A Beginner's Guide to Structural Equation Modeling by Randall E. Schumacker, Tiffany A. Whittaker
The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research.
This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.
This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.
This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.
This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.
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