本书是与Wiley合作出版。其国内版列入《应用统计学丛书》。

本书以通俗易懂的方式系统地阐述结构方程模型的基本概念和统计原理，侧重各种结构方程模型的实际运用。本书采用国际著名SEM软件Mplus，使用真实数据来演示各种常见的以及某些新近发展起来的较高级的结构方程模型，提供相应的Mplus程序，并详细解读程序输出结果。参照本书提供的例题和相应的计算机程序，读者便能自己实践各种SEM模型。

本书可作为大学社会科学及公共卫生学院研究生以及u00A0 统计和生物统计专业本科生教材，也可作为相关学科的研究人员从事统计分析的工具书。

front matter
1 Introduction
  1.1 Model formulation
   1.1.1 Measurement model
   1.1.2 Structural model
   1.1.3 Model formulation in equations
  1.2 Model identification
  1.3 Model estimation
  1.4 Model evaluation
  1.5 Model modification
  1.6 Computer programs for SEM
  Appendix 1.A Expressing variances and covariances among observed variables as functions of model parameters
  Appendix 1.B Maximum likelihood function for SEM
2 Confirmatory factor analysis
  2.1 Basics of CFA model
  2.2 CFA model with continuous indicators
  2.3 CFA model with non-normal and censored continuous indicators
   2.3.1 Testing non-normality
   2.3.2 CFA model with non-normal indicators
   2.3.3 CFA model with censored data
  2.4 CFA model with categorical indicators
   2.4.1 CFA model with binary indicators
   2.4.2 CFA model with ordered categorical indicators
  2.5 Higher order CFA model
  Appendix 2.A BSI-18 instrument
  Appendix 2.B Item reliability
  Appendix 2.C Cronbach’s alpha coefficient
  Appendix 2.D Calculating probabilities using PROBIT regression coefficients
3 Structural equations with latent variables
  3.1 MIMIC model
  3.2 Structural equation model
  3.3 Correcting for measurement errors in single indicator variables
  3.4 Testing interactions involving latent variables
  Appendix 3.A Influence of measurement errors
4 Latent growth models for longitudinal data analysis
  4.1 Linear LGM
  4.2 Nonlinear LGM
  4.3 Multi-process LGM
  4.4 Two-part LGM
  4.5 LGM with categorical outcomes
5 Multi-group modeling
  5.1 Multi-group CFA model
   5.1.1 Multi-group first-order CFA
   5.1.2 Multi-group second-order CFA
  5.2 Multi-group SEM model
  5.3 Multi-group LGM
6 Mixture modeling
  6.1 LCA model
   6.1.1 Example of LCA
   6.1.2 Example of LCA model with covariates
  6.2 LTA model
   6.2.1 Example of LTA
  6.3 Growth mixture model
   6.3.1 Example of GMM
  6.4 Factor mixture model
  Appendix 6.A Including covariate in the LTA model
7 Sample size for structural equation modeling
  7.1 The rules of thumb for sample size needed for SEM
  7.2 Satorra and Saris’s method for sample size estimation
   7.2.1 Application of Satorra and Saris’s method to CFA model
   7.2.2 Application of Satorra and Saris’s method to LGM
  7.3 Monte Carlo simulation for sample size estimation
   7.3.1 Application of Monte Carlo simulation to CFA model
   7.3.2 Application of Monte Carlo simulation to LGM
   7.3.3 Application of Monte Carlo simulation to LGM with covariate
   7.3.4 Application of Monte Carlo simulation to LGM with missing values
  7.4 Estimate sample size for SEM based on model fit indices
   7.4.1 Application of MacCallum, Browne and Sugawara’s method
   7.4.2 Application of Kim’s method
References
Index
版权

 王济川 1947年出生。1982年四川大学经济系毕业。1986年获美国康乃尔大学社会学硕士学位，1990年获该校社会学博士学位。1989年9月至1990年8月于美国密西根大学人口中心作博士后研究。1991年9月任职美国俄亥俄州怀特州立大学医学院社区卫生系，2000年7月至今任该系教授。2002年被聘为山东大学客座教授，2006年被聘为山东大学流行病与卫生统计学专业博士研究生兼职导师。王济川博士的主要研究领域为社会科学定量分析方法、人口分析方法及公共卫生和疾病预防研究。 

应用统计学丛书

摘自美国亚马逊读者评论

Only book that has discussed variables other than continuous!

By Mash on July 6, 2014

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.

Mplus is a wonderful program, but unfortunately it is difficult to use ...

By Alberto F. Cabrera on July 6, 2015

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.

I recommend the book warmly

By Dr. Gabriel Liberman on September 15, 2013

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.

 

Excellent book on SEM using Mplus, the best I have seen!!

By Seungyoung Hwang on July 7, 2015

I decided to buy this book because this is the only book that covers a MIMIC model (Chapter 3.1) and latent transition analysis with a covariate and interaction with latent status at time 1 with two different parameterization approaches (Appendix 6.A).



Highly recommended for those starting out in SEM using Mplus as well as refreshers who need advanced techniques along with theoretical foundations.

GREAT

By John R. Turner on April 10, 2014

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.

This is a very good applied book on structural equation modelling using Mplus

By MM on May 3, 2015

This is a very good applied book on structural equation modelling using Mplus. The book illustrates many basic as well as advanced SEM applications. The authors explain these applications in a very clear manner and provide all the necessary Mplus codes and output. I would certainly recommend the book to students/scholars wanting to use Mplus for structural equation modelling.

I would like to highly recommend this book.

By glb on January 8, 2014

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.

Excellent Book

By Eric Kim on June 6, 2013

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.