**A First Course in Structural Equation Modeling** is a textbook designed for an introductory course in structural equation modeling (SEM), aimed at advanced undergraduate students, graduate students, and researchers from various disciplines who have limited or no prior exposure to this method. The second edition updates the first, providing a conceptual and nonmathematical introduction to SEM, which is increasingly popular in the social and behavioral sciences. The book avoids technical jargon and focuses on the fundamental principles of SEM, making it accessible to a broad audience. It uses the software EQS, LISREL, and Mplus to illustrate the concepts, but does not compare the programs or imply any preference. The text emphasizes understanding the core aspects of SEM, such as modeling relationships between observed and latent variables, accounting for measurement error, and testing hypotheses about these relationships. It also discusses various types of SEM models, including path analysis, confirmatory factor analysis, structural regression models, and latent change models. The book is structured to guide readers through the fundamentals of SEM, including model specification, estimation, and evaluation, with a focus on practical applications and interpretation of results. It also addresses the importance of model fit and the role of software in SEM analysis. The text is intended to serve as a foundation for more advanced studies in SEM and is written in a clear, concise manner to facilitate learning and application.**A First Course in Structural Equation Modeling** is a textbook designed for an introductory course in structural equation modeling (SEM), aimed at advanced undergraduate students, graduate students, and researchers from various disciplines who have limited or no prior exposure to this method. The second edition updates the first, providing a conceptual and nonmathematical introduction to SEM, which is increasingly popular in the social and behavioral sciences. The book avoids technical jargon and focuses on the fundamental principles of SEM, making it accessible to a broad audience. It uses the software EQS, LISREL, and Mplus to illustrate the concepts, but does not compare the programs or imply any preference. The text emphasizes understanding the core aspects of SEM, such as modeling relationships between observed and latent variables, accounting for measurement error, and testing hypotheses about these relationships. It also discusses various types of SEM models, including path analysis, confirmatory factor analysis, structural regression models, and latent change models. The book is structured to guide readers through the fundamentals of SEM, including model specification, estimation, and evaluation, with a focus on practical applications and interpretation of results. It also addresses the importance of model fit and the role of software in SEM analysis. The text is intended to serve as a foundation for more advanced studies in SEM and is written in a clear, concise manner to facilitate learning and application.