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, Second Edition (Methodology In The Social Sciences) Review

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(More customer reviews)Kline's book provides a very readable introduction to and explanation of structural equation modeling. The book does not include statistical proofs, so it would not serve well as an advanced text. But if you are looking for a book that explains what SEM is and how it fits within the larger framework of inferential statistics, I recommend it.

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The bestselling text that has been so popular with graduate students and researchers for providing an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) has now been carefully revised to be even more useful.New to this edition are: * The first SEM text web page, offering free access to data and program syntax files for many of the research examples in the book, electronic overheads that readers can download and print, and links to other useful sites. * Separate chapters that review fundamental statistical concepts: one on correlation and regression (providing a foundation for less advanced readers), and another on data preparation and screening. * More coverage of the relation between measurement models and structural models in Chapter 8, which directly compares both types of models. * New, separate chapters on nonrecursive models of multiple-sample SEM in Part III, including extensive explanations of latent growth models in Chapter 10 and multilevel SEM in Chapter 13.

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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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Basics of Structural Equation Modeling Review

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(More customer reviews)I'm working on my Ph.D. using SEM. This book was very, very helpful. In fact out of about 20 books that I used, this was the most simple and easy to understand.
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With the availability of software programs such as LISREL, EQS, and AMOS, modeling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and for testing the plausibility of hypothesizing for a particular data set. However, the popularity of these techniques has often led to misunderstandings of them, particularly by students being exposed to them for the first time. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data.The book provides an overview and history of methods for path models, basic approaches to structural modeling confirmatory factor analysis techniques, measurement issues when multiple measures of theoretical variables are available, and latent variable structural equation modeling. Throughout the book, Maruyama uses a single data set (the relationships between peer acceptance and achievement) to demonstrate a variety of techniques ranging from path analysis to panel analysis to confirmatory analysis to latent variable structural equation modelling. In addition, Maruyama provides carefully constructed exercises both within and at the end of chapters so that readers can apply the material they've just read. This book will provide readers with a good understanding of how and why structural equation approaches have come to be used.

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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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Principles and Practice of Structural Equation Modeling, Third Edition (Methodology In The Social Sciences) Review

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(More customer reviews)We use this book in my the SEM class in my doctoral program and my professor has used it for years. Great book, easy to follow and understand!

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This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools.

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