This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling. The concepts behind measurement models are introduced to illustrate how measurement error impacts statistical analyses, and structural models are presented that indicate how latent variable relationships can be established. Examples are included throughout to make the concepts clear to the reader. The structural equation modelling examples are presented using either EQS5.0 or LISREL8-SIMPLIS programming language, both of which have an easy-to-use set of commands to specify measurement and strucural models. No complicated programming is required, nor does the reader need an advanced understanding of statistics of matrix algebra. A goal in writing this volume was to focus conceptually on the steps one takes in analyzing theoretical models. These steps encompass: specifying a model based upon theory or prior research; determining whether the model can be identified to have unique estimates for variables in the model; selecting an appropriate estimation method based on the distributional assumptions of variables; testing the model and interpreting fit indices; and finally respecifying a model based on suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. The resources and references provided in this book should equip faculty, students and researchers to enhance their working knowledge of structural equation modelling. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modelling. Assuming that the reader has a basic understanding of correlation, the authors have built upon this understanding to present these basic ideas and principles.
"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
