A Beginner's Guide to Structural Equation Modeling - Softcover

Críticas

to conduct an SEM analysis. Checklists are provided for each step, from model specification through validation. The accompanying CD provides all of the programs needed to do the analysis, with accompanying screen shots and directions in the book to allow the reader to self-teach the software application. This book would serve well as either a reference text or a required text for a course."
—Technometrics

"This book would be an invaluable resource for any student or researcher who wishes to begin exploring SEM, path analysis, or confirmatory factor analysis and who is willing to conscientiously treat this book as a self-taught course....a book which manages to be clear and understandable without spoon-feeding or talking down to the reader."
—Journal of Personality Assessment

"Before Schumacker and Lomax, I had a difficult time identifying the 'one source' that would provide a very straightforward introduction to SEM, enough detail about these modeling methods and current issues in the field, and enough of a base for a reader to take the next step and formally consider their own data in SEM...I am a great fan of this book."
—Abigail T. Panter, Ph.D.
University of North Carolina, Chapel Hill

Reseña del editor

The second edition features:

• a CD with all of the book's Amos, EQS, and LISREL programs and data sets;
• new chapters on importing data issues related to data editing and on how to report research;
• an updated introduction to matrix notation and programs that illustrate how to compute these calculations;
• many more computer program examples and chapter exercises; and
• increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues.

The new edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged.