PDF Free Download | Handbook of Structural Equation Modeling by Rick H. Hoyle.
Preface to Handbook of Structural Equation Modeling
Structural equation modeling (SEM) has come of age. As recently as the 1980s, SEM was perceived by many students and researchers in the social and behavioral sciences as virtually intractable—esoteric notation, difficult-to-use computer programs, and little published guidance targeted to would-be users with basic graduate-level training in statistical methods.
The traditional LISREL notation system is now more familiar to many students and researchers, and alternative, more intuitive systems have been developed.
Whereas there was once only LISREL for mainframe computers, there now are multiple computer programs for implementing SEM that run on desktop computers using syntax that does not require knowledge of matrix algebra.
And one could now fill a shelf with textbooks and edited volumes devoted to SEM and SEM-related topics.
A statistical approach that once was accessible only to social and behavioral scientists with advanced coursework in statistical methods and strong computing skills is now part of the methodological mainstream.
Despite the growing literature on SEM targeted to graduate students and researchers, there has, to date, been no single resource that offers broad and deep coverage of both the mechanics of SEM and specific SEM strategies and applications.
This handbook is that resource. If offers comprehensive coverage of SEM, beginning with background issues, continuing through statistical underpinnings and steps in implementation, then moving into basic and advanced applications of SEM.
In a single volume, it offers virtually complete coverage of SEM and its use. The book is intended for advanced graduate students and postgraduate researchers with graduate-level training in applied statistical methods that include multiple regression analysis and at least basic coverage of factor analysis.
The structure of the book, described below, is designed to lead readers from basic, foundational material through coverage of the increasing number of modeling approaches and model types for which SEM is appropriate.
As such, the book could serve as the primary textbook for a graduate-level course on SEM. Alternatively, it could serve as a resource for students and researchers who have completed their statistical training but need to know more about how SEM works and how it could be used in their work.
In either case, the goal is to provide coverage at a level suitable for graduate students and postgraduate researchers who have had basic statistical training typical of the social and behavioral sciences.
To that end, the authors, of whom many are at the forefront of developments related to the topic about which they have written, were challenged with producing focused chapters that balance sophis-ication and accessibility.
\The level of sophistication necessarily varies but, generally, increases from early to later chapters. Some chapters in the last part of the book cover highly specialized applications at a level that assumes a solid grasp of the statistical underpinnings of SEM.
Yet, even in these chapters, the authors have provided conceptually oriented descriptions and revealing examples.
Many of the chapters offer fully explicated analyses, including access to data and syntax files for readers interested in trying their hand at reproducing the authors’ results.
(These can be accessed at the website for the Handbook: www.handbookofsem.com.) The result is a set of chapters that provide up-to-date, accessible, and practical coverage of the full array of SEM topics.
The 40 chapters are arrayed in five parts designed to move the reader from foundational material through the statistical underpinnings and practicalities of using SEM, to basic and advanced applications.
The chapters in Part I provide important background, beginning with a historical account of key advances and including material on path diagrams, latent variables, causality, and simulation methods.
Part II is the “nuts-and-bolts” portion of the book, comprising chapters on assumptions, specification, estimation, statistical power, fit, model modification, and equivalent models. Also included is a chapter on the use of categorical data in SEM.
Part III, a practically oriented “how-to” portion of the book, covers preparing data, managing missing data, bootstrapping, choosing computer software, and writing the SEM research report. Parts IV and V cover the many types of models and data for which SEM is appropriate.
Part V includes chapters on “basic” applications—those that have been in use for the longest period of time and/or serve as building blocks for newer, more complex or specialized applications.
These include confirmatory factor analysis; models of mediation and moderation; models of longitudinal data; models focused on means; models for the construction and development of measurement scales; and models for evaluating measurement equivalence for different populations.
Part V includes a dozen chapters that showcase the newest and most specialized SEM models and modeling strategies. Some chapters focus on the use of SEM to model data generated by relatively new methods such as brain imaging, genotyping, and geocoding.
Others cover strategies for more general types of data that pose particular challenges but offer unique opportunities; these include multilevel data, categorical measurement data, longitudinal growth data, data from intensive longitudinal assessments, dyadic data, and data from heterogeneous samples for which the source of heterogeneity is not observed.
Also included in Part V are chapters on emerging strategies—Bayesian methods and automated model specification.
Together, these parts form a coherent whole that provides comprehensive, in-depth, coverage of SEM in a style appropriate for advanced graduate students and researchers in the social and behavioral sciences.
Handbook of Structural Equation Modeling Contents
- Part I. Background
- Part II. Fundamentals
- Part III. Implementation
- Part V. Advanced Applications
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