Structural Equation Modeling Using R/SAS 1st Edition

Structural Equation Modeling Using R/SAS, 1st Edition by Ding-Geng Chen and Yiu-Fai Yung is a clear, practical guide that brings rigorous SEM theory into hands-on application. Ideal for graduate students, applied researchers, and data analysts, this volume bridges statistical foundations with real-world modeling using both R and SAS—making it indispensable for audiences across academia, healthcare, social sciences, and business analytics worldwide.

Begin confidently: concise explanations introduce latent variable modeling, confirmatory factor analysis, path models, and model identification. Progress naturally to estimation methods, goodness-of-fit assessment, multi-group comparisons, and longitudinal SEM, all explained with approachable language and illustrative examples.

Why readers choose this text: step-by-step code demonstrations in R and SAS illuminate implementation choices and interpretation of output, helping you translate theory into reproducible analysis. Emphasis on practical diagnostics and model evaluation equips users to handle messy, real-world datasets encountered in North America, Europe, Asia, and beyond.

The result is a reference that balances mathematical rigor with applied accessibility. Whether you’re preparing a thesis, conducting policy evaluation, or building predictive latent-variable models for healthcare or market research, this book strengthens your SEM toolkit and boosts research credibility.

Add this authoritative resource by Chen and Yung to your shelf to advance your modeling skills and deliver robust, defensible results. Order now to deepen your understanding of structural equation modeling with pragmatic guidance for both R and SAS users.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.