Handbook of Structural Equation Modeling by Hoyle

Handbook of Structural Equation Modeling Contents

1. Half I. Background
2. Half II. Fundamentals
3. Half III. Implementation
4. Half V. Superior Purposes

Preface to Handbook of Structural Equation Modeling

Structural equation modeling (SEM) has come of age. As lately because the 1980s, SEM was perceived by many college students and researchers within the social and behavioral sciences as nearly intractable—esoteric notation, difficult-to-use pc packages, and little revealed steering focused to would-be customers with primary graduate-level coaching in statistical strategies.

The standard LISREL notation system is now extra acquainted to many college students and researchers, and different, extra intuitive techniques have been developed.

Whereas there was as soon as solely LISREL for mainframe computer systems, there now are a number of pc packages for implementing SEM that run on desktop computer systems utilizing syntax that doesn’t require data of matrix algebra.

And one might now fill a shelf with textbooks and edited volumes dedicated to SEM and SEM-related subjects.

A statistical method that after was accessible solely to social and behavioral scientists with superior coursework in statistical strategies and powerful computing abilities is now half of the methodological mainstream.

Regardless of the rising literature on SEM focused to graduate college students and researchers, there has, thus far, been no single useful resource that gives broad and deep protection of each the mechanics of SEM and particular SEM methods and purposes.

This handbook is that useful resource. If affords complete protection of SEM, starting with background points, persevering with by means of statistical underpinnings and steps in implementation, then shifting into primary and superior purposes of SEM.

In a single quantity, it affords nearly full protection of SEM and its use. The guide is meant for superior graduate college students and postgraduate researchers with graduate-level coaching in utilized statistical strategies that embrace a number of regression evaluation and at the least primary protection of issue evaluation.

The construction of the guide, described under, is designed to steer readers from primary, foundational materials by means of protection of the growing quantity of modeling approaches and mannequin sorts for which SEM is suitable.

As such, the guide might function the first textbook for a graduate-level course on SEM. Alternatively, it might function a useful resource for college students and researchers who’ve accomplished their statistical coaching however must know extra about how SEM works and the way it could possibly be used of their work.

In both case, the objective is to offer protection at a stage appropriate for graduate college students and postgraduate researchers who’ve had primary statistical coaching typical of the social and behavioral sciences.

To that finish, the authors, of whom many are on the forefront of developments associated to the subject about which they’ve written, have been challenged with producing centered chapters that steadiness sophis-ication and accessibility.

The extent of sophistication essentially varies however, typically, will increase from early to later chapters. Some chapters within the final half of the guide cowl extremely specialised purposes at a stage that assumes a strong grasp of the statistical underpinnings of SEM.

But, even in these chapters, the authors have offered conceptually oriented descriptions and revealing examples.

Many of the chapters provide absolutely explicated analyses, together with entry to information and syntax information for readers all for making an attempt their hand at reproducing the authors’ outcomes.

(These could be accessed on the web site for the Handbook: www.handbookofsem.com.) The result’s a set of chapters that present up-to-date, accessible, and sensible protection of the complete array of SEM subjects.

The 40 chapters are arrayed in 5 components designed to maneuver the reader from foundational materials by means of the statistical underpinnings and practicalities of utilizing SEM, to primary and superior purposes.

The chapters in Half I present necessary background, starting with a historic account of key advances and together with materials on path diagrams, latent variables, causality, and simulation strategies.

Half II is the “nuts-and-bolts” portion of the guide, comprising chapters on assumptions, specification, estimation, statistical energy, match, mannequin modification, and equal fashions. Additionally included is a chapter on the use of categorical information in SEM.

Half III, a virtually oriented “how-to” portion of the guide, covers making ready information, managing lacking information, bootstrapping, selecting pc software program, and writing the SEM analysis report. Elements IV and V cowl the various sorts of fashions and information for which SEM is suitable.

Half V contains chapters on “primary” purposes—these which have been in use for the longest interval of time and/or function constructing blocks for newer, extra complicated or specialised purposes.

These embrace confirmatory issue evaluation; fashions of mediation and moderation; fashions of longitudinal information; fashions centered on means; fashions for the development and growth of measurement scales; and fashions for evaluating measurement equivalence for various populations.

Half V features a dozen chapters that showcase the latest and most specialised SEM fashions and modeling methods. Some chapters give attention to the use of SEM to mannequin information generated by comparatively new strategies akin to mind imaging, genotyping, and geocoding.

Others cowl methods for extra normal sorts of information that pose specific challenges however provide distinctive alternatives; these embrace multilevel information, categorical measurement information, longitudinal development information, information from intensive longitudinal assessments, dyadic information, and information from heterogeneous samples for which the supply of heterogeneity isn’t noticed.

Additionally included in Half V are chapters on rising methods—Bayesian strategies and automatic mannequin specification.

Collectively, these components type a coherent entire that gives complete, in-depth, protection of SEM in a method applicable for superior graduate college students and researchers within the social and behavioral sciences.

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