There were two primary goals for this book. The first one was to present the recent methodological advances on integrating meta-analysis and SEM—the SEM-based meta-analysis (using SEM to conducting meta-analysis) and meta-analytic struc- tural equation modeling (conducting meta-analysis on correlation matrices for the purpose of fitting structural equation models on the pooled correlation…
Structural equation modeling (SEM) is a general, cross-sectional statistical modeling technique. The chapters in this book propose a Bayesian approach based on SEM; an examination of predictors and outcomes related to school climate using latent class analysis and the testing of specific effects and contrasts in three types of mediation models followed by a discussion on the common types o…
academics, industry, government, and individuals are confronted with an explosion of data. The data increasingly are emerging from sources such as web traffic, social networking interactions, search behavior, sensors that track suppliers, customers, and shipments, and GPS systems that monitor traffic, to name only some of the more visible sources. This trend, often referred to as the age o…
Multiple Regression and Beyond is designed to provide a conceptually oriented introduction to multiple regression along with more complex methods that fl ow naturally from multiple regres- sion: path analysis, confi rmatory factor analysis, and structural equation modeling. Multiple regression (MR) and related methods have become indispensable tools for modern social science researchers. MR…
It’s not often in life that you get three chances at something. Thus, it was a privilege for me to write the third edition of this book. This edition builds on the strengths of the second by presenting structural equation modeling (SEM) in a clear, accessible way for readers without extensive quantitative backgrounds. Many new examples of the applica- tion of SEM to actual research proble…
This book presents a basic introduction to structural equation modeling (SEM). Readers will find that we have followed our tradition of keeping examples rudi- mentary and easy to follow. The reader is provided with a review of correlation and covariance, followed by multiple regression, path, and factor analysis in order to better understand the building blocks of SEM. We then describe the …