Showing posts with label cfa. Show all posts
Showing posts with label cfa. Show all posts

Introduction to Structural Equation Modelling Using SPSS and Amos Review

Introduction to Structural Equation Modelling Using SPSS and Amos
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The quality of this book is low. It has many errors making it hard to follow. I purchased the book based on the fact that the author was making the files associated with the analysis in the book available. Well the data files do not match what is in the book. Second, there is very little help in showing how to use SPSS or AMOS properly. Your better choice are the texts from Barbara Byrne.

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New software (Lisrel and AMOS) has made the techniques of Structural Equation Modelling (SEM) increasingly available to students and researchers, while the recent adoption of AMOS as part of the SPSS suite has improved access still further.





As an alternative to existing books on the subject, which are customarily very long, very high-level and very mathematical, not to mention expensive, Niels Blunch's introduction has been designed for advanced undergraduates and Masters students who are new to SEM and still relatively new to statistics.



Illustrated with screenshots, cases and exercises and accompanied by a companion website containing datasets that can be easily uploaded onto SPSS and AMOS, this handy introduction keeps maths to a minimum and contains an appendix covering basic forms of statistical analysis.


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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis Review

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis
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I read this book's earlier edition so that I know this book is a good one. I bought a kindle version of it and found that the 4th edition is meeting my expectation. Only troubling issue that I found from this kindle version is that the data cd coming with a paper version of this book is missing in kindle version. Neither the publisher nor the amazon.com provide a link to the data cd. My complain to amazon.com is that they didn't inform their customer about this.

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Basics of Structural Equation Modeling Review

Basics of Structural Equation Modeling
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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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Structural Equation Modeling With Eqs: Basic Concepts, Applications, And Programming (Multivariate Applications) (Multivariate Applications Series) Review

Structural Equation Modeling With Eqs: Basic Concepts, Applications, And Programming (Multivariate Applications) (Multivariate Applications Series)
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Byrne offers a more readable alternative to the hard core approach of learning EQS from its official manual. The latter presents a heavily mathematical framework that might be offputting to some readers who lack a strong statistical background.
Instead, her book goes through much of what EQS can do for you. But at a gentler pace. Giving a brief walkthrough of multivariate statistical modelling. The book starts off explaining the format of the EQS input file. It uses a graphical approach similar to what electrical engineers have for laying out circuits. More intuitive for the user. Where you can draw regressions paths from one component in the diagram to other components, for example. This is reminiscent of how SPICE went from a text input file that described a circuit to a graphical approach that was far easier to understand.
Much of the book then goes into how you can test for causality in your model. EQS has formidable abilities to do so, and you need to master how to control these.

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Researchers and students who want a less mathematical alternative to the EQS manual will find exactly what they're looking for in this practical text. Written specifically for those with little to no knowledge of structural equation modeling (SEM) or EQS, the author's goal is to provide a non-mathematical introduction to the basic concepts of SEM by applying these principles to EQS, Version 6.1. The book clearly demonstrates a wide variety of SEM/EQS applications that include confirmatory factor analytic and full latent variable models. Analyses are based on a wide variety of data representing single and multiple-group models; these include data that are normal/non-normal, complete/incomplete, and continuous/categorical. Written in a "user-friendly" style, the author "walks" the reader through the varied steps involved in the process of testing SEM models. These include model specification and estimation, assessment of model fit, description of EQS output, and interpretation of findings. Each of the book's applications is accompanied by: a statement of the hypothesis being tested, a schematic representation of the model, explanations and interpretations of the related EQS input and output files, tips on how to use the associated pull-down menus and icons, and the data file upon which the application is based. Beginning with an overview of the basic concepts of SEM and the EQS program, the book carefully works through applications starting with relatively simple single group analyses, through to more advanced applications, such as a multi-group, latent growth curve, and multilevel modeling.The new edition features: •many new applications that include a latent growth curve model, a multilevel model, a second-order model based on categorical data, a missing data multigroup model based on the EM algorithm, and the testing for latent mean differences related to a higher-order model; •a CD enclosed with the book that includes all application data; •vignettes illustrating procedural and/or data management tasks using a Windows interface; •description of how to build models both interactively using the BUILDULEQ interface and graphically using the EQS Diagrammer.

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A First Course in Structural Equation Modeling Review

A First Course in Structural Equation Modeling
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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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Structural Equation Modeling with LISREL: Essentials and Advances Review

Structural Equation Modeling with LISREL: Essentials and Advances
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The book is best at presenting the theory and has excellent discussions of advanced estimation issues. The book, however, is not as useful in conveying the nuts and bolts of how to use LISREL.

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A Beginner's Guide to Structural Equation Modeling: Third Edition Review

A Beginner's Guide to Structural Equation Modeling: Third Edition
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No one expects statistics to be easy reading, especially when it concerns complicated models such as structural equation modeling (SEM). Nevertheless, this book manages to do just that.
Schumacker and Lomax have successfully put together a guide that explains to beginners (like myself) in simple terms how the whole thing works. As with most books that treat complex models, some basic knowledge of statistics is preferable before you begin to read it. But if your statistics is rusty and you have only vague impressions of probability sketches in your memory, fear not! Schumacker and Lomax are kind to us poor souls, and begin by introducing some basics in chapter 1 to prod your memory: terminology, variable scales, how to treat missing data, outliers and normality. And in chapter 2, they discuss correlation and covariance.
Before talking about structural equation models, Schumacker and Lomax dedicate chapter 3 to a number of statistical methods on which SEM is built. This chapter gives a basic overview of regression, path analysis and factor analysis. The review of these methods helps you to understand SEM better later on. They also provide an excellent understanding of the methods, in case you have not used them before or it's been a while ...
The rest of the guide covers SEM: how to develop and measure a model (chapters 4 and 5), how the model parameters are estimated and how you can check for reliability and validity (chapter 6), and checking for goodness of fit of your model (chapter 7).
In chapter 8, you are shown some examples of computer outputs by two software packages that can conduct SEM, EQS5 and LISREL8-SIMPLIS.
Chapter 9 goes into more detail on models and diagrams (regression, analysis of covariance, path, measurement and structural models). For those that feel by this point that they've gained enough experience, advanced topics such as cross validation, simulation, bootstrap and jacknife methods as well as multiple same and interaction models are covered in chapter 10. And for the super-keen, the technical bits are covered in chapter 11 (health warning: you better be up to speed on matrix algebra).
The great thing about this book is that you most likely will be able to run models and interpret results by chapter 7, and you don't need to go into the nitty-gritty if you don't want to. On the other hand, the details are there if you need them. In essence, the authors start at the beginning, building up slowly until you are able to handle a basic model, before going into more complex issues.
One drawback, I have found, is that this book was published in 1996. That's nearly a decade ago, and (fortunately) computer power and statistical modeling has come a long way since then. The authors, for example, are convinced that WordPerfect is the software of choice for word processing and that at some point in the future it would be possible to copy and paste diagrams into a word processing program. We've come a long way since then. Today, many SEM packages exist that are much more user friendly than some of the older stodgy packages that require you to enter data in a very specific way and interpret results by going through reams of data output. As such, the authors (or publishers) probably ought to update it with a second edition.
Nevertheless, the strength of the book lies in its guidance and explanatory power. And even if you use a different package, you can skim through the data outputs they use, and focus on your model, how to construct it, and of what pitfalls to beware.
I highly recommend this for anyone starting on SEM - your modeling days will be much happier with this guide.

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This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding.The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters.Highlights of the new edition include:A website with raw data sets for the book's examples and exercises so they can be used with any SEM program, all of the book's exercises, hotlinks to related websites, and answers to all of the exercises for Instructor's onlyNew troubleshooting tips on how to address the most frequently encountered problemsExamples now reference the free student version of Lisrel 8.8Expanded coverage of advanced models with more on multiple-group, multi-level, & mixture modeling (Chs. 13 & 15), second-order and dynamic factor models (Ch. 14), and Monte Carlo methods (Ch. 16)Increased coverage of sample size and power (Ch. 5) and reporting research (Ch. 11)New journal article references help readers better understand published research (Chs. 13 - 17) and 25 % new exercises with answers to half in the book for student review.Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.

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Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Second Edition (Multivariate Applications Series) Review

Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Second Edition (Multivariate Applications Series)
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For a book that presumes to describe the use of a particular software (AMOS) for conducting Structural Equation Modeling, this book misses the mark by quite a bit. Many of the terms and windows figures the author uses do not correspond well to those actually used in AMOS. Also, the description of what the author is doing is often not as clear as one would hope, and I found myself often at a lost as to what the intent or purpose was for performing each step and why one may want to perform each procedure discussed. Lastly, this book spends the vast majority of its page space discussing factor analyses (confirmatory factor analysis) and the use of modification indices. While this may be of some importance to some researchers, the more theorectically and empirically meaningful discussion of assessing and comparing various causal models of association are tersely covered and and the rationale behind such analytical tools poorly explained. Also, the author seems to advocate for some practices in SEM that many other experts may not condone.

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This bestselling text provides a practical guide to the basic concepts of structural equation modeling (SEM) and the AMOS program (Versions 17 & 18). The author reviews SEM applications based on actual data taken from her research. Noted for its non-mathematical language, this book is written for the novice SEM user. With each chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed an illustration of the hypothesized and posthoc models tested AMOS input and output with accompanying interpretation and explanationThe function of the AMOS toolbar icons and their related pull-down menusThe data and published reference upon which the model was based.With over 50% new material, highlights of the new edition include:All new screen shots featuring Version 17 of the AMOS program  All data files now available at www.psypress.com/sem-with-amos Application of a multitrait-mulitimethod model, latent growth curve model, and second-order model based on categorical dataAll applications based on the most commonly used graphical interfaceThe automated multi-group approach to testing for equivalenceThe book opens with an introduction to the fundamental concepts of SEM and the basics of the AMOS program. The next 3 sections present applications that focus on single-group, multiple-group, and multitrait-mutimethod and latent growth curve models. The book concludes with a discussion about non-normal and missing (incomplete) data and  two applications capable of addressing these issues. Intended for researchers, practitioners, and students who use SEM and AMOS in their work, this book is an ideal resource for graduate level courses on SEM taught in departments of psychology, education, business, and other social and health sciences and/or as a supplement in courses on applied statistics, multivariate statistics, statistics II, intermediate or advanced statistics, and/or research design. Appropriate for those with limited or no previous exposure to SEM, a prerequisite of basic statistics through regression analysis is recommended.

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Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science) Review

Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science)
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This book fills an important niche, a sweet spot in between basic SEM texts such as Rex Kline's helpful beginner's book and more complex, advanced treatments of SEM such as Ken Bollen's classic 1989 Wiley text. Methodologists might argue that the latter text is intermediate rather than advanced, but practitioners and applied users of SEM who are not in the business of creating new methods but instead want to use SEM in a rigorous, productive way on applied analysis problems will find this text to be just the ticket to getting things done using SEM and tackling typical problems such as how to handle missing data and how to calculate power for goodness-of-fit tests and parameter estimates.
The editors have done a terrific job in working with the chapter authors to make all chapters accessible with helpful examples and consistent notation and terminology. This book, along with Loehlin's Latent Variable Models, is one I find myself pulling off my bookcase repeatedly to solve applied problems or to learn more about a particular SEM issue (modeling options for complex survey data, mixture modeling) quickly, yet comprehensively. Highly recommended.

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A volume in Quantitative Methods in Education and the Behavioral Sciences:Issues, Research, and Teaching
(sponsored by the American Educational Research Association's Special Interest Group:Educational Statisticians)
Series EditorRonald C. Serlin, University of Wisconsin-Madison
This volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussedin introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understandingof foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types ofresearch questions that might not have arisen during a first course. Chapters focus on the clear explanation and application oftopics, rather than on analytical derivations, and contain syntax and partial output files from popular SEM software.
CONTENTS: Introduction to Series, Ronald C. Serlin. Preface, Richard G. Lomax. Dedication. Acknowledgements. Introduction,Gregory R. Hancock & Ralph O. Mueller. Part I: Foundations. The Problem of Equivalent Structural Models, Scott L.Hershberger. Formative Measurement and Feedback Loops, Rex B. Kline. Power Analysis in Covariance Structure Modeling,Gregory R. Hancock. Part II: Extensions. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S.Thompson & Samuel B. Green. Using Latent Growth Models to Evaluate Longitudinal Change, Gregory R. Hancock & FrankR. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagné. Structural Equation Models of Latent Interactionand Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and CategoricalData in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models withMissing Data, Craig K. Enders. Using Multilevel Structural Equation Modeling Techniques with Complex Sample Data,Laura M. Stapleton. The Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos. Aboutthe Authors.

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