7 Structural Equation Modeling Books That Separate Experts from Amateurs

Drawing from his extensive background as a psychology and neuroscience professor at Duke University, Rick H. Hoyle offers a thorough guide to structural equation modeling (SEM) that advances beyond basics into specialized topics like bifactor models and dynamic SEM. You’ll find detailed chapters that walk through model selection, assumptions, estimation methods, and handling missing data, complemented by practical examples using Mplus and R code. This book suits researchers and advanced students keen on mastering SEM's complexities, especially those working with social and behavioral data. While dense, it clarifies nuanced modeling techniques that deepen your analytical skills rather than just scratching the surface.

The authoritative expertise behind this book comes from Rex B. Kline, a clinical psychologist and Concordia University professor with deep experience in psychometrics and behavioral assessment. Kline expands the scope of structural equation modeling (SEM) by covering covariance-based SEM, Pearl’s structural causal models, and composite SEM, all while integrating accessible tools like the R lavaan package. You’ll gain practical insights into SEM phases—from significance testing and regression basics to mediation analysis and handling missing data—all illustrated through examples across multiple disciplines. This edition also guides you through common pitfalls and recent advances, making it a solid choice if you want a broad, methodical understanding of SEM techniques and software.

This tailored book on Structural Equation Modeling (SEM) explores the subject through a lens finely tuned to your experience and goals. It covers foundational concepts, model specification, estimation techniques, and interpretation of results, all customized to focus on the SEM topics you find most relevant. By weaving together core principles with your unique background, this resource reveals a clear, personalized pathway through the complexities of SEM. Designed to match your learning objectives, it examines advanced topics such as measurement invariance, longitudinal SEM, and software application nuances, ensuring you gain deep, applicable knowledge without wading through unrelated material. This personalized approach helps you efficiently master SEM techniques aligned with your interests.

Christian Geiser, PhD, leverages his extensive background in quantitative psychology and statistical consulting to present a targeted exploration of longitudinal structural equation modeling through the lens of latent state–trait theory. You’ll gain a nuanced understanding of confirmatory factor analysis and structural equation modeling in Mplus, with clear guidance on applying and interpreting models such as autoregressive, change score, and growth curve models. The book’s chapters thoughtfully balance theory with practical application, featuring data examples, Mplus syntax, and real output to deepen your skills in measurement equivalence testing and handling intensive longitudinal data. This resource is best suited for researchers and advanced practitioners ready to sharpen their proficiency in longitudinal SEM techniques rather than casual learners.

While working as a psychology professor, Barbara M. Byrne noticed many students struggled to apply Structural Equation Modeling (SEM) concepts using software tools. This book demystifies SEM through Mplus software by guiding you step-by-step with real data examples from Byrne’s own research. You’ll gain practical skills in interpreting outputs, testing models, and handling complex SEM applications like multiple-group analyses and latent growth curves. It’s especially suited for those new to SEM or Mplus, offering clear explanations without heavy math jargon, making it a solid choice if you want to build foundational expertise in SEM software application.

What started as a rigorous academic pursuit by Leslie A. Hayduk, a sociology professor at the University of Alberta, became a detailed guide to LISREL applications in Structural Equation Modeling. You’ll find clarity in complex areas like matrix algebra and regression, with chapters that walk through both basic and advanced program features. This book suits those who already grasp mathematical concepts and want to deepen their understanding of LISREL’s capabilities. For example, the explanations on model specification and assessment give you practical insight into how LISREL handles complex statistical modeling scenarios.

This tailored book explores the essential techniques and practical steps for accelerating your mastery of Structural Equation Modeling (SEM) within a focused 30-day period. It delves into core SEM concepts, model specification, and interpretation, emphasizing actionable progress matched to your background and specific goals. By concentrating on your interests, this personalized guide examines advanced model evaluation and software applications, providing a clear pathway to skill growth. Each chapter reveals crucial insights into SEM's nuances, helping you build confidence in applying these methods to your unique research questions.

Drawing from her role as Associate Professor of Quantitative Methods at UC Merced, Sarah Depaoli presents a detailed introduction to applying Bayesian frameworks within structural equation modeling. You’ll find clear explanations of Bayesian adaptations for each SEM model, accompanied by practical examples spanning social science fields that navigate common estimation challenges. The book goes beyond theory by walking you through data analysis plans and publication-ready result write-ups, supported by annotated code in Mplus and R. This resource suits researchers and graduate students aiming to deepen their statistical toolkit with Bayesian SEM techniques and improve the clarity of their empirical reporting.

Barbara M. Byrne, a seasoned professor emeritus with decades of experience in psychology and structural equation modeling, offers a clear, approachable guide tailored for those new to SEM and the EQS software. This book walks you through foundational concepts without heavy math, focusing on practical skills like model specification, fit assessment, and interpreting EQS output. You’ll find detailed examples ranging from simple single-group analyses to complex multilevel and latent growth curve models, complete with data files and visual schematics to solidify your understanding. If you’re aiming to grasp SEM through applied practice rather than abstract theory, this book suits you well, though seasoned statisticians seeking deep mathematical proofs might look elsewhere.