Showing posts with label multivariate analysis. Show all posts
Showing posts with label multivariate analysis. Show all posts

Principles and Practice of Structural Equation Modeling Review

Principles and Practice of Structural Equation Modeling
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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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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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