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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
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.
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: [...] .
5.0 out of 5 stars Excellent Book 7 Jun. 2013
By Eric Kim - Published on Amazon.com
Format: Hardcover
I'm a grad student and I've taken one SEM course (which was taught using LISREL). I also read Kline's SEM book (which is excellent).

I found this book to be easy to read. All the concepts were explained clearly. The authors went through several examples and provided the syntax, output, and described the output quite well. I would highly recommend this book for people trying to figure out how to implement their knowledge of SEM in Mplus. If you are new to SEM, I would suggest taking a class or reading Kline first, then reading this book.
5.0 out of 5 stars Dr. G. Leonard Burns 8 Jan. 2014
By glb - Published on Amazon.com
Format: Hardcover
I read this book last semester. It was so good that I decided to use it in my graduate applied SEM course this spring semester. The many applied examples from the social and behavioral sciences and the integration of the MPlus code into the book are also strengths of this book. Many of the graduate students in the class will need to use SEM procedures for their dissertation research and this book will be most helpful in this regard as well. I would like to highly recommend this book.
5.0 out of 5 stars Mplus is a wonderful program, but unfortunately it is difficult to use ... 6 July 2015
By Alberto F. Cabrera - Published on Amazon.com
Format: Hardcover Verified Purchase
This book provides an invaluable introduction to using Mplus for estimating latent class analyses. Mplus is a wonderful program, but unfortunately it is difficult to use if one were to rely solely on its manual. In contrast, Jichuan Wang and Xiaoqian Wang provide excellent examples as to how to unlock the potential of this incredible statistical program, while sharing with the reader some important tips for using Mplus for mixture modeling.
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