data with both continuous and categorical outcomes.
  • Provides step–by–step instructions of model specification and estimation, as well as detail interpretation of Mplus results.
  • Explores different methods for sample size estimate and statistical power analysis for SEM.

By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

About the Author

Jichuan Wang, Children s National Medical Center, The George Washington University, USA

Xiaoqian Wang, Mobley Group Pacific Ltd., P.R. China

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Amazon.com: 4.7 out of 5 stars 16 reviews
4.0 out of 5 stars GREAT 10 April 2014
By John R. Turner - Published on Amazon.com
Format: Hardcover Verified Purchase
This book is an excellent guide to be used in conjunction with with the Mplus user guide. The descriptions about CFA, SEM in this book are as good as any that I have read in other books on CFA and SEM. Good set of examples are provided and a fair overview of interpreting each output is also provided. This would be a great graduate course book for those using Mplus.
5.0 out of 5 stars Excelent 10 Jun. 2014
By Hugo Cogo Moreira - Published on Amazon.com
Format: Hardcover Verified Purchase
I do recommend the book (clear, straight to the point, and very applied). Indeed, the author gently provided a prompt answers to emerged issues throughout the reading.
3 of 7 people found the following review helpful
1.0 out of 5 stars very dissapointing book 11 April 2014
By Alexandra - Published on Amazon.com
Format: Hardcover Verified Purchase
I bought this book in a hope to learn MPlus and SEM technique (I do have extensive training in econometrics and I am comfortable with a bunch of other statistical packages...somehow SEM and MPLus were missing from my toolbox).

The book unfortunately proved to be useless and really frustrating for the following reasons:
1. The data sets used for book examples are not available. This means I cannot recreate the results reported to see how it really works in MPlus.
2. It is not clear what version of MPlus the book uses (I see that the book mentions version 5 somewhere in the middle of the text). When I try to use the codes provided in the book using my data set and MPlus version 6, in some cases I am getting an error message, in some cases the output is not provided as it should, according to the book (for example, no standardized results are returned for CFA analysis when I copy the code from p. 43).
3. The book is full (i mean FULL) of typos. To detect some of them, you need to be familiar with matrix algebra.
4. The book contains a lot of distantly relevant material that should be in footnotes at best (like error structure assumptions used in LISREL... seriously, this book is not about LISREL, no need to show off the authors' knowledge of other statistical packages, IMHO).

All in all, this book might be helpful for somebody who already works with SEM/MPlus. For me, it was a waste of money.
5.0 out of 5 stars I recommend the book warmly 15 Sept. 2013
By Dr. Gabriel Liberman - Published on Amazon.com
Format: Hardcover
I have just finished reading "Structural Equation Modeling" by Wang and Wang. I find the book extremely contributing to my knowledge of SEM. As a person who works with SEM for years and supports many studies and researches, this book advances my knowledge and allows me to get much deeper into complex SEM and puts me in the most advance modeling techniques. First, the book provides clear introduction on the mathematics and the algebra of SEM with helpful examples of graphical illustrations and the matrix algebra that generates these models. This is, of course, not the focus of the book, but only stands at the back of modeling examples. Then, the authors explain how to use different measurements for goodness of fit and quality of the model. They also discuss events when these measurements exceed the expected range and how to treat such cases. I am using the Mplus examples and they save time usually necessary for experimenting with the program before building the final model. Beyond these advantages, my experience with directly asking the authors more complex questions on topics which do not appear in the book, receives immediate clear answers. I recommend the book warmly for those who'd like to get into SEM and those who already into SEM, but would like to go further with this statistical technique.
Dr. Gabriel Liberman – Data-Graph, Research and Statistical Consulting at: [...] .
1 of 1 people found the following review helpful
5.0 out of 5 stars Only book that has discussed variables other than continuous! 6 July 2014
By Mash - Published on Amazon.com
Format: Hardcover
The only book on Mplus, which has touched on different variables than just continuous. In the real world it is necessary so for me this was a breath of fresh air. The author has done a good job in explaining concepts I have not found elsewhere. The only drawback is that formative models are not covered and the emphasis is on reflective models. Will look forward to an update, which discusses more details including formative models and how to interpret them.

Overall, an excellent source for those who wish to learn how to use SEM beyond linear SEM.
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