Structural Equation Modeling: Foundations and Extensions – A Comprehensive Guide to SEM Methods and Applications by Davi

Introduction

Structural Equation Modeling (SEM) stands as one of the most powerful statistical techniques in the social and behavioural sciences, enabling researchers to test complex theoretical frameworks with empirical data. David Kaplan’s Structural Equation Modeling: Foundations and Extensions, published by Sage Publications, Inc, offers a rigorous yet accessible gateway into this sophisticated methodology. Designed for graduate students and practising researchers in India and worldwide, this hardcover edition provides a comprehensive treatment of SEM’s core principles, advanced extensions, and practical applications. Whether you are exploring causal pathways, latent variable models, or measurement invariance, this book equips you with the conceptual clarity and technical skills needed to conduct robust SEM analyses.

Book Overview

Now in its second edition, this volume builds on the strengths of the first by integrating updated examples, new extensions, and deeper coverage of foundational assumptions. Kaplan’s approach is distinctly empirical: each chapter grounds theoretical discussion in real-world data from the social and behavioural sciences, making abstract concepts tangible. The book bridges the gap between introductory statistics and advanced SEM topics, ensuring readers can move from understanding basic regression to designing and evaluating complex structural models. With a focus on critical assumptions—normality, sample size, model identification, and fit indices—the text prepares readers to apply SEM responsibly and interpret results with confidence.

Key Highlights

• Empirical focus: Detailed real-data examples from psychology, education, and sociology illustrate each technique.
• Foundations first: Thorough coverage of path analysis, confirmatory factor analysis, and latent variable modelling before moving to extensions.
• Critical assumptions: In-depth discussion of normality, missing data, and model identification to avoid common pitfalls.
• Advanced extensions: Chapters on multilevel SEM, latent growth curves, mixture models, and Bayesian SEM.
• Software-agnostic: Concepts are explained independently, though references to popular SEM software (e.g., LISREL, Mplus) are included.
• Updated second edition: New material on effect sizes, power analysis, and handling non-normal and categorical data.

Inside the Book

The book is structured to guide readers from foundational concepts to cutting-edge extensions. Part I covers the logic of SEM, including covariance structure analysis, model specification, identification, estimation, and fit evaluation. Part II delves into confirmatory factor analysis and structural regression models, with emphasis on measurement theory. Part III introduces advanced topics such as multiple-group SEM, latent growth modelling, and mixture models for unobserved heterogeneity. Each chapter includes worked examples, annotated output, and exercises that reinforce learning. Kaplan’s writing is clear and methodical, avoiding unnecessary jargon while maintaining statistical rigour.

Key Topics

• Path analysis and covariance structure modelling
• Confirmatory factor analysis (CFA) and measurement invariance
• Structural regression models with latent variables
• Model fit assessment: chi-square, RMSEA, CFI, TLI, SRMR
• Handling non-normal and categorical data
• Multilevel SEM (clustered data)
• Latent growth curve modelling for longitudinal data
• Mixture models and finite mixture SEM
• Bayesian approaches to SEM
• Power analysis and sample size planning

Reader Benefits

By studying this book, you will develop a deep understanding of SEM’s logic and limitations. You will learn to specify, estimate, and evaluate models that reflect your substantive theories. The empirical examples help you see how SEM illuminates research questions—from testing mediation hypotheses to examining developmental trajectories. The focus on assumptions ensures you avoid common errors, such as misinterpreting fit indices or ignoring measurement error. Indian students and researchers will find the content directly applicable to fields like educational psychology, marketing, public health, and organisational behaviour, where SEM is increasingly used for thesis work and journal publications.

Learning Outcomes

• Understand the mathematical and conceptual foundations of SEM
• Specify and identify structural equation models correctly
• Conduct confirmatory factor analysis and assess measurement models
• Evaluate model fit using appropriate indices and guidelines
• Handle missing data, non-normality, and categorical variables
• Apply advanced extensions like multilevel SEM and latent growth curves
• Interpret SEM results for academic papers and dissertations
• Critically evaluate published SEM studies

Who Should Read

This book is ideal for postgraduate students in psychology, sociology, education, economics, and business who have completed introductory courses in multivariate statistics. Researchers in Indian universities, research institutes, and market research firms will find it a valuable reference for conducting SEM-based studies. Faculty members teaching advanced quantitative methods will appreciate the balanced blend of theory and application. Professionals in data analytics and survey research who wish to move beyond regression to causal modelling will also benefit. The book assumes basic knowledge of regression and factor analysis, but no prior SEM experience is required.

About the Author

David Kaplan is a professor of quantitative methods in the School of Education at the University of Wisconsin–Madison. He has published extensively on SEM, Bayesian statistics, and educational measurement. His research focuses on the integration of statistical modelling with policy analysis, and he has served on editorial boards of leading journals. Kaplan’s teaching experience informs the clarity and pedagogical structure of this book, making it a trusted resource for students and researchers worldwide.

About the Publisher

Sage Publications, Inc is a renowned academic and professional publisher with a strong presence in India. Known for its rigorous editorial standards and commitment to advancing social science research, Sage publishes influential textbooks, reference works, and journals. This hardcover edition reflects Sage’s dedication to producing durable, high-quality academic books that support learning and scholarship across disciplines.

Conclusion

Structural Equation Modeling: Foundations and Extensions is more than a textbook—it is a mentor guiding you through one of statistics’ most powerful frameworks. With its empirical grounding, clear exposition, and coverage of both classic and modern extensions, this book deserves a place on the shelf of every serious quantitative researcher. For Indian students and academics aiming to produce rigorous, publishable SEM work, this second edition is an indispensable companion. Order your copy from Bookshops.in today and take your analytical skills to the next level.