Showing posts with label spss. Show all posts
Showing posts with label spss. 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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Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming Review

Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming
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Barbara Byrne manages to make a very complicated topic seem manageable and understandable. This book is ideal for people familiar with the basics of psychology statistics, but relatively new at structural equation modeling. My only complaints are that the index is a bit sparse, so I found myself thumbing through the book frequently; and sometimes the details of how to apply the concepts directly to EQS commands were left a bit unclear. However, overall this was an excellent starter book for structural equation newbies!

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Designed to help beginners estimate and test structural equation modeling (SEM) using the EQS approach, this book demonstrates a variety of SEM//EQS applications that include both partial factor analytic and full latent variable models. Beginning with an overview of the basic concepts of SEM and the EQS program, the author works through applications starting with a single sample approach to more advanced applications, such as a multi-sample approach. The book concludes with a section on using EQS for modeling with Windows.


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An Introduction to Multilevel Modeling Techniques: Second Edition (Quantitative Methodology Series) Review

An Introduction to Multilevel Modeling Techniques: Second Edition (Quantitative Methodology Series)
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This isn't the most "introductory" introduction to multilevel modeling, but it's an excellent book. Heck and Thomas present multilevel models from an integrated structural equation modeling perspective, in the vein of Muthen and the Mplus software or Skrondal & Rabe-Hesketh and the GLLAM software.
The authors illustrate how traditional multilevel models, such as those estimated with the HLM software, can be extended to models with latent predictors and latent outcomes, and they describe advanced extensions (e.g., finite-mixture models and models with categorical indicators) as well as similarities with other methods (e.g., latent growth curve models). It is nice to find a book that is both conceptually integrative and practical.
If you plan to use Mplus for your multilevel analyses, this is the single best book to buy. If you're new to multilevel models, you should start with other books (such as Multilevel Modeling (Quantitative Applications in the Social Sciences)) before digging into this one.

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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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