This comprehensive guide serves as an accessible introduction to Structural Equation Modeling (SEM), renowned for its clear explanations and broad coverage. This resource equips readers with the knowledge to apply SEM effectively to diverse research questions by emphasizing fundamental concepts and various models. Minimal use of programming details and complex algebra ensures that readers can readily grasp the core principles, conduct independent analyses, and critically evaluate existing research. The guide distinguishes itself through its focus on statistical power and model validation, incorporating key concepts, real-world examples, tables, figures, summaries, and exercises in each chapter.
The extensively revised 4th edition features several key enhancements:
- Diverse SEM Software Applications: Demonstrates applications using various SEM software packages, including Amos, EQS, LISREL, Mplus, and R, rather than focusing solely on Lisrel.
- Detailed Introduction to Statistical Methods: Provides a thorough introduction to statistical methods crucial for SEM, such as correlation, regression, and factor analysis (Chapters 1-6).
- The 5-Step Modeling Approach: The core process of modeling data (specification, identification, estimation, testing, and modification) is now comprehensively covered before the modeling chapters, providing a more integrated perspective on model creation and result interpretation (Chapter 7).
- Emphasis on Hypothesis Testing and Model Fit: Increased discussion of hypothesis testing, power, sampling, effect sizes, and model fit, which are essential for novice modelers (Chapter 7).
- Focused Model Chapters: Each model chapter concentrates on a single technique, enhancing understanding through detailed descriptions, assumptions, result interpretations, and analysis-related exercises (Chapters 8-15).
Alt text: An example of a path diagram created in SPSS AMOS, illustrating the relationships between variables in a structural equation model.
- SPSS AMOS Diagrams: Utilizes SPSS AMOS diagrams to visually represent theoretical models.
- Software Package Overviews: Outlines the key features of each software package (Chapter 1).
- SEM Research Reporting Guidelines: Offers guidelines for reporting SEM research findings (Chapter 16).
- Online Resources: A companion website (www.routledge.com/9781138811935) provides access to data sets compatible with any program, links to additional SEM examples, relevant readings, journal articles, and more.
The revised edition begins with an in-depth introduction to SEM, covering available software packages. Subsequent chapters address data entry, editing, and the importance of correlation in understanding how missing data, non-normality, measurement issues, and score range restrictions impact SEM analysis. Multiple regression, path analysis, and factor models are then reviewed, followed by introductions to exploratory and confirmatory factor analysis. These chapters demonstrate how observed variables share variance to define latent variables and how measurement error can be eliminated from observed variables. Chapter 7 details the 5 SEM modeling steps: model specification, identification, estimation, testing, and modification, along with discussions on hypothesis testing, power, sample size, and effect sizes.
Chapters 8 through 15 offer comprehensive introductions to various SEM models, including Multiple Group, Second-Order CFA, Dynamic Factor, Multiple-Indicator Multiple-Cause (MIMIC), Mixed Variable and Mixture, Multi-Level, Latent Growth, and SEM Interaction Models. Each of the 5 SEM modeling steps is explained for each model, accompanied by a practical application. Chapter exercises provide hands-on experience and deepen the understanding of each model’s analysis. The book concludes with a review of SEM guidelines for reporting research.
Alt text: A visual representation of a structural equation model depicting the relationships between several constructs, including anxiety, coping strategies, and academic performance.
This guide is designed for introductory graduate courses in structural equation modeling, factor analysis, advanced multivariate statistics, applied statistics, quantitative techniques, or statistics II, taught across disciplines such as psychology, education, business, social sciences, and healthcare sciences. It is also a valuable resource for researchers in these fields. A prerequisite understanding of intermediate statistics, covering correlation and regression principles, is recommended.
This resource serves as a valuable “a beginner’s guide to structural equation modeling,” providing the fundamental knowledge for those starting to explore the world of SEM. It enables researchers and students to grasp core concepts and apply them effectively in various research scenarios.
