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!
Randall Schumacker and Richard Lomax's book introduces structural equation modeling (SEM) to researchers with some previous background in applied statistics. It begins with the basics of correlation and then outlines the logic of multiple regression, path analysis, and factor analysis. It presents SEM as a combination of these three analytical techniques. The final chapters discuss more complex SEM procedures. There are also useful discussions of how to report SEM studies and check the validity of SEM results.
The SEM model development process of data preparation, specification, identification, estimation, testing, and modification is used to structure discussion across chapters. This organization works well and makes the book useful as a reference as well as a text. Most chapters contain example analyses which are explained in detail. There are also end-of-chapter exercises with answers to odd-numbered problems in an appendix.
A key strength is the book's integration with the LISREL 8.80 Student software, which is available for free download from the web. Early chapters explain the basics of LISREL and how to import data. Subsequent chapters use LISREL code and output in examples and exercises. This kind of hands-on work is essential to learning SEM.
I recommend the book as a text or for independent study for those learning about SEM for the first time.
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.