A First Course in Structural Equation Modeling Review

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(More customer reviews)If you haven't been exposed to SEM , this book is right for you. Basic, straightforward, well planned and nonmathematical. However, this book cannot be the "only" book in your library if you are planning to go deep in SEM. This book is a good step for moving to intermediate or advanced levels.
The book is well desinged. It starts with fundamentals of SEM and EQS and LISREL programs. This book is NOT an EQS or LISREL reference, however, in each chapter, Raykov and Marcoulides cover an example showing the syntax and output of each program. The syntax and the output are deeply explained.
After introducing SEM and EQS and LISREL, the authors start to cover different approaches in SEM. Path analysis, Confirmatory Factor Analysis, Structural Regression Models and finally there is chapter about Latent Change Analysis.
In overall, the book is useful for only beginners as the authors' claim.

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This book is designed to introduce students to the basics of structural equation modeling through a conceptual, nonmathematical approach. The few mathematical formulas included are used in a conceptual or illustrative nature, rather than a computational one. The book features examples from LISREL and EQS. For that reason, the book can also be used as a beginning guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. Intended as an introduction for graduate students or researchers in psychology, education, business, and other applied social and health sciences. The only prerequisite is a basic statistics course.

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A First Course in Structural Equation Modeling Review

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(More customer reviews)This book does a good job of explaining the basic concepts involved in SEM, path analysis, and CFA in easy to understand terms. Also, it lays out syntax and output for running popular SEM programs, such as LISREL, EQS, and Mplus. This book is perfect for the person who is trying to learn SEM on their own. I think it gives you enough to be able to know how to run a SEM, but it isn't enough to really make you a pro at it.

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In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the second edition include:• review of latent change (growth) analysis models at an introductory level; •coverage of the popular Mplus program; •updated examples of LISREL and EQS; and •a CD that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

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Principles and Practice of Structural Equation Modeling Review

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(More customer reviews)Rex Kline easy writing style will take you through step-by-step in one of the most comprehesive yet accessible introductions to multivariate analysis and structural equation modeling. After outlining the building blocks of SEM (multiple regression, path analysis, and factor analysis), Kline gets the reader ready to tackle popular SEM software with examples for AMOS, LISREL and EQS. There is also a great chapter on what NOT to do with this often misused technique. For a preview, see Kline's article in the Journal of Clinical Psychology (1991), Latent variable path analysis: A beginner's tour guide. This book is an excellent read, and a must have for researchers and statisticians.

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Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) in the social sciences. This is an accessible volume which covers introductory techniques, including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods, such as the evaluation of nonlinear effects, the analysis of means in covariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the author offers clear instructions on the preparation and screening of data, common mistakes to avoid, and features of widely used software programs (Amos, EQS, and LISREL). Readers will acquire the skills necessary to begin to use SEM in their own research and to interpret and critique the use of the method by others.

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Latent Curve Models: A Structural Equation Perspective (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Bollen's name should be well known to anyone with an interest in structural equation modeling (SEM). His 1989-book 'Structural Equation Modeling With Latent Variables' is still a cornerstone in the SEM-literature - and many of us are eagerly awaiting a second edition!
In the last few years Bollen and his colleague Curran have authored - or co-authored - several journal articles on the use of SEM for longitudinal analysis. Now they have brought their experiences together in this book.
If you have a basic understanding of SEM and want to develop your skills in using SEM methodology in analysis of panel-like data, then this is the book for you.
Well-written and suitable for researchers, it is pedagogical enough to be used as a textbook at graduate level.
The data used in the examples can be downloaded from a website.
This book is without any doubt the best book on the subject. It will be the main reference on the subject for many years to come.
Highly recommended!Niels

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An effective technique for data analysis in the social sciences
The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories.
The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs.
This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include:
Chapter introductions and summaries that provide a quick overview of highlights
Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications
Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics
Extensive footnoting that points the way to the primary literature for more information on particular topics

With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.

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An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (Quantitative Methodology) (Quantitative Methodology Series) Review

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(More customer reviews)This is an excellent book for anyone who wishes to not only understand the theory behind latent growth curve modeling but also seeing how it is directly applied in a number of situations. For a reader like me who depends upon the literature to help understand newer statistical approaches, a book like this is a breath of fresh air. The book presents very clearly how to set up a basic LGC model and includes other topics such as dealing with missing data, interaction effects and multilevel approaches to longitudinal data analysis. The appendix contains a number of example LGCM models in the software language of EQS, LISREL and AMOS. I most highly recommend this text for beginners and more advanced modelers alike!

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This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters–growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

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