Structural Equation Modeling With AMOS

This bestselling text provides a practical guide to the basic concepts of structural equation modeling (SEM) and the AMOS program (Versions 17 & 18). The author reviews SEM applications based on actual data taken from her research. Noted for its non-mathematical language, this book is written for the novice SEM user. With each chapter, the author "walks" the reader through all steps involved in testing the SEM model including:

• an explanation of the issues addressed
• an illustration of the hypothesized and posthoc models tested
• AMOS input and output with accompanying interpretation and explanation
• The function of the AMOS toolbar icons and their related pull-down menus
• The data and published reference upon which the model was based.

With over 50% new material, highlights of the new edition include:

• All new screen shots featuring Version 17 of the AMOS program
• All data files now available at www.routledge.com/9780805863734
• Application of a multitrait-mulitimethod model, latent growth curve model, and　second-order model based on categorical data
• All applications based on the most commonly used graphical interface
• The automated multi-group approach to testing for equivalence

The book opens with an introduction to the fundamental concepts of SEM and the basics of the AMOS program. The next 3 sections present applications that focus on single-group, multiple-group, and multitrait-mutimethod and latent growth curve models. The book concludes with a discussion about non-normal and missing (incomplete) data and two applications capable of addressing these issues.

Intended for researchers, practitioners, and students who use SEM and AMOS in their work, this book is an ideal resource for graduate level courses on SEM taught in departments of psychology, education, business, and other social and health sciences and/or as a supplement in courses on applied statistics, multivariate statistics, statistics II, intermediate or advanced statistics, and/or research design. Appropriate for those with limited or no previous exposure to SEM, a prerequisite of basic statistics through regression analysis is recommended.