7 Next-Gen Structural Equation Modeling Books Leading 2025

Drawing from their extensive expertise in psychometrics and quantitative methods, Michael Eid, Christian Geiser, and Tobias Koch delve into the nuanced challenges of analyzing data collected from multiple raters. You learn how to tailor structural equation models based on the specific measurement design and rater types, rather than applying generic approaches. For example, the authors guide you through modeling strategies that accommodate both cross-sectional and longitudinal designs, emphasizing the latent variables' substantive interpretation. If your work involves complex personality or attitude assessments using multiple observations, this book equips you with practical model selection frameworks and code examples in Mplus and R to implement them effectively.

Jonathan Sarwono’s latest work delves into three distinct approaches to structural equation modeling (SEM), presenting them without bias to let you grasp their unique strengths and limitations. You’ll explore covariance-based SEM, widely accepted for advanced statistical analysis, alongside Partial Least Square SEM, which relaxes stringent assumptions like normality and large sample sizes, and a newer component-based method designed to address weaknesses in the latter. The book’s clarity in explaining these methods side-by-side equips you to choose the right approach for your research or application. If your work demands a nuanced understanding of SEM’s evolving methodologies, this book lays a solid foundation without pushing one method over another.

This personalized book explores groundbreaking developments in Structural Equation Modeling (SEM) as of 2025, tailored to your specific interests and background. It examines the newest research findings, emerging techniques, and software advancements that are reshaping SEM applications across various fields. By focusing on the latest discoveries, this book offers a unique opportunity to engage with cutting-edge insights that match your goals and experience level. Through a tailored approach, you delve into specialized topics such as advanced model specification, novel estimation methods, and innovative ways to handle complex data structures. This focused exploration equips you with up-to-date knowledge, aligning with your aspirations to stay at the forefront of SEM research and practice.

What happens when a clear focus on practical application meets Structural Equation Modeling? William V. Jenkins offers a manual that cuts through jargon to teach you how to use EQS software effectively for SEM analysis. You’ll learn to prepare data meticulously, specify models with precision, and interpret results with confidence, all grounded in solid theory tailored for social science research. The book’s concise format, including examples and stepwise guidance, makes it approachable if you’re looking to move beyond theory to actual implementation. This manual suits anyone aiming to build foundational SEM skills using EQS, especially students and researchers seeking a straightforward pathway.

Drawing from decades of expertise in educational psychology and statistical methodology, Todd D. Little updates this edition to reflect significant advances in longitudinal structural equation modeling. You gain a thorough understanding of modeling within-person change over time through clear explanations of longitudinal confirmatory factor analysis, panel models, and hybrid approaches. New chapters guide you through handling missing data, mixture modeling, and Bayesian SEM, with practical examples like adolescent emotion studies and healthy aging. The book suits researchers and advanced students aiming to deepen their technical skills in analyzing complex longitudinal data structures.

After decades of academic and practical experience, J.P. Verma and Priyam Verma crafted this manual to address the increasing need for clarity in Structural Equation Modeling (SEM) across diverse research fields. The book methodically guides you through foundational concepts like variance and correlation before advancing to complex topics such as mediation, moderation, and confirmatory factor analysis. Notably, it integrates hands-on guidance for using IBM SPSS and Amos software, helping you translate theory into applied modeling. If your work spans psychology, business, or political science, this book equips you with the conceptual understanding and practical skills to apply SEM effectively in your research.

This tailored book explores emerging developments in Structural Equation Modeling (SEM) with a sharp focus on your individual interests and background. It delves into the latest 2025 advances, examining cutting-edge techniques and newly discovered insights that redefine SEM applications across various research domains. By addressing trends relevant to your specific goals, it reveals how to stay at the forefront of this evolving field. Your personalized edition carefully matches content to your expertise and objectives, making complex innovations accessible and directly applicable to your work. This approach ensures a focused learning experience that highlights the newest SEM knowledge you need to navigate future challenges and opportunities confidently.

After analyzing numerous studies presented at the 2022 PLS-SEM conference, the authors compiled cutting-edge methodological advances and diverse applications of Partial Least Squares Structural Equation Modeling. This book not only updates your statistical toolkit with new algorithms and evaluation metrics but also showcases how PLS-SEM is applied across social sciences fields like consumer behavior and organizational studies. You’ll gain insight into integrating complementary methods for richer analysis, with examples spanning hospitality to entrepreneurship. If you’re seeking to deepen your understanding of modern PLS-SEM and expand its practical use in research, this volume serves as a detailed guide tailored for advanced scholars and practitioners.

When Ding-Geng Chen and Yiu-Fai Yung set out to write this book, their goal was clear: to bridge the gap between complex structural equation modeling theory and practical application using R and SAS software. You’ll find detailed explanations of key models, from basics to advanced topics, paired with real datasets that clarify the process step-by-step. Particularly useful is their clear exposition on how to implement these models in widely used statistical tools, making it a solid resource if you’re working with applied statistics or data analytics. Whether you’re a graduate student, public health analyst, or data scientist, this book equips you with hands-on skills and conceptual understanding to tackle real-world data with confidence.